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"shape" : "[1, 57]", + "name" : "tokens", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1 × 57)", + "shortDescription" : "", + "shape" : "[1, 57]", + "name" : "attention_mask", + "type" : "MultiArray" + } + ], + "generatedClassName" : "bert_fp16", + "method" : "predict" + } +] \ No newline at end of file diff --git a/iteration_3/compiled/bert_fp16.mlmodelc/model.mil b/iteration_3/compiled/bert_fp16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a5897b24e63c9a9a5bf7efc05258a79ac83cd022 --- /dev/null +++ b/iteration_3/compiled/bert_fp16.mlmodelc/model.mil @@ -0,0 +1,442 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor attention_mask, tensor tokens) { + int32 inputs_embeds_batch_dims_0 = const()[name = string("inputs_embeds_batch_dims_0"), val = int32(0)]; + bool inputs_embeds_validate_indices_0 = const()[name = string("inputs_embeds_validate_indices_0"), val = bool(false)]; + tensor bert_embeddings_word_embeddings_weight_to_fp16 = const()[name = string("bert_embeddings_word_embeddings_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string tokens_to_int16_dtype_0 = const()[name = string("tokens_to_int16_dtype_0"), val = string("int16")]; + string cast_53_dtype_0 = const()[name = string("cast_53_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor tokens_to_int16 = cast(dtype = tokens_to_int16_dtype_0, x = tokens)[name = string("cast_58")]; + tensor cast_53 = cast(dtype = cast_53_dtype_0, x = tokens_to_int16)[name = string("cast_57")]; + tensor greater_equal_0 = greater_equal(x = cast_53, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(178)]; + tensor add_0 = add(x = cast_53, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_53, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + int32 inputs_embeds_cast_fp16_cast_uint16_axis_0 = const()[name = string("inputs_embeds_cast_fp16_cast_uint16_axis_0"), val = int32(0)]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_56")]; + tensor inputs_embeds_cast_fp16_cast_uint16_cast_uint16 = gather(axis = inputs_embeds_cast_fp16_cast_uint16_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = select_0_to_int16, validate_indices = inputs_embeds_validate_indices_0, x = bert_embeddings_word_embeddings_weight_to_fp16)[name = string("inputs_embeds_cast_fp16_cast_uint16_cast_uint16")]; + tensor token_type_embeddings_1_to_fp16 = const()[name = string("token_type_embeddings_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45696)))]; + tensor embeddings_1_cast_fp16 = add(x = inputs_embeds_cast_fp16_cast_uint16_cast_uint16, y = token_type_embeddings_1_to_fp16)[name = string("embeddings_1_cast_fp16")]; + tensor position_embeddings_1_to_fp16 = const()[name = string("position_embeddings_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60352)))]; + tensor input_5_cast_fp16 = add(x = embeddings_1_cast_fp16, y = position_embeddings_1_to_fp16)[name = string("input_5_cast_fp16")]; + tensor input_7_axes_0 = const()[name = string("input_7_axes_0"), val = tensor([-1])]; + tensor bert_embeddings_LayerNorm_weight_to_fp16 = const()[name = string("bert_embeddings_LayerNorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75008)))]; + tensor bert_embeddings_LayerNorm_bias_to_fp16 = const()[name = string("bert_embeddings_LayerNorm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75328)))]; + fp16 var_34_to_fp16 = const()[name = string("op_34_to_fp16"), val = fp16(0x1p-24)]; + tensor input_7_cast_fp16 = layer_norm(axes = input_7_axes_0, beta = bert_embeddings_LayerNorm_bias_to_fp16, epsilon = var_34_to_fp16, gamma = bert_embeddings_LayerNorm_weight_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_79_axes_0 = const()[name = string("op_79_axes_0"), val = tensor([1])]; + tensor var_79 = expand_dims(axes = var_79_axes_0, x = attention_mask)[name = string("op_79")]; + tensor var_81_axes_0 = const()[name = string("op_81_axes_0"), val = tensor([2])]; + tensor var_81 = expand_dims(axes = var_81_axes_0, x = var_79)[name = string("op_81")]; + tensor var_90_reps_0 = const()[name = string("op_90_reps_0"), val = tensor([1, 1, 57, 1])]; + tensor var_90 = tile(reps = var_90_reps_0, x = var_81)[name = string("op_90")]; + fp16 var_96_to_fp16 = const()[name = string("op_96_to_fp16"), val = fp16(0x1p+0)]; + string var_95_to_fp16_dtype_0 = const()[name = string("op_95_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_90_to_fp16 = cast(dtype = var_95_to_fp16_dtype_0, x = var_90)[name = string("cast_55")]; + tensor inverted_mask_cast_fp16 = sub(x = var_96_to_fp16, y = var_90_to_fp16)[name = string("inverted_mask_cast_fp16")]; + string var_103_dtype_0 = const()[name = string("op_103_dtype_0"), val = string("bool")]; + fp16 var_104_to_fp16 = const()[name = string("op_104_to_fp16"), val = fp16(-inf)]; + tensor inverted_mask_cast_fp16_to_bool = cast(dtype = var_103_dtype_0, x = inverted_mask_cast_fp16)[name = string("cast_54")]; + tensor attention_mask_cast_fp16 = select(a = var_104_to_fp16, b = inverted_mask_cast_fp16, cond = inverted_mask_cast_fp16_to_bool)[name = string("attention_mask_cast_fp16")]; + tensor bert_encoder_embedding_hidden_mapping_in_weight_to_fp16 = const()[name = string("bert_encoder_embedding_hidden_mapping_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75648)))]; + tensor bert_encoder_embedding_hidden_mapping_in_bias_to_fp16 = const()[name = string("bert_encoder_embedding_hidden_mapping_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272320)))]; + tensor linear_0_cast_fp16 = linear(bias = bert_encoder_embedding_hidden_mapping_in_bias_to_fp16, weight = bert_encoder_embedding_hidden_mapping_in_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273920)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1453632)))]; + tensor linear_1_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = linear_0_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_143 = const()[name = string("op_143"), val = tensor([1, 57, 12, 64])]; + tensor x_3_cast_fp16 = reshape(shape = var_143, x = linear_1_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1455232)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2634944)))]; + tensor linear_2_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = linear_0_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_152 = const()[name = string("op_152"), val = tensor([1, 57, 12, 64])]; + tensor x_7_cast_fp16 = reshape(shape = var_152, x = linear_2_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2636544)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3816256)))]; + tensor linear_3_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = linear_0_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_161 = const()[name = string("op_161"), val = tensor([1, 57, 12, 64])]; + tensor x_11_cast_fp16 = reshape(shape = var_161, x = linear_3_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = x_11_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = x_7_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = x_3_cast_fp16)[name = string("transpose_156")]; + tensor attention_output_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("attention_output_1_cast_fp16")]; + tensor attention_output_3_perm_0 = const()[name = string("attention_output_3_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_167 = const()[name = string("op_167"), val = tensor([1, 57, 768])]; + tensor attention_output_3_cast_fp16 = transpose(perm = attention_output_3_perm_0, x = attention_output_1_cast_fp16)[name = string("transpose_153")]; + tensor input_9_cast_fp16 = reshape(shape = var_167, x = attention_output_3_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3817856)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4997568)))]; + tensor linear_4_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = linear_0_cast_fp16, y = linear_4_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor input_13_axes_0 = const()[name = string("input_13_axes_0"), val = tensor([-1])]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4999168)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5000768)))]; + fp16 var_118_to_fp16 = const()[name = string("op_118_to_fp16"), val = fp16(0x1p-24)]; + tensor input_13_cast_fp16 = layer_norm(axes = input_13_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5002368)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8148160)))]; + tensor linear_5_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_13_cast_fp16)[name = string("linear_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = linear_5_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8152320)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11298112)))]; + tensor linear_6_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_17_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = linear_6_cast_fp16, y = input_13_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor hidden_states_3_axes_0 = const()[name = string("hidden_states_3_axes_0"), val = tensor([-1])]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11299712)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11301312)))]; + tensor hidden_states_3_cast_fp16 = layer_norm(axes = hidden_states_3_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_19_cast_fp16)[name = string("hidden_states_3_cast_fp16")]; + tensor linear_7_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_3_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_218 = const()[name = string("op_218"), val = tensor([1, 57, 12, 64])]; + tensor x_15_cast_fp16 = reshape(shape = var_218, x = linear_7_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor linear_8_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_3_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_227 = const()[name = string("op_227"), val = tensor([1, 57, 12, 64])]; + tensor x_19_cast_fp16 = reshape(shape = var_227, x = linear_8_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor linear_9_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_3_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_236 = const()[name = string("op_236"), val = tensor([1, 57, 12, 64])]; + tensor x_23_cast_fp16 = reshape(shape = var_236, x = linear_9_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = x_23_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = x_19_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = x_15_cast_fp16)[name = string("transpose_152")]; + tensor attention_output_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("attention_output_5_cast_fp16")]; + tensor attention_output_7_perm_0 = const()[name = string("attention_output_7_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_242 = const()[name = string("op_242"), val = tensor([1, 57, 768])]; + tensor attention_output_7_cast_fp16 = transpose(perm = attention_output_7_perm_0, x = attention_output_5_cast_fp16)[name = string("transpose_149")]; + tensor input_21_cast_fp16 = reshape(shape = var_242, x = attention_output_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor linear_10_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_21_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = hidden_states_3_cast_fp16, y = linear_10_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor input_25_axes_0 = const()[name = string("input_25_axes_0"), val = tensor([-1])]; + tensor input_25_cast_fp16 = layer_norm(axes = input_25_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor linear_11_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_25_cast_fp16)[name = string("linear_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = linear_11_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor linear_12_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_29_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = linear_12_cast_fp16, y = input_25_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor hidden_states_5_axes_0 = const()[name = string("hidden_states_5_axes_0"), val = tensor([-1])]; + tensor hidden_states_5_cast_fp16 = layer_norm(axes = hidden_states_5_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_31_cast_fp16)[name = string("hidden_states_5_cast_fp16")]; + tensor linear_13_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_5_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_293 = const()[name = string("op_293"), val = tensor([1, 57, 12, 64])]; + tensor x_27_cast_fp16 = reshape(shape = var_293, x = linear_13_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor linear_14_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_5_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_302 = const()[name = string("op_302"), val = tensor([1, 57, 12, 64])]; + tensor x_31_cast_fp16 = reshape(shape = var_302, x = linear_14_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor linear_15_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_5_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 57, 12, 64])]; + tensor x_35_cast_fp16 = reshape(shape = var_311, x = linear_15_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = x_35_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = x_31_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = x_27_cast_fp16)[name = string("transpose_148")]; + tensor attention_output_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("attention_output_9_cast_fp16")]; + tensor attention_output_11_perm_0 = const()[name = string("attention_output_11_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_317 = const()[name = string("op_317"), val = tensor([1, 57, 768])]; + tensor attention_output_11_cast_fp16 = transpose(perm = attention_output_11_perm_0, x = attention_output_9_cast_fp16)[name = string("transpose_145")]; + tensor input_33_cast_fp16 = reshape(shape = var_317, x = attention_output_11_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor linear_16_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_33_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor input_35_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = linear_16_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor input_37_axes_0 = const()[name = string("input_37_axes_0"), val = tensor([-1])]; + tensor input_37_cast_fp16 = layer_norm(axes = input_37_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")]; + tensor linear_17_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_37_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = linear_17_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor linear_18_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_41_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = linear_18_cast_fp16, y = input_37_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor hidden_states_7_axes_0 = const()[name = string("hidden_states_7_axes_0"), val = tensor([-1])]; + tensor hidden_states_7_cast_fp16 = layer_norm(axes = hidden_states_7_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_43_cast_fp16)[name = string("hidden_states_7_cast_fp16")]; + tensor linear_19_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_368 = const()[name = string("op_368"), val = tensor([1, 57, 12, 64])]; + tensor x_39_cast_fp16 = reshape(shape = var_368, x = linear_19_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor linear_20_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_377 = const()[name = string("op_377"), val = tensor([1, 57, 12, 64])]; + tensor x_43_cast_fp16 = reshape(shape = var_377, x = linear_20_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor linear_21_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_386 = const()[name = string("op_386"), val = tensor([1, 57, 12, 64])]; + tensor x_47_cast_fp16 = reshape(shape = var_386, x = linear_21_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = x_47_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = x_43_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = x_39_cast_fp16)[name = string("transpose_144")]; + tensor attention_output_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("attention_output_13_cast_fp16")]; + tensor attention_output_15_perm_0 = const()[name = string("attention_output_15_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_392 = const()[name = string("op_392"), val = tensor([1, 57, 768])]; + tensor attention_output_15_cast_fp16 = transpose(perm = attention_output_15_perm_0, x = attention_output_13_cast_fp16)[name = string("transpose_141")]; + tensor input_45_cast_fp16 = reshape(shape = var_392, x = attention_output_15_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor linear_22_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_45_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = hidden_states_7_cast_fp16, y = linear_22_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor input_49_axes_0 = const()[name = string("input_49_axes_0"), val = tensor([-1])]; + tensor input_49_cast_fp16 = layer_norm(axes = input_49_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_47_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor linear_23_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_49_cast_fp16)[name = string("linear_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = linear_23_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor linear_24_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_53_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor input_55_cast_fp16 = add(x = linear_24_cast_fp16, y = input_49_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor hidden_states_9_axes_0 = const()[name = string("hidden_states_9_axes_0"), val = tensor([-1])]; + tensor hidden_states_9_cast_fp16 = layer_norm(axes = hidden_states_9_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_55_cast_fp16)[name = string("hidden_states_9_cast_fp16")]; + tensor linear_25_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_443 = const()[name = string("op_443"), val = tensor([1, 57, 12, 64])]; + tensor x_51_cast_fp16 = reshape(shape = var_443, x = linear_25_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor linear_26_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 57, 12, 64])]; + tensor x_55_cast_fp16 = reshape(shape = var_452, x = linear_26_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor linear_27_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_461 = const()[name = string("op_461"), val = tensor([1, 57, 12, 64])]; + tensor x_59_cast_fp16 = reshape(shape = var_461, x = linear_27_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = x_59_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = x_55_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = x_51_cast_fp16)[name = string("transpose_140")]; + tensor attention_output_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("attention_output_17_cast_fp16")]; + tensor attention_output_19_perm_0 = const()[name = string("attention_output_19_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_467 = const()[name = string("op_467"), val = tensor([1, 57, 768])]; + tensor attention_output_19_cast_fp16 = transpose(perm = attention_output_19_perm_0, x = attention_output_17_cast_fp16)[name = string("transpose_137")]; + tensor input_57_cast_fp16 = reshape(shape = var_467, x = attention_output_19_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor linear_28_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_57_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = linear_28_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_axes_0 = const()[name = string("input_61_axes_0"), val = tensor([-1])]; + tensor input_61_cast_fp16 = layer_norm(axes = input_61_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor linear_29_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_61_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_65_mode_0 = const()[name = string("input_65_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_65_cast_fp16 = gelu(mode = input_65_mode_0, x = linear_29_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor linear_30_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_65_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor input_67_cast_fp16 = add(x = linear_30_cast_fp16, y = input_61_cast_fp16)[name = string("input_67_cast_fp16")]; + tensor hidden_states_11_axes_0 = const()[name = string("hidden_states_11_axes_0"), val = tensor([-1])]; + tensor hidden_states_11_cast_fp16 = layer_norm(axes = hidden_states_11_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_67_cast_fp16)[name = string("hidden_states_11_cast_fp16")]; + tensor linear_31_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_518 = const()[name = string("op_518"), val = tensor([1, 57, 12, 64])]; + tensor x_63_cast_fp16 = reshape(shape = var_518, x = linear_31_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor linear_32_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_527 = const()[name = string("op_527"), val = tensor([1, 57, 12, 64])]; + tensor x_67_cast_fp16 = reshape(shape = var_527, x = linear_32_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor linear_33_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_536 = const()[name = string("op_536"), val = tensor([1, 57, 12, 64])]; + tensor x_71_cast_fp16 = reshape(shape = var_536, x = linear_33_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = x_71_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = x_67_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = x_63_cast_fp16)[name = string("transpose_136")]; + tensor attention_output_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("attention_output_21_cast_fp16")]; + tensor attention_output_23_perm_0 = const()[name = string("attention_output_23_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_542 = const()[name = string("op_542"), val = tensor([1, 57, 768])]; + tensor attention_output_23_cast_fp16 = transpose(perm = attention_output_23_perm_0, x = attention_output_21_cast_fp16)[name = string("transpose_133")]; + tensor input_69_cast_fp16 = reshape(shape = var_542, x = attention_output_23_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor linear_34_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_69_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = hidden_states_11_cast_fp16, y = linear_34_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor input_73_axes_0 = const()[name = string("input_73_axes_0"), val = tensor([-1])]; + tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_71_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor linear_35_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_73_cast_fp16)[name = string("linear_35_cast_fp16")]; + string input_77_mode_0 = const()[name = string("input_77_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_77_cast_fp16 = gelu(mode = input_77_mode_0, x = linear_35_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor linear_36_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_77_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor input_79_cast_fp16 = add(x = linear_36_cast_fp16, y = input_73_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor hidden_states_13_axes_0 = const()[name = string("hidden_states_13_axes_0"), val = tensor([-1])]; + tensor hidden_states_13_cast_fp16 = layer_norm(axes = hidden_states_13_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("hidden_states_13_cast_fp16")]; + tensor linear_37_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_593 = const()[name = string("op_593"), val = tensor([1, 57, 12, 64])]; + tensor x_75_cast_fp16 = reshape(shape = var_593, x = linear_37_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor linear_38_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_602 = const()[name = string("op_602"), val = tensor([1, 57, 12, 64])]; + tensor x_79_cast_fp16 = reshape(shape = var_602, x = linear_38_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor linear_39_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_611 = const()[name = string("op_611"), val = tensor([1, 57, 12, 64])]; + tensor x_83_cast_fp16 = reshape(shape = var_611, x = linear_39_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = x_83_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = x_79_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = x_75_cast_fp16)[name = string("transpose_132")]; + tensor attention_output_25_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("attention_output_25_cast_fp16")]; + tensor attention_output_27_perm_0 = const()[name = string("attention_output_27_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_617 = const()[name = string("op_617"), val = tensor([1, 57, 768])]; + tensor attention_output_27_cast_fp16 = transpose(perm = attention_output_27_perm_0, x = attention_output_25_cast_fp16)[name = string("transpose_129")]; + tensor input_81_cast_fp16 = reshape(shape = var_617, x = attention_output_27_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor linear_40_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_81_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = hidden_states_13_cast_fp16, y = linear_40_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_axes_0 = const()[name = string("input_85_axes_0"), val = tensor([-1])]; + tensor input_85_cast_fp16 = layer_norm(axes = input_85_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor linear_41_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_85_cast_fp16)[name = string("linear_41_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_41_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor linear_42_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_89_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = linear_42_cast_fp16, y = input_85_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor hidden_states_15_axes_0 = const()[name = string("hidden_states_15_axes_0"), val = tensor([-1])]; + tensor hidden_states_15_cast_fp16 = layer_norm(axes = hidden_states_15_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_91_cast_fp16)[name = string("hidden_states_15_cast_fp16")]; + tensor linear_43_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_15_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_668 = const()[name = string("op_668"), val = tensor([1, 57, 12, 64])]; + tensor x_87_cast_fp16 = reshape(shape = var_668, x = linear_43_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor linear_44_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_15_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_677 = const()[name = string("op_677"), val = tensor([1, 57, 12, 64])]; + tensor x_91_cast_fp16 = reshape(shape = var_677, x = linear_44_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor linear_45_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_15_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_686 = const()[name = string("op_686"), val = tensor([1, 57, 12, 64])]; + tensor x_95_cast_fp16 = reshape(shape = var_686, x = linear_45_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = x_95_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = x_91_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = x_87_cast_fp16)[name = string("transpose_128")]; + tensor attention_output_29_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("attention_output_29_cast_fp16")]; + tensor attention_output_31_perm_0 = const()[name = string("attention_output_31_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_692 = const()[name = string("op_692"), val = tensor([1, 57, 768])]; + tensor attention_output_31_cast_fp16 = transpose(perm = attention_output_31_perm_0, x = attention_output_29_cast_fp16)[name = string("transpose_125")]; + tensor input_93_cast_fp16 = reshape(shape = var_692, x = attention_output_31_cast_fp16)[name = string("input_93_cast_fp16")]; + tensor linear_46_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_93_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor input_95_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = linear_46_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor input_97_axes_0 = const()[name = string("input_97_axes_0"), val = tensor([-1])]; + tensor input_97_cast_fp16 = layer_norm(axes = input_97_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_95_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor linear_47_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_97_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_101_mode_0 = const()[name = string("input_101_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_101_cast_fp16 = gelu(mode = input_101_mode_0, x = linear_47_cast_fp16)[name = string("input_101_cast_fp16")]; + tensor linear_48_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_101_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = linear_48_cast_fp16, y = input_97_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor hidden_states_17_axes_0 = const()[name = string("hidden_states_17_axes_0"), val = tensor([-1])]; + tensor hidden_states_17_cast_fp16 = layer_norm(axes = hidden_states_17_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_103_cast_fp16)[name = string("hidden_states_17_cast_fp16")]; + tensor linear_49_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_17_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_743 = const()[name = string("op_743"), val = tensor([1, 57, 12, 64])]; + tensor x_99_cast_fp16 = reshape(shape = var_743, x = linear_49_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor linear_50_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_17_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_752 = const()[name = string("op_752"), val = tensor([1, 57, 12, 64])]; + tensor x_103_cast_fp16 = reshape(shape = var_752, x = linear_50_cast_fp16)[name = string("x_103_cast_fp16")]; + tensor linear_51_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_17_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_761 = const()[name = string("op_761"), val = tensor([1, 57, 12, 64])]; + tensor x_107_cast_fp16 = reshape(shape = var_761, x = linear_51_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = x_107_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = x_103_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = x_99_cast_fp16)[name = string("transpose_124")]; + tensor attention_output_33_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("attention_output_33_cast_fp16")]; + tensor attention_output_35_perm_0 = const()[name = string("attention_output_35_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_767 = const()[name = string("op_767"), val = tensor([1, 57, 768])]; + tensor attention_output_35_cast_fp16 = transpose(perm = attention_output_35_perm_0, x = attention_output_33_cast_fp16)[name = string("transpose_121")]; + tensor input_105_cast_fp16 = reshape(shape = var_767, x = attention_output_35_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor linear_52_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_105_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor input_107_cast_fp16 = add(x = hidden_states_17_cast_fp16, y = linear_52_cast_fp16)[name = string("input_107_cast_fp16")]; + tensor input_109_axes_0 = const()[name = string("input_109_axes_0"), val = tensor([-1])]; + tensor input_109_cast_fp16 = layer_norm(axes = input_109_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_107_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor linear_53_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_109_cast_fp16)[name = string("linear_53_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = linear_53_cast_fp16)[name = string("input_113_cast_fp16")]; + tensor linear_54_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_113_cast_fp16)[name = string("linear_54_cast_fp16")]; + tensor input_115_cast_fp16 = add(x = linear_54_cast_fp16, y = input_109_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor hidden_states_19_axes_0 = const()[name = string("hidden_states_19_axes_0"), val = tensor([-1])]; + tensor hidden_states_19_cast_fp16 = layer_norm(axes = hidden_states_19_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("hidden_states_19_cast_fp16")]; + tensor linear_55_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_818 = const()[name = string("op_818"), val = tensor([1, 57, 12, 64])]; + tensor x_111_cast_fp16 = reshape(shape = var_818, x = linear_55_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor linear_56_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_827 = const()[name = string("op_827"), val = tensor([1, 57, 12, 64])]; + tensor x_115_cast_fp16 = reshape(shape = var_827, x = linear_56_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor linear_57_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_836 = const()[name = string("op_836"), val = tensor([1, 57, 12, 64])]; + tensor x_119_cast_fp16 = reshape(shape = var_836, x = linear_57_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = x_119_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = x_115_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = x_111_cast_fp16)[name = string("transpose_120")]; + tensor attention_output_37_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("attention_output_37_cast_fp16")]; + tensor attention_output_39_perm_0 = const()[name = string("attention_output_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_842 = const()[name = string("op_842"), val = tensor([1, 57, 768])]; + tensor attention_output_39_cast_fp16 = transpose(perm = attention_output_39_perm_0, x = attention_output_37_cast_fp16)[name = string("transpose_117")]; + tensor input_117_cast_fp16 = reshape(shape = var_842, x = attention_output_39_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor linear_58_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_117_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = linear_58_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor input_121_axes_0 = const()[name = string("input_121_axes_0"), val = tensor([-1])]; + tensor input_121_cast_fp16 = layer_norm(axes = input_121_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_119_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor linear_59_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_121_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = linear_59_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor linear_60_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_125_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor input_127_cast_fp16 = add(x = linear_60_cast_fp16, y = input_121_cast_fp16)[name = string("input_127_cast_fp16")]; + tensor hidden_states_21_axes_0 = const()[name = string("hidden_states_21_axes_0"), val = tensor([-1])]; + tensor hidden_states_21_cast_fp16 = layer_norm(axes = hidden_states_21_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("hidden_states_21_cast_fp16")]; + tensor linear_61_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_21_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_893 = const()[name = string("op_893"), val = tensor([1, 57, 12, 64])]; + tensor x_123_cast_fp16 = reshape(shape = var_893, x = linear_61_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor linear_62_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_21_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_902 = const()[name = string("op_902"), val = tensor([1, 57, 12, 64])]; + tensor x_127_cast_fp16 = reshape(shape = var_902, x = linear_62_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor linear_63_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_21_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_911 = const()[name = string("op_911"), val = tensor([1, 57, 12, 64])]; + tensor x_131_cast_fp16 = reshape(shape = var_911, x = linear_63_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = x_131_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = x_127_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = x_123_cast_fp16)[name = string("transpose_116")]; + tensor attention_output_41_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("attention_output_41_cast_fp16")]; + tensor attention_output_43_perm_0 = const()[name = string("attention_output_43_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_917 = const()[name = string("op_917"), val = tensor([1, 57, 768])]; + tensor attention_output_43_cast_fp16 = transpose(perm = attention_output_43_perm_0, x = attention_output_41_cast_fp16)[name = string("transpose_113")]; + tensor input_129_cast_fp16 = reshape(shape = var_917, x = attention_output_43_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor linear_64_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_129_cast_fp16)[name = string("linear_64_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = hidden_states_21_cast_fp16, y = linear_64_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor input_133_axes_0 = const()[name = string("input_133_axes_0"), val = tensor([-1])]; + tensor input_133_cast_fp16 = layer_norm(axes = input_133_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_131_cast_fp16)[name = string("input_133_cast_fp16")]; + tensor linear_65_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_133_cast_fp16)[name = string("linear_65_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = linear_65_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor linear_66_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_137_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor input_139_cast_fp16 = add(x = linear_66_cast_fp16, y = input_133_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor hidden_states_axes_0 = const()[name = string("hidden_states_axes_0"), val = tensor([-1])]; + tensor hidden_states_cast_fp16 = layer_norm(axes = hidden_states_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("hidden_states_cast_fp16")]; + tensor linear_67_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_968 = const()[name = string("op_968"), val = tensor([1, 57, 12, 64])]; + tensor x_135_cast_fp16 = reshape(shape = var_968, x = linear_67_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor linear_68_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_977 = const()[name = string("op_977"), val = tensor([1, 57, 12, 64])]; + tensor x_139_cast_fp16 = reshape(shape = var_977, x = linear_68_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor linear_69_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_cast_fp16)[name = string("linear_69_cast_fp16")]; + tensor var_986 = const()[name = string("op_986"), val = tensor([1, 57, 12, 64])]; + tensor x_cast_fp16 = reshape(shape = var_986, x = linear_69_cast_fp16)[name = string("x_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = x_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = x_139_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = x_135_cast_fp16)[name = string("transpose_112")]; + tensor attention_output_45_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("attention_output_45_cast_fp16")]; + tensor attention_output_perm_0 = const()[name = string("attention_output_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_992 = const()[name = string("op_992"), val = tensor([1, 57, 768])]; + tensor attention_output_cast_fp16 = transpose(perm = attention_output_perm_0, x = attention_output_45_cast_fp16)[name = string("transpose_109")]; + tensor input_141_cast_fp16 = reshape(shape = var_992, x = attention_output_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor linear_70_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_141_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = hidden_states_cast_fp16, y = linear_70_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor input_145_axes_0 = const()[name = string("input_145_axes_0"), val = tensor([-1])]; + tensor input_145_cast_fp16 = layer_norm(axes = input_145_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_143_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor linear_71_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_145_cast_fp16)[name = string("linear_71_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_71_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor linear_72_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_149_cast_fp16)[name = string("linear_72_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = linear_72_cast_fp16, y = input_145_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor sequence_output_axes_0 = const()[name = string("sequence_output_axes_0"), val = tensor([-1])]; + tensor sequence_output = layer_norm(axes = sequence_output_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("sequence_output_cast_fp16")]; + tensor bert_encoder_weight_to_fp16 = const()[name = string("bert_encoder_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11302912)))]; + tensor bert_encoder_bias_to_fp16 = const()[name = string("bert_encoder_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12089408)))]; + tensor linear_73_cast_fp16 = linear(bias = bert_encoder_bias_to_fp16, weight = bert_encoder_weight_to_fp16, x = sequence_output)[name = string("linear_73_cast_fp16")]; + tensor var_1030_perm_0 = const()[name = string("op_1030_perm_0"), val = tensor([0, -1, -2])]; + tensor var_1030 = transpose(perm = var_1030_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + } -> (sequence_output, var_1030); +} \ No newline at end of file diff --git a/iteration_3/compiled/bert_fp16.mlmodelc/weights/weight.bin b/iteration_3/compiled/bert_fp16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..5b3e19b1351e318a29951b98f6fe5f32a56f940e --- /dev/null +++ b/iteration_3/compiled/bert_fp16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc4a9fb3870729f9572b0830993351524b04b99eba6cab982cef2a17507d9ba0 +size 12090496 diff --git a/iteration_3/compiled/decoder_pre_fp16.mlmodelc/analytics/coremldata.bin b/iteration_3/compiled/decoder_pre_fp16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e2749dbc8fc8582f7f57a0851ae1fb4d0c857369 --- /dev/null +++ b/iteration_3/compiled/decoder_pre_fp16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b5d73b4fd5e456dd180b2e38abf8348b265ebe3fca95c7c671da76d64e1d0cd +size 243 diff --git a/iteration_3/compiled/decoder_pre_fp16.mlmodelc/coremldata.bin b/iteration_3/compiled/decoder_pre_fp16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..f56bda355efc20462e86801d31500d8405817119 --- /dev/null +++ b/iteration_3/compiled/decoder_pre_fp16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:42eac5d6b57aa155f4be14e845e550857b4a22289034b26eb37184ea3e80a85a +size 490 diff --git a/iteration_3/compiled/decoder_pre_fp16.mlmodelc/metadata.json b/iteration_3/compiled/decoder_pre_fp16.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..950d2f5078bc3e12054a9eb8e2e61759d59edf6d --- /dev/null +++ b/iteration_3/compiled/decoder_pre_fp16.mlmodelc/metadata.json @@ -0,0 +1,111 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Float16", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16)", + "shortDescription" : "", + "shape" : "[]", + "name" : "var_436", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 9, + "mlProgramOperationTypeHistogram" : { + "Ios18.instanceNorm" : 10, + "Ios18.linear" : 10, + "Ios18.convTranspose" : 1, + "Ios18.leakyRelu" : 10, + "Ios18.expandDims" : 3, + "Ios18.conv" : 18, + "Ios18.concat" : 5, + "Ios18.add" : 25, + "UpsampleNearestNeighbor" : 1, + "Ios18.sliceByIndex" : 1, + "Ios18.cast" : 4, + "Ios18.squeeze" : 1, + "Ios18.reshape" : 10, + "Split" : 10, + "Ios18.mul" : 15 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "15.0", + "tvOS" : "18.0", + "visionOS" : "2.0", + "watchOS" : "11.0", + "iOS" : "18.0", + "macCatalyst" : "18.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-05-08", + "com.github.apple.coremltools.source" : "torch==2.11.0", + "com.github.apple.coremltools.version" : "9.0", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "inputSchema" : [ + { + "dataType" : "Float32", + "hasShapeFlexibility" : "1", + "isOptional" : "0", + "shapeFlexibility" : "1 × 512 × 1...2048", + "shapeRange" : "[[1, 1], [512, 512], [1, 2048]]", + "formattedType" : "MultiArray (Float32 1 × 512 × 147)", + "type" : "MultiArray", + "shape" : "[1, 512, 147]", + "name" : "asr", + "shortDescription" : "" + }, + { + "dataType" : "Float32", + "hasShapeFlexibility" : "1", + "isOptional" : "0", + "shapeFlexibility" : "1 × 2...4096", + "shapeRange" : "[[1, 1], [2, 4096]]", + "formattedType" : "MultiArray (Float32 1 × 294)", + "type" : "MultiArray", + "shape" : "[1, 294]", + "name" : "f0_pred", + "shortDescription" : "" + }, + { + "dataType" : "Float32", + "hasShapeFlexibility" : "1", + "isOptional" : "0", + "shapeFlexibility" : "1 × 2...4096", + "shapeRange" : "[[1, 1], [2, 4096]]", + "formattedType" : "MultiArray (Float32 1 × 294)", + "type" : "MultiArray", + "shape" : "[1, 294]", + "name" : "n_pred", + "shortDescription" : "" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 128)", + "shortDescription" : "", + "shape" : "[1, 128]", + "name" : "ref", + "type" : "MultiArray" + } + ], + "generatedClassName" : "decoder_pre_fp16", + "method" : "predict" + } +] \ No newline at end of file diff --git a/iteration_3/compiled/decoder_pre_fp16.mlmodelc/model.mil b/iteration_3/compiled/decoder_pre_fp16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..5d46018b2c55056fbae7d2a2e0daa5244d768e47 --- /dev/null +++ b/iteration_3/compiled/decoder_pre_fp16.mlmodelc/model.mil @@ -0,0 +1,357 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor asr, tensor f0_pred, tensor n_pred, tensor ref) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"asr", [1, 512, 147]}, {"f0_pred", [1, 294]}, {"n_pred", [1, 294]}}), ("RangeDims", {{"asr", [[1, 1], [512, 512], [1, 2048]]}, {"f0_pred", [[1, 1], [2, 4096]]}, {"n_pred", [[1, 1], [2, 4096]]}})))] { + tensor input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor([1])]; + string f0_pred_to_fp16_dtype_0 = const()[name = string("f0_pred_to_fp16_dtype_0"), val = string("fp16")]; + tensor f0_pred_to_fp16 = cast(dtype = f0_pred_to_fp16_dtype_0, x = f0_pred)[name = string("cast_24")]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = f0_pred_to_fp16)[name = string("input_1_cast_fp16")]; + string F0_pad_type_0 = const()[name = string("F0_pad_type_0"), val = string("custom")]; + tensor F0_pad_0 = const()[name = string("F0_pad_0"), val = tensor([1, 1])]; + tensor F0_strides_0 = const()[name = string("F0_strides_0"), val = tensor([2])]; + tensor F0_dilations_0 = const()[name = string("F0_dilations_0"), val = tensor([1])]; + int32 F0_groups_0 = const()[name = string("F0_groups_0"), val = int32(1)]; + tensor decoder_F0_conv_weight_to_fp16 = const()[name = string("decoder_F0_conv_weight_to_fp16"), val = tensor([[[0x1.568p-5, -0x1.864p-5, -0x1.504p-4]]])]; + tensor decoder_F0_conv_bias_to_fp16 = const()[name = string("decoder_F0_conv_bias_to_fp16"), val = tensor([0x1.844p-2])]; + tensor F0_cast_fp16 = conv(bias = decoder_F0_conv_bias_to_fp16, dilations = F0_dilations_0, groups = F0_groups_0, pad = F0_pad_0, pad_type = F0_pad_type_0, strides = F0_strides_0, weight = decoder_F0_conv_weight_to_fp16, x = input_1_cast_fp16)[name = string("F0_cast_fp16")]; + tensor input_3_axes_0 = const()[name = string("input_3_axes_0"), val = tensor([1])]; + string n_pred_to_fp16_dtype_0 = const()[name = string("n_pred_to_fp16_dtype_0"), val = string("fp16")]; + tensor n_pred_to_fp16 = cast(dtype = n_pred_to_fp16_dtype_0, x = n_pred)[name = string("cast_23")]; + tensor input_3_cast_fp16 = expand_dims(axes = input_3_axes_0, x = n_pred_to_fp16)[name = string("input_3_cast_fp16")]; + string N_pad_type_0 = const()[name = string("N_pad_type_0"), val = string("custom")]; + tensor N_pad_0 = const()[name = string("N_pad_0"), val = tensor([1, 1])]; + tensor N_strides_0 = const()[name = string("N_strides_0"), val = tensor([2])]; + tensor N_dilations_0 = const()[name = string("N_dilations_0"), val = tensor([1])]; + int32 N_groups_0 = const()[name = string("N_groups_0"), val = int32(1)]; + tensor decoder_N_conv_weight_to_fp16 = const()[name = string("decoder_N_conv_weight_to_fp16"), val = tensor([[[0x1.1d4p-5, -0x1.71p-1, -0x1.89cp-1]]])]; + tensor decoder_N_conv_bias_to_fp16 = const()[name = string("decoder_N_conv_bias_to_fp16"), val = tensor([0x1.44cp-1])]; + tensor N_cast_fp16 = conv(bias = decoder_N_conv_bias_to_fp16, dilations = N_dilations_0, groups = N_groups_0, pad = N_pad_0, pad_type = N_pad_type_0, strides = N_strides_0, weight = decoder_N_conv_weight_to_fp16, x = input_3_cast_fp16)[name = string("N_cast_fp16")]; + int32 var_54 = const()[name = string("op_54"), val = int32(1)]; + bool input_7_interleave_0 = const()[name = string("input_7_interleave_0"), val = bool(false)]; + string asr_to_fp16_dtype_0 = const()[name = string("asr_to_fp16_dtype_0"), val = string("fp16")]; + tensor asr_to_fp16 = cast(dtype = asr_to_fp16_dtype_0, x = asr)[name = string("cast_22")]; + tensor input_7_cast_fp16 = concat(axis = var_54, interleave = input_7_interleave_0, values = (asr_to_fp16, F0_cast_fp16, N_cast_fp16))[name = string("input_7_cast_fp16")]; + string ref_to_fp16_dtype_0 = const()[name = string("ref_to_fp16_dtype_0"), val = string("fp16")]; + fp32 var_61 = const()[name = string("op_61"), val = fp32(0x1.99999ap-3)]; + tensor decoder_encode_norm1_fc_weight_to_fp16 = const()[name = string("decoder_encode_norm1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor decoder_encode_norm1_fc_bias_to_fp16 = const()[name = string("decoder_encode_norm1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263296)))]; + tensor ref_to_fp16 = cast(dtype = ref_to_fp16_dtype_0, x = ref)[name = string("cast_21")]; + tensor linear_0_cast_fp16 = linear(bias = decoder_encode_norm1_fc_bias_to_fp16, weight = decoder_encode_norm1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_79 = const()[name = string("op_79"), val = tensor([1, 1028, 1])]; + tensor h_3_cast_fp16 = reshape(shape = var_79, x = linear_0_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor var_81_split_sizes_0 = const()[name = string("op_81_split_sizes_0"), val = tensor([514, 514])]; + int32 var_81_axis_0 = const()[name = string("op_81_axis_0"), val = int32(1)]; + tensor var_81_cast_fp16_0, tensor var_81_cast_fp16_1 = split(axis = var_81_axis_0, split_sizes = var_81_split_sizes_0, x = h_3_cast_fp16)[name = string("op_81_cast_fp16")]; + fp16 var_83_promoted_to_fp16 = const()[name = string("op_83_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_84_cast_fp16 = add(x = var_81_cast_fp16_0, y = var_83_promoted_to_fp16)[name = string("op_84_cast_fp16")]; + fp16 var_64_to_fp16 = const()[name = string("op_64_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_85_cast_fp16 = instance_norm(epsilon = var_64_to_fp16, x = input_7_cast_fp16)[name = string("op_85_cast_fp16")]; + tensor var_86_cast_fp16 = mul(x = var_84_cast_fp16, y = var_85_cast_fp16)[name = string("op_86_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_86_cast_fp16, y = var_81_cast_fp16_1)[name = string("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = leaky_relu(alpha = var_61, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")]; + tensor input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor([1, 1])]; + tensor input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor([1])]; + tensor input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor([1])]; + int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)]; + tensor decoder_encode_conv1_weight_to_fp16 = const()[name = string("decoder_encode_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265472)))]; + tensor decoder_encode_conv1_bias_to_fp16 = const()[name = string("decoder_encode_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3423552)))]; + tensor input_13_cast_fp16 = conv(bias = decoder_encode_conv1_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = decoder_encode_conv1_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor decoder_encode_norm2_fc_weight_to_fp16 = const()[name = string("decoder_encode_norm2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3425664)))]; + tensor decoder_encode_norm2_fc_bias_to_fp16 = const()[name = string("decoder_encode_norm2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3950016)))]; + tensor linear_1_cast_fp16 = linear(bias = decoder_encode_norm2_fc_bias_to_fp16, weight = decoder_encode_norm2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_102 = const()[name = string("op_102"), val = tensor([1, 2048, 1])]; + tensor h_7_cast_fp16 = reshape(shape = var_102, x = linear_1_cast_fp16)[name = string("h_7_cast_fp16")]; + tensor var_104_split_sizes_0 = const()[name = string("op_104_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_104_axis_0 = const()[name = string("op_104_axis_0"), val = int32(1)]; + tensor var_104_cast_fp16_0, tensor var_104_cast_fp16_1 = split(axis = var_104_axis_0, split_sizes = var_104_split_sizes_0, x = h_7_cast_fp16)[name = string("op_104_cast_fp16")]; + fp16 var_106_promoted_to_fp16 = const()[name = string("op_106_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_107_cast_fp16 = add(x = var_104_cast_fp16_0, y = var_106_promoted_to_fp16)[name = string("op_107_cast_fp16")]; + tensor var_108_cast_fp16 = instance_norm(epsilon = var_64_to_fp16, x = input_13_cast_fp16)[name = string("op_108_cast_fp16")]; + tensor var_109_cast_fp16 = mul(x = var_107_cast_fp16, y = var_108_cast_fp16)[name = string("op_109_cast_fp16")]; + tensor input_15_cast_fp16 = add(x = var_109_cast_fp16, y = var_104_cast_fp16_1)[name = string("input_15_cast_fp16")]; + tensor input_17_cast_fp16 = leaky_relu(alpha = var_61, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + string out_1_pad_type_0 = const()[name = string("out_1_pad_type_0"), val = string("custom")]; + tensor out_1_pad_0 = const()[name = string("out_1_pad_0"), val = tensor([1, 1])]; + tensor out_1_strides_0 = const()[name = string("out_1_strides_0"), val = tensor([1])]; + tensor out_1_dilations_0 = const()[name = string("out_1_dilations_0"), val = tensor([1])]; + int32 out_1_groups_0 = const()[name = string("out_1_groups_0"), val = int32(1)]; + tensor decoder_encode_conv2_weight_to_fp16 = const()[name = string("decoder_encode_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3954176)))]; + tensor decoder_encode_conv2_bias_to_fp16 = const()[name = string("decoder_encode_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10245696)))]; + tensor out_1_cast_fp16 = conv(bias = decoder_encode_conv2_bias_to_fp16, dilations = out_1_dilations_0, groups = out_1_groups_0, pad = out_1_pad_0, pad_type = out_1_pad_type_0, strides = out_1_strides_0, weight = decoder_encode_conv2_weight_to_fp16, x = input_17_cast_fp16)[name = string("out_1_cast_fp16")]; + string var_124_pad_type_0 = const()[name = string("op_124_pad_type_0"), val = string("valid")]; + tensor var_124_strides_0 = const()[name = string("op_124_strides_0"), val = tensor([1])]; + tensor var_124_pad_0 = const()[name = string("op_124_pad_0"), val = tensor([0, 0])]; + tensor var_124_dilations_0 = const()[name = string("op_124_dilations_0"), val = tensor([1])]; + int32 var_124_groups_0 = const()[name = string("op_124_groups_0"), val = int32(1)]; + tensor decoder_encode_conv1x1_weight_to_fp16 = const()[name = string("decoder_encode_conv1x1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10247808)))]; + tensor var_124_cast_fp16 = conv(dilations = var_124_dilations_0, groups = var_124_groups_0, pad = var_124_pad_0, pad_type = var_124_pad_type_0, strides = var_124_strides_0, weight = decoder_encode_conv1x1_weight_to_fp16, x = input_7_cast_fp16)[name = string("op_124_cast_fp16")]; + tensor var_125_cast_fp16 = add(x = out_1_cast_fp16, y = var_124_cast_fp16)[name = string("op_125_cast_fp16")]; + fp16 _inversed_x_1_y_0_to_fp16 = const()[name = string("_inversed_x_1_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_x_1_cast_fp16 = mul(x = var_125_cast_fp16, y = _inversed_x_1_y_0_to_fp16)[name = string("_inversed_x_1_cast_fp16")]; + string asr_res_1_pad_type_0 = const()[name = string("asr_res_1_pad_type_0"), val = string("valid")]; + tensor asr_res_1_strides_0 = const()[name = string("asr_res_1_strides_0"), val = tensor([1])]; + tensor asr_res_1_pad_0 = const()[name = string("asr_res_1_pad_0"), val = tensor([0, 0])]; + tensor asr_res_1_dilations_0 = const()[name = string("asr_res_1_dilations_0"), val = tensor([1])]; + int32 asr_res_1_groups_0 = const()[name = string("asr_res_1_groups_0"), val = int32(1)]; + tensor decoder_asr_res_0_weight_to_fp16 = const()[name = string("decoder_asr_res_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11300544)))]; + tensor decoder_asr_res_0_bias_to_fp16 = const()[name = string("decoder_asr_res_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11366144)))]; + tensor asr_res_1_cast_fp16 = conv(bias = decoder_asr_res_0_bias_to_fp16, dilations = asr_res_1_dilations_0, groups = asr_res_1_groups_0, pad = asr_res_1_pad_0, pad_type = asr_res_1_pad_type_0, strides = asr_res_1_strides_0, weight = decoder_asr_res_0_weight_to_fp16, x = asr_to_fp16)[name = string("asr_res_1_cast_fp16")]; + int32 var_141 = const()[name = string("op_141"), val = int32(1)]; + bool input_19_interleave_0 = const()[name = string("input_19_interleave_0"), val = bool(false)]; + tensor input_19_cast_fp16 = concat(axis = var_141, interleave = input_19_interleave_0, values = (_inversed_x_1_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_cast_fp16))[name = string("input_19_cast_fp16")]; + fp32 var_144 = const()[name = string("op_144"), val = fp32(0x1.99999ap-3)]; + tensor decoder_decode_0_norm1_fc_weight_to_fp16 = const()[name = string("decoder_decode_0_norm1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11366336)))]; + tensor decoder_decode_0_norm1_fc_bias_to_fp16 = const()[name = string("decoder_decode_0_norm1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11924480)))]; + tensor linear_2_cast_fp16 = linear(bias = decoder_decode_0_norm1_fc_bias_to_fp16, weight = decoder_decode_0_norm1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_162 = const()[name = string("op_162"), val = tensor([1, 2180, 1])]; + tensor h_11_cast_fp16 = reshape(shape = var_162, x = linear_2_cast_fp16)[name = string("h_11_cast_fp16")]; + tensor var_164_split_sizes_0 = const()[name = string("op_164_split_sizes_0"), val = tensor([1090, 1090])]; + int32 var_164_axis_0 = const()[name = string("op_164_axis_0"), val = int32(1)]; + tensor var_164_cast_fp16_0, tensor var_164_cast_fp16_1 = split(axis = var_164_axis_0, split_sizes = var_164_split_sizes_0, x = h_11_cast_fp16)[name = string("op_164_cast_fp16")]; + fp16 var_166_promoted_to_fp16 = const()[name = string("op_166_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = add(x = var_164_cast_fp16_0, y = var_166_promoted_to_fp16)[name = string("op_167_cast_fp16")]; + fp16 var_147_to_fp16 = const()[name = string("op_147_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_168_cast_fp16 = instance_norm(epsilon = var_147_to_fp16, x = input_19_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = var_167_cast_fp16, y = var_168_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_169_cast_fp16, y = var_164_cast_fp16_1)[name = string("input_21_cast_fp16")]; + tensor input_23_cast_fp16 = leaky_relu(alpha = var_144, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")]; + tensor input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor([1, 1])]; + tensor input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor([1])]; + tensor input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor([1])]; + int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)]; + tensor decoder_decode_0_conv1_weight_to_fp16 = const()[name = string("decoder_decode_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11928960)))]; + tensor decoder_decode_0_conv1_bias_to_fp16 = const()[name = string("decoder_decode_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18625984)))]; + tensor input_25_cast_fp16 = conv(bias = decoder_decode_0_conv1_bias_to_fp16, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = decoder_decode_0_conv1_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor decoder_decode_0_norm2_fc_weight_to_fp16 = const()[name = string("decoder_decode_0_norm2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18628096)))]; + tensor decoder_decode_0_norm2_fc_bias_to_fp16 = const()[name = string("decoder_decode_0_norm2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152448)))]; + tensor linear_3_cast_fp16 = linear(bias = decoder_decode_0_norm2_fc_bias_to_fp16, weight = decoder_decode_0_norm2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_185 = const()[name = string("op_185"), val = tensor([1, 2048, 1])]; + tensor h_15_cast_fp16 = reshape(shape = var_185, x = linear_3_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor var_187_split_sizes_0 = const()[name = string("op_187_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_187_axis_0 = const()[name = string("op_187_axis_0"), val = int32(1)]; + tensor var_187_cast_fp16_0, tensor var_187_cast_fp16_1 = split(axis = var_187_axis_0, split_sizes = var_187_split_sizes_0, x = h_15_cast_fp16)[name = string("op_187_cast_fp16")]; + fp16 var_189_promoted_to_fp16 = const()[name = string("op_189_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_190_cast_fp16 = add(x = var_187_cast_fp16_0, y = var_189_promoted_to_fp16)[name = string("op_190_cast_fp16")]; + tensor var_191_cast_fp16 = instance_norm(epsilon = var_147_to_fp16, x = input_25_cast_fp16)[name = string("op_191_cast_fp16")]; + tensor var_192_cast_fp16 = mul(x = var_190_cast_fp16, y = var_191_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_27_cast_fp16 = add(x = var_192_cast_fp16, y = var_187_cast_fp16_1)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = leaky_relu(alpha = var_144, x = input_27_cast_fp16)[name = string("input_29_cast_fp16")]; + string out_3_pad_type_0 = const()[name = string("out_3_pad_type_0"), val = string("custom")]; + tensor out_3_pad_0 = const()[name = string("out_3_pad_0"), val = tensor([1, 1])]; + tensor out_3_strides_0 = const()[name = string("out_3_strides_0"), val = tensor([1])]; + tensor out_3_dilations_0 = const()[name = string("out_3_dilations_0"), val = tensor([1])]; + int32 out_3_groups_0 = const()[name = string("out_3_groups_0"), val = int32(1)]; + tensor decoder_decode_0_conv2_weight_to_fp16 = const()[name = string("decoder_decode_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19156608)))]; + tensor decoder_decode_0_conv2_bias_to_fp16 = const()[name = string("decoder_decode_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25448128)))]; + tensor out_3_cast_fp16 = conv(bias = decoder_decode_0_conv2_bias_to_fp16, dilations = out_3_dilations_0, groups = out_3_groups_0, pad = out_3_pad_0, pad_type = out_3_pad_type_0, strides = out_3_strides_0, weight = decoder_decode_0_conv2_weight_to_fp16, x = input_29_cast_fp16)[name = string("out_3_cast_fp16")]; + string var_207_pad_type_0 = const()[name = string("op_207_pad_type_0"), val = string("valid")]; + tensor var_207_strides_0 = const()[name = string("op_207_strides_0"), val = tensor([1])]; + tensor var_207_pad_0 = const()[name = string("op_207_pad_0"), val = tensor([0, 0])]; + tensor var_207_dilations_0 = const()[name = string("op_207_dilations_0"), val = tensor([1])]; + int32 var_207_groups_0 = const()[name = string("op_207_groups_0"), val = int32(1)]; + tensor decoder_decode_0_conv1x1_weight_to_fp16 = const()[name = string("decoder_decode_0_conv1x1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25450240)))]; + tensor var_207_cast_fp16 = conv(dilations = var_207_dilations_0, groups = var_207_groups_0, pad = var_207_pad_0, pad_type = var_207_pad_type_0, strides = var_207_strides_0, weight = decoder_decode_0_conv1x1_weight_to_fp16, x = input_19_cast_fp16)[name = string("op_207_cast_fp16")]; + tensor var_208_cast_fp16 = add(x = out_3_cast_fp16, y = var_207_cast_fp16)[name = string("op_208_cast_fp16")]; + fp16 _inversed_x_3_y_0_to_fp16 = const()[name = string("_inversed_x_3_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_x_3_cast_fp16 = mul(x = var_208_cast_fp16, y = _inversed_x_3_y_0_to_fp16)[name = string("_inversed_x_3_cast_fp16")]; + int32 var_212 = const()[name = string("op_212"), val = int32(1)]; + bool input_31_interleave_0 = const()[name = string("input_31_interleave_0"), val = bool(false)]; + tensor input_31_cast_fp16 = concat(axis = var_212, interleave = input_31_interleave_0, values = (_inversed_x_3_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_cast_fp16))[name = string("input_31_cast_fp16")]; + fp32 var_215 = const()[name = string("op_215"), val = fp32(0x1.99999ap-3)]; + tensor decoder_decode_1_norm1_fc_weight_to_fp16 = const()[name = string("decoder_decode_1_norm1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27682624)))]; + tensor decoder_decode_1_norm1_fc_bias_to_fp16 = const()[name = string("decoder_decode_1_norm1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28240768)))]; + tensor linear_4_cast_fp16 = linear(bias = decoder_decode_1_norm1_fc_bias_to_fp16, weight = decoder_decode_1_norm1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_233 = const()[name = string("op_233"), val = tensor([1, 2180, 1])]; + tensor h_19_cast_fp16 = reshape(shape = var_233, x = linear_4_cast_fp16)[name = string("h_19_cast_fp16")]; + tensor var_235_split_sizes_0 = const()[name = string("op_235_split_sizes_0"), val = tensor([1090, 1090])]; + int32 var_235_axis_0 = const()[name = string("op_235_axis_0"), val = int32(1)]; + tensor var_235_cast_fp16_0, tensor var_235_cast_fp16_1 = split(axis = var_235_axis_0, split_sizes = var_235_split_sizes_0, x = h_19_cast_fp16)[name = string("op_235_cast_fp16")]; + fp16 var_237_promoted_to_fp16 = const()[name = string("op_237_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_238_cast_fp16 = add(x = var_235_cast_fp16_0, y = var_237_promoted_to_fp16)[name = string("op_238_cast_fp16")]; + fp16 var_218_to_fp16 = const()[name = string("op_218_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_239_cast_fp16 = instance_norm(epsilon = var_218_to_fp16, x = input_31_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor var_240_cast_fp16 = mul(x = var_238_cast_fp16, y = var_239_cast_fp16)[name = string("op_240_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_240_cast_fp16, y = var_235_cast_fp16_1)[name = string("input_33_cast_fp16")]; + tensor input_35_cast_fp16 = leaky_relu(alpha = var_215, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")]; + tensor input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor([1, 1])]; + tensor input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor([1])]; + tensor input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor([1])]; + int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)]; + tensor decoder_decode_1_conv1_weight_to_fp16 = const()[name = string("decoder_decode_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28245248)))]; + tensor decoder_decode_1_conv1_bias_to_fp16 = const()[name = string("decoder_decode_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34942272)))]; + tensor input_37_cast_fp16 = conv(bias = decoder_decode_1_conv1_bias_to_fp16, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = decoder_decode_1_conv1_weight_to_fp16, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")]; + tensor decoder_decode_1_norm2_fc_weight_to_fp16 = const()[name = string("decoder_decode_1_norm2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34944384)))]; + tensor decoder_decode_1_norm2_fc_bias_to_fp16 = const()[name = string("decoder_decode_1_norm2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35468736)))]; + tensor linear_5_cast_fp16 = linear(bias = decoder_decode_1_norm2_fc_bias_to_fp16, weight = decoder_decode_1_norm2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_256 = const()[name = string("op_256"), val = tensor([1, 2048, 1])]; + tensor h_23_cast_fp16 = reshape(shape = var_256, x = linear_5_cast_fp16)[name = string("h_23_cast_fp16")]; + tensor var_258_split_sizes_0 = const()[name = string("op_258_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_258_axis_0 = const()[name = string("op_258_axis_0"), val = int32(1)]; + tensor var_258_cast_fp16_0, tensor var_258_cast_fp16_1 = split(axis = var_258_axis_0, split_sizes = var_258_split_sizes_0, x = h_23_cast_fp16)[name = string("op_258_cast_fp16")]; + fp16 var_260_promoted_to_fp16 = const()[name = string("op_260_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_261_cast_fp16 = add(x = var_258_cast_fp16_0, y = var_260_promoted_to_fp16)[name = string("op_261_cast_fp16")]; + tensor var_262_cast_fp16 = instance_norm(epsilon = var_218_to_fp16, x = input_37_cast_fp16)[name = string("op_262_cast_fp16")]; + tensor var_263_cast_fp16 = mul(x = var_261_cast_fp16, y = var_262_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = var_263_cast_fp16, y = var_258_cast_fp16_1)[name = string("input_39_cast_fp16")]; + tensor input_41_cast_fp16 = leaky_relu(alpha = var_215, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + string out_5_pad_type_0 = const()[name = string("out_5_pad_type_0"), val = string("custom")]; + tensor out_5_pad_0 = const()[name = string("out_5_pad_0"), val = tensor([1, 1])]; + tensor out_5_strides_0 = const()[name = string("out_5_strides_0"), val = tensor([1])]; + tensor out_5_dilations_0 = const()[name = string("out_5_dilations_0"), val = tensor([1])]; + int32 out_5_groups_0 = const()[name = string("out_5_groups_0"), val = int32(1)]; + tensor decoder_decode_1_conv2_weight_to_fp16 = const()[name = string("decoder_decode_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35472896)))]; + tensor decoder_decode_1_conv2_bias_to_fp16 = const()[name = string("decoder_decode_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41764416)))]; + tensor out_5_cast_fp16 = conv(bias = decoder_decode_1_conv2_bias_to_fp16, dilations = out_5_dilations_0, groups = out_5_groups_0, pad = out_5_pad_0, pad_type = out_5_pad_type_0, strides = out_5_strides_0, weight = decoder_decode_1_conv2_weight_to_fp16, x = input_41_cast_fp16)[name = string("out_5_cast_fp16")]; + string var_278_pad_type_0 = const()[name = string("op_278_pad_type_0"), val = string("valid")]; + tensor var_278_strides_0 = const()[name = string("op_278_strides_0"), val = tensor([1])]; + tensor var_278_pad_0 = const()[name = string("op_278_pad_0"), val = tensor([0, 0])]; + tensor var_278_dilations_0 = const()[name = string("op_278_dilations_0"), val = tensor([1])]; + int32 var_278_groups_0 = const()[name = string("op_278_groups_0"), val = int32(1)]; + tensor decoder_decode_1_conv1x1_weight_to_fp16 = const()[name = string("decoder_decode_1_conv1x1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41766528)))]; + tensor var_278_cast_fp16 = conv(dilations = var_278_dilations_0, groups = var_278_groups_0, pad = var_278_pad_0, pad_type = var_278_pad_type_0, strides = var_278_strides_0, weight = decoder_decode_1_conv1x1_weight_to_fp16, x = input_31_cast_fp16)[name = string("op_278_cast_fp16")]; + tensor var_279_cast_fp16 = add(x = out_5_cast_fp16, y = var_278_cast_fp16)[name = string("op_279_cast_fp16")]; + fp16 _inversed_x_5_y_0_to_fp16 = const()[name = string("_inversed_x_5_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_x_5_cast_fp16 = mul(x = var_279_cast_fp16, y = _inversed_x_5_y_0_to_fp16)[name = string("_inversed_x_5_cast_fp16")]; + int32 var_283 = const()[name = string("op_283"), val = int32(1)]; + bool input_43_interleave_0 = const()[name = string("input_43_interleave_0"), val = bool(false)]; + tensor input_43_cast_fp16 = concat(axis = var_283, interleave = input_43_interleave_0, values = (_inversed_x_5_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_cast_fp16))[name = string("input_43_cast_fp16")]; + fp32 var_286 = const()[name = string("op_286"), val = fp32(0x1.99999ap-3)]; + tensor decoder_decode_2_norm1_fc_weight_to_fp16 = const()[name = string("decoder_decode_2_norm1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43998912)))]; + tensor decoder_decode_2_norm1_fc_bias_to_fp16 = const()[name = string("decoder_decode_2_norm1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44557056)))]; + tensor linear_6_cast_fp16 = linear(bias = decoder_decode_2_norm1_fc_bias_to_fp16, weight = decoder_decode_2_norm1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_304 = const()[name = string("op_304"), val = tensor([1, 2180, 1])]; + tensor h_27_cast_fp16 = reshape(shape = var_304, x = linear_6_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor var_306_split_sizes_0 = const()[name = string("op_306_split_sizes_0"), val = tensor([1090, 1090])]; + int32 var_306_axis_0 = const()[name = string("op_306_axis_0"), val = int32(1)]; + tensor var_306_cast_fp16_0, tensor var_306_cast_fp16_1 = split(axis = var_306_axis_0, split_sizes = var_306_split_sizes_0, x = h_27_cast_fp16)[name = string("op_306_cast_fp16")]; + fp16 var_308_promoted_to_fp16 = const()[name = string("op_308_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_309_cast_fp16 = add(x = var_306_cast_fp16_0, y = var_308_promoted_to_fp16)[name = string("op_309_cast_fp16")]; + fp16 var_289_to_fp16 = const()[name = string("op_289_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_310_cast_fp16 = instance_norm(epsilon = var_289_to_fp16, x = input_43_cast_fp16)[name = string("op_310_cast_fp16")]; + tensor var_311_cast_fp16 = mul(x = var_309_cast_fp16, y = var_310_cast_fp16)[name = string("op_311_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_311_cast_fp16, y = var_306_cast_fp16_1)[name = string("input_45_cast_fp16")]; + tensor input_47_cast_fp16 = leaky_relu(alpha = var_286, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")]; + tensor input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor([1, 1])]; + tensor input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor([1])]; + tensor input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor([1])]; + int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)]; + tensor decoder_decode_2_conv1_weight_to_fp16 = const()[name = string("decoder_decode_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44561536)))]; + tensor decoder_decode_2_conv1_bias_to_fp16 = const()[name = string("decoder_decode_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51258560)))]; + tensor input_49_cast_fp16 = conv(bias = decoder_decode_2_conv1_bias_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = decoder_decode_2_conv1_weight_to_fp16, x = input_47_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor decoder_decode_2_norm2_fc_weight_to_fp16 = const()[name = string("decoder_decode_2_norm2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51260672)))]; + tensor decoder_decode_2_norm2_fc_bias_to_fp16 = const()[name = string("decoder_decode_2_norm2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51785024)))]; + tensor linear_7_cast_fp16 = linear(bias = decoder_decode_2_norm2_fc_bias_to_fp16, weight = decoder_decode_2_norm2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_327 = const()[name = string("op_327"), val = tensor([1, 2048, 1])]; + tensor h_31_cast_fp16 = reshape(shape = var_327, x = linear_7_cast_fp16)[name = string("h_31_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = h_31_cast_fp16)[name = string("op_329_cast_fp16")]; + fp16 var_331_promoted_to_fp16 = const()[name = string("op_331_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_332_cast_fp16 = add(x = var_329_cast_fp16_0, y = var_331_promoted_to_fp16)[name = string("op_332_cast_fp16")]; + tensor var_333_cast_fp16 = instance_norm(epsilon = var_289_to_fp16, x = input_49_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = var_332_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_334_cast_fp16, y = var_329_cast_fp16_1)[name = string("input_51_cast_fp16")]; + tensor input_53_cast_fp16 = leaky_relu(alpha = var_286, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")]; + string out_7_pad_type_0 = const()[name = string("out_7_pad_type_0"), val = string("custom")]; + tensor out_7_pad_0 = const()[name = string("out_7_pad_0"), val = tensor([1, 1])]; + tensor out_7_strides_0 = const()[name = string("out_7_strides_0"), val = tensor([1])]; + tensor out_7_dilations_0 = const()[name = string("out_7_dilations_0"), val = tensor([1])]; + int32 out_7_groups_0 = const()[name = string("out_7_groups_0"), val = int32(1)]; + tensor decoder_decode_2_conv2_weight_to_fp16 = const()[name = string("decoder_decode_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51789184)))]; + tensor decoder_decode_2_conv2_bias_to_fp16 = const()[name = string("decoder_decode_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58080704)))]; + tensor out_7_cast_fp16 = conv(bias = decoder_decode_2_conv2_bias_to_fp16, dilations = out_7_dilations_0, groups = out_7_groups_0, pad = out_7_pad_0, pad_type = out_7_pad_type_0, strides = out_7_strides_0, weight = decoder_decode_2_conv2_weight_to_fp16, x = input_53_cast_fp16)[name = string("out_7_cast_fp16")]; + string var_349_pad_type_0 = const()[name = string("op_349_pad_type_0"), val = string("valid")]; + tensor var_349_strides_0 = const()[name = string("op_349_strides_0"), val = tensor([1])]; + tensor var_349_pad_0 = const()[name = string("op_349_pad_0"), val = tensor([0, 0])]; + tensor var_349_dilations_0 = const()[name = string("op_349_dilations_0"), val = tensor([1])]; + int32 var_349_groups_0 = const()[name = string("op_349_groups_0"), val = int32(1)]; + tensor decoder_decode_2_conv1x1_weight_to_fp16 = const()[name = string("decoder_decode_2_conv1x1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58082816)))]; + tensor var_349_cast_fp16 = conv(dilations = var_349_dilations_0, groups = var_349_groups_0, pad = var_349_pad_0, pad_type = var_349_pad_type_0, strides = var_349_strides_0, weight = decoder_decode_2_conv1x1_weight_to_fp16, x = input_43_cast_fp16)[name = string("op_349_cast_fp16")]; + tensor var_350_cast_fp16 = add(x = out_7_cast_fp16, y = var_349_cast_fp16)[name = string("op_350_cast_fp16")]; + fp16 _inversed_x_y_0_to_fp16 = const()[name = string("_inversed_x_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_x_cast_fp16 = mul(x = var_350_cast_fp16, y = _inversed_x_y_0_to_fp16)[name = string("_inversed_x_cast_fp16")]; + int32 var_354 = const()[name = string("op_354"), val = int32(1)]; + bool input_55_interleave_0 = const()[name = string("input_55_interleave_0"), val = bool(false)]; + tensor input_55_cast_fp16 = concat(axis = var_354, interleave = input_55_interleave_0, values = (_inversed_x_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_cast_fp16))[name = string("input_55_cast_fp16")]; + fp32 var_359 = const()[name = string("op_359"), val = fp32(0x1.99999ap-3)]; + tensor decoder_decode_3_norm1_fc_weight_to_fp16 = const()[name = string("decoder_decode_3_norm1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60315200)))]; + tensor decoder_decode_3_norm1_fc_bias_to_fp16 = const()[name = string("decoder_decode_3_norm1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60873344)))]; + tensor linear_8_cast_fp16 = linear(bias = decoder_decode_3_norm1_fc_bias_to_fp16, weight = decoder_decode_3_norm1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_379 = const()[name = string("op_379"), val = tensor([1, 2180, 1])]; + tensor h_35_cast_fp16 = reshape(shape = var_379, x = linear_8_cast_fp16)[name = string("h_35_cast_fp16")]; + tensor var_381_split_sizes_0 = const()[name = string("op_381_split_sizes_0"), val = tensor([1090, 1090])]; + int32 var_381_axis_0 = const()[name = string("op_381_axis_0"), val = int32(1)]; + tensor var_381_cast_fp16_0, tensor var_381_cast_fp16_1 = split(axis = var_381_axis_0, split_sizes = var_381_split_sizes_0, x = h_35_cast_fp16)[name = string("op_381_cast_fp16")]; + fp16 var_383_promoted_to_fp16 = const()[name = string("op_383_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_384_cast_fp16 = add(x = var_381_cast_fp16_0, y = var_383_promoted_to_fp16)[name = string("op_384_cast_fp16")]; + fp16 var_363_to_fp16 = const()[name = string("op_363_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_385_cast_fp16 = instance_norm(epsilon = var_363_to_fp16, x = input_55_cast_fp16)[name = string("op_385_cast_fp16")]; + tensor var_386_cast_fp16 = mul(x = var_384_cast_fp16, y = var_385_cast_fp16)[name = string("op_386_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = var_386_cast_fp16, y = var_381_cast_fp16_1)[name = string("input_57_cast_fp16")]; + tensor input_59_cast_fp16 = leaky_relu(alpha = var_359, x = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + string conv_transpose_0_pad_type_0 = const()[name = string("conv_transpose_0_pad_type_0"), val = string("custom")]; + tensor conv_transpose_0_pad_0 = const()[name = string("conv_transpose_0_pad_0"), val = tensor([0, 0])]; + tensor conv_transpose_0_strides_0 = const()[name = string("conv_transpose_0_strides_0"), val = tensor([2])]; + int32 conv_transpose_0_groups_0 = const()[name = string("conv_transpose_0_groups_0"), val = int32(1090)]; + tensor conv_transpose_0_dilations_0 = const()[name = string("conv_transpose_0_dilations_0"), val = tensor([1])]; + tensor decoder_decode_3_pool_weight_to_fp16 = const()[name = string("decoder_decode_3_pool_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60877824)))]; + tensor decoder_decode_3_pool_bias_to_fp16 = const()[name = string("decoder_decode_3_pool_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60884480)))]; + tensor conv_transpose_0_cast_fp16 = conv_transpose(bias = decoder_decode_3_pool_bias_to_fp16, dilations = conv_transpose_0_dilations_0, groups = conv_transpose_0_groups_0, pad = conv_transpose_0_pad_0, pad_type = conv_transpose_0_pad_type_0, strides = conv_transpose_0_strides_0, weight = decoder_decode_3_pool_weight_to_fp16, x = input_59_cast_fp16)[name = string("conv_transpose_0_cast_fp16")]; + tensor input_61_begin_0 = const()[name = string("input_61_begin_0"), val = tensor([0, 0, 1])]; + tensor input_61_end_0 = const()[name = string("input_61_end_0"), val = tensor([0, 0, 0])]; + tensor input_61_begin_mask_0 = const()[name = string("input_61_begin_mask_0"), val = tensor([true, true, false])]; + tensor input_61_end_mask_0 = const()[name = string("input_61_end_mask_0"), val = tensor([true, true, true])]; + tensor input_61_cast_fp16 = slice_by_index(begin = input_61_begin_0, begin_mask = input_61_begin_mask_0, end = input_61_end_0, end_mask = input_61_end_mask_0, x = conv_transpose_0_cast_fp16)[name = string("input_61_cast_fp16")]; + string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")]; + tensor input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor([1, 1])]; + tensor input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor([1])]; + tensor input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor([1])]; + int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)]; + tensor decoder_decode_3_conv1_weight_to_fp16 = const()[name = string("decoder_decode_3_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60886784)))]; + tensor decoder_decode_3_conv1_bias_to_fp16 = const()[name = string("decoder_decode_3_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64235328)))]; + tensor input_63_cast_fp16 = conv(bias = decoder_decode_3_conv1_bias_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = decoder_decode_3_conv1_weight_to_fp16, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor decoder_decode_3_norm2_fc_weight_to_fp16 = const()[name = string("decoder_decode_3_norm2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64236416)))]; + tensor decoder_decode_3_norm2_fc_bias_to_fp16 = const()[name = string("decoder_decode_3_norm2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64498624)))]; + tensor linear_9_cast_fp16 = linear(bias = decoder_decode_3_norm2_fc_bias_to_fp16, weight = decoder_decode_3_norm2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 1024, 1])]; + tensor h_cast_fp16 = reshape(shape = var_409, x = linear_9_cast_fp16)[name = string("h_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([512, 512])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(1)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = h_cast_fp16)[name = string("op_411_cast_fp16")]; + fp16 var_413_promoted_to_fp16 = const()[name = string("op_413_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_414_cast_fp16 = add(x = var_411_cast_fp16_0, y = var_413_promoted_to_fp16)[name = string("op_414_cast_fp16")]; + tensor var_415_cast_fp16 = instance_norm(epsilon = var_363_to_fp16, x = input_63_cast_fp16)[name = string("op_415_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = var_414_cast_fp16, y = var_415_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_416_cast_fp16, y = var_411_cast_fp16_1)[name = string("input_65_cast_fp16")]; + tensor input_67_cast_fp16 = leaky_relu(alpha = var_359, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")]; + string out_pad_type_0 = const()[name = string("out_pad_type_0"), val = string("custom")]; + tensor out_pad_0 = const()[name = string("out_pad_0"), val = tensor([1, 1])]; + tensor out_strides_0 = const()[name = string("out_strides_0"), val = tensor([1])]; + tensor out_dilations_0 = const()[name = string("out_dilations_0"), val = tensor([1])]; + int32 out_groups_0 = const()[name = string("out_groups_0"), val = int32(1)]; + tensor decoder_decode_3_conv2_weight_to_fp16 = const()[name = string("decoder_decode_3_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64500736)))]; + tensor decoder_decode_3_conv2_bias_to_fp16 = const()[name = string("decoder_decode_3_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66073664)))]; + tensor out_cast_fp16 = conv(bias = decoder_decode_3_conv2_bias_to_fp16, dilations = out_dilations_0, groups = out_groups_0, pad = out_pad_0, pad_type = out_pad_type_0, strides = out_strides_0, weight = decoder_decode_3_conv2_weight_to_fp16, x = input_67_cast_fp16)[name = string("out_cast_fp16")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_55_cast_fp16)[name = string("expand_dims_0_cast_fp16")]; + int32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = int32(2)]; + int32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = int32(1)]; + tensor upsample_nearest_neighbor_0_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0_cast_fp16)[name = string("upsample_nearest_neighbor_0_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([3])]; + tensor input_cast_fp16 = squeeze(axes = input_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = string("input_cast_fp16")]; + string var_433_pad_type_0 = const()[name = string("op_433_pad_type_0"), val = string("valid")]; + tensor var_433_strides_0 = const()[name = string("op_433_strides_0"), val = tensor([1])]; + tensor var_433_pad_0 = const()[name = string("op_433_pad_0"), val = tensor([0, 0])]; + tensor var_433_dilations_0 = const()[name = string("op_433_dilations_0"), val = tensor([1])]; + int32 var_433_groups_0 = const()[name = string("op_433_groups_0"), val = int32(1)]; + tensor decoder_decode_3_conv1x1_weight_to_fp16 = const()[name = string("decoder_decode_3_conv1x1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66074752)))]; + tensor var_433_cast_fp16 = conv(dilations = var_433_dilations_0, groups = var_433_groups_0, pad = var_433_pad_0, pad_type = var_433_pad_type_0, strides = var_433_strides_0, weight = decoder_decode_3_conv1x1_weight_to_fp16, x = input_cast_fp16)[name = string("op_433_cast_fp16")]; + tensor var_434_cast_fp16 = add(x = out_cast_fp16, y = var_433_cast_fp16)[name = string("op_434_cast_fp16")]; + fp16 _inversed_436_y_0_to_fp16 = const()[name = string("_inversed_436_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor var_436 = mul(x = var_434_cast_fp16, y = _inversed_436_y_0_to_fp16)[name = string("_inversed_436_cast_fp16")]; + } -> (var_436); +} \ No newline at end of file diff --git a/iteration_3/compiled/decoder_pre_fp16.mlmodelc/weights/weight.bin b/iteration_3/compiled/decoder_pre_fp16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..4bbec4a0d74d4fe7d4b83167aa3eb2032990681e --- /dev/null +++ b/iteration_3/compiled/decoder_pre_fp16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db81849a38ce1959ea345219332051947f22f00dc2445cb9b7a119673ca4bf93 +size 67190976 diff --git 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467 diff --git a/iteration_3/compiled/decoder_upsample_fp16.mlmodelc/metadata.json b/iteration_3/compiled/decoder_upsample_fp16.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9615611c7ed8e0fb9b5edcb701dff4c85f2a3518 --- /dev/null +++ b/iteration_3/compiled/decoder_upsample_fp16.mlmodelc/metadata.json @@ -0,0 +1,97 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Float16", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16)", + "shortDescription" : "", + "shape" : "[]", + "name" : "var_3711", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 9, + "mlProgramOperationTypeHistogram" : { + "Ios18.linear" : 96, + "Ios18.convTranspose" : 4, + "Ios18.conv" : 101, + "Ios18.mul" : 302, + "Ios18.sin" : 101, + "Ios18.add" : 353, + "Ios18.tanh" : 1, + "Ios18.pow" : 101, + "Ios18.sliceByIndex" : 2, + "Ios18.cast" : 3, + "Ios18.reshape" : 96, + "Split" : 96, + "Ios18.instanceNorm" : 88 + }, + "computePrecision" : "Mixed (Float16, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "15.0", + "tvOS" : "18.0", + "visionOS" : "2.0", + "watchOS" : "11.0", + "iOS" : "18.0", + "macCatalyst" : "18.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-05-08", + "com.github.apple.coremltools.source" : "torch==2.11.0", + "com.github.apple.coremltools.version" : "9.0", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "inputSchema" : [ + { + "dataType" : "Float32", + "hasShapeFlexibility" : "1", + "isOptional" : "0", + "shapeFlexibility" : "1 × 512 × 2...4096", + "shapeRange" : "[[1, 1], [512, 512], [2, 4096]]", + "formattedType" : "MultiArray (Float32 1 × 512 × 294)", + "type" : "MultiArray", + "shape" : "[1, 512, 294]", + "name" : "x_pre", + "shortDescription" : "" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 128)", + "shortDescription" : "", + "shape" : "[1, 128]", + "name" : "ref", + "type" : "MultiArray" + }, + { + "dataType" : "Float32", + "hasShapeFlexibility" : "1", + "isOptional" : "0", + "shapeFlexibility" : "1 × 1 × 600...1228800", + "shapeRange" : "[[1, 1], [1, 1], [600, 1228800]]", + "formattedType" : "MultiArray (Float32 1 × 1 × 88200)", + "type" : "MultiArray", + "shape" : "[1, 1, 88200]", + "name" : "har_source", + "shortDescription" : "" + } + ], + "generatedClassName" : "decoder_upsample_fp16", + "method" : "predict" + } +] \ No newline at end of file diff --git a/iteration_3/compiled/decoder_upsample_fp16.mlmodelc/model.mil b/iteration_3/compiled/decoder_upsample_fp16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..37302d6ebba72577e5c3891f34fee7f8fbdff4fb --- /dev/null +++ b/iteration_3/compiled/decoder_upsample_fp16.mlmodelc/model.mil @@ -0,0 +1,2980 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor har_source, tensor ref, tensor x_pre) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"har_source", [1, 1, 88200]}, {"x_pre", [1, 512, 294]}}), ("RangeDims", {{"har_source", [[1, 1], [1, 1], [600, 1228800]]}, {"x_pre", [[1, 1], [512, 512], [2, 4096]]}})))] { + string ref_to_fp16_dtype_0 = const()[name = string("ref_to_fp16_dtype_0"), val = string("fp16")]; + tensor generator_alphas_0_to_fp16 = const()[name = string("generator_alphas_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string x_pre_to_fp16_dtype_0 = const()[name = string("x_pre_to_fp16_dtype_0"), val = string("fp16")]; + tensor x_pre_to_fp16 = cast(dtype = x_pre_to_fp16_dtype_0, x = x_pre)[name = string("cast_106")]; + tensor var_87_cast_fp16 = mul(x = generator_alphas_0_to_fp16, y = x_pre_to_fp16)[name = string("op_87_cast_fp16")]; + tensor var_88_cast_fp16 = sin(x = var_87_cast_fp16)[name = string("op_88_cast_fp16")]; + fp16 var_24_promoted_to_fp16 = const()[name = string("op_24_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_89_cast_fp16 = pow(x = var_88_cast_fp16, y = var_24_promoted_to_fp16)[name = string("op_89_cast_fp16")]; + tensor var_84_to_fp16 = const()[name = string("op_84_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152)))]; + tensor var_90_cast_fp16 = mul(x = var_84_to_fp16, y = var_89_cast_fp16)[name = string("op_90_cast_fp16")]; + tensor input_27_cast_fp16 = add(x = x_pre_to_fp16, y = var_90_cast_fp16)[name = string("input_27_cast_fp16")]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([15, 15])]; + tensor input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor([30])]; + tensor input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor([1])]; + int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; + string har_source_to_fp16_dtype_0 = const()[name = string("har_source_to_fp16_dtype_0"), val = string("fp16")]; + tensor generator_noise_convs_0_weight_to_fp16 = const()[name = string("generator_noise_convs_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2240)))]; + tensor generator_noise_convs_0_bias_to_fp16 = const()[name = string("generator_noise_convs_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33024)))]; + tensor har_source_to_fp16 = cast(dtype = har_source_to_fp16_dtype_0, x = har_source)[name = string("cast_105")]; + tensor input_3_cast_fp16 = conv(bias = generator_noise_convs_0_bias_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = generator_noise_convs_0_weight_to_fp16, x = har_source_to_fp16)[name = string("input_3_cast_fp16")]; + tensor generator_noise_res_0_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_noise_res_0_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33600)))]; + tensor generator_noise_res_0_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_noise_res_0_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164736)))]; + tensor ref_to_fp16 = cast(dtype = ref_to_fp16_dtype_0, x = ref)[name = string("cast_107")]; + tensor linear_0_cast_fp16 = linear(bias = generator_noise_res_0_adain1_0_fc_bias_to_fp16, weight = generator_noise_res_0_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_141 = const()[name = string("op_141"), val = tensor([1, 512, 1])]; + tensor h_3_cast_fp16 = reshape(shape = var_141, x = linear_0_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor var_143_split_sizes_0 = const()[name = string("op_143_split_sizes_0"), val = tensor([256, 256])]; + int32 var_143_axis_0 = const()[name = string("op_143_axis_0"), val = int32(1)]; + tensor var_143_cast_fp16_0, tensor var_143_cast_fp16_1 = split(axis = var_143_axis_0, split_sizes = var_143_split_sizes_0, x = h_3_cast_fp16)[name = string("op_143_cast_fp16")]; + fp16 var_145_promoted_to_fp16 = const()[name = string("op_145_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_146_cast_fp16 = add(x = var_143_cast_fp16_0, y = var_145_promoted_to_fp16)[name = string("op_146_cast_fp16")]; + fp16 var_14_to_fp16 = const()[name = string("op_14_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_147_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_3_cast_fp16)[name = string("op_147_cast_fp16")]; + tensor var_148_cast_fp16 = mul(x = var_146_cast_fp16, y = var_147_cast_fp16)[name = string("op_148_cast_fp16")]; + tensor xt_1_cast_fp16 = add(x = var_148_cast_fp16, y = var_143_cast_fp16_1)[name = string("xt_1_cast_fp16")]; + tensor generator_noise_res_0_alpha1_0_to_fp16 = const()[name = string("generator_noise_res_0_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165824)))]; + tensor var_153_cast_fp16 = mul(x = generator_noise_res_0_alpha1_0_to_fp16, y = xt_1_cast_fp16)[name = string("op_153_cast_fp16")]; + tensor var_154_cast_fp16 = sin(x = var_153_cast_fp16)[name = string("op_154_cast_fp16")]; + fp16 var_24_promoted_1_to_fp16 = const()[name = string("op_24_promoted_1_to_fp16"), val = fp16(0x1p+1)]; + tensor var_155_cast_fp16 = pow(x = var_154_cast_fp16, y = var_24_promoted_1_to_fp16)[name = string("op_155_cast_fp16")]; + tensor var_150_to_fp16 = const()[name = string("op_150_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166400)))]; + tensor var_156_cast_fp16 = mul(x = var_150_to_fp16, y = var_155_cast_fp16)[name = string("op_156_cast_fp16")]; + tensor input_5_cast_fp16 = add(x = xt_1_cast_fp16, y = var_156_cast_fp16)[name = string("input_5_cast_fp16")]; + string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")]; + tensor input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor([3, 3])]; + tensor input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor([1])]; + tensor input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor([1])]; + int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)]; + tensor generator_noise_res_0_convs1_0_weight_to_fp16 = const()[name = string("generator_noise_res_0_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166976)))]; + tensor generator_noise_res_0_convs1_0_bias_to_fp16 = const()[name = string("generator_noise_res_0_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1084544)))]; + tensor input_7_cast_fp16 = conv(bias = generator_noise_res_0_convs1_0_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = generator_noise_res_0_convs1_0_weight_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor generator_noise_res_0_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_noise_res_0_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1085120)))]; + tensor generator_noise_res_0_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_noise_res_0_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1216256)))]; + tensor linear_1_cast_fp16 = linear(bias = generator_noise_res_0_adain2_0_fc_bias_to_fp16, weight = generator_noise_res_0_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_171 = const()[name = string("op_171"), val = tensor([1, 512, 1])]; + tensor h_7_cast_fp16 = reshape(shape = var_171, x = linear_1_cast_fp16)[name = string("h_7_cast_fp16")]; + tensor var_173_split_sizes_0 = const()[name = string("op_173_split_sizes_0"), val = tensor([256, 256])]; + int32 var_173_axis_0 = const()[name = string("op_173_axis_0"), val = int32(1)]; + tensor var_173_cast_fp16_0, tensor var_173_cast_fp16_1 = split(axis = var_173_axis_0, split_sizes = var_173_split_sizes_0, x = h_7_cast_fp16)[name = string("op_173_cast_fp16")]; + fp16 var_175_promoted_to_fp16 = const()[name = string("op_175_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_176_cast_fp16 = add(x = var_173_cast_fp16_0, y = var_175_promoted_to_fp16)[name = string("op_176_cast_fp16")]; + tensor var_177_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_7_cast_fp16)[name = string("op_177_cast_fp16")]; + tensor var_178_cast_fp16 = mul(x = var_176_cast_fp16, y = var_177_cast_fp16)[name = string("op_178_cast_fp16")]; + tensor xt_3_cast_fp16 = add(x = var_178_cast_fp16, y = var_173_cast_fp16_1)[name = string("xt_3_cast_fp16")]; + tensor generator_noise_res_0_alpha2_0_to_fp16 = const()[name = string("generator_noise_res_0_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1217344)))]; + tensor var_183_cast_fp16 = mul(x = generator_noise_res_0_alpha2_0_to_fp16, y = xt_3_cast_fp16)[name = string("op_183_cast_fp16")]; + tensor var_184_cast_fp16 = sin(x = var_183_cast_fp16)[name = string("op_184_cast_fp16")]; + fp16 var_24_promoted_2_to_fp16 = const()[name = string("op_24_promoted_2_to_fp16"), val = fp16(0x1p+1)]; + tensor var_185_cast_fp16 = pow(x = var_184_cast_fp16, y = var_24_promoted_2_to_fp16)[name = string("op_185_cast_fp16")]; + tensor var_180_to_fp16 = const()[name = string("op_180_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1217920)))]; + tensor var_186_cast_fp16 = mul(x = var_180_to_fp16, y = var_185_cast_fp16)[name = string("op_186_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = xt_3_cast_fp16, y = var_186_cast_fp16)[name = string("input_9_cast_fp16")]; + string xt_5_pad_type_0 = const()[name = string("xt_5_pad_type_0"), val = string("custom")]; + tensor xt_5_pad_0 = const()[name = string("xt_5_pad_0"), val = tensor([3, 3])]; + tensor xt_5_strides_0 = const()[name = string("xt_5_strides_0"), val = tensor([1])]; + tensor xt_5_dilations_0 = const()[name = string("xt_5_dilations_0"), val = tensor([1])]; + int32 xt_5_groups_0 = const()[name = string("xt_5_groups_0"), val = int32(1)]; + tensor generator_noise_res_0_convs2_0_weight_to_fp16 = const()[name = string("generator_noise_res_0_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1218496)))]; + tensor generator_noise_res_0_convs2_0_bias_to_fp16 = const()[name = string("generator_noise_res_0_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2136064)))]; + tensor xt_5_cast_fp16 = conv(bias = generator_noise_res_0_convs2_0_bias_to_fp16, dilations = xt_5_dilations_0, groups = xt_5_groups_0, pad = xt_5_pad_0, pad_type = xt_5_pad_type_0, strides = xt_5_strides_0, weight = generator_noise_res_0_convs2_0_weight_to_fp16, x = input_9_cast_fp16)[name = string("xt_5_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = xt_5_cast_fp16, y = input_3_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor generator_noise_res_0_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_noise_res_0_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2136640)))]; + tensor generator_noise_res_0_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_noise_res_0_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2267776)))]; + tensor linear_2_cast_fp16 = linear(bias = generator_noise_res_0_adain1_1_fc_bias_to_fp16, weight = generator_noise_res_0_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_202 = const()[name = string("op_202"), val = tensor([1, 512, 1])]; + tensor h_11_cast_fp16 = reshape(shape = var_202, x = linear_2_cast_fp16)[name = string("h_11_cast_fp16")]; + tensor var_204_split_sizes_0 = const()[name = string("op_204_split_sizes_0"), val = tensor([256, 256])]; + int32 var_204_axis_0 = const()[name = string("op_204_axis_0"), val = int32(1)]; + tensor var_204_cast_fp16_0, tensor var_204_cast_fp16_1 = split(axis = var_204_axis_0, split_sizes = var_204_split_sizes_0, x = h_11_cast_fp16)[name = string("op_204_cast_fp16")]; + fp16 var_206_promoted_to_fp16 = const()[name = string("op_206_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_207_cast_fp16 = add(x = var_204_cast_fp16_0, y = var_206_promoted_to_fp16)[name = string("op_207_cast_fp16")]; + tensor var_208_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_11_cast_fp16)[name = string("op_208_cast_fp16")]; + tensor var_209_cast_fp16 = mul(x = var_207_cast_fp16, y = var_208_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor xt_7_cast_fp16 = add(x = var_209_cast_fp16, y = var_204_cast_fp16_1)[name = string("xt_7_cast_fp16")]; + tensor generator_noise_res_0_alpha1_1_to_fp16 = const()[name = string("generator_noise_res_0_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2268864)))]; + tensor var_214_cast_fp16 = mul(x = generator_noise_res_0_alpha1_1_to_fp16, y = xt_7_cast_fp16)[name = string("op_214_cast_fp16")]; + tensor var_215_cast_fp16 = sin(x = var_214_cast_fp16)[name = string("op_215_cast_fp16")]; + fp16 var_24_promoted_3_to_fp16 = const()[name = string("op_24_promoted_3_to_fp16"), val = fp16(0x1p+1)]; + tensor var_216_cast_fp16 = pow(x = var_215_cast_fp16, y = var_24_promoted_3_to_fp16)[name = string("op_216_cast_fp16")]; + tensor var_211_to_fp16 = const()[name = string("op_211_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2269440)))]; + tensor var_217_cast_fp16 = mul(x = var_211_to_fp16, y = var_216_cast_fp16)[name = string("op_217_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = xt_7_cast_fp16, y = var_217_cast_fp16)[name = string("input_13_cast_fp16")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([9, 9])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([3])]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor generator_noise_res_0_convs1_1_weight_to_fp16 = const()[name = string("generator_noise_res_0_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2270016)))]; + tensor generator_noise_res_0_convs1_1_bias_to_fp16 = const()[name = string("generator_noise_res_0_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3187584)))]; + tensor input_15_cast_fp16 = conv(bias = generator_noise_res_0_convs1_1_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = generator_noise_res_0_convs1_1_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor generator_noise_res_0_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_noise_res_0_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3188160)))]; + tensor generator_noise_res_0_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_noise_res_0_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3319296)))]; + tensor linear_3_cast_fp16 = linear(bias = generator_noise_res_0_adain2_1_fc_bias_to_fp16, weight = generator_noise_res_0_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_232 = const()[name = string("op_232"), val = tensor([1, 512, 1])]; + tensor h_15_cast_fp16 = reshape(shape = var_232, x = linear_3_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor var_234_split_sizes_0 = const()[name = string("op_234_split_sizes_0"), val = tensor([256, 256])]; + int32 var_234_axis_0 = const()[name = string("op_234_axis_0"), val = int32(1)]; + tensor var_234_cast_fp16_0, tensor var_234_cast_fp16_1 = split(axis = var_234_axis_0, split_sizes = var_234_split_sizes_0, x = h_15_cast_fp16)[name = string("op_234_cast_fp16")]; + fp16 var_236_promoted_to_fp16 = const()[name = string("op_236_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_237_cast_fp16 = add(x = var_234_cast_fp16_0, y = var_236_promoted_to_fp16)[name = string("op_237_cast_fp16")]; + tensor var_238_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_15_cast_fp16)[name = string("op_238_cast_fp16")]; + tensor var_239_cast_fp16 = mul(x = var_237_cast_fp16, y = var_238_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor xt_9_cast_fp16 = add(x = var_239_cast_fp16, y = var_234_cast_fp16_1)[name = string("xt_9_cast_fp16")]; + tensor generator_noise_res_0_alpha2_1_to_fp16 = const()[name = string("generator_noise_res_0_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3320384)))]; + tensor var_244_cast_fp16 = mul(x = generator_noise_res_0_alpha2_1_to_fp16, y = xt_9_cast_fp16)[name = string("op_244_cast_fp16")]; + tensor var_245_cast_fp16 = sin(x = var_244_cast_fp16)[name = string("op_245_cast_fp16")]; + fp16 var_24_promoted_4_to_fp16 = const()[name = string("op_24_promoted_4_to_fp16"), val = fp16(0x1p+1)]; + tensor var_246_cast_fp16 = pow(x = var_245_cast_fp16, y = var_24_promoted_4_to_fp16)[name = string("op_246_cast_fp16")]; + tensor var_241_to_fp16 = const()[name = string("op_241_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3320960)))]; + tensor var_247_cast_fp16 = mul(x = var_241_to_fp16, y = var_246_cast_fp16)[name = string("op_247_cast_fp16")]; + tensor input_17_cast_fp16 = add(x = xt_9_cast_fp16, y = var_247_cast_fp16)[name = string("input_17_cast_fp16")]; + string xt_11_pad_type_0 = const()[name = string("xt_11_pad_type_0"), val = string("custom")]; + tensor xt_11_pad_0 = const()[name = string("xt_11_pad_0"), val = tensor([3, 3])]; + tensor xt_11_strides_0 = const()[name = string("xt_11_strides_0"), val = tensor([1])]; + tensor xt_11_dilations_0 = const()[name = string("xt_11_dilations_0"), val = tensor([1])]; + int32 xt_11_groups_0 = const()[name = string("xt_11_groups_0"), val = int32(1)]; + tensor generator_noise_res_0_convs2_1_weight_to_fp16 = const()[name = string("generator_noise_res_0_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3321536)))]; + tensor generator_noise_res_0_convs2_1_bias_to_fp16 = const()[name = string("generator_noise_res_0_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4239104)))]; + tensor xt_11_cast_fp16 = conv(bias = generator_noise_res_0_convs2_1_bias_to_fp16, dilations = xt_11_dilations_0, groups = xt_11_groups_0, pad = xt_11_pad_0, pad_type = xt_11_pad_type_0, strides = xt_11_strides_0, weight = generator_noise_res_0_convs2_1_weight_to_fp16, x = input_17_cast_fp16)[name = string("xt_11_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = xt_11_cast_fp16, y = input_11_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor generator_noise_res_0_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_noise_res_0_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4239680)))]; + tensor generator_noise_res_0_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_noise_res_0_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4370816)))]; + tensor linear_4_cast_fp16 = linear(bias = generator_noise_res_0_adain1_2_fc_bias_to_fp16, weight = generator_noise_res_0_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_263 = const()[name = string("op_263"), val = tensor([1, 512, 1])]; + tensor h_19_cast_fp16 = reshape(shape = var_263, x = linear_4_cast_fp16)[name = string("h_19_cast_fp16")]; + tensor var_265_split_sizes_0 = const()[name = string("op_265_split_sizes_0"), val = tensor([256, 256])]; + int32 var_265_axis_0 = const()[name = string("op_265_axis_0"), val = int32(1)]; + tensor var_265_cast_fp16_0, tensor var_265_cast_fp16_1 = split(axis = var_265_axis_0, split_sizes = var_265_split_sizes_0, x = h_19_cast_fp16)[name = string("op_265_cast_fp16")]; + fp16 var_267_promoted_to_fp16 = const()[name = string("op_267_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_268_cast_fp16 = add(x = var_265_cast_fp16_0, y = var_267_promoted_to_fp16)[name = string("op_268_cast_fp16")]; + tensor var_269_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_19_cast_fp16)[name = string("op_269_cast_fp16")]; + tensor var_270_cast_fp16 = mul(x = var_268_cast_fp16, y = var_269_cast_fp16)[name = string("op_270_cast_fp16")]; + tensor xt_13_cast_fp16 = add(x = var_270_cast_fp16, y = var_265_cast_fp16_1)[name = string("xt_13_cast_fp16")]; + tensor generator_noise_res_0_alpha1_2_to_fp16 = const()[name = string("generator_noise_res_0_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4371904)))]; + tensor var_275_cast_fp16 = mul(x = generator_noise_res_0_alpha1_2_to_fp16, y = xt_13_cast_fp16)[name = string("op_275_cast_fp16")]; + tensor var_276_cast_fp16 = sin(x = var_275_cast_fp16)[name = string("op_276_cast_fp16")]; + fp16 var_24_promoted_5_to_fp16 = const()[name = string("op_24_promoted_5_to_fp16"), val = fp16(0x1p+1)]; + tensor var_277_cast_fp16 = pow(x = var_276_cast_fp16, y = var_24_promoted_5_to_fp16)[name = string("op_277_cast_fp16")]; + tensor var_272_to_fp16 = const()[name = string("op_272_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4372480)))]; + tensor var_278_cast_fp16 = mul(x = var_272_to_fp16, y = var_277_cast_fp16)[name = string("op_278_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = xt_13_cast_fp16, y = var_278_cast_fp16)[name = string("input_21_cast_fp16")]; + string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("custom")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([15, 15])]; + tensor input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor([5])]; + tensor input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor([1])]; + int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)]; + tensor generator_noise_res_0_convs1_2_weight_to_fp16 = const()[name = string("generator_noise_res_0_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4373056)))]; + tensor generator_noise_res_0_convs1_2_bias_to_fp16 = const()[name = string("generator_noise_res_0_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5290624)))]; + tensor input_23_cast_fp16 = conv(bias = generator_noise_res_0_convs1_2_bias_to_fp16, dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = generator_noise_res_0_convs1_2_weight_to_fp16, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor generator_noise_res_0_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_noise_res_0_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5291200)))]; + tensor generator_noise_res_0_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_noise_res_0_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5422336)))]; + tensor linear_5_cast_fp16 = linear(bias = generator_noise_res_0_adain2_2_fc_bias_to_fp16, weight = generator_noise_res_0_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_293 = const()[name = string("op_293"), val = tensor([1, 512, 1])]; + tensor h_23_cast_fp16 = reshape(shape = var_293, x = linear_5_cast_fp16)[name = string("h_23_cast_fp16")]; + tensor var_295_split_sizes_0 = const()[name = string("op_295_split_sizes_0"), val = tensor([256, 256])]; + int32 var_295_axis_0 = const()[name = string("op_295_axis_0"), val = int32(1)]; + tensor var_295_cast_fp16_0, tensor var_295_cast_fp16_1 = split(axis = var_295_axis_0, split_sizes = var_295_split_sizes_0, x = h_23_cast_fp16)[name = string("op_295_cast_fp16")]; + fp16 var_297_promoted_to_fp16 = const()[name = string("op_297_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_298_cast_fp16 = add(x = var_295_cast_fp16_0, y = var_297_promoted_to_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_23_cast_fp16)[name = string("op_299_cast_fp16")]; + tensor var_300_cast_fp16 = mul(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor xt_15_cast_fp16 = add(x = var_300_cast_fp16, y = var_295_cast_fp16_1)[name = string("xt_15_cast_fp16")]; + tensor generator_noise_res_0_alpha2_2_to_fp16 = const()[name = string("generator_noise_res_0_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5423424)))]; + tensor var_305_cast_fp16 = mul(x = generator_noise_res_0_alpha2_2_to_fp16, y = xt_15_cast_fp16)[name = string("op_305_cast_fp16")]; + tensor var_306_cast_fp16 = sin(x = var_305_cast_fp16)[name = string("op_306_cast_fp16")]; + fp16 var_24_promoted_6_to_fp16 = const()[name = string("op_24_promoted_6_to_fp16"), val = fp16(0x1p+1)]; + tensor var_307_cast_fp16 = pow(x = var_306_cast_fp16, y = var_24_promoted_6_to_fp16)[name = string("op_307_cast_fp16")]; + tensor var_302_to_fp16 = const()[name = string("op_302_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5424000)))]; + tensor var_308_cast_fp16 = mul(x = var_302_to_fp16, y = var_307_cast_fp16)[name = string("op_308_cast_fp16")]; + tensor input_25_cast_fp16 = add(x = xt_15_cast_fp16, y = var_308_cast_fp16)[name = string("input_25_cast_fp16")]; + string xt_17_pad_type_0 = const()[name = string("xt_17_pad_type_0"), val = string("custom")]; + tensor xt_17_pad_0 = const()[name = string("xt_17_pad_0"), val = tensor([3, 3])]; + tensor xt_17_strides_0 = const()[name = string("xt_17_strides_0"), val = tensor([1])]; + tensor xt_17_dilations_0 = const()[name = string("xt_17_dilations_0"), val = tensor([1])]; + int32 xt_17_groups_0 = const()[name = string("xt_17_groups_0"), val = int32(1)]; + tensor generator_noise_res_0_convs2_2_weight_to_fp16 = const()[name = string("generator_noise_res_0_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5424576)))]; + tensor generator_noise_res_0_convs2_2_bias_to_fp16 = const()[name = string("generator_noise_res_0_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6342144)))]; + tensor xt_17_cast_fp16 = conv(bias = generator_noise_res_0_convs2_2_bias_to_fp16, dilations = xt_17_dilations_0, groups = xt_17_groups_0, pad = xt_17_pad_0, pad_type = xt_17_pad_type_0, strides = xt_17_strides_0, weight = generator_noise_res_0_convs2_2_weight_to_fp16, x = input_25_cast_fp16)[name = string("xt_17_cast_fp16")]; + tensor x_source_1_cast_fp16 = add(x = xt_17_cast_fp16, y = input_19_cast_fp16)[name = string("x_source_1_cast_fp16")]; + string x_1_pad_type_0 = const()[name = string("x_1_pad_type_0"), val = string("custom")]; + tensor x_1_pad_0 = const()[name = string("x_1_pad_0"), val = tensor([5, 5])]; + tensor x_1_strides_0 = const()[name = string("x_1_strides_0"), val = tensor([10])]; + tensor x_1_dilations_0 = const()[name = string("x_1_dilations_0"), val = tensor([1])]; + int32 x_1_groups_0 = const()[name = string("x_1_groups_0"), val = int32(1)]; + tensor generator_ups_0_weight_to_fp16 = const()[name = string("generator_ups_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6342720)))]; + tensor generator_ups_0_bias_to_fp16 = const()[name = string("generator_ups_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11585664)))]; + tensor x_1_cast_fp16 = conv_transpose(bias = generator_ups_0_bias_to_fp16, dilations = x_1_dilations_0, groups = x_1_groups_0, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = x_1_strides_0, weight = generator_ups_0_weight_to_fp16, x = input_27_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor input_29_cast_fp16 = add(x = x_1_cast_fp16, y = x_source_1_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor generator_resblocks_0_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_0_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11586240)))]; + tensor generator_resblocks_0_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_0_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11717376)))]; + tensor linear_6_cast_fp16 = linear(bias = generator_resblocks_0_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_0_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_368 = const()[name = string("op_368"), val = tensor([1, 512, 1])]; + tensor h_27_cast_fp16 = reshape(shape = var_368, x = linear_6_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor var_370_split_sizes_0 = const()[name = string("op_370_split_sizes_0"), val = tensor([256, 256])]; + int32 var_370_axis_0 = const()[name = string("op_370_axis_0"), val = int32(1)]; + tensor var_370_cast_fp16_0, tensor var_370_cast_fp16_1 = split(axis = var_370_axis_0, split_sizes = var_370_split_sizes_0, x = h_27_cast_fp16)[name = string("op_370_cast_fp16")]; + fp16 var_372_promoted_to_fp16 = const()[name = string("op_372_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_373_cast_fp16 = add(x = var_370_cast_fp16_0, y = var_372_promoted_to_fp16)[name = string("op_373_cast_fp16")]; + tensor var_374_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_29_cast_fp16)[name = string("op_374_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_373_cast_fp16, y = var_374_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor xt_19_cast_fp16 = add(x = var_375_cast_fp16, y = var_370_cast_fp16_1)[name = string("xt_19_cast_fp16")]; + tensor generator_resblocks_0_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_0_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11718464)))]; + tensor var_380_cast_fp16 = mul(x = generator_resblocks_0_alpha1_0_to_fp16, y = xt_19_cast_fp16)[name = string("op_380_cast_fp16")]; + tensor var_381_cast_fp16 = sin(x = var_380_cast_fp16)[name = string("op_381_cast_fp16")]; + fp16 var_24_promoted_7_to_fp16 = const()[name = string("op_24_promoted_7_to_fp16"), val = fp16(0x1p+1)]; + tensor var_382_cast_fp16 = pow(x = var_381_cast_fp16, y = var_24_promoted_7_to_fp16)[name = string("op_382_cast_fp16")]; + tensor var_377_to_fp16 = const()[name = string("op_377_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11719040)))]; + tensor var_383_cast_fp16 = mul(x = var_377_to_fp16, y = var_382_cast_fp16)[name = string("op_383_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = xt_19_cast_fp16, y = var_383_cast_fp16)[name = string("input_31_cast_fp16")]; + string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")]; + tensor input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor([1, 1])]; + tensor input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor([1])]; + tensor input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor([1])]; + int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)]; + tensor generator_resblocks_0_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_0_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11719616)))]; + tensor generator_resblocks_0_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_0_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12112896)))]; + tensor input_33_cast_fp16 = conv(bias = generator_resblocks_0_convs1_0_bias_to_fp16, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = generator_resblocks_0_convs1_0_weight_to_fp16, x = input_31_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor generator_resblocks_0_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_0_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12113472)))]; + tensor generator_resblocks_0_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_0_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12244608)))]; + tensor linear_7_cast_fp16 = linear(bias = generator_resblocks_0_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_0_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_398 = const()[name = string("op_398"), val = tensor([1, 512, 1])]; + tensor h_31_cast_fp16 = reshape(shape = var_398, x = linear_7_cast_fp16)[name = string("h_31_cast_fp16")]; + tensor var_400_split_sizes_0 = const()[name = string("op_400_split_sizes_0"), val = tensor([256, 256])]; + int32 var_400_axis_0 = const()[name = string("op_400_axis_0"), val = int32(1)]; + tensor var_400_cast_fp16_0, tensor var_400_cast_fp16_1 = split(axis = var_400_axis_0, split_sizes = var_400_split_sizes_0, x = h_31_cast_fp16)[name = string("op_400_cast_fp16")]; + fp16 var_402_promoted_to_fp16 = const()[name = string("op_402_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_400_cast_fp16_0, y = var_402_promoted_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_33_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_cast_fp16 = mul(x = var_403_cast_fp16, y = var_404_cast_fp16)[name = string("op_405_cast_fp16")]; + tensor xt_21_cast_fp16 = add(x = var_405_cast_fp16, y = var_400_cast_fp16_1)[name = string("xt_21_cast_fp16")]; + tensor generator_resblocks_0_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_0_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12245696)))]; + tensor var_410_cast_fp16 = mul(x = generator_resblocks_0_alpha2_0_to_fp16, y = xt_21_cast_fp16)[name = string("op_410_cast_fp16")]; + tensor var_411_cast_fp16 = sin(x = var_410_cast_fp16)[name = string("op_411_cast_fp16")]; + fp16 var_24_promoted_8_to_fp16 = const()[name = string("op_24_promoted_8_to_fp16"), val = fp16(0x1p+1)]; + tensor var_412_cast_fp16 = pow(x = var_411_cast_fp16, y = var_24_promoted_8_to_fp16)[name = string("op_412_cast_fp16")]; + tensor var_407_to_fp16 = const()[name = string("op_407_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12246272)))]; + tensor var_413_cast_fp16 = mul(x = var_407_to_fp16, y = var_412_cast_fp16)[name = string("op_413_cast_fp16")]; + tensor input_35_cast_fp16 = add(x = xt_21_cast_fp16, y = var_413_cast_fp16)[name = string("input_35_cast_fp16")]; + string xt_23_pad_type_0 = const()[name = string("xt_23_pad_type_0"), val = string("custom")]; + tensor xt_23_pad_0 = const()[name = string("xt_23_pad_0"), val = tensor([1, 1])]; + tensor xt_23_strides_0 = const()[name = string("xt_23_strides_0"), val = tensor([1])]; + tensor xt_23_dilations_0 = const()[name = string("xt_23_dilations_0"), val = tensor([1])]; + int32 xt_23_groups_0 = const()[name = string("xt_23_groups_0"), val = int32(1)]; + tensor generator_resblocks_0_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_0_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12246848)))]; + tensor generator_resblocks_0_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_0_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12640128)))]; + tensor xt_23_cast_fp16 = conv(bias = generator_resblocks_0_convs2_0_bias_to_fp16, dilations = xt_23_dilations_0, groups = xt_23_groups_0, pad = xt_23_pad_0, pad_type = xt_23_pad_type_0, strides = xt_23_strides_0, weight = generator_resblocks_0_convs2_0_weight_to_fp16, x = input_35_cast_fp16)[name = string("xt_23_cast_fp16")]; + tensor input_37_cast_fp16 = add(x = xt_23_cast_fp16, y = input_29_cast_fp16)[name = string("input_37_cast_fp16")]; + tensor generator_resblocks_0_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_0_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12640704)))]; + tensor generator_resblocks_0_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_0_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12771840)))]; + tensor linear_8_cast_fp16 = linear(bias = generator_resblocks_0_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_0_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_429 = const()[name = string("op_429"), val = tensor([1, 512, 1])]; + tensor h_35_cast_fp16 = reshape(shape = var_429, x = linear_8_cast_fp16)[name = string("h_35_cast_fp16")]; + tensor var_431_split_sizes_0 = const()[name = string("op_431_split_sizes_0"), val = tensor([256, 256])]; + int32 var_431_axis_0 = const()[name = string("op_431_axis_0"), val = int32(1)]; + tensor var_431_cast_fp16_0, tensor var_431_cast_fp16_1 = split(axis = var_431_axis_0, split_sizes = var_431_split_sizes_0, x = h_35_cast_fp16)[name = string("op_431_cast_fp16")]; + fp16 var_433_promoted_to_fp16 = const()[name = string("op_433_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_434_cast_fp16 = add(x = var_431_cast_fp16_0, y = var_433_promoted_to_fp16)[name = string("op_434_cast_fp16")]; + tensor var_435_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_37_cast_fp16)[name = string("op_435_cast_fp16")]; + tensor var_436_cast_fp16 = mul(x = var_434_cast_fp16, y = var_435_cast_fp16)[name = string("op_436_cast_fp16")]; + tensor xt_25_cast_fp16 = add(x = var_436_cast_fp16, y = var_431_cast_fp16_1)[name = string("xt_25_cast_fp16")]; + tensor generator_resblocks_0_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_0_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12772928)))]; + tensor var_441_cast_fp16 = mul(x = generator_resblocks_0_alpha1_1_to_fp16, y = xt_25_cast_fp16)[name = string("op_441_cast_fp16")]; + tensor var_442_cast_fp16 = sin(x = var_441_cast_fp16)[name = string("op_442_cast_fp16")]; + fp16 var_24_promoted_9_to_fp16 = const()[name = string("op_24_promoted_9_to_fp16"), val = fp16(0x1p+1)]; + tensor var_443_cast_fp16 = pow(x = var_442_cast_fp16, y = var_24_promoted_9_to_fp16)[name = string("op_443_cast_fp16")]; + tensor var_438_to_fp16 = const()[name = string("op_438_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12773504)))]; + tensor var_444_cast_fp16 = mul(x = var_438_to_fp16, y = var_443_cast_fp16)[name = string("op_444_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = xt_25_cast_fp16, y = var_444_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("custom")]; + tensor input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor([3, 3])]; + tensor input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor([3])]; + tensor input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor([1])]; + int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(1)]; + tensor generator_resblocks_0_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_0_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12774080)))]; + tensor generator_resblocks_0_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_0_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13167360)))]; + tensor input_41_cast_fp16 = conv(bias = generator_resblocks_0_convs1_1_bias_to_fp16, dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = generator_resblocks_0_convs1_1_weight_to_fp16, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor generator_resblocks_0_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_0_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13167936)))]; + tensor generator_resblocks_0_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_0_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13299072)))]; + tensor linear_9_cast_fp16 = linear(bias = generator_resblocks_0_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_0_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_459 = const()[name = string("op_459"), val = tensor([1, 512, 1])]; + tensor h_39_cast_fp16 = reshape(shape = var_459, x = linear_9_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor var_461_split_sizes_0 = const()[name = string("op_461_split_sizes_0"), val = tensor([256, 256])]; + int32 var_461_axis_0 = const()[name = string("op_461_axis_0"), val = int32(1)]; + tensor var_461_cast_fp16_0, tensor var_461_cast_fp16_1 = split(axis = var_461_axis_0, split_sizes = var_461_split_sizes_0, x = h_39_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_463_promoted_to_fp16 = const()[name = string("op_463_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_464_cast_fp16 = add(x = var_461_cast_fp16_0, y = var_463_promoted_to_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_41_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor var_466_cast_fp16 = mul(x = var_464_cast_fp16, y = var_465_cast_fp16)[name = string("op_466_cast_fp16")]; + tensor xt_27_cast_fp16 = add(x = var_466_cast_fp16, y = var_461_cast_fp16_1)[name = string("xt_27_cast_fp16")]; + tensor generator_resblocks_0_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_0_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13300160)))]; + tensor var_471_cast_fp16 = mul(x = generator_resblocks_0_alpha2_1_to_fp16, y = xt_27_cast_fp16)[name = string("op_471_cast_fp16")]; + tensor var_472_cast_fp16 = sin(x = var_471_cast_fp16)[name = string("op_472_cast_fp16")]; + fp16 var_24_promoted_10_to_fp16 = const()[name = string("op_24_promoted_10_to_fp16"), val = fp16(0x1p+1)]; + tensor var_473_cast_fp16 = pow(x = var_472_cast_fp16, y = var_24_promoted_10_to_fp16)[name = string("op_473_cast_fp16")]; + tensor var_468_to_fp16 = const()[name = string("op_468_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13300736)))]; + tensor var_474_cast_fp16 = mul(x = var_468_to_fp16, y = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = xt_27_cast_fp16, y = var_474_cast_fp16)[name = string("input_43_cast_fp16")]; + string xt_29_pad_type_0 = const()[name = string("xt_29_pad_type_0"), val = string("custom")]; + tensor xt_29_pad_0 = const()[name = string("xt_29_pad_0"), val = tensor([1, 1])]; + tensor xt_29_strides_0 = const()[name = string("xt_29_strides_0"), val = tensor([1])]; + tensor xt_29_dilations_0 = const()[name = string("xt_29_dilations_0"), val = tensor([1])]; + int32 xt_29_groups_0 = const()[name = string("xt_29_groups_0"), val = int32(1)]; + tensor generator_resblocks_0_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_0_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13301312)))]; + tensor generator_resblocks_0_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_0_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13694592)))]; + tensor xt_29_cast_fp16 = conv(bias = generator_resblocks_0_convs2_1_bias_to_fp16, dilations = xt_29_dilations_0, groups = xt_29_groups_0, pad = xt_29_pad_0, pad_type = xt_29_pad_type_0, strides = xt_29_strides_0, weight = generator_resblocks_0_convs2_1_weight_to_fp16, x = input_43_cast_fp16)[name = string("xt_29_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = xt_29_cast_fp16, y = input_37_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor generator_resblocks_0_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_0_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13695168)))]; + tensor generator_resblocks_0_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_0_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13826304)))]; + tensor linear_10_cast_fp16 = linear(bias = generator_resblocks_0_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_0_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_490 = const()[name = string("op_490"), val = tensor([1, 512, 1])]; + tensor h_43_cast_fp16 = reshape(shape = var_490, x = linear_10_cast_fp16)[name = string("h_43_cast_fp16")]; + tensor var_492_split_sizes_0 = const()[name = string("op_492_split_sizes_0"), val = tensor([256, 256])]; + int32 var_492_axis_0 = const()[name = string("op_492_axis_0"), val = int32(1)]; + tensor var_492_cast_fp16_0, tensor var_492_cast_fp16_1 = split(axis = var_492_axis_0, split_sizes = var_492_split_sizes_0, x = h_43_cast_fp16)[name = string("op_492_cast_fp16")]; + fp16 var_494_promoted_to_fp16 = const()[name = string("op_494_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_495_cast_fp16 = add(x = var_492_cast_fp16_0, y = var_494_promoted_to_fp16)[name = string("op_495_cast_fp16")]; + tensor var_496_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_45_cast_fp16)[name = string("op_496_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = var_495_cast_fp16, y = var_496_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor xt_31_cast_fp16 = add(x = var_497_cast_fp16, y = var_492_cast_fp16_1)[name = string("xt_31_cast_fp16")]; + tensor generator_resblocks_0_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_0_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13827392)))]; + tensor var_502_cast_fp16 = mul(x = generator_resblocks_0_alpha1_2_to_fp16, y = xt_31_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_cast_fp16 = sin(x = var_502_cast_fp16)[name = string("op_503_cast_fp16")]; + fp16 var_24_promoted_11_to_fp16 = const()[name = string("op_24_promoted_11_to_fp16"), val = fp16(0x1p+1)]; + tensor var_504_cast_fp16 = pow(x = var_503_cast_fp16, y = var_24_promoted_11_to_fp16)[name = string("op_504_cast_fp16")]; + tensor var_499_to_fp16 = const()[name = string("op_499_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13827968)))]; + tensor var_505_cast_fp16 = mul(x = var_499_to_fp16, y = var_504_cast_fp16)[name = string("op_505_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = xt_31_cast_fp16, y = var_505_cast_fp16)[name = string("input_47_cast_fp16")]; + string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")]; + tensor input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor([5, 5])]; + tensor input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor([5])]; + tensor input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor([1])]; + int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)]; + tensor generator_resblocks_0_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_0_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13828544)))]; + tensor generator_resblocks_0_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_0_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14221824)))]; + tensor input_49_cast_fp16 = conv(bias = generator_resblocks_0_convs1_2_bias_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = generator_resblocks_0_convs1_2_weight_to_fp16, x = input_47_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor generator_resblocks_0_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_0_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14222400)))]; + tensor generator_resblocks_0_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_0_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14353536)))]; + tensor linear_11_cast_fp16 = linear(bias = generator_resblocks_0_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_0_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_520 = const()[name = string("op_520"), val = tensor([1, 512, 1])]; + tensor h_47_cast_fp16 = reshape(shape = var_520, x = linear_11_cast_fp16)[name = string("h_47_cast_fp16")]; + tensor var_522_split_sizes_0 = const()[name = string("op_522_split_sizes_0"), val = tensor([256, 256])]; + int32 var_522_axis_0 = const()[name = string("op_522_axis_0"), val = int32(1)]; + tensor var_522_cast_fp16_0, tensor var_522_cast_fp16_1 = split(axis = var_522_axis_0, split_sizes = var_522_split_sizes_0, x = h_47_cast_fp16)[name = string("op_522_cast_fp16")]; + fp16 var_524_promoted_to_fp16 = const()[name = string("op_524_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_525_cast_fp16 = add(x = var_522_cast_fp16_0, y = var_524_promoted_to_fp16)[name = string("op_525_cast_fp16")]; + tensor var_526_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_49_cast_fp16)[name = string("op_526_cast_fp16")]; + tensor var_527_cast_fp16 = mul(x = var_525_cast_fp16, y = var_526_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor xt_33_cast_fp16 = add(x = var_527_cast_fp16, y = var_522_cast_fp16_1)[name = string("xt_33_cast_fp16")]; + tensor generator_resblocks_0_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_0_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14354624)))]; + tensor var_532_cast_fp16 = mul(x = generator_resblocks_0_alpha2_2_to_fp16, y = xt_33_cast_fp16)[name = string("op_532_cast_fp16")]; + tensor var_533_cast_fp16 = sin(x = var_532_cast_fp16)[name = string("op_533_cast_fp16")]; + fp16 var_24_promoted_12_to_fp16 = const()[name = string("op_24_promoted_12_to_fp16"), val = fp16(0x1p+1)]; + tensor var_534_cast_fp16 = pow(x = var_533_cast_fp16, y = var_24_promoted_12_to_fp16)[name = string("op_534_cast_fp16")]; + tensor var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14355200)))]; + tensor var_535_cast_fp16 = mul(x = var_529_to_fp16, y = var_534_cast_fp16)[name = string("op_535_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = xt_33_cast_fp16, y = var_535_cast_fp16)[name = string("input_51_cast_fp16")]; + string xt_35_pad_type_0 = const()[name = string("xt_35_pad_type_0"), val = string("custom")]; + tensor xt_35_pad_0 = const()[name = string("xt_35_pad_0"), val = tensor([1, 1])]; + tensor xt_35_strides_0 = const()[name = string("xt_35_strides_0"), val = tensor([1])]; + tensor xt_35_dilations_0 = const()[name = string("xt_35_dilations_0"), val = tensor([1])]; + int32 xt_35_groups_0 = const()[name = string("xt_35_groups_0"), val = int32(1)]; + tensor generator_resblocks_0_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_0_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14355776)))]; + tensor generator_resblocks_0_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_0_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14749056)))]; + tensor xt_35_cast_fp16 = conv(bias = generator_resblocks_0_convs2_2_bias_to_fp16, dilations = xt_35_dilations_0, groups = xt_35_groups_0, pad = xt_35_pad_0, pad_type = xt_35_pad_type_0, strides = xt_35_strides_0, weight = generator_resblocks_0_convs2_2_weight_to_fp16, x = input_51_cast_fp16)[name = string("xt_35_cast_fp16")]; + tensor xs_1_cast_fp16 = add(x = xt_35_cast_fp16, y = input_45_cast_fp16)[name = string("xs_1_cast_fp16")]; + tensor generator_resblocks_1_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_1_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14749632)))]; + tensor generator_resblocks_1_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_1_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14880768)))]; + tensor linear_12_cast_fp16 = linear(bias = generator_resblocks_1_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_1_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_587 = const()[name = string("op_587"), val = tensor([1, 512, 1])]; + tensor h_51_cast_fp16 = reshape(shape = var_587, x = linear_12_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor var_589_split_sizes_0 = const()[name = string("op_589_split_sizes_0"), val = tensor([256, 256])]; + int32 var_589_axis_0 = const()[name = string("op_589_axis_0"), val = int32(1)]; + tensor var_589_cast_fp16_0, tensor var_589_cast_fp16_1 = split(axis = var_589_axis_0, split_sizes = var_589_split_sizes_0, x = h_51_cast_fp16)[name = string("op_589_cast_fp16")]; + fp16 var_591_promoted_to_fp16 = const()[name = string("op_591_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_592_cast_fp16 = add(x = var_589_cast_fp16_0, y = var_591_promoted_to_fp16)[name = string("op_592_cast_fp16")]; + tensor var_594_cast_fp16 = mul(x = var_592_cast_fp16, y = var_374_cast_fp16)[name = string("op_594_cast_fp16")]; + tensor xt_37_cast_fp16 = add(x = var_594_cast_fp16, y = var_589_cast_fp16_1)[name = string("xt_37_cast_fp16")]; + tensor generator_resblocks_1_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_1_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14881856)))]; + tensor var_599_cast_fp16 = mul(x = generator_resblocks_1_alpha1_0_to_fp16, y = xt_37_cast_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = sin(x = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + fp16 var_24_promoted_13_to_fp16 = const()[name = string("op_24_promoted_13_to_fp16"), val = fp16(0x1p+1)]; + tensor var_601_cast_fp16 = pow(x = var_600_cast_fp16, y = var_24_promoted_13_to_fp16)[name = string("op_601_cast_fp16")]; + tensor var_596_to_fp16 = const()[name = string("op_596_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14882432)))]; + tensor var_602_cast_fp16 = mul(x = var_596_to_fp16, y = var_601_cast_fp16)[name = string("op_602_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = xt_37_cast_fp16, y = var_602_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")]; + tensor input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor([3, 3])]; + tensor input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor([1])]; + tensor input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor([1])]; + int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)]; + tensor generator_resblocks_1_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_1_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14883008)))]; + tensor generator_resblocks_1_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_1_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15800576)))]; + tensor input_55_cast_fp16 = conv(bias = generator_resblocks_1_convs1_0_bias_to_fp16, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = generator_resblocks_1_convs1_0_weight_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor generator_resblocks_1_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_1_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15801152)))]; + tensor generator_resblocks_1_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_1_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15932288)))]; + tensor linear_13_cast_fp16 = linear(bias = generator_resblocks_1_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_1_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_617 = const()[name = string("op_617"), val = tensor([1, 512, 1])]; + tensor h_55_cast_fp16 = reshape(shape = var_617, x = linear_13_cast_fp16)[name = string("h_55_cast_fp16")]; + tensor var_619_split_sizes_0 = const()[name = string("op_619_split_sizes_0"), val = tensor([256, 256])]; + int32 var_619_axis_0 = const()[name = string("op_619_axis_0"), val = int32(1)]; + tensor var_619_cast_fp16_0, tensor var_619_cast_fp16_1 = split(axis = var_619_axis_0, split_sizes = var_619_split_sizes_0, x = h_55_cast_fp16)[name = string("op_619_cast_fp16")]; + fp16 var_621_promoted_to_fp16 = const()[name = string("op_621_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_622_cast_fp16 = add(x = var_619_cast_fp16_0, y = var_621_promoted_to_fp16)[name = string("op_622_cast_fp16")]; + tensor var_623_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_55_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_624_cast_fp16 = mul(x = var_622_cast_fp16, y = var_623_cast_fp16)[name = string("op_624_cast_fp16")]; + tensor xt_39_cast_fp16 = add(x = var_624_cast_fp16, y = var_619_cast_fp16_1)[name = string("xt_39_cast_fp16")]; + tensor generator_resblocks_1_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_1_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15933376)))]; + tensor var_629_cast_fp16 = mul(x = generator_resblocks_1_alpha2_0_to_fp16, y = xt_39_cast_fp16)[name = string("op_629_cast_fp16")]; + tensor var_630_cast_fp16 = sin(x = var_629_cast_fp16)[name = string("op_630_cast_fp16")]; + fp16 var_24_promoted_14_to_fp16 = const()[name = string("op_24_promoted_14_to_fp16"), val = fp16(0x1p+1)]; + tensor var_631_cast_fp16 = pow(x = var_630_cast_fp16, y = var_24_promoted_14_to_fp16)[name = string("op_631_cast_fp16")]; + tensor var_626_to_fp16 = const()[name = string("op_626_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15933952)))]; + tensor var_632_cast_fp16 = mul(x = var_626_to_fp16, y = var_631_cast_fp16)[name = string("op_632_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = xt_39_cast_fp16, y = var_632_cast_fp16)[name = string("input_57_cast_fp16")]; + string xt_41_pad_type_0 = const()[name = string("xt_41_pad_type_0"), val = string("custom")]; + tensor xt_41_pad_0 = const()[name = string("xt_41_pad_0"), val = tensor([3, 3])]; + tensor xt_41_strides_0 = const()[name = string("xt_41_strides_0"), val = tensor([1])]; + tensor xt_41_dilations_0 = const()[name = string("xt_41_dilations_0"), val = tensor([1])]; + int32 xt_41_groups_0 = const()[name = string("xt_41_groups_0"), val = int32(1)]; + tensor generator_resblocks_1_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_1_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15934528)))]; + tensor generator_resblocks_1_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_1_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16852096)))]; + tensor xt_41_cast_fp16 = conv(bias = generator_resblocks_1_convs2_0_bias_to_fp16, dilations = xt_41_dilations_0, groups = xt_41_groups_0, pad = xt_41_pad_0, pad_type = xt_41_pad_type_0, strides = xt_41_strides_0, weight = generator_resblocks_1_convs2_0_weight_to_fp16, x = input_57_cast_fp16)[name = string("xt_41_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = xt_41_cast_fp16, y = input_29_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor generator_resblocks_1_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_1_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16852672)))]; + tensor generator_resblocks_1_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_1_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983808)))]; + tensor linear_14_cast_fp16 = linear(bias = generator_resblocks_1_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_1_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 512, 1])]; + tensor h_59_cast_fp16 = reshape(shape = var_648, x = linear_14_cast_fp16)[name = string("h_59_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([256, 256])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(1)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = h_59_cast_fp16)[name = string("op_650_cast_fp16")]; + fp16 var_652_promoted_to_fp16 = const()[name = string("op_652_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_653_cast_fp16 = add(x = var_650_cast_fp16_0, y = var_652_promoted_to_fp16)[name = string("op_653_cast_fp16")]; + tensor var_654_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_59_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = var_653_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor xt_43_cast_fp16 = add(x = var_655_cast_fp16, y = var_650_cast_fp16_1)[name = string("xt_43_cast_fp16")]; + tensor generator_resblocks_1_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_1_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984896)))]; + tensor var_660_cast_fp16 = mul(x = generator_resblocks_1_alpha1_1_to_fp16, y = xt_43_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = sin(x = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + fp16 var_24_promoted_15_to_fp16 = const()[name = string("op_24_promoted_15_to_fp16"), val = fp16(0x1p+1)]; + tensor var_662_cast_fp16 = pow(x = var_661_cast_fp16, y = var_24_promoted_15_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_657_to_fp16 = const()[name = string("op_657_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16985472)))]; + tensor var_663_cast_fp16 = mul(x = var_657_to_fp16, y = var_662_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = xt_43_cast_fp16, y = var_663_cast_fp16)[name = string("input_61_cast_fp16")]; + string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")]; + tensor input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor([9, 9])]; + tensor input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor([3])]; + tensor input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor([1])]; + int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)]; + tensor generator_resblocks_1_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_1_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16986048)))]; + tensor generator_resblocks_1_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_1_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17903616)))]; + tensor input_63_cast_fp16 = conv(bias = generator_resblocks_1_convs1_1_bias_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = generator_resblocks_1_convs1_1_weight_to_fp16, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor generator_resblocks_1_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_1_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17904192)))]; + tensor generator_resblocks_1_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_1_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18035328)))]; + tensor linear_15_cast_fp16 = linear(bias = generator_resblocks_1_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_1_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_678 = const()[name = string("op_678"), val = tensor([1, 512, 1])]; + tensor h_63_cast_fp16 = reshape(shape = var_678, x = linear_15_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor var_680_split_sizes_0 = const()[name = string("op_680_split_sizes_0"), val = tensor([256, 256])]; + int32 var_680_axis_0 = const()[name = string("op_680_axis_0"), val = int32(1)]; + tensor var_680_cast_fp16_0, tensor var_680_cast_fp16_1 = split(axis = var_680_axis_0, split_sizes = var_680_split_sizes_0, x = h_63_cast_fp16)[name = string("op_680_cast_fp16")]; + fp16 var_682_promoted_to_fp16 = const()[name = string("op_682_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_683_cast_fp16 = add(x = var_680_cast_fp16_0, y = var_682_promoted_to_fp16)[name = string("op_683_cast_fp16")]; + tensor var_684_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_63_cast_fp16)[name = string("op_684_cast_fp16")]; + tensor var_685_cast_fp16 = mul(x = var_683_cast_fp16, y = var_684_cast_fp16)[name = string("op_685_cast_fp16")]; + tensor xt_45_cast_fp16 = add(x = var_685_cast_fp16, y = var_680_cast_fp16_1)[name = string("xt_45_cast_fp16")]; + tensor generator_resblocks_1_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_1_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18036416)))]; + tensor var_690_cast_fp16 = mul(x = generator_resblocks_1_alpha2_1_to_fp16, y = xt_45_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_cast_fp16 = sin(x = var_690_cast_fp16)[name = string("op_691_cast_fp16")]; + fp16 var_24_promoted_16_to_fp16 = const()[name = string("op_24_promoted_16_to_fp16"), val = fp16(0x1p+1)]; + tensor var_692_cast_fp16 = pow(x = var_691_cast_fp16, y = var_24_promoted_16_to_fp16)[name = string("op_692_cast_fp16")]; + tensor var_687_to_fp16 = const()[name = string("op_687_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18036992)))]; + tensor var_693_cast_fp16 = mul(x = var_687_to_fp16, y = var_692_cast_fp16)[name = string("op_693_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = xt_45_cast_fp16, y = var_693_cast_fp16)[name = string("input_65_cast_fp16")]; + string xt_47_pad_type_0 = const()[name = string("xt_47_pad_type_0"), val = string("custom")]; + tensor xt_47_pad_0 = const()[name = string("xt_47_pad_0"), val = tensor([3, 3])]; + tensor xt_47_strides_0 = const()[name = string("xt_47_strides_0"), val = tensor([1])]; + tensor xt_47_dilations_0 = const()[name = string("xt_47_dilations_0"), val = tensor([1])]; + int32 xt_47_groups_0 = const()[name = string("xt_47_groups_0"), val = int32(1)]; + tensor generator_resblocks_1_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_1_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18037568)))]; + tensor generator_resblocks_1_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_1_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18955136)))]; + tensor xt_47_cast_fp16 = conv(bias = generator_resblocks_1_convs2_1_bias_to_fp16, dilations = xt_47_dilations_0, groups = xt_47_groups_0, pad = xt_47_pad_0, pad_type = xt_47_pad_type_0, strides = xt_47_strides_0, weight = generator_resblocks_1_convs2_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("xt_47_cast_fp16")]; + tensor input_67_cast_fp16 = add(x = xt_47_cast_fp16, y = input_59_cast_fp16)[name = string("input_67_cast_fp16")]; + tensor generator_resblocks_1_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_1_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18955712)))]; + tensor generator_resblocks_1_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_1_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19086848)))]; + tensor linear_16_cast_fp16 = linear(bias = generator_resblocks_1_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_1_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_709 = const()[name = string("op_709"), val = tensor([1, 512, 1])]; + tensor h_67_cast_fp16 = reshape(shape = var_709, x = linear_16_cast_fp16)[name = string("h_67_cast_fp16")]; + tensor var_711_split_sizes_0 = const()[name = string("op_711_split_sizes_0"), val = tensor([256, 256])]; + int32 var_711_axis_0 = const()[name = string("op_711_axis_0"), val = int32(1)]; + tensor var_711_cast_fp16_0, tensor var_711_cast_fp16_1 = split(axis = var_711_axis_0, split_sizes = var_711_split_sizes_0, x = h_67_cast_fp16)[name = string("op_711_cast_fp16")]; + fp16 var_713_promoted_to_fp16 = const()[name = string("op_713_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_714_cast_fp16 = add(x = var_711_cast_fp16_0, y = var_713_promoted_to_fp16)[name = string("op_714_cast_fp16")]; + tensor var_715_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_67_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = var_714_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor xt_49_cast_fp16 = add(x = var_716_cast_fp16, y = var_711_cast_fp16_1)[name = string("xt_49_cast_fp16")]; + tensor generator_resblocks_1_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_1_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19087936)))]; + tensor var_721_cast_fp16 = mul(x = generator_resblocks_1_alpha1_2_to_fp16, y = xt_49_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_722_cast_fp16 = sin(x = var_721_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_24_promoted_17_to_fp16 = const()[name = string("op_24_promoted_17_to_fp16"), val = fp16(0x1p+1)]; + tensor var_723_cast_fp16 = pow(x = var_722_cast_fp16, y = var_24_promoted_17_to_fp16)[name = string("op_723_cast_fp16")]; + tensor var_718_to_fp16 = const()[name = string("op_718_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19088512)))]; + tensor var_724_cast_fp16 = mul(x = var_718_to_fp16, y = var_723_cast_fp16)[name = string("op_724_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = xt_49_cast_fp16, y = var_724_cast_fp16)[name = string("input_69_cast_fp16")]; + string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")]; + tensor input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor([15, 15])]; + tensor input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor([5])]; + tensor input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor([1])]; + int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)]; + tensor generator_resblocks_1_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_1_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19089088)))]; + tensor generator_resblocks_1_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_1_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20006656)))]; + tensor input_71_cast_fp16 = conv(bias = generator_resblocks_1_convs1_2_bias_to_fp16, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = generator_resblocks_1_convs1_2_weight_to_fp16, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor generator_resblocks_1_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_1_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20007232)))]; + tensor generator_resblocks_1_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_1_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20138368)))]; + tensor linear_17_cast_fp16 = linear(bias = generator_resblocks_1_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_1_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 512, 1])]; + tensor h_71_cast_fp16 = reshape(shape = var_739, x = linear_17_cast_fp16)[name = string("h_71_cast_fp16")]; + tensor var_741_split_sizes_0 = const()[name = string("op_741_split_sizes_0"), val = tensor([256, 256])]; + int32 var_741_axis_0 = const()[name = string("op_741_axis_0"), val = int32(1)]; + tensor var_741_cast_fp16_0, tensor var_741_cast_fp16_1 = split(axis = var_741_axis_0, split_sizes = var_741_split_sizes_0, x = h_71_cast_fp16)[name = string("op_741_cast_fp16")]; + fp16 var_743_promoted_to_fp16 = const()[name = string("op_743_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_744_cast_fp16 = add(x = var_741_cast_fp16_0, y = var_743_promoted_to_fp16)[name = string("op_744_cast_fp16")]; + tensor var_745_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_71_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor var_746_cast_fp16 = mul(x = var_744_cast_fp16, y = var_745_cast_fp16)[name = string("op_746_cast_fp16")]; + tensor xt_51_cast_fp16 = add(x = var_746_cast_fp16, y = var_741_cast_fp16_1)[name = string("xt_51_cast_fp16")]; + tensor generator_resblocks_1_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_1_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20139456)))]; + tensor var_751_cast_fp16 = mul(x = generator_resblocks_1_alpha2_2_to_fp16, y = xt_51_cast_fp16)[name = string("op_751_cast_fp16")]; + tensor var_752_cast_fp16 = sin(x = var_751_cast_fp16)[name = string("op_752_cast_fp16")]; + fp16 var_24_promoted_18_to_fp16 = const()[name = string("op_24_promoted_18_to_fp16"), val = fp16(0x1p+1)]; + tensor var_753_cast_fp16 = pow(x = var_752_cast_fp16, y = var_24_promoted_18_to_fp16)[name = string("op_753_cast_fp16")]; + tensor var_748_to_fp16 = const()[name = string("op_748_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20140032)))]; + tensor var_754_cast_fp16 = mul(x = var_748_to_fp16, y = var_753_cast_fp16)[name = string("op_754_cast_fp16")]; + tensor input_73_cast_fp16 = add(x = xt_51_cast_fp16, y = var_754_cast_fp16)[name = string("input_73_cast_fp16")]; + string xt_53_pad_type_0 = const()[name = string("xt_53_pad_type_0"), val = string("custom")]; + tensor xt_53_pad_0 = const()[name = string("xt_53_pad_0"), val = tensor([3, 3])]; + tensor xt_53_strides_0 = const()[name = string("xt_53_strides_0"), val = tensor([1])]; + tensor xt_53_dilations_0 = const()[name = string("xt_53_dilations_0"), val = tensor([1])]; + int32 xt_53_groups_0 = const()[name = string("xt_53_groups_0"), val = int32(1)]; + tensor generator_resblocks_1_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_1_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20140608)))]; + tensor generator_resblocks_1_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_1_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21058176)))]; + tensor xt_53_cast_fp16 = conv(bias = generator_resblocks_1_convs2_2_bias_to_fp16, dilations = xt_53_dilations_0, groups = xt_53_groups_0, pad = xt_53_pad_0, pad_type = xt_53_pad_type_0, strides = xt_53_strides_0, weight = generator_resblocks_1_convs2_2_weight_to_fp16, x = input_73_cast_fp16)[name = string("xt_53_cast_fp16")]; + tensor var_763_cast_fp16 = add(x = xt_53_cast_fp16, y = input_67_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor xs_3_cast_fp16 = add(x = xs_1_cast_fp16, y = var_763_cast_fp16)[name = string("xs_3_cast_fp16")]; + tensor generator_resblocks_2_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_2_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21058752)))]; + tensor generator_resblocks_2_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_2_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21189888)))]; + tensor linear_18_cast_fp16 = linear(bias = generator_resblocks_2_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_2_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_807 = const()[name = string("op_807"), val = tensor([1, 512, 1])]; + tensor h_75_cast_fp16 = reshape(shape = var_807, x = linear_18_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor var_809_split_sizes_0 = const()[name = string("op_809_split_sizes_0"), val = tensor([256, 256])]; + int32 var_809_axis_0 = const()[name = string("op_809_axis_0"), val = int32(1)]; + tensor var_809_cast_fp16_0, tensor var_809_cast_fp16_1 = split(axis = var_809_axis_0, split_sizes = var_809_split_sizes_0, x = h_75_cast_fp16)[name = string("op_809_cast_fp16")]; + fp16 var_811_promoted_to_fp16 = const()[name = string("op_811_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_812_cast_fp16 = add(x = var_809_cast_fp16_0, y = var_811_promoted_to_fp16)[name = string("op_812_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = var_812_cast_fp16, y = var_374_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor xt_55_cast_fp16 = add(x = var_814_cast_fp16, y = var_809_cast_fp16_1)[name = string("xt_55_cast_fp16")]; + tensor generator_resblocks_2_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_2_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21190976)))]; + tensor var_819_cast_fp16 = mul(x = generator_resblocks_2_alpha1_0_to_fp16, y = xt_55_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_820_cast_fp16 = sin(x = var_819_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_24_promoted_19_to_fp16 = const()[name = string("op_24_promoted_19_to_fp16"), val = fp16(0x1p+1)]; + tensor var_821_cast_fp16 = pow(x = var_820_cast_fp16, y = var_24_promoted_19_to_fp16)[name = string("op_821_cast_fp16")]; + tensor var_816_to_fp16 = const()[name = string("op_816_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21191552)))]; + tensor var_822_cast_fp16 = mul(x = var_816_to_fp16, y = var_821_cast_fp16)[name = string("op_822_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = xt_55_cast_fp16, y = var_822_cast_fp16)[name = string("input_75_cast_fp16")]; + string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")]; + tensor input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor([5, 5])]; + tensor input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor([1])]; + tensor input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor([1])]; + int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)]; + tensor generator_resblocks_2_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_2_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21192128)))]; + tensor generator_resblocks_2_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_2_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22633984)))]; + tensor input_77_cast_fp16 = conv(bias = generator_resblocks_2_convs1_0_bias_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = generator_resblocks_2_convs1_0_weight_to_fp16, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor generator_resblocks_2_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_2_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22634560)))]; + tensor generator_resblocks_2_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_2_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22765696)))]; + tensor linear_19_cast_fp16 = linear(bias = generator_resblocks_2_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_2_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 512, 1])]; + tensor h_79_cast_fp16 = reshape(shape = var_837, x = linear_19_cast_fp16)[name = string("h_79_cast_fp16")]; + tensor var_839_split_sizes_0 = const()[name = string("op_839_split_sizes_0"), val = tensor([256, 256])]; + int32 var_839_axis_0 = const()[name = string("op_839_axis_0"), val = int32(1)]; + tensor var_839_cast_fp16_0, tensor var_839_cast_fp16_1 = split(axis = var_839_axis_0, split_sizes = var_839_split_sizes_0, x = h_79_cast_fp16)[name = string("op_839_cast_fp16")]; + fp16 var_841_promoted_to_fp16 = const()[name = string("op_841_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_842_cast_fp16 = add(x = var_839_cast_fp16_0, y = var_841_promoted_to_fp16)[name = string("op_842_cast_fp16")]; + tensor var_843_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_77_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor var_844_cast_fp16 = mul(x = var_842_cast_fp16, y = var_843_cast_fp16)[name = string("op_844_cast_fp16")]; + tensor xt_57_cast_fp16 = add(x = var_844_cast_fp16, y = var_839_cast_fp16_1)[name = string("xt_57_cast_fp16")]; + tensor generator_resblocks_2_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_2_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22766784)))]; + tensor var_849_cast_fp16 = mul(x = generator_resblocks_2_alpha2_0_to_fp16, y = xt_57_cast_fp16)[name = string("op_849_cast_fp16")]; + tensor var_850_cast_fp16 = sin(x = var_849_cast_fp16)[name = string("op_850_cast_fp16")]; + fp16 var_24_promoted_20_to_fp16 = const()[name = string("op_24_promoted_20_to_fp16"), val = fp16(0x1p+1)]; + tensor var_851_cast_fp16 = pow(x = var_850_cast_fp16, y = var_24_promoted_20_to_fp16)[name = string("op_851_cast_fp16")]; + tensor var_846_to_fp16 = const()[name = string("op_846_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22767360)))]; + tensor var_852_cast_fp16 = mul(x = var_846_to_fp16, y = var_851_cast_fp16)[name = string("op_852_cast_fp16")]; + tensor input_79_cast_fp16 = add(x = xt_57_cast_fp16, y = var_852_cast_fp16)[name = string("input_79_cast_fp16")]; + string xt_59_pad_type_0 = const()[name = string("xt_59_pad_type_0"), val = string("custom")]; + tensor xt_59_pad_0 = const()[name = string("xt_59_pad_0"), val = tensor([5, 5])]; + tensor xt_59_strides_0 = const()[name = string("xt_59_strides_0"), val = tensor([1])]; + tensor xt_59_dilations_0 = const()[name = string("xt_59_dilations_0"), val = tensor([1])]; + int32 xt_59_groups_0 = const()[name = string("xt_59_groups_0"), val = int32(1)]; + tensor generator_resblocks_2_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_2_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22767936)))]; + tensor generator_resblocks_2_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_2_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24209792)))]; + tensor xt_59_cast_fp16 = conv(bias = generator_resblocks_2_convs2_0_bias_to_fp16, dilations = xt_59_dilations_0, groups = xt_59_groups_0, pad = xt_59_pad_0, pad_type = xt_59_pad_type_0, strides = xt_59_strides_0, weight = generator_resblocks_2_convs2_0_weight_to_fp16, x = input_79_cast_fp16)[name = string("xt_59_cast_fp16")]; + tensor input_81_cast_fp16 = add(x = xt_59_cast_fp16, y = input_29_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor generator_resblocks_2_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_2_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24210368)))]; + tensor generator_resblocks_2_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_2_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24341504)))]; + tensor linear_20_cast_fp16 = linear(bias = generator_resblocks_2_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_2_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_868 = const()[name = string("op_868"), val = tensor([1, 512, 1])]; + tensor h_83_cast_fp16 = reshape(shape = var_868, x = linear_20_cast_fp16)[name = string("h_83_cast_fp16")]; + tensor var_870_split_sizes_0 = const()[name = string("op_870_split_sizes_0"), val = tensor([256, 256])]; + int32 var_870_axis_0 = const()[name = string("op_870_axis_0"), val = int32(1)]; + tensor var_870_cast_fp16_0, tensor var_870_cast_fp16_1 = split(axis = var_870_axis_0, split_sizes = var_870_split_sizes_0, x = h_83_cast_fp16)[name = string("op_870_cast_fp16")]; + fp16 var_872_promoted_to_fp16 = const()[name = string("op_872_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_873_cast_fp16 = add(x = var_870_cast_fp16_0, y = var_872_promoted_to_fp16)[name = string("op_873_cast_fp16")]; + tensor var_874_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_81_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_875_cast_fp16 = mul(x = var_873_cast_fp16, y = var_874_cast_fp16)[name = string("op_875_cast_fp16")]; + tensor xt_61_cast_fp16 = add(x = var_875_cast_fp16, y = var_870_cast_fp16_1)[name = string("xt_61_cast_fp16")]; + tensor generator_resblocks_2_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_2_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24342592)))]; + tensor var_880_cast_fp16 = mul(x = generator_resblocks_2_alpha1_1_to_fp16, y = xt_61_cast_fp16)[name = string("op_880_cast_fp16")]; + tensor var_881_cast_fp16 = sin(x = var_880_cast_fp16)[name = string("op_881_cast_fp16")]; + fp16 var_24_promoted_21_to_fp16 = const()[name = string("op_24_promoted_21_to_fp16"), val = fp16(0x1p+1)]; + tensor var_882_cast_fp16 = pow(x = var_881_cast_fp16, y = var_24_promoted_21_to_fp16)[name = string("op_882_cast_fp16")]; + tensor var_877_to_fp16 = const()[name = string("op_877_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24343168)))]; + tensor var_883_cast_fp16 = mul(x = var_877_to_fp16, y = var_882_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = xt_61_cast_fp16, y = var_883_cast_fp16)[name = string("input_83_cast_fp16")]; + string input_85_pad_type_0 = const()[name = string("input_85_pad_type_0"), val = string("custom")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([15, 15])]; + tensor input_85_dilations_0 = const()[name = string("input_85_dilations_0"), val = tensor([3])]; + tensor input_85_strides_0 = const()[name = string("input_85_strides_0"), val = tensor([1])]; + int32 input_85_groups_0 = const()[name = string("input_85_groups_0"), val = int32(1)]; + tensor generator_resblocks_2_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_2_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24343744)))]; + tensor generator_resblocks_2_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_2_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25785600)))]; + tensor input_85_cast_fp16 = conv(bias = generator_resblocks_2_convs1_1_bias_to_fp16, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = generator_resblocks_2_convs1_1_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor generator_resblocks_2_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_2_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25786176)))]; + tensor generator_resblocks_2_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_2_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25917312)))]; + tensor linear_21_cast_fp16 = linear(bias = generator_resblocks_2_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_2_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_898 = const()[name = string("op_898"), val = tensor([1, 512, 1])]; + tensor h_87_cast_fp16 = reshape(shape = var_898, x = linear_21_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor var_900_split_sizes_0 = const()[name = string("op_900_split_sizes_0"), val = tensor([256, 256])]; + int32 var_900_axis_0 = const()[name = string("op_900_axis_0"), val = int32(1)]; + tensor var_900_cast_fp16_0, tensor var_900_cast_fp16_1 = split(axis = var_900_axis_0, split_sizes = var_900_split_sizes_0, x = h_87_cast_fp16)[name = string("op_900_cast_fp16")]; + fp16 var_902_promoted_to_fp16 = const()[name = string("op_902_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_903_cast_fp16 = add(x = var_900_cast_fp16_0, y = var_902_promoted_to_fp16)[name = string("op_903_cast_fp16")]; + tensor var_904_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_85_cast_fp16)[name = string("op_904_cast_fp16")]; + tensor var_905_cast_fp16 = mul(x = var_903_cast_fp16, y = var_904_cast_fp16)[name = string("op_905_cast_fp16")]; + tensor xt_63_cast_fp16 = add(x = var_905_cast_fp16, y = var_900_cast_fp16_1)[name = string("xt_63_cast_fp16")]; + tensor generator_resblocks_2_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_2_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25918400)))]; + tensor var_910_cast_fp16 = mul(x = generator_resblocks_2_alpha2_1_to_fp16, y = xt_63_cast_fp16)[name = string("op_910_cast_fp16")]; + tensor var_911_cast_fp16 = sin(x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + fp16 var_24_promoted_22_to_fp16 = const()[name = string("op_24_promoted_22_to_fp16"), val = fp16(0x1p+1)]; + tensor var_912_cast_fp16 = pow(x = var_911_cast_fp16, y = var_24_promoted_22_to_fp16)[name = string("op_912_cast_fp16")]; + tensor var_907_to_fp16 = const()[name = string("op_907_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25918976)))]; + tensor var_913_cast_fp16 = mul(x = var_907_to_fp16, y = var_912_cast_fp16)[name = string("op_913_cast_fp16")]; + tensor input_87_cast_fp16 = add(x = xt_63_cast_fp16, y = var_913_cast_fp16)[name = string("input_87_cast_fp16")]; + string xt_65_pad_type_0 = const()[name = string("xt_65_pad_type_0"), val = string("custom")]; + tensor xt_65_pad_0 = const()[name = string("xt_65_pad_0"), val = tensor([5, 5])]; + tensor xt_65_strides_0 = const()[name = string("xt_65_strides_0"), val = tensor([1])]; + tensor xt_65_dilations_0 = const()[name = string("xt_65_dilations_0"), val = tensor([1])]; + int32 xt_65_groups_0 = const()[name = string("xt_65_groups_0"), val = int32(1)]; + tensor generator_resblocks_2_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_2_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25919552)))]; + tensor generator_resblocks_2_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_2_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27361408)))]; + tensor xt_65_cast_fp16 = conv(bias = generator_resblocks_2_convs2_1_bias_to_fp16, dilations = xt_65_dilations_0, groups = xt_65_groups_0, pad = xt_65_pad_0, pad_type = xt_65_pad_type_0, strides = xt_65_strides_0, weight = generator_resblocks_2_convs2_1_weight_to_fp16, x = input_87_cast_fp16)[name = string("xt_65_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = xt_65_cast_fp16, y = input_81_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor generator_resblocks_2_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_2_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27361984)))]; + tensor generator_resblocks_2_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_2_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27493120)))]; + tensor linear_22_cast_fp16 = linear(bias = generator_resblocks_2_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_2_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_929 = const()[name = string("op_929"), val = tensor([1, 512, 1])]; + tensor h_91_cast_fp16 = reshape(shape = var_929, x = linear_22_cast_fp16)[name = string("h_91_cast_fp16")]; + tensor var_931_split_sizes_0 = const()[name = string("op_931_split_sizes_0"), val = tensor([256, 256])]; + int32 var_931_axis_0 = const()[name = string("op_931_axis_0"), val = int32(1)]; + tensor var_931_cast_fp16_0, tensor var_931_cast_fp16_1 = split(axis = var_931_axis_0, split_sizes = var_931_split_sizes_0, x = h_91_cast_fp16)[name = string("op_931_cast_fp16")]; + fp16 var_933_promoted_to_fp16 = const()[name = string("op_933_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_934_cast_fp16 = add(x = var_931_cast_fp16_0, y = var_933_promoted_to_fp16)[name = string("op_934_cast_fp16")]; + tensor var_935_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_89_cast_fp16)[name = string("op_935_cast_fp16")]; + tensor var_936_cast_fp16 = mul(x = var_934_cast_fp16, y = var_935_cast_fp16)[name = string("op_936_cast_fp16")]; + tensor xt_67_cast_fp16 = add(x = var_936_cast_fp16, y = var_931_cast_fp16_1)[name = string("xt_67_cast_fp16")]; + tensor generator_resblocks_2_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_2_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27494208)))]; + tensor var_941_cast_fp16 = mul(x = generator_resblocks_2_alpha1_2_to_fp16, y = xt_67_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor var_942_cast_fp16 = sin(x = var_941_cast_fp16)[name = string("op_942_cast_fp16")]; + fp16 var_24_promoted_23_to_fp16 = const()[name = string("op_24_promoted_23_to_fp16"), val = fp16(0x1p+1)]; + tensor var_943_cast_fp16 = pow(x = var_942_cast_fp16, y = var_24_promoted_23_to_fp16)[name = string("op_943_cast_fp16")]; + tensor var_938_to_fp16 = const()[name = string("op_938_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27494784)))]; + tensor var_944_cast_fp16 = mul(x = var_938_to_fp16, y = var_943_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = xt_67_cast_fp16, y = var_944_cast_fp16)[name = string("input_91_cast_fp16")]; + string input_93_pad_type_0 = const()[name = string("input_93_pad_type_0"), val = string("custom")]; + tensor input_93_pad_0 = const()[name = string("input_93_pad_0"), val = tensor([25, 25])]; + tensor input_93_dilations_0 = const()[name = string("input_93_dilations_0"), val = tensor([5])]; + tensor input_93_strides_0 = const()[name = string("input_93_strides_0"), val = tensor([1])]; + int32 input_93_groups_0 = const()[name = string("input_93_groups_0"), val = int32(1)]; + tensor generator_resblocks_2_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_2_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27495360)))]; + tensor generator_resblocks_2_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_2_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28937216)))]; + tensor input_93_cast_fp16 = conv(bias = generator_resblocks_2_convs1_2_bias_to_fp16, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = generator_resblocks_2_convs1_2_weight_to_fp16, x = input_91_cast_fp16)[name = string("input_93_cast_fp16")]; + tensor generator_resblocks_2_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_2_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28937792)))]; + tensor generator_resblocks_2_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_2_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29068928)))]; + tensor linear_23_cast_fp16 = linear(bias = generator_resblocks_2_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_2_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_959 = const()[name = string("op_959"), val = tensor([1, 512, 1])]; + tensor h_95_cast_fp16 = reshape(shape = var_959, x = linear_23_cast_fp16)[name = string("h_95_cast_fp16")]; + tensor var_961_split_sizes_0 = const()[name = string("op_961_split_sizes_0"), val = tensor([256, 256])]; + int32 var_961_axis_0 = const()[name = string("op_961_axis_0"), val = int32(1)]; + tensor var_961_cast_fp16_0, tensor var_961_cast_fp16_1 = split(axis = var_961_axis_0, split_sizes = var_961_split_sizes_0, x = h_95_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_963_promoted_to_fp16 = const()[name = string("op_963_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_964_cast_fp16 = add(x = var_961_cast_fp16_0, y = var_963_promoted_to_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_93_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor var_966_cast_fp16 = mul(x = var_964_cast_fp16, y = var_965_cast_fp16)[name = string("op_966_cast_fp16")]; + tensor xt_69_cast_fp16 = add(x = var_966_cast_fp16, y = var_961_cast_fp16_1)[name = string("xt_69_cast_fp16")]; + tensor generator_resblocks_2_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_2_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29070016)))]; + tensor var_971_cast_fp16 = mul(x = generator_resblocks_2_alpha2_2_to_fp16, y = xt_69_cast_fp16)[name = string("op_971_cast_fp16")]; + tensor var_972_cast_fp16 = sin(x = var_971_cast_fp16)[name = string("op_972_cast_fp16")]; + fp16 var_24_promoted_24_to_fp16 = const()[name = string("op_24_promoted_24_to_fp16"), val = fp16(0x1p+1)]; + tensor var_973_cast_fp16 = pow(x = var_972_cast_fp16, y = var_24_promoted_24_to_fp16)[name = string("op_973_cast_fp16")]; + tensor var_968_to_fp16 = const()[name = string("op_968_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29070592)))]; + tensor var_974_cast_fp16 = mul(x = var_968_to_fp16, y = var_973_cast_fp16)[name = string("op_974_cast_fp16")]; + tensor input_95_cast_fp16 = add(x = xt_69_cast_fp16, y = var_974_cast_fp16)[name = string("input_95_cast_fp16")]; + string xt_71_pad_type_0 = const()[name = string("xt_71_pad_type_0"), val = string("custom")]; + tensor xt_71_pad_0 = const()[name = string("xt_71_pad_0"), val = tensor([5, 5])]; + tensor xt_71_strides_0 = const()[name = string("xt_71_strides_0"), val = tensor([1])]; + tensor xt_71_dilations_0 = const()[name = string("xt_71_dilations_0"), val = tensor([1])]; + int32 xt_71_groups_0 = const()[name = string("xt_71_groups_0"), val = int32(1)]; + tensor generator_resblocks_2_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_2_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29071168)))]; + tensor generator_resblocks_2_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_2_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30513024)))]; + tensor xt_71_cast_fp16 = conv(bias = generator_resblocks_2_convs2_2_bias_to_fp16, dilations = xt_71_dilations_0, groups = xt_71_groups_0, pad = xt_71_pad_0, pad_type = xt_71_pad_type_0, strides = xt_71_strides_0, weight = generator_resblocks_2_convs2_2_weight_to_fp16, x = input_95_cast_fp16)[name = string("xt_71_cast_fp16")]; + tensor var_983_cast_fp16 = add(x = xt_71_cast_fp16, y = input_89_cast_fp16)[name = string("op_983_cast_fp16")]; + tensor xs_5_cast_fp16 = add(x = xs_3_cast_fp16, y = var_983_cast_fp16)[name = string("xs_5_cast_fp16")]; + fp16 _inversed_x_3_y_0_to_fp16 = const()[name = string("_inversed_x_3_y_0_to_fp16"), val = fp16(0x1.554p-2)]; + tensor _inversed_x_3_cast_fp16 = mul(x = xs_5_cast_fp16, y = _inversed_x_3_y_0_to_fp16)[name = string("_inversed_x_3_cast_fp16")]; + tensor generator_alphas_1_to_fp16 = const()[name = string("generator_alphas_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30513600)))]; + tensor var_990_cast_fp16 = mul(x = generator_alphas_1_to_fp16, y = _inversed_x_3_cast_fp16)[name = string("op_990_cast_fp16")]; + tensor var_991_cast_fp16 = sin(x = var_990_cast_fp16)[name = string("op_991_cast_fp16")]; + fp16 var_24_promoted_25_to_fp16 = const()[name = string("op_24_promoted_25_to_fp16"), val = fp16(0x1p+1)]; + tensor var_992_cast_fp16 = pow(x = var_991_cast_fp16, y = var_24_promoted_25_to_fp16)[name = string("op_992_cast_fp16")]; + tensor var_987_to_fp16 = const()[name = string("op_987_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30514176)))]; + tensor var_993_cast_fp16 = mul(x = var_987_to_fp16, y = var_992_cast_fp16)[name = string("op_993_cast_fp16")]; + tensor input_121_cast_fp16 = add(x = _inversed_x_3_cast_fp16, y = var_993_cast_fp16)[name = string("input_121_cast_fp16")]; + string input_97_pad_type_0 = const()[name = string("input_97_pad_type_0"), val = string("custom")]; + tensor input_97_pad_0 = const()[name = string("input_97_pad_0"), val = tensor([3, 3])]; + tensor input_97_strides_0 = const()[name = string("input_97_strides_0"), val = tensor([6])]; + tensor input_97_dilations_0 = const()[name = string("input_97_dilations_0"), val = tensor([1])]; + int32 input_97_groups_0 = const()[name = string("input_97_groups_0"), val = int32(1)]; + tensor generator_noise_convs_1_weight_to_fp16 = const()[name = string("generator_noise_convs_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30514752)))]; + tensor generator_noise_convs_1_bias_to_fp16 = const()[name = string("generator_noise_convs_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30517888)))]; + tensor input_97_cast_fp16 = conv(bias = generator_noise_convs_1_bias_to_fp16, dilations = input_97_dilations_0, groups = input_97_groups_0, pad = input_97_pad_0, pad_type = input_97_pad_type_0, strides = input_97_strides_0, weight = generator_noise_convs_1_weight_to_fp16, x = har_source_to_fp16)[name = string("input_97_cast_fp16")]; + tensor generator_noise_res_1_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_noise_res_1_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30518208)))]; + tensor generator_noise_res_1_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_noise_res_1_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30583808)))]; + tensor linear_24_cast_fp16 = linear(bias = generator_noise_res_1_adain1_0_fc_bias_to_fp16, weight = generator_noise_res_1_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_1044 = const()[name = string("op_1044"), val = tensor([1, 256, 1])]; + tensor h_99_cast_fp16 = reshape(shape = var_1044, x = linear_24_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor var_1046_split_sizes_0 = const()[name = string("op_1046_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1046_axis_0 = const()[name = string("op_1046_axis_0"), val = int32(1)]; + tensor var_1046_cast_fp16_0, tensor var_1046_cast_fp16_1 = split(axis = var_1046_axis_0, split_sizes = var_1046_split_sizes_0, x = h_99_cast_fp16)[name = string("op_1046_cast_fp16")]; + fp16 var_1048_promoted_to_fp16 = const()[name = string("op_1048_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1049_cast_fp16 = add(x = var_1046_cast_fp16_0, y = var_1048_promoted_to_fp16)[name = string("op_1049_cast_fp16")]; + tensor var_1050_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_97_cast_fp16)[name = string("op_1050_cast_fp16")]; + tensor var_1051_cast_fp16 = mul(x = var_1049_cast_fp16, y = var_1050_cast_fp16)[name = string("op_1051_cast_fp16")]; + tensor xt_73_cast_fp16 = add(x = var_1051_cast_fp16, y = var_1046_cast_fp16_1)[name = string("xt_73_cast_fp16")]; + tensor generator_noise_res_1_alpha1_0_to_fp16 = const()[name = string("generator_noise_res_1_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30584384)))]; + tensor var_1056_cast_fp16 = mul(x = generator_noise_res_1_alpha1_0_to_fp16, y = xt_73_cast_fp16)[name = string("op_1056_cast_fp16")]; + tensor var_1057_cast_fp16 = sin(x = var_1056_cast_fp16)[name = string("op_1057_cast_fp16")]; + fp16 var_24_promoted_26_to_fp16 = const()[name = string("op_24_promoted_26_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1058_cast_fp16 = pow(x = var_1057_cast_fp16, y = var_24_promoted_26_to_fp16)[name = string("op_1058_cast_fp16")]; + tensor var_1053_to_fp16 = const()[name = string("op_1053_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30584704)))]; + tensor var_1059_cast_fp16 = mul(x = var_1053_to_fp16, y = var_1058_cast_fp16)[name = string("op_1059_cast_fp16")]; + tensor input_99_cast_fp16 = add(x = xt_73_cast_fp16, y = var_1059_cast_fp16)[name = string("input_99_cast_fp16")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([3, 3])]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1])]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)]; + tensor generator_noise_res_1_convs1_0_weight_to_fp16 = const()[name = string("generator_noise_res_1_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30585024)))]; + tensor generator_noise_res_1_convs1_0_bias_to_fp16 = const()[name = string("generator_noise_res_1_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30814464)))]; + tensor input_101_cast_fp16 = conv(bias = generator_noise_res_1_convs1_0_bias_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = generator_noise_res_1_convs1_0_weight_to_fp16, x = input_99_cast_fp16)[name = string("input_101_cast_fp16")]; + tensor generator_noise_res_1_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_noise_res_1_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30814784)))]; + tensor generator_noise_res_1_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_noise_res_1_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30880384)))]; + tensor linear_25_cast_fp16 = linear(bias = generator_noise_res_1_adain2_0_fc_bias_to_fp16, weight = generator_noise_res_1_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_1074 = const()[name = string("op_1074"), val = tensor([1, 256, 1])]; + tensor h_103_cast_fp16 = reshape(shape = var_1074, x = linear_25_cast_fp16)[name = string("h_103_cast_fp16")]; + tensor var_1076_split_sizes_0 = const()[name = string("op_1076_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1076_axis_0 = const()[name = string("op_1076_axis_0"), val = int32(1)]; + tensor var_1076_cast_fp16_0, tensor var_1076_cast_fp16_1 = split(axis = var_1076_axis_0, split_sizes = var_1076_split_sizes_0, x = h_103_cast_fp16)[name = string("op_1076_cast_fp16")]; + fp16 var_1078_promoted_to_fp16 = const()[name = string("op_1078_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1079_cast_fp16 = add(x = var_1076_cast_fp16_0, y = var_1078_promoted_to_fp16)[name = string("op_1079_cast_fp16")]; + tensor var_1080_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_101_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_cast_fp16 = mul(x = var_1079_cast_fp16, y = var_1080_cast_fp16)[name = string("op_1081_cast_fp16")]; + tensor xt_75_cast_fp16 = add(x = var_1081_cast_fp16, y = var_1076_cast_fp16_1)[name = string("xt_75_cast_fp16")]; + tensor generator_noise_res_1_alpha2_0_to_fp16 = const()[name = string("generator_noise_res_1_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30880960)))]; + tensor var_1086_cast_fp16 = mul(x = generator_noise_res_1_alpha2_0_to_fp16, y = xt_75_cast_fp16)[name = string("op_1086_cast_fp16")]; + tensor var_1087_cast_fp16 = sin(x = var_1086_cast_fp16)[name = string("op_1087_cast_fp16")]; + fp16 var_24_promoted_27_to_fp16 = const()[name = string("op_24_promoted_27_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1088_cast_fp16 = pow(x = var_1087_cast_fp16, y = var_24_promoted_27_to_fp16)[name = string("op_1088_cast_fp16")]; + tensor var_1083_to_fp16 = const()[name = string("op_1083_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30881280)))]; + tensor var_1089_cast_fp16 = mul(x = var_1083_to_fp16, y = var_1088_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = xt_75_cast_fp16, y = var_1089_cast_fp16)[name = string("input_103_cast_fp16")]; + string xt_77_pad_type_0 = const()[name = string("xt_77_pad_type_0"), val = string("custom")]; + tensor xt_77_pad_0 = const()[name = string("xt_77_pad_0"), val = tensor([3, 3])]; + tensor xt_77_strides_0 = const()[name = string("xt_77_strides_0"), val = tensor([1])]; + tensor xt_77_dilations_0 = const()[name = string("xt_77_dilations_0"), val = tensor([1])]; + int32 xt_77_groups_0 = const()[name = string("xt_77_groups_0"), val = int32(1)]; + tensor generator_noise_res_1_convs2_0_weight_to_fp16 = const()[name = string("generator_noise_res_1_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30881600)))]; + tensor generator_noise_res_1_convs2_0_bias_to_fp16 = const()[name = string("generator_noise_res_1_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31111040)))]; + tensor xt_77_cast_fp16 = conv(bias = generator_noise_res_1_convs2_0_bias_to_fp16, dilations = xt_77_dilations_0, groups = xt_77_groups_0, pad = xt_77_pad_0, pad_type = xt_77_pad_type_0, strides = xt_77_strides_0, weight = generator_noise_res_1_convs2_0_weight_to_fp16, x = input_103_cast_fp16)[name = string("xt_77_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = xt_77_cast_fp16, y = input_97_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor generator_noise_res_1_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_noise_res_1_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31111360)))]; + tensor generator_noise_res_1_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_noise_res_1_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31176960)))]; + tensor linear_26_cast_fp16 = linear(bias = generator_noise_res_1_adain1_1_fc_bias_to_fp16, weight = generator_noise_res_1_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_1105 = const()[name = string("op_1105"), val = tensor([1, 256, 1])]; + tensor h_107_cast_fp16 = reshape(shape = var_1105, x = linear_26_cast_fp16)[name = string("h_107_cast_fp16")]; + tensor var_1107_split_sizes_0 = const()[name = string("op_1107_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1107_axis_0 = const()[name = string("op_1107_axis_0"), val = int32(1)]; + tensor var_1107_cast_fp16_0, tensor var_1107_cast_fp16_1 = split(axis = var_1107_axis_0, split_sizes = var_1107_split_sizes_0, x = h_107_cast_fp16)[name = string("op_1107_cast_fp16")]; + fp16 var_1109_promoted_to_fp16 = const()[name = string("op_1109_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1110_cast_fp16 = add(x = var_1107_cast_fp16_0, y = var_1109_promoted_to_fp16)[name = string("op_1110_cast_fp16")]; + tensor var_1111_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_105_cast_fp16)[name = string("op_1111_cast_fp16")]; + tensor var_1112_cast_fp16 = mul(x = var_1110_cast_fp16, y = var_1111_cast_fp16)[name = string("op_1112_cast_fp16")]; + tensor xt_79_cast_fp16 = add(x = var_1112_cast_fp16, y = var_1107_cast_fp16_1)[name = string("xt_79_cast_fp16")]; + tensor generator_noise_res_1_alpha1_1_to_fp16 = const()[name = string("generator_noise_res_1_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31177536)))]; + tensor var_1117_cast_fp16 = mul(x = generator_noise_res_1_alpha1_1_to_fp16, y = xt_79_cast_fp16)[name = string("op_1117_cast_fp16")]; + tensor var_1118_cast_fp16 = sin(x = var_1117_cast_fp16)[name = string("op_1118_cast_fp16")]; + fp16 var_24_promoted_28_to_fp16 = const()[name = string("op_24_promoted_28_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1119_cast_fp16 = pow(x = var_1118_cast_fp16, y = var_24_promoted_28_to_fp16)[name = string("op_1119_cast_fp16")]; + tensor var_1114_to_fp16 = const()[name = string("op_1114_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31177856)))]; + tensor var_1120_cast_fp16 = mul(x = var_1114_to_fp16, y = var_1119_cast_fp16)[name = string("op_1120_cast_fp16")]; + tensor input_107_cast_fp16 = add(x = xt_79_cast_fp16, y = var_1120_cast_fp16)[name = string("input_107_cast_fp16")]; + string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("custom")]; + tensor input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor([9, 9])]; + tensor input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor([3])]; + tensor input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor([1])]; + int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)]; + tensor generator_noise_res_1_convs1_1_weight_to_fp16 = const()[name = string("generator_noise_res_1_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31178176)))]; + tensor generator_noise_res_1_convs1_1_bias_to_fp16 = const()[name = string("generator_noise_res_1_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31407616)))]; + tensor input_109_cast_fp16 = conv(bias = generator_noise_res_1_convs1_1_bias_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = generator_noise_res_1_convs1_1_weight_to_fp16, x = input_107_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor generator_noise_res_1_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_noise_res_1_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31407936)))]; + tensor generator_noise_res_1_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_noise_res_1_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31473536)))]; + tensor linear_27_cast_fp16 = linear(bias = generator_noise_res_1_adain2_1_fc_bias_to_fp16, weight = generator_noise_res_1_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1135 = const()[name = string("op_1135"), val = tensor([1, 256, 1])]; + tensor h_111_cast_fp16 = reshape(shape = var_1135, x = linear_27_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor var_1137_split_sizes_0 = const()[name = string("op_1137_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1137_axis_0 = const()[name = string("op_1137_axis_0"), val = int32(1)]; + tensor var_1137_cast_fp16_0, tensor var_1137_cast_fp16_1 = split(axis = var_1137_axis_0, split_sizes = var_1137_split_sizes_0, x = h_111_cast_fp16)[name = string("op_1137_cast_fp16")]; + fp16 var_1139_promoted_to_fp16 = const()[name = string("op_1139_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1140_cast_fp16 = add(x = var_1137_cast_fp16_0, y = var_1139_promoted_to_fp16)[name = string("op_1140_cast_fp16")]; + tensor var_1141_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_109_cast_fp16)[name = string("op_1141_cast_fp16")]; + tensor var_1142_cast_fp16 = mul(x = var_1140_cast_fp16, y = var_1141_cast_fp16)[name = string("op_1142_cast_fp16")]; + tensor xt_81_cast_fp16 = add(x = var_1142_cast_fp16, y = var_1137_cast_fp16_1)[name = string("xt_81_cast_fp16")]; + tensor generator_noise_res_1_alpha2_1_to_fp16 = const()[name = string("generator_noise_res_1_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31474112)))]; + tensor var_1147_cast_fp16 = mul(x = generator_noise_res_1_alpha2_1_to_fp16, y = xt_81_cast_fp16)[name = string("op_1147_cast_fp16")]; + tensor var_1148_cast_fp16 = sin(x = var_1147_cast_fp16)[name = string("op_1148_cast_fp16")]; + fp16 var_24_promoted_29_to_fp16 = const()[name = string("op_24_promoted_29_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1149_cast_fp16 = pow(x = var_1148_cast_fp16, y = var_24_promoted_29_to_fp16)[name = string("op_1149_cast_fp16")]; + tensor var_1144_to_fp16 = const()[name = string("op_1144_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31474432)))]; + tensor var_1150_cast_fp16 = mul(x = var_1144_to_fp16, y = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = xt_81_cast_fp16, y = var_1150_cast_fp16)[name = string("input_111_cast_fp16")]; + string xt_83_pad_type_0 = const()[name = string("xt_83_pad_type_0"), val = string("custom")]; + tensor xt_83_pad_0 = const()[name = string("xt_83_pad_0"), val = tensor([3, 3])]; + tensor xt_83_strides_0 = const()[name = string("xt_83_strides_0"), val = tensor([1])]; + tensor xt_83_dilations_0 = const()[name = string("xt_83_dilations_0"), val = tensor([1])]; + int32 xt_83_groups_0 = const()[name = string("xt_83_groups_0"), val = int32(1)]; + tensor generator_noise_res_1_convs2_1_weight_to_fp16 = const()[name = string("generator_noise_res_1_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31474752)))]; + tensor generator_noise_res_1_convs2_1_bias_to_fp16 = const()[name = string("generator_noise_res_1_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31704192)))]; + tensor xt_83_cast_fp16 = conv(bias = generator_noise_res_1_convs2_1_bias_to_fp16, dilations = xt_83_dilations_0, groups = xt_83_groups_0, pad = xt_83_pad_0, pad_type = xt_83_pad_type_0, strides = xt_83_strides_0, weight = generator_noise_res_1_convs2_1_weight_to_fp16, x = input_111_cast_fp16)[name = string("xt_83_cast_fp16")]; + tensor input_113_cast_fp16 = add(x = xt_83_cast_fp16, y = input_105_cast_fp16)[name = string("input_113_cast_fp16")]; + tensor generator_noise_res_1_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_noise_res_1_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31704512)))]; + tensor generator_noise_res_1_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_noise_res_1_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31770112)))]; + tensor linear_28_cast_fp16 = linear(bias = generator_noise_res_1_adain1_2_fc_bias_to_fp16, weight = generator_noise_res_1_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_1166 = const()[name = string("op_1166"), val = tensor([1, 256, 1])]; + tensor h_115_cast_fp16 = reshape(shape = var_1166, x = linear_28_cast_fp16)[name = string("h_115_cast_fp16")]; + tensor var_1168_split_sizes_0 = const()[name = string("op_1168_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1168_axis_0 = const()[name = string("op_1168_axis_0"), val = int32(1)]; + tensor var_1168_cast_fp16_0, tensor var_1168_cast_fp16_1 = split(axis = var_1168_axis_0, split_sizes = var_1168_split_sizes_0, x = h_115_cast_fp16)[name = string("op_1168_cast_fp16")]; + fp16 var_1170_promoted_to_fp16 = const()[name = string("op_1170_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1171_cast_fp16 = add(x = var_1168_cast_fp16_0, y = var_1170_promoted_to_fp16)[name = string("op_1171_cast_fp16")]; + tensor var_1172_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_113_cast_fp16)[name = string("op_1172_cast_fp16")]; + tensor var_1173_cast_fp16 = mul(x = var_1171_cast_fp16, y = var_1172_cast_fp16)[name = string("op_1173_cast_fp16")]; + tensor xt_85_cast_fp16 = add(x = var_1173_cast_fp16, y = var_1168_cast_fp16_1)[name = string("xt_85_cast_fp16")]; + tensor generator_noise_res_1_alpha1_2_to_fp16 = const()[name = string("generator_noise_res_1_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31770688)))]; + tensor var_1178_cast_fp16 = mul(x = generator_noise_res_1_alpha1_2_to_fp16, y = xt_85_cast_fp16)[name = string("op_1178_cast_fp16")]; + tensor var_1179_cast_fp16 = sin(x = var_1178_cast_fp16)[name = string("op_1179_cast_fp16")]; + fp16 var_24_promoted_30_to_fp16 = const()[name = string("op_24_promoted_30_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1180_cast_fp16 = pow(x = var_1179_cast_fp16, y = var_24_promoted_30_to_fp16)[name = string("op_1180_cast_fp16")]; + tensor var_1175_to_fp16 = const()[name = string("op_1175_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31771008)))]; + tensor var_1181_cast_fp16 = mul(x = var_1175_to_fp16, y = var_1180_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor input_115_cast_fp16 = add(x = xt_85_cast_fp16, y = var_1181_cast_fp16)[name = string("input_115_cast_fp16")]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("custom")]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([15, 15])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([5])]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor generator_noise_res_1_convs1_2_weight_to_fp16 = const()[name = string("generator_noise_res_1_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31771328)))]; + tensor generator_noise_res_1_convs1_2_bias_to_fp16 = const()[name = string("generator_noise_res_1_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32000768)))]; + tensor input_117_cast_fp16 = conv(bias = generator_noise_res_1_convs1_2_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = generator_noise_res_1_convs1_2_weight_to_fp16, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor generator_noise_res_1_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_noise_res_1_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32001088)))]; + tensor generator_noise_res_1_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_noise_res_1_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32066688)))]; + tensor linear_29_cast_fp16 = linear(bias = generator_noise_res_1_adain2_2_fc_bias_to_fp16, weight = generator_noise_res_1_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_1196 = const()[name = string("op_1196"), val = tensor([1, 256, 1])]; + tensor h_119_cast_fp16 = reshape(shape = var_1196, x = linear_29_cast_fp16)[name = string("h_119_cast_fp16")]; + tensor var_1198_split_sizes_0 = const()[name = string("op_1198_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1198_axis_0 = const()[name = string("op_1198_axis_0"), val = int32(1)]; + tensor var_1198_cast_fp16_0, tensor var_1198_cast_fp16_1 = split(axis = var_1198_axis_0, split_sizes = var_1198_split_sizes_0, x = h_119_cast_fp16)[name = string("op_1198_cast_fp16")]; + fp16 var_1200_promoted_to_fp16 = const()[name = string("op_1200_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1201_cast_fp16 = add(x = var_1198_cast_fp16_0, y = var_1200_promoted_to_fp16)[name = string("op_1201_cast_fp16")]; + tensor var_1202_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_117_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor var_1203_cast_fp16 = mul(x = var_1201_cast_fp16, y = var_1202_cast_fp16)[name = string("op_1203_cast_fp16")]; + tensor xt_87_cast_fp16 = add(x = var_1203_cast_fp16, y = var_1198_cast_fp16_1)[name = string("xt_87_cast_fp16")]; + tensor generator_noise_res_1_alpha2_2_to_fp16 = const()[name = string("generator_noise_res_1_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32067264)))]; + tensor var_1208_cast_fp16 = mul(x = generator_noise_res_1_alpha2_2_to_fp16, y = xt_87_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_cast_fp16 = sin(x = var_1208_cast_fp16)[name = string("op_1209_cast_fp16")]; + fp16 var_24_promoted_31_to_fp16 = const()[name = string("op_24_promoted_31_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1210_cast_fp16 = pow(x = var_1209_cast_fp16, y = var_24_promoted_31_to_fp16)[name = string("op_1210_cast_fp16")]; + tensor var_1205_to_fp16 = const()[name = string("op_1205_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32067584)))]; + tensor var_1211_cast_fp16 = mul(x = var_1205_to_fp16, y = var_1210_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = xt_87_cast_fp16, y = var_1211_cast_fp16)[name = string("input_119_cast_fp16")]; + string xt_89_pad_type_0 = const()[name = string("xt_89_pad_type_0"), val = string("custom")]; + tensor xt_89_pad_0 = const()[name = string("xt_89_pad_0"), val = tensor([3, 3])]; + tensor xt_89_strides_0 = const()[name = string("xt_89_strides_0"), val = tensor([1])]; + tensor xt_89_dilations_0 = const()[name = string("xt_89_dilations_0"), val = tensor([1])]; + int32 xt_89_groups_0 = const()[name = string("xt_89_groups_0"), val = int32(1)]; + tensor generator_noise_res_1_convs2_2_weight_to_fp16 = const()[name = string("generator_noise_res_1_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32067904)))]; + tensor generator_noise_res_1_convs2_2_bias_to_fp16 = const()[name = string("generator_noise_res_1_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32297344)))]; + tensor xt_89_cast_fp16 = conv(bias = generator_noise_res_1_convs2_2_bias_to_fp16, dilations = xt_89_dilations_0, groups = xt_89_groups_0, pad = xt_89_pad_0, pad_type = xt_89_pad_type_0, strides = xt_89_strides_0, weight = generator_noise_res_1_convs2_2_weight_to_fp16, x = input_119_cast_fp16)[name = string("xt_89_cast_fp16")]; + tensor x_source_3_cast_fp16 = add(x = xt_89_cast_fp16, y = input_113_cast_fp16)[name = string("x_source_3_cast_fp16")]; + string conv_transpose_0_pad_type_0 = const()[name = string("conv_transpose_0_pad_type_0"), val = string("custom")]; + tensor conv_transpose_0_pad_0 = const()[name = string("conv_transpose_0_pad_0"), val = tensor([0, 0])]; + tensor conv_transpose_0_strides_0 = const()[name = string("conv_transpose_0_strides_0"), val = tensor([5])]; + tensor conv_transpose_0_dilations_0 = const()[name = string("conv_transpose_0_dilations_0"), val = tensor([1])]; + int32 conv_transpose_0_groups_0 = const()[name = string("conv_transpose_0_groups_0"), val = int32(1)]; + tensor generator_ups_1_weight_to_fp16 = const()[name = string("generator_ups_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32297664)))]; + tensor generator_ups_1_bias_to_fp16 = const()[name = string("generator_ups_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32953088)))]; + tensor conv_transpose_0_cast_fp16 = conv_transpose(bias = generator_ups_1_bias_to_fp16, dilations = conv_transpose_0_dilations_0, groups = conv_transpose_0_groups_0, pad = conv_transpose_0_pad_0, pad_type = conv_transpose_0_pad_type_0, strides = conv_transpose_0_strides_0, weight = generator_ups_1_weight_to_fp16, x = input_121_cast_fp16)[name = string("conv_transpose_0_cast_fp16")]; + tensor x_5_begin_0 = const()[name = string("x_5_begin_0"), val = tensor([0, 0, 3])]; + tensor x_5_end_0 = const()[name = string("x_5_end_0"), val = tensor([0, 0, -2])]; + tensor x_5_begin_mask_0 = const()[name = string("x_5_begin_mask_0"), val = tensor([true, true, false])]; + tensor x_5_end_mask_0 = const()[name = string("x_5_end_mask_0"), val = tensor([true, true, false])]; + tensor x_5_cast_fp16 = slice_by_index(begin = x_5_begin_0, begin_mask = x_5_begin_mask_0, end = x_5_end_0, end_mask = x_5_end_mask_0, x = conv_transpose_0_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = x_5_cast_fp16, y = x_source_3_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor generator_resblocks_3_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_3_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32953408)))]; + tensor generator_resblocks_3_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_3_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33019008)))]; + tensor linear_30_cast_fp16 = linear(bias = generator_resblocks_3_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_3_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_1271 = const()[name = string("op_1271"), val = tensor([1, 256, 1])]; + tensor h_123_cast_fp16 = reshape(shape = var_1271, x = linear_30_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor var_1273_split_sizes_0 = const()[name = string("op_1273_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1273_axis_0 = const()[name = string("op_1273_axis_0"), val = int32(1)]; + tensor var_1273_cast_fp16_0, tensor var_1273_cast_fp16_1 = split(axis = var_1273_axis_0, split_sizes = var_1273_split_sizes_0, x = h_123_cast_fp16)[name = string("op_1273_cast_fp16")]; + fp16 var_1275_promoted_to_fp16 = const()[name = string("op_1275_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1276_cast_fp16 = add(x = var_1273_cast_fp16_0, y = var_1275_promoted_to_fp16)[name = string("op_1276_cast_fp16")]; + tensor var_1277_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_123_cast_fp16)[name = string("op_1277_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1276_cast_fp16, y = var_1277_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor xt_91_cast_fp16 = add(x = var_1278_cast_fp16, y = var_1273_cast_fp16_1)[name = string("xt_91_cast_fp16")]; + tensor generator_resblocks_3_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_3_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33019584)))]; + tensor var_1283_cast_fp16 = mul(x = generator_resblocks_3_alpha1_0_to_fp16, y = xt_91_cast_fp16)[name = string("op_1283_cast_fp16")]; + tensor var_1284_cast_fp16 = sin(x = var_1283_cast_fp16)[name = string("op_1284_cast_fp16")]; + fp16 var_24_promoted_32_to_fp16 = const()[name = string("op_24_promoted_32_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1285_cast_fp16 = pow(x = var_1284_cast_fp16, y = var_24_promoted_32_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1280_to_fp16 = const()[name = string("op_1280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33019904)))]; + tensor var_1286_cast_fp16 = mul(x = var_1280_to_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = xt_91_cast_fp16, y = var_1286_cast_fp16)[name = string("input_125_cast_fp16")]; + string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")]; + tensor input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor([1, 1])]; + tensor input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor([1])]; + tensor input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor([1])]; + int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)]; + tensor generator_resblocks_3_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_3_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33020224)))]; + tensor generator_resblocks_3_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_3_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33118592)))]; + tensor input_127_cast_fp16 = conv(bias = generator_resblocks_3_convs1_0_bias_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = generator_resblocks_3_convs1_0_weight_to_fp16, x = input_125_cast_fp16)[name = string("input_127_cast_fp16")]; + tensor generator_resblocks_3_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_3_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33118912)))]; + tensor generator_resblocks_3_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_3_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33184512)))]; + tensor linear_31_cast_fp16 = linear(bias = generator_resblocks_3_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_3_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_1301 = const()[name = string("op_1301"), val = tensor([1, 256, 1])]; + tensor h_127_cast_fp16 = reshape(shape = var_1301, x = linear_31_cast_fp16)[name = string("h_127_cast_fp16")]; + tensor var_1303_split_sizes_0 = const()[name = string("op_1303_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1303_axis_0 = const()[name = string("op_1303_axis_0"), val = int32(1)]; + tensor var_1303_cast_fp16_0, tensor var_1303_cast_fp16_1 = split(axis = var_1303_axis_0, split_sizes = var_1303_split_sizes_0, x = h_127_cast_fp16)[name = string("op_1303_cast_fp16")]; + fp16 var_1305_promoted_to_fp16 = const()[name = string("op_1305_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1306_cast_fp16 = add(x = var_1303_cast_fp16_0, y = var_1305_promoted_to_fp16)[name = string("op_1306_cast_fp16")]; + tensor var_1307_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_127_cast_fp16)[name = string("op_1307_cast_fp16")]; + tensor var_1308_cast_fp16 = mul(x = var_1306_cast_fp16, y = var_1307_cast_fp16)[name = string("op_1308_cast_fp16")]; + tensor xt_93_cast_fp16 = add(x = var_1308_cast_fp16, y = var_1303_cast_fp16_1)[name = string("xt_93_cast_fp16")]; + tensor generator_resblocks_3_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_3_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33185088)))]; + tensor var_1313_cast_fp16 = mul(x = generator_resblocks_3_alpha2_0_to_fp16, y = xt_93_cast_fp16)[name = string("op_1313_cast_fp16")]; + tensor var_1314_cast_fp16 = sin(x = var_1313_cast_fp16)[name = string("op_1314_cast_fp16")]; + fp16 var_24_promoted_33_to_fp16 = const()[name = string("op_24_promoted_33_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1315_cast_fp16 = pow(x = var_1314_cast_fp16, y = var_24_promoted_33_to_fp16)[name = string("op_1315_cast_fp16")]; + tensor var_1310_to_fp16 = const()[name = string("op_1310_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33185408)))]; + tensor var_1316_cast_fp16 = mul(x = var_1310_to_fp16, y = var_1315_cast_fp16)[name = string("op_1316_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = xt_93_cast_fp16, y = var_1316_cast_fp16)[name = string("input_129_cast_fp16")]; + string xt_95_pad_type_0 = const()[name = string("xt_95_pad_type_0"), val = string("custom")]; + tensor xt_95_pad_0 = const()[name = string("xt_95_pad_0"), val = tensor([1, 1])]; + tensor xt_95_strides_0 = const()[name = string("xt_95_strides_0"), val = tensor([1])]; + tensor xt_95_dilations_0 = const()[name = string("xt_95_dilations_0"), val = tensor([1])]; + int32 xt_95_groups_0 = const()[name = string("xt_95_groups_0"), val = int32(1)]; + tensor generator_resblocks_3_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_3_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33185728)))]; + tensor generator_resblocks_3_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_3_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33284096)))]; + tensor xt_95_cast_fp16 = conv(bias = generator_resblocks_3_convs2_0_bias_to_fp16, dilations = xt_95_dilations_0, groups = xt_95_groups_0, pad = xt_95_pad_0, pad_type = xt_95_pad_type_0, strides = xt_95_strides_0, weight = generator_resblocks_3_convs2_0_weight_to_fp16, x = input_129_cast_fp16)[name = string("xt_95_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = xt_95_cast_fp16, y = input_123_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor generator_resblocks_3_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_3_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33284416)))]; + tensor generator_resblocks_3_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_3_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33350016)))]; + tensor linear_32_cast_fp16 = linear(bias = generator_resblocks_3_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_3_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_1332 = const()[name = string("op_1332"), val = tensor([1, 256, 1])]; + tensor h_131_cast_fp16 = reshape(shape = var_1332, x = linear_32_cast_fp16)[name = string("h_131_cast_fp16")]; + tensor var_1334_split_sizes_0 = const()[name = string("op_1334_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1334_axis_0 = const()[name = string("op_1334_axis_0"), val = int32(1)]; + tensor var_1334_cast_fp16_0, tensor var_1334_cast_fp16_1 = split(axis = var_1334_axis_0, split_sizes = var_1334_split_sizes_0, x = h_131_cast_fp16)[name = string("op_1334_cast_fp16")]; + fp16 var_1336_promoted_to_fp16 = const()[name = string("op_1336_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1337_cast_fp16 = add(x = var_1334_cast_fp16_0, y = var_1336_promoted_to_fp16)[name = string("op_1337_cast_fp16")]; + tensor var_1338_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_131_cast_fp16)[name = string("op_1338_cast_fp16")]; + tensor var_1339_cast_fp16 = mul(x = var_1337_cast_fp16, y = var_1338_cast_fp16)[name = string("op_1339_cast_fp16")]; + tensor xt_97_cast_fp16 = add(x = var_1339_cast_fp16, y = var_1334_cast_fp16_1)[name = string("xt_97_cast_fp16")]; + tensor generator_resblocks_3_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_3_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33350592)))]; + tensor var_1344_cast_fp16 = mul(x = generator_resblocks_3_alpha1_1_to_fp16, y = xt_97_cast_fp16)[name = string("op_1344_cast_fp16")]; + tensor var_1345_cast_fp16 = sin(x = var_1344_cast_fp16)[name = string("op_1345_cast_fp16")]; + fp16 var_24_promoted_34_to_fp16 = const()[name = string("op_24_promoted_34_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1346_cast_fp16 = pow(x = var_1345_cast_fp16, y = var_24_promoted_34_to_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1341_to_fp16 = const()[name = string("op_1341_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33350912)))]; + tensor var_1347_cast_fp16 = mul(x = var_1341_to_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor input_133_cast_fp16 = add(x = xt_97_cast_fp16, y = var_1347_cast_fp16)[name = string("input_133_cast_fp16")]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("custom")]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([3, 3])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([3])]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor generator_resblocks_3_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_3_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33351232)))]; + tensor generator_resblocks_3_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_3_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33449600)))]; + tensor input_135_cast_fp16 = conv(bias = generator_resblocks_3_convs1_1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = generator_resblocks_3_convs1_1_weight_to_fp16, x = input_133_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor generator_resblocks_3_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_3_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33449920)))]; + tensor generator_resblocks_3_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_3_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33515520)))]; + tensor linear_33_cast_fp16 = linear(bias = generator_resblocks_3_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_3_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_1362 = const()[name = string("op_1362"), val = tensor([1, 256, 1])]; + tensor h_135_cast_fp16 = reshape(shape = var_1362, x = linear_33_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor var_1364_split_sizes_0 = const()[name = string("op_1364_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1364_axis_0 = const()[name = string("op_1364_axis_0"), val = int32(1)]; + tensor var_1364_cast_fp16_0, tensor var_1364_cast_fp16_1 = split(axis = var_1364_axis_0, split_sizes = var_1364_split_sizes_0, x = h_135_cast_fp16)[name = string("op_1364_cast_fp16")]; + fp16 var_1366_promoted_to_fp16 = const()[name = string("op_1366_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1367_cast_fp16 = add(x = var_1364_cast_fp16_0, y = var_1366_promoted_to_fp16)[name = string("op_1367_cast_fp16")]; + tensor var_1368_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_135_cast_fp16)[name = string("op_1368_cast_fp16")]; + tensor var_1369_cast_fp16 = mul(x = var_1367_cast_fp16, y = var_1368_cast_fp16)[name = string("op_1369_cast_fp16")]; + tensor xt_99_cast_fp16 = add(x = var_1369_cast_fp16, y = var_1364_cast_fp16_1)[name = string("xt_99_cast_fp16")]; + tensor generator_resblocks_3_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_3_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33516096)))]; + tensor var_1374_cast_fp16 = mul(x = generator_resblocks_3_alpha2_1_to_fp16, y = xt_99_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1375_cast_fp16 = sin(x = var_1374_cast_fp16)[name = string("op_1375_cast_fp16")]; + fp16 var_24_promoted_35_to_fp16 = const()[name = string("op_24_promoted_35_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1376_cast_fp16 = pow(x = var_1375_cast_fp16, y = var_24_promoted_35_to_fp16)[name = string("op_1376_cast_fp16")]; + tensor var_1371_to_fp16 = const()[name = string("op_1371_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33516416)))]; + tensor var_1377_cast_fp16 = mul(x = var_1371_to_fp16, y = var_1376_cast_fp16)[name = string("op_1377_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = xt_99_cast_fp16, y = var_1377_cast_fp16)[name = string("input_137_cast_fp16")]; + string xt_101_pad_type_0 = const()[name = string("xt_101_pad_type_0"), val = string("custom")]; + tensor xt_101_pad_0 = const()[name = string("xt_101_pad_0"), val = tensor([1, 1])]; + tensor xt_101_strides_0 = const()[name = string("xt_101_strides_0"), val = tensor([1])]; + tensor xt_101_dilations_0 = const()[name = string("xt_101_dilations_0"), val = tensor([1])]; + int32 xt_101_groups_0 = const()[name = string("xt_101_groups_0"), val = int32(1)]; + tensor generator_resblocks_3_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_3_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33516736)))]; + tensor generator_resblocks_3_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_3_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33615104)))]; + tensor xt_101_cast_fp16 = conv(bias = generator_resblocks_3_convs2_1_bias_to_fp16, dilations = xt_101_dilations_0, groups = xt_101_groups_0, pad = xt_101_pad_0, pad_type = xt_101_pad_type_0, strides = xt_101_strides_0, weight = generator_resblocks_3_convs2_1_weight_to_fp16, x = input_137_cast_fp16)[name = string("xt_101_cast_fp16")]; + tensor input_139_cast_fp16 = add(x = xt_101_cast_fp16, y = input_131_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor generator_resblocks_3_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_3_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33615424)))]; + tensor generator_resblocks_3_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_3_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33681024)))]; + tensor linear_34_cast_fp16 = linear(bias = generator_resblocks_3_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_3_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_1393 = const()[name = string("op_1393"), val = tensor([1, 256, 1])]; + tensor h_139_cast_fp16 = reshape(shape = var_1393, x = linear_34_cast_fp16)[name = string("h_139_cast_fp16")]; + tensor var_1395_split_sizes_0 = const()[name = string("op_1395_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1395_axis_0 = const()[name = string("op_1395_axis_0"), val = int32(1)]; + tensor var_1395_cast_fp16_0, tensor var_1395_cast_fp16_1 = split(axis = var_1395_axis_0, split_sizes = var_1395_split_sizes_0, x = h_139_cast_fp16)[name = string("op_1395_cast_fp16")]; + fp16 var_1397_promoted_to_fp16 = const()[name = string("op_1397_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1398_cast_fp16 = add(x = var_1395_cast_fp16_0, y = var_1397_promoted_to_fp16)[name = string("op_1398_cast_fp16")]; + tensor var_1399_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_139_cast_fp16)[name = string("op_1399_cast_fp16")]; + tensor var_1400_cast_fp16 = mul(x = var_1398_cast_fp16, y = var_1399_cast_fp16)[name = string("op_1400_cast_fp16")]; + tensor xt_103_cast_fp16 = add(x = var_1400_cast_fp16, y = var_1395_cast_fp16_1)[name = string("xt_103_cast_fp16")]; + tensor generator_resblocks_3_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_3_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33681600)))]; + tensor var_1405_cast_fp16 = mul(x = generator_resblocks_3_alpha1_2_to_fp16, y = xt_103_cast_fp16)[name = string("op_1405_cast_fp16")]; + tensor var_1406_cast_fp16 = sin(x = var_1405_cast_fp16)[name = string("op_1406_cast_fp16")]; + fp16 var_24_promoted_36_to_fp16 = const()[name = string("op_24_promoted_36_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1407_cast_fp16 = pow(x = var_1406_cast_fp16, y = var_24_promoted_36_to_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1402_to_fp16 = const()[name = string("op_1402_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33681920)))]; + tensor var_1408_cast_fp16 = mul(x = var_1402_to_fp16, y = var_1407_cast_fp16)[name = string("op_1408_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = xt_103_cast_fp16, y = var_1408_cast_fp16)[name = string("input_141_cast_fp16")]; + string input_143_pad_type_0 = const()[name = string("input_143_pad_type_0"), val = string("custom")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([5, 5])]; + tensor input_143_dilations_0 = const()[name = string("input_143_dilations_0"), val = tensor([5])]; + tensor input_143_strides_0 = const()[name = string("input_143_strides_0"), val = tensor([1])]; + int32 input_143_groups_0 = const()[name = string("input_143_groups_0"), val = int32(1)]; + tensor generator_resblocks_3_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_3_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33682240)))]; + tensor generator_resblocks_3_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_3_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33780608)))]; + tensor input_143_cast_fp16 = conv(bias = generator_resblocks_3_convs1_2_bias_to_fp16, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = generator_resblocks_3_convs1_2_weight_to_fp16, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor generator_resblocks_3_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_3_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33780928)))]; + tensor generator_resblocks_3_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_3_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33846528)))]; + tensor linear_35_cast_fp16 = linear(bias = generator_resblocks_3_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_3_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_1423 = const()[name = string("op_1423"), val = tensor([1, 256, 1])]; + tensor h_143_cast_fp16 = reshape(shape = var_1423, x = linear_35_cast_fp16)[name = string("h_143_cast_fp16")]; + tensor var_1425_split_sizes_0 = const()[name = string("op_1425_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1425_axis_0 = const()[name = string("op_1425_axis_0"), val = int32(1)]; + tensor var_1425_cast_fp16_0, tensor var_1425_cast_fp16_1 = split(axis = var_1425_axis_0, split_sizes = var_1425_split_sizes_0, x = h_143_cast_fp16)[name = string("op_1425_cast_fp16")]; + fp16 var_1427_promoted_to_fp16 = const()[name = string("op_1427_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1428_cast_fp16 = add(x = var_1425_cast_fp16_0, y = var_1427_promoted_to_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1429_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_143_cast_fp16)[name = string("op_1429_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_1428_cast_fp16, y = var_1429_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor xt_105_cast_fp16 = add(x = var_1430_cast_fp16, y = var_1425_cast_fp16_1)[name = string("xt_105_cast_fp16")]; + tensor generator_resblocks_3_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_3_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33847104)))]; + tensor var_1435_cast_fp16 = mul(x = generator_resblocks_3_alpha2_2_to_fp16, y = xt_105_cast_fp16)[name = string("op_1435_cast_fp16")]; + tensor var_1436_cast_fp16 = sin(x = var_1435_cast_fp16)[name = string("op_1436_cast_fp16")]; + fp16 var_24_promoted_37_to_fp16 = const()[name = string("op_24_promoted_37_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1437_cast_fp16 = pow(x = var_1436_cast_fp16, y = var_24_promoted_37_to_fp16)[name = string("op_1437_cast_fp16")]; + tensor var_1432_to_fp16 = const()[name = string("op_1432_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33847424)))]; + tensor var_1438_cast_fp16 = mul(x = var_1432_to_fp16, y = var_1437_cast_fp16)[name = string("op_1438_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = xt_105_cast_fp16, y = var_1438_cast_fp16)[name = string("input_145_cast_fp16")]; + string xt_107_pad_type_0 = const()[name = string("xt_107_pad_type_0"), val = string("custom")]; + tensor xt_107_pad_0 = const()[name = string("xt_107_pad_0"), val = tensor([1, 1])]; + tensor xt_107_strides_0 = const()[name = string("xt_107_strides_0"), val = tensor([1])]; + tensor xt_107_dilations_0 = const()[name = string("xt_107_dilations_0"), val = tensor([1])]; + int32 xt_107_groups_0 = const()[name = string("xt_107_groups_0"), val = int32(1)]; + tensor generator_resblocks_3_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_3_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33847744)))]; + tensor generator_resblocks_3_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_3_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33946112)))]; + tensor xt_107_cast_fp16 = conv(bias = generator_resblocks_3_convs2_2_bias_to_fp16, dilations = xt_107_dilations_0, groups = xt_107_groups_0, pad = xt_107_pad_0, pad_type = xt_107_pad_type_0, strides = xt_107_strides_0, weight = generator_resblocks_3_convs2_2_weight_to_fp16, x = input_145_cast_fp16)[name = string("xt_107_cast_fp16")]; + tensor xs_7_cast_fp16 = add(x = xt_107_cast_fp16, y = input_139_cast_fp16)[name = string("xs_7_cast_fp16")]; + tensor generator_resblocks_4_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_4_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33946432)))]; + tensor generator_resblocks_4_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_4_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34012032)))]; + tensor linear_36_cast_fp16 = linear(bias = generator_resblocks_4_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_4_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_1490 = const()[name = string("op_1490"), val = tensor([1, 256, 1])]; + tensor h_147_cast_fp16 = reshape(shape = var_1490, x = linear_36_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor var_1492_split_sizes_0 = const()[name = string("op_1492_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1492_axis_0 = const()[name = string("op_1492_axis_0"), val = int32(1)]; + tensor var_1492_cast_fp16_0, tensor var_1492_cast_fp16_1 = split(axis = var_1492_axis_0, split_sizes = var_1492_split_sizes_0, x = h_147_cast_fp16)[name = string("op_1492_cast_fp16")]; + fp16 var_1494_promoted_to_fp16 = const()[name = string("op_1494_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1495_cast_fp16 = add(x = var_1492_cast_fp16_0, y = var_1494_promoted_to_fp16)[name = string("op_1495_cast_fp16")]; + tensor var_1497_cast_fp16 = mul(x = var_1495_cast_fp16, y = var_1277_cast_fp16)[name = string("op_1497_cast_fp16")]; + tensor xt_109_cast_fp16 = add(x = var_1497_cast_fp16, y = var_1492_cast_fp16_1)[name = string("xt_109_cast_fp16")]; + tensor generator_resblocks_4_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_4_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34012608)))]; + tensor var_1502_cast_fp16 = mul(x = generator_resblocks_4_alpha1_0_to_fp16, y = xt_109_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor var_1503_cast_fp16 = sin(x = var_1502_cast_fp16)[name = string("op_1503_cast_fp16")]; + fp16 var_24_promoted_38_to_fp16 = const()[name = string("op_24_promoted_38_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1504_cast_fp16 = pow(x = var_1503_cast_fp16, y = var_24_promoted_38_to_fp16)[name = string("op_1504_cast_fp16")]; + tensor var_1499_to_fp16 = const()[name = string("op_1499_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34012928)))]; + tensor var_1505_cast_fp16 = mul(x = var_1499_to_fp16, y = var_1504_cast_fp16)[name = string("op_1505_cast_fp16")]; + tensor input_147_cast_fp16 = add(x = xt_109_cast_fp16, y = var_1505_cast_fp16)[name = string("input_147_cast_fp16")]; + string input_149_pad_type_0 = const()[name = string("input_149_pad_type_0"), val = string("custom")]; + tensor input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor([3, 3])]; + tensor input_149_strides_0 = const()[name = string("input_149_strides_0"), val = tensor([1])]; + tensor input_149_dilations_0 = const()[name = string("input_149_dilations_0"), val = tensor([1])]; + int32 input_149_groups_0 = const()[name = string("input_149_groups_0"), val = int32(1)]; + tensor generator_resblocks_4_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_4_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34013248)))]; + tensor generator_resblocks_4_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_4_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34242688)))]; + tensor input_149_cast_fp16 = conv(bias = generator_resblocks_4_convs1_0_bias_to_fp16, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = generator_resblocks_4_convs1_0_weight_to_fp16, x = input_147_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor generator_resblocks_4_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_4_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34243008)))]; + tensor generator_resblocks_4_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_4_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34308608)))]; + tensor linear_37_cast_fp16 = linear(bias = generator_resblocks_4_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_4_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_1520 = const()[name = string("op_1520"), val = tensor([1, 256, 1])]; + tensor h_151_cast_fp16 = reshape(shape = var_1520, x = linear_37_cast_fp16)[name = string("h_151_cast_fp16")]; + tensor var_1522_split_sizes_0 = const()[name = string("op_1522_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1522_axis_0 = const()[name = string("op_1522_axis_0"), val = int32(1)]; + tensor var_1522_cast_fp16_0, tensor var_1522_cast_fp16_1 = split(axis = var_1522_axis_0, split_sizes = var_1522_split_sizes_0, x = h_151_cast_fp16)[name = string("op_1522_cast_fp16")]; + fp16 var_1524_promoted_to_fp16 = const()[name = string("op_1524_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1525_cast_fp16 = add(x = var_1522_cast_fp16_0, y = var_1524_promoted_to_fp16)[name = string("op_1525_cast_fp16")]; + tensor var_1526_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_149_cast_fp16)[name = string("op_1526_cast_fp16")]; + tensor var_1527_cast_fp16 = mul(x = var_1525_cast_fp16, y = var_1526_cast_fp16)[name = string("op_1527_cast_fp16")]; + tensor xt_111_cast_fp16 = add(x = var_1527_cast_fp16, y = var_1522_cast_fp16_1)[name = string("xt_111_cast_fp16")]; + tensor generator_resblocks_4_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_4_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34309184)))]; + tensor var_1532_cast_fp16 = mul(x = generator_resblocks_4_alpha2_0_to_fp16, y = xt_111_cast_fp16)[name = string("op_1532_cast_fp16")]; + tensor var_1533_cast_fp16 = sin(x = var_1532_cast_fp16)[name = string("op_1533_cast_fp16")]; + fp16 var_24_promoted_39_to_fp16 = const()[name = string("op_24_promoted_39_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1534_cast_fp16 = pow(x = var_1533_cast_fp16, y = var_24_promoted_39_to_fp16)[name = string("op_1534_cast_fp16")]; + tensor var_1529_to_fp16 = const()[name = string("op_1529_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34309504)))]; + tensor var_1535_cast_fp16 = mul(x = var_1529_to_fp16, y = var_1534_cast_fp16)[name = string("op_1535_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = xt_111_cast_fp16, y = var_1535_cast_fp16)[name = string("input_151_cast_fp16")]; + string xt_113_pad_type_0 = const()[name = string("xt_113_pad_type_0"), val = string("custom")]; + tensor xt_113_pad_0 = const()[name = string("xt_113_pad_0"), val = tensor([3, 3])]; + tensor xt_113_strides_0 = const()[name = string("xt_113_strides_0"), val = tensor([1])]; + tensor xt_113_dilations_0 = const()[name = string("xt_113_dilations_0"), val = tensor([1])]; + int32 xt_113_groups_0 = const()[name = string("xt_113_groups_0"), val = int32(1)]; + tensor generator_resblocks_4_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_4_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34309824)))]; + tensor generator_resblocks_4_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_4_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34539264)))]; + tensor xt_113_cast_fp16 = conv(bias = generator_resblocks_4_convs2_0_bias_to_fp16, dilations = xt_113_dilations_0, groups = xt_113_groups_0, pad = xt_113_pad_0, pad_type = xt_113_pad_type_0, strides = xt_113_strides_0, weight = generator_resblocks_4_convs2_0_weight_to_fp16, x = input_151_cast_fp16)[name = string("xt_113_cast_fp16")]; + tensor input_153_cast_fp16 = add(x = xt_113_cast_fp16, y = input_123_cast_fp16)[name = string("input_153_cast_fp16")]; + tensor generator_resblocks_4_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_4_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34539584)))]; + tensor generator_resblocks_4_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_4_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34605184)))]; + tensor linear_38_cast_fp16 = linear(bias = generator_resblocks_4_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_4_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_1551 = const()[name = string("op_1551"), val = tensor([1, 256, 1])]; + tensor h_155_cast_fp16 = reshape(shape = var_1551, x = linear_38_cast_fp16)[name = string("h_155_cast_fp16")]; + tensor var_1553_split_sizes_0 = const()[name = string("op_1553_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1553_axis_0 = const()[name = string("op_1553_axis_0"), val = int32(1)]; + tensor var_1553_cast_fp16_0, tensor var_1553_cast_fp16_1 = split(axis = var_1553_axis_0, split_sizes = var_1553_split_sizes_0, x = h_155_cast_fp16)[name = string("op_1553_cast_fp16")]; + fp16 var_1555_promoted_to_fp16 = const()[name = string("op_1555_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1556_cast_fp16 = add(x = var_1553_cast_fp16_0, y = var_1555_promoted_to_fp16)[name = string("op_1556_cast_fp16")]; + tensor var_1557_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_153_cast_fp16)[name = string("op_1557_cast_fp16")]; + tensor var_1558_cast_fp16 = mul(x = var_1556_cast_fp16, y = var_1557_cast_fp16)[name = string("op_1558_cast_fp16")]; + tensor xt_115_cast_fp16 = add(x = var_1558_cast_fp16, y = var_1553_cast_fp16_1)[name = string("xt_115_cast_fp16")]; + tensor generator_resblocks_4_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_4_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34605760)))]; + tensor var_1563_cast_fp16 = mul(x = generator_resblocks_4_alpha1_1_to_fp16, y = xt_115_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor var_1564_cast_fp16 = sin(x = var_1563_cast_fp16)[name = string("op_1564_cast_fp16")]; + fp16 var_24_promoted_40_to_fp16 = const()[name = string("op_24_promoted_40_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1565_cast_fp16 = pow(x = var_1564_cast_fp16, y = var_24_promoted_40_to_fp16)[name = string("op_1565_cast_fp16")]; + tensor var_1560_to_fp16 = const()[name = string("op_1560_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34606080)))]; + tensor var_1566_cast_fp16 = mul(x = var_1560_to_fp16, y = var_1565_cast_fp16)[name = string("op_1566_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = xt_115_cast_fp16, y = var_1566_cast_fp16)[name = string("input_155_cast_fp16")]; + string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("custom")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([9, 9])]; + tensor input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor([3])]; + tensor input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor([1])]; + int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)]; + tensor generator_resblocks_4_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_4_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34606400)))]; + tensor generator_resblocks_4_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_4_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34835840)))]; + tensor input_157_cast_fp16 = conv(bias = generator_resblocks_4_convs1_1_bias_to_fp16, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = generator_resblocks_4_convs1_1_weight_to_fp16, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor generator_resblocks_4_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_4_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34836160)))]; + tensor generator_resblocks_4_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_4_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34901760)))]; + tensor linear_39_cast_fp16 = linear(bias = generator_resblocks_4_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_4_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_1581 = const()[name = string("op_1581"), val = tensor([1, 256, 1])]; + tensor h_159_cast_fp16 = reshape(shape = var_1581, x = linear_39_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor var_1583_split_sizes_0 = const()[name = string("op_1583_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1583_axis_0 = const()[name = string("op_1583_axis_0"), val = int32(1)]; + tensor var_1583_cast_fp16_0, tensor var_1583_cast_fp16_1 = split(axis = var_1583_axis_0, split_sizes = var_1583_split_sizes_0, x = h_159_cast_fp16)[name = string("op_1583_cast_fp16")]; + fp16 var_1585_promoted_to_fp16 = const()[name = string("op_1585_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1586_cast_fp16 = add(x = var_1583_cast_fp16_0, y = var_1585_promoted_to_fp16)[name = string("op_1586_cast_fp16")]; + tensor var_1587_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_157_cast_fp16)[name = string("op_1587_cast_fp16")]; + tensor var_1588_cast_fp16 = mul(x = var_1586_cast_fp16, y = var_1587_cast_fp16)[name = string("op_1588_cast_fp16")]; + tensor xt_117_cast_fp16 = add(x = var_1588_cast_fp16, y = var_1583_cast_fp16_1)[name = string("xt_117_cast_fp16")]; + tensor generator_resblocks_4_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_4_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34902336)))]; + tensor var_1593_cast_fp16 = mul(x = generator_resblocks_4_alpha2_1_to_fp16, y = xt_117_cast_fp16)[name = string("op_1593_cast_fp16")]; + tensor var_1594_cast_fp16 = sin(x = var_1593_cast_fp16)[name = string("op_1594_cast_fp16")]; + fp16 var_24_promoted_41_to_fp16 = const()[name = string("op_24_promoted_41_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1595_cast_fp16 = pow(x = var_1594_cast_fp16, y = var_24_promoted_41_to_fp16)[name = string("op_1595_cast_fp16")]; + tensor var_1590_to_fp16 = const()[name = string("op_1590_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34902656)))]; + tensor var_1596_cast_fp16 = mul(x = var_1590_to_fp16, y = var_1595_cast_fp16)[name = string("op_1596_cast_fp16")]; + tensor input_159_cast_fp16 = add(x = xt_117_cast_fp16, y = var_1596_cast_fp16)[name = string("input_159_cast_fp16")]; + string xt_119_pad_type_0 = const()[name = string("xt_119_pad_type_0"), val = string("custom")]; + tensor xt_119_pad_0 = const()[name = string("xt_119_pad_0"), val = tensor([3, 3])]; + tensor xt_119_strides_0 = const()[name = string("xt_119_strides_0"), val = tensor([1])]; + tensor xt_119_dilations_0 = const()[name = string("xt_119_dilations_0"), val = tensor([1])]; + int32 xt_119_groups_0 = const()[name = string("xt_119_groups_0"), val = int32(1)]; + tensor generator_resblocks_4_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_4_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34902976)))]; + tensor generator_resblocks_4_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_4_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35132416)))]; + tensor xt_119_cast_fp16 = conv(bias = generator_resblocks_4_convs2_1_bias_to_fp16, dilations = xt_119_dilations_0, groups = xt_119_groups_0, pad = xt_119_pad_0, pad_type = xt_119_pad_type_0, strides = xt_119_strides_0, weight = generator_resblocks_4_convs2_1_weight_to_fp16, x = input_159_cast_fp16)[name = string("xt_119_cast_fp16")]; + tensor input_161_cast_fp16 = add(x = xt_119_cast_fp16, y = input_153_cast_fp16)[name = string("input_161_cast_fp16")]; + tensor generator_resblocks_4_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_4_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35132736)))]; + tensor generator_resblocks_4_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_4_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35198336)))]; + tensor linear_40_cast_fp16 = linear(bias = generator_resblocks_4_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_4_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_1612 = const()[name = string("op_1612"), val = tensor([1, 256, 1])]; + tensor h_163_cast_fp16 = reshape(shape = var_1612, x = linear_40_cast_fp16)[name = string("h_163_cast_fp16")]; + tensor var_1614_split_sizes_0 = const()[name = string("op_1614_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1614_axis_0 = const()[name = string("op_1614_axis_0"), val = int32(1)]; + tensor var_1614_cast_fp16_0, tensor var_1614_cast_fp16_1 = split(axis = var_1614_axis_0, split_sizes = var_1614_split_sizes_0, x = h_163_cast_fp16)[name = string("op_1614_cast_fp16")]; + fp16 var_1616_promoted_to_fp16 = const()[name = string("op_1616_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1617_cast_fp16 = add(x = var_1614_cast_fp16_0, y = var_1616_promoted_to_fp16)[name = string("op_1617_cast_fp16")]; + tensor var_1618_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_161_cast_fp16)[name = string("op_1618_cast_fp16")]; + tensor var_1619_cast_fp16 = mul(x = var_1617_cast_fp16, y = var_1618_cast_fp16)[name = string("op_1619_cast_fp16")]; + tensor xt_121_cast_fp16 = add(x = var_1619_cast_fp16, y = var_1614_cast_fp16_1)[name = string("xt_121_cast_fp16")]; + tensor generator_resblocks_4_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_4_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35198912)))]; + tensor var_1624_cast_fp16 = mul(x = generator_resblocks_4_alpha1_2_to_fp16, y = xt_121_cast_fp16)[name = string("op_1624_cast_fp16")]; + tensor var_1625_cast_fp16 = sin(x = var_1624_cast_fp16)[name = string("op_1625_cast_fp16")]; + fp16 var_24_promoted_42_to_fp16 = const()[name = string("op_24_promoted_42_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1626_cast_fp16 = pow(x = var_1625_cast_fp16, y = var_24_promoted_42_to_fp16)[name = string("op_1626_cast_fp16")]; + tensor var_1621_to_fp16 = const()[name = string("op_1621_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35199232)))]; + tensor var_1627_cast_fp16 = mul(x = var_1621_to_fp16, y = var_1626_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = xt_121_cast_fp16, y = var_1627_cast_fp16)[name = string("input_163_cast_fp16")]; + string input_165_pad_type_0 = const()[name = string("input_165_pad_type_0"), val = string("custom")]; + tensor input_165_pad_0 = const()[name = string("input_165_pad_0"), val = tensor([15, 15])]; + tensor input_165_dilations_0 = const()[name = string("input_165_dilations_0"), val = tensor([5])]; + tensor input_165_strides_0 = const()[name = string("input_165_strides_0"), val = tensor([1])]; + int32 input_165_groups_0 = const()[name = string("input_165_groups_0"), val = int32(1)]; + tensor generator_resblocks_4_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_4_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35199552)))]; + tensor generator_resblocks_4_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_4_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35428992)))]; + tensor input_165_cast_fp16 = conv(bias = generator_resblocks_4_convs1_2_bias_to_fp16, dilations = input_165_dilations_0, groups = input_165_groups_0, pad = input_165_pad_0, pad_type = input_165_pad_type_0, strides = input_165_strides_0, weight = generator_resblocks_4_convs1_2_weight_to_fp16, x = input_163_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor generator_resblocks_4_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_4_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35429312)))]; + tensor generator_resblocks_4_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_4_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35494912)))]; + tensor linear_41_cast_fp16 = linear(bias = generator_resblocks_4_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_4_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_1642 = const()[name = string("op_1642"), val = tensor([1, 256, 1])]; + tensor h_167_cast_fp16 = reshape(shape = var_1642, x = linear_41_cast_fp16)[name = string("h_167_cast_fp16")]; + tensor var_1644_split_sizes_0 = const()[name = string("op_1644_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1644_axis_0 = const()[name = string("op_1644_axis_0"), val = int32(1)]; + tensor var_1644_cast_fp16_0, tensor var_1644_cast_fp16_1 = split(axis = var_1644_axis_0, split_sizes = var_1644_split_sizes_0, x = h_167_cast_fp16)[name = string("op_1644_cast_fp16")]; + fp16 var_1646_promoted_to_fp16 = const()[name = string("op_1646_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1647_cast_fp16 = add(x = var_1644_cast_fp16_0, y = var_1646_promoted_to_fp16)[name = string("op_1647_cast_fp16")]; + tensor var_1648_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_165_cast_fp16)[name = string("op_1648_cast_fp16")]; + tensor var_1649_cast_fp16 = mul(x = var_1647_cast_fp16, y = var_1648_cast_fp16)[name = string("op_1649_cast_fp16")]; + tensor xt_123_cast_fp16 = add(x = var_1649_cast_fp16, y = var_1644_cast_fp16_1)[name = string("xt_123_cast_fp16")]; + tensor generator_resblocks_4_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_4_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35495488)))]; + tensor var_1654_cast_fp16 = mul(x = generator_resblocks_4_alpha2_2_to_fp16, y = xt_123_cast_fp16)[name = string("op_1654_cast_fp16")]; + tensor var_1655_cast_fp16 = sin(x = var_1654_cast_fp16)[name = string("op_1655_cast_fp16")]; + fp16 var_24_promoted_43_to_fp16 = const()[name = string("op_24_promoted_43_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1656_cast_fp16 = pow(x = var_1655_cast_fp16, y = var_24_promoted_43_to_fp16)[name = string("op_1656_cast_fp16")]; + tensor var_1651_to_fp16 = const()[name = string("op_1651_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35495808)))]; + tensor var_1657_cast_fp16 = mul(x = var_1651_to_fp16, y = var_1656_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor input_167_cast_fp16 = add(x = xt_123_cast_fp16, y = var_1657_cast_fp16)[name = string("input_167_cast_fp16")]; + string xt_125_pad_type_0 = const()[name = string("xt_125_pad_type_0"), val = string("custom")]; + tensor xt_125_pad_0 = const()[name = string("xt_125_pad_0"), val = tensor([3, 3])]; + tensor xt_125_strides_0 = const()[name = string("xt_125_strides_0"), val = tensor([1])]; + tensor xt_125_dilations_0 = const()[name = string("xt_125_dilations_0"), val = tensor([1])]; + int32 xt_125_groups_0 = const()[name = string("xt_125_groups_0"), val = int32(1)]; + tensor generator_resblocks_4_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_4_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35496128)))]; + tensor generator_resblocks_4_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_4_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35725568)))]; + tensor xt_125_cast_fp16 = conv(bias = generator_resblocks_4_convs2_2_bias_to_fp16, dilations = xt_125_dilations_0, groups = xt_125_groups_0, pad = xt_125_pad_0, pad_type = xt_125_pad_type_0, strides = xt_125_strides_0, weight = generator_resblocks_4_convs2_2_weight_to_fp16, x = input_167_cast_fp16)[name = string("xt_125_cast_fp16")]; + tensor var_1666_cast_fp16 = add(x = xt_125_cast_fp16, y = input_161_cast_fp16)[name = string("op_1666_cast_fp16")]; + tensor xs_9_cast_fp16 = add(x = xs_7_cast_fp16, y = var_1666_cast_fp16)[name = string("xs_9_cast_fp16")]; + tensor generator_resblocks_5_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_5_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35725888)))]; + tensor generator_resblocks_5_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_5_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35791488)))]; + tensor linear_42_cast_fp16 = linear(bias = generator_resblocks_5_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_5_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_1710 = const()[name = string("op_1710"), val = tensor([1, 256, 1])]; + tensor h_171_cast_fp16 = reshape(shape = var_1710, x = linear_42_cast_fp16)[name = string("h_171_cast_fp16")]; + tensor var_1712_split_sizes_0 = const()[name = string("op_1712_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1712_axis_0 = const()[name = string("op_1712_axis_0"), val = int32(1)]; + tensor var_1712_cast_fp16_0, tensor var_1712_cast_fp16_1 = split(axis = var_1712_axis_0, split_sizes = var_1712_split_sizes_0, x = h_171_cast_fp16)[name = string("op_1712_cast_fp16")]; + fp16 var_1714_promoted_to_fp16 = const()[name = string("op_1714_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1715_cast_fp16 = add(x = var_1712_cast_fp16_0, y = var_1714_promoted_to_fp16)[name = string("op_1715_cast_fp16")]; + tensor var_1717_cast_fp16 = mul(x = var_1715_cast_fp16, y = var_1277_cast_fp16)[name = string("op_1717_cast_fp16")]; + tensor xt_127_cast_fp16 = add(x = var_1717_cast_fp16, y = var_1712_cast_fp16_1)[name = string("xt_127_cast_fp16")]; + tensor generator_resblocks_5_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_5_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35792064)))]; + tensor var_1722_cast_fp16 = mul(x = generator_resblocks_5_alpha1_0_to_fp16, y = xt_127_cast_fp16)[name = string("op_1722_cast_fp16")]; + tensor var_1723_cast_fp16 = sin(x = var_1722_cast_fp16)[name = string("op_1723_cast_fp16")]; + fp16 var_24_promoted_44_to_fp16 = const()[name = string("op_24_promoted_44_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1724_cast_fp16 = pow(x = var_1723_cast_fp16, y = var_24_promoted_44_to_fp16)[name = string("op_1724_cast_fp16")]; + tensor var_1719_to_fp16 = const()[name = string("op_1719_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35792384)))]; + tensor var_1725_cast_fp16 = mul(x = var_1719_to_fp16, y = var_1724_cast_fp16)[name = string("op_1725_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = xt_127_cast_fp16, y = var_1725_cast_fp16)[name = string("input_169_cast_fp16")]; + string input_171_pad_type_0 = const()[name = string("input_171_pad_type_0"), val = string("custom")]; + tensor input_171_pad_0 = const()[name = string("input_171_pad_0"), val = tensor([5, 5])]; + tensor input_171_strides_0 = const()[name = string("input_171_strides_0"), val = tensor([1])]; + tensor input_171_dilations_0 = const()[name = string("input_171_dilations_0"), val = tensor([1])]; + int32 input_171_groups_0 = const()[name = string("input_171_groups_0"), val = int32(1)]; + tensor generator_resblocks_5_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_5_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35792704)))]; + tensor generator_resblocks_5_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_5_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36153216)))]; + tensor input_171_cast_fp16 = conv(bias = generator_resblocks_5_convs1_0_bias_to_fp16, dilations = input_171_dilations_0, groups = input_171_groups_0, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = input_171_strides_0, weight = generator_resblocks_5_convs1_0_weight_to_fp16, x = input_169_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor generator_resblocks_5_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_5_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36153536)))]; + tensor generator_resblocks_5_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_5_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36219136)))]; + tensor linear_43_cast_fp16 = linear(bias = generator_resblocks_5_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_5_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_1740 = const()[name = string("op_1740"), val = tensor([1, 256, 1])]; + tensor h_175_cast_fp16 = reshape(shape = var_1740, x = linear_43_cast_fp16)[name = string("h_175_cast_fp16")]; + tensor var_1742_split_sizes_0 = const()[name = string("op_1742_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1742_axis_0 = const()[name = string("op_1742_axis_0"), val = int32(1)]; + tensor var_1742_cast_fp16_0, tensor var_1742_cast_fp16_1 = split(axis = var_1742_axis_0, split_sizes = var_1742_split_sizes_0, x = h_175_cast_fp16)[name = string("op_1742_cast_fp16")]; + fp16 var_1744_promoted_to_fp16 = const()[name = string("op_1744_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1745_cast_fp16 = add(x = var_1742_cast_fp16_0, y = var_1744_promoted_to_fp16)[name = string("op_1745_cast_fp16")]; + tensor var_1746_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_171_cast_fp16)[name = string("op_1746_cast_fp16")]; + tensor var_1747_cast_fp16 = mul(x = var_1745_cast_fp16, y = var_1746_cast_fp16)[name = string("op_1747_cast_fp16")]; + tensor xt_129_cast_fp16 = add(x = var_1747_cast_fp16, y = var_1742_cast_fp16_1)[name = string("xt_129_cast_fp16")]; + tensor generator_resblocks_5_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_5_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36219712)))]; + tensor var_1752_cast_fp16 = mul(x = generator_resblocks_5_alpha2_0_to_fp16, y = xt_129_cast_fp16)[name = string("op_1752_cast_fp16")]; + tensor var_1753_cast_fp16 = sin(x = var_1752_cast_fp16)[name = string("op_1753_cast_fp16")]; + fp16 var_24_promoted_45_to_fp16 = const()[name = string("op_24_promoted_45_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1754_cast_fp16 = pow(x = var_1753_cast_fp16, y = var_24_promoted_45_to_fp16)[name = string("op_1754_cast_fp16")]; + tensor var_1749_to_fp16 = const()[name = string("op_1749_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36220032)))]; + tensor var_1755_cast_fp16 = mul(x = var_1749_to_fp16, y = var_1754_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor input_173_cast_fp16 = add(x = xt_129_cast_fp16, y = var_1755_cast_fp16)[name = string("input_173_cast_fp16")]; + string xt_131_pad_type_0 = const()[name = string("xt_131_pad_type_0"), val = string("custom")]; + tensor xt_131_pad_0 = const()[name = string("xt_131_pad_0"), val = tensor([5, 5])]; + tensor xt_131_strides_0 = const()[name = string("xt_131_strides_0"), val = tensor([1])]; + tensor xt_131_dilations_0 = const()[name = string("xt_131_dilations_0"), val = tensor([1])]; + int32 xt_131_groups_0 = const()[name = string("xt_131_groups_0"), val = int32(1)]; + tensor generator_resblocks_5_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_5_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36220352)))]; + tensor generator_resblocks_5_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_5_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36580864)))]; + tensor xt_131_cast_fp16 = conv(bias = generator_resblocks_5_convs2_0_bias_to_fp16, dilations = xt_131_dilations_0, groups = xt_131_groups_0, pad = xt_131_pad_0, pad_type = xt_131_pad_type_0, strides = xt_131_strides_0, weight = generator_resblocks_5_convs2_0_weight_to_fp16, x = input_173_cast_fp16)[name = string("xt_131_cast_fp16")]; + tensor input_175_cast_fp16 = add(x = xt_131_cast_fp16, y = input_123_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor generator_resblocks_5_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_5_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36581184)))]; + tensor generator_resblocks_5_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_5_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36646784)))]; + tensor linear_44_cast_fp16 = linear(bias = generator_resblocks_5_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_5_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_1771 = const()[name = string("op_1771"), val = tensor([1, 256, 1])]; + tensor h_179_cast_fp16 = reshape(shape = var_1771, x = linear_44_cast_fp16)[name = string("h_179_cast_fp16")]; + tensor var_1773_split_sizes_0 = const()[name = string("op_1773_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1773_axis_0 = const()[name = string("op_1773_axis_0"), val = int32(1)]; + tensor var_1773_cast_fp16_0, tensor var_1773_cast_fp16_1 = split(axis = var_1773_axis_0, split_sizes = var_1773_split_sizes_0, x = h_179_cast_fp16)[name = string("op_1773_cast_fp16")]; + fp16 var_1775_promoted_to_fp16 = const()[name = string("op_1775_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1776_cast_fp16 = add(x = var_1773_cast_fp16_0, y = var_1775_promoted_to_fp16)[name = string("op_1776_cast_fp16")]; + tensor var_1777_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_175_cast_fp16)[name = string("op_1777_cast_fp16")]; + tensor var_1778_cast_fp16 = mul(x = var_1776_cast_fp16, y = var_1777_cast_fp16)[name = string("op_1778_cast_fp16")]; + tensor xt_133_cast_fp16 = add(x = var_1778_cast_fp16, y = var_1773_cast_fp16_1)[name = string("xt_133_cast_fp16")]; + tensor generator_resblocks_5_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_5_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36647360)))]; + tensor var_1783_cast_fp16 = mul(x = generator_resblocks_5_alpha1_1_to_fp16, y = xt_133_cast_fp16)[name = string("op_1783_cast_fp16")]; + tensor var_1784_cast_fp16 = sin(x = var_1783_cast_fp16)[name = string("op_1784_cast_fp16")]; + fp16 var_24_promoted_46_to_fp16 = const()[name = string("op_24_promoted_46_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1785_cast_fp16 = pow(x = var_1784_cast_fp16, y = var_24_promoted_46_to_fp16)[name = string("op_1785_cast_fp16")]; + tensor var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36647680)))]; + tensor var_1786_cast_fp16 = mul(x = var_1780_to_fp16, y = var_1785_cast_fp16)[name = string("op_1786_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = xt_133_cast_fp16, y = var_1786_cast_fp16)[name = string("input_177_cast_fp16")]; + string input_179_pad_type_0 = const()[name = string("input_179_pad_type_0"), val = string("custom")]; + tensor input_179_pad_0 = const()[name = string("input_179_pad_0"), val = tensor([15, 15])]; + tensor input_179_dilations_0 = const()[name = string("input_179_dilations_0"), val = tensor([3])]; + tensor input_179_strides_0 = const()[name = string("input_179_strides_0"), val = tensor([1])]; + int32 input_179_groups_0 = const()[name = string("input_179_groups_0"), val = int32(1)]; + tensor generator_resblocks_5_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_5_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36648000)))]; + tensor generator_resblocks_5_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_5_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37008512)))]; + tensor input_179_cast_fp16 = conv(bias = generator_resblocks_5_convs1_1_bias_to_fp16, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = generator_resblocks_5_convs1_1_weight_to_fp16, x = input_177_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor generator_resblocks_5_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_5_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37008832)))]; + tensor generator_resblocks_5_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_5_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37074432)))]; + tensor linear_45_cast_fp16 = linear(bias = generator_resblocks_5_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_5_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1801 = const()[name = string("op_1801"), val = tensor([1, 256, 1])]; + tensor h_183_cast_fp16 = reshape(shape = var_1801, x = linear_45_cast_fp16)[name = string("h_183_cast_fp16")]; + tensor var_1803_split_sizes_0 = const()[name = string("op_1803_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1803_axis_0 = const()[name = string("op_1803_axis_0"), val = int32(1)]; + tensor var_1803_cast_fp16_0, tensor var_1803_cast_fp16_1 = split(axis = var_1803_axis_0, split_sizes = var_1803_split_sizes_0, x = h_183_cast_fp16)[name = string("op_1803_cast_fp16")]; + fp16 var_1805_promoted_to_fp16 = const()[name = string("op_1805_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1806_cast_fp16 = add(x = var_1803_cast_fp16_0, y = var_1805_promoted_to_fp16)[name = string("op_1806_cast_fp16")]; + tensor var_1807_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_179_cast_fp16)[name = string("op_1807_cast_fp16")]; + tensor var_1808_cast_fp16 = mul(x = var_1806_cast_fp16, y = var_1807_cast_fp16)[name = string("op_1808_cast_fp16")]; + tensor xt_135_cast_fp16 = add(x = var_1808_cast_fp16, y = var_1803_cast_fp16_1)[name = string("xt_135_cast_fp16")]; + tensor generator_resblocks_5_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_5_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37075008)))]; + tensor var_1813_cast_fp16 = mul(x = generator_resblocks_5_alpha2_1_to_fp16, y = xt_135_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1814_cast_fp16 = sin(x = var_1813_cast_fp16)[name = string("op_1814_cast_fp16")]; + fp16 var_24_promoted_47_to_fp16 = const()[name = string("op_24_promoted_47_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1815_cast_fp16 = pow(x = var_1814_cast_fp16, y = var_24_promoted_47_to_fp16)[name = string("op_1815_cast_fp16")]; + tensor var_1810_to_fp16 = const()[name = string("op_1810_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37075328)))]; + tensor var_1816_cast_fp16 = mul(x = var_1810_to_fp16, y = var_1815_cast_fp16)[name = string("op_1816_cast_fp16")]; + tensor input_181_cast_fp16 = add(x = xt_135_cast_fp16, y = var_1816_cast_fp16)[name = string("input_181_cast_fp16")]; + string xt_137_pad_type_0 = const()[name = string("xt_137_pad_type_0"), val = string("custom")]; + tensor xt_137_pad_0 = const()[name = string("xt_137_pad_0"), val = tensor([5, 5])]; + tensor xt_137_strides_0 = const()[name = string("xt_137_strides_0"), val = tensor([1])]; + tensor xt_137_dilations_0 = const()[name = string("xt_137_dilations_0"), val = tensor([1])]; + int32 xt_137_groups_0 = const()[name = string("xt_137_groups_0"), val = int32(1)]; + tensor generator_resblocks_5_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_5_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37075648)))]; + tensor generator_resblocks_5_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_5_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37436160)))]; + tensor xt_137_cast_fp16 = conv(bias = generator_resblocks_5_convs2_1_bias_to_fp16, dilations = xt_137_dilations_0, groups = xt_137_groups_0, pad = xt_137_pad_0, pad_type = xt_137_pad_type_0, strides = xt_137_strides_0, weight = generator_resblocks_5_convs2_1_weight_to_fp16, x = input_181_cast_fp16)[name = string("xt_137_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = xt_137_cast_fp16, y = input_175_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor generator_resblocks_5_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_5_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37436480)))]; + tensor generator_resblocks_5_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_5_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37502080)))]; + tensor linear_46_cast_fp16 = linear(bias = generator_resblocks_5_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_5_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1832 = const()[name = string("op_1832"), val = tensor([1, 256, 1])]; + tensor h_187_cast_fp16 = reshape(shape = var_1832, x = linear_46_cast_fp16)[name = string("h_187_cast_fp16")]; + tensor var_1834_split_sizes_0 = const()[name = string("op_1834_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1834_axis_0 = const()[name = string("op_1834_axis_0"), val = int32(1)]; + tensor var_1834_cast_fp16_0, tensor var_1834_cast_fp16_1 = split(axis = var_1834_axis_0, split_sizes = var_1834_split_sizes_0, x = h_187_cast_fp16)[name = string("op_1834_cast_fp16")]; + fp16 var_1836_promoted_to_fp16 = const()[name = string("op_1836_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1837_cast_fp16 = add(x = var_1834_cast_fp16_0, y = var_1836_promoted_to_fp16)[name = string("op_1837_cast_fp16")]; + tensor var_1838_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_183_cast_fp16)[name = string("op_1838_cast_fp16")]; + tensor var_1839_cast_fp16 = mul(x = var_1837_cast_fp16, y = var_1838_cast_fp16)[name = string("op_1839_cast_fp16")]; + tensor xt_139_cast_fp16 = add(x = var_1839_cast_fp16, y = var_1834_cast_fp16_1)[name = string("xt_139_cast_fp16")]; + tensor generator_resblocks_5_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_5_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37502656)))]; + tensor var_1844_cast_fp16 = mul(x = generator_resblocks_5_alpha1_2_to_fp16, y = xt_139_cast_fp16)[name = string("op_1844_cast_fp16")]; + tensor var_1845_cast_fp16 = sin(x = var_1844_cast_fp16)[name = string("op_1845_cast_fp16")]; + fp16 var_24_promoted_48_to_fp16 = const()[name = string("op_24_promoted_48_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1846_cast_fp16 = pow(x = var_1845_cast_fp16, y = var_24_promoted_48_to_fp16)[name = string("op_1846_cast_fp16")]; + tensor var_1841_to_fp16 = const()[name = string("op_1841_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37502976)))]; + tensor var_1847_cast_fp16 = mul(x = var_1841_to_fp16, y = var_1846_cast_fp16)[name = string("op_1847_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = xt_139_cast_fp16, y = var_1847_cast_fp16)[name = string("input_185_cast_fp16")]; + string input_187_pad_type_0 = const()[name = string("input_187_pad_type_0"), val = string("custom")]; + tensor input_187_pad_0 = const()[name = string("input_187_pad_0"), val = tensor([25, 25])]; + tensor input_187_dilations_0 = const()[name = string("input_187_dilations_0"), val = tensor([5])]; + tensor input_187_strides_0 = const()[name = string("input_187_strides_0"), val = tensor([1])]; + int32 input_187_groups_0 = const()[name = string("input_187_groups_0"), val = int32(1)]; + tensor generator_resblocks_5_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_5_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37503296)))]; + tensor generator_resblocks_5_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_5_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37863808)))]; + tensor input_187_cast_fp16 = conv(bias = generator_resblocks_5_convs1_2_bias_to_fp16, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = generator_resblocks_5_convs1_2_weight_to_fp16, x = input_185_cast_fp16)[name = string("input_187_cast_fp16")]; + tensor generator_resblocks_5_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_5_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37864128)))]; + tensor generator_resblocks_5_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_5_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37929728)))]; + tensor linear_47_cast_fp16 = linear(bias = generator_resblocks_5_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_5_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1862 = const()[name = string("op_1862"), val = tensor([1, 256, 1])]; + tensor h_191_cast_fp16 = reshape(shape = var_1862, x = linear_47_cast_fp16)[name = string("h_191_cast_fp16")]; + tensor var_1864_split_sizes_0 = const()[name = string("op_1864_split_sizes_0"), val = tensor([128, 128])]; + int32 var_1864_axis_0 = const()[name = string("op_1864_axis_0"), val = int32(1)]; + tensor var_1864_cast_fp16_0, tensor var_1864_cast_fp16_1 = split(axis = var_1864_axis_0, split_sizes = var_1864_split_sizes_0, x = h_191_cast_fp16)[name = string("op_1864_cast_fp16")]; + fp16 var_1866_promoted_to_fp16 = const()[name = string("op_1866_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1867_cast_fp16 = add(x = var_1864_cast_fp16_0, y = var_1866_promoted_to_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1868_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_187_cast_fp16)[name = string("op_1868_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_1867_cast_fp16, y = var_1868_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor xt_141_cast_fp16 = add(x = var_1869_cast_fp16, y = var_1864_cast_fp16_1)[name = string("xt_141_cast_fp16")]; + tensor generator_resblocks_5_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_5_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37930304)))]; + tensor var_1874_cast_fp16 = mul(x = generator_resblocks_5_alpha2_2_to_fp16, y = xt_141_cast_fp16)[name = string("op_1874_cast_fp16")]; + tensor var_1875_cast_fp16 = sin(x = var_1874_cast_fp16)[name = string("op_1875_cast_fp16")]; + fp16 var_24_promoted_49_to_fp16 = const()[name = string("op_24_promoted_49_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1876_cast_fp16 = pow(x = var_1875_cast_fp16, y = var_24_promoted_49_to_fp16)[name = string("op_1876_cast_fp16")]; + tensor var_1871_to_fp16 = const()[name = string("op_1871_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37930624)))]; + tensor var_1877_cast_fp16 = mul(x = var_1871_to_fp16, y = var_1876_cast_fp16)[name = string("op_1877_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = xt_141_cast_fp16, y = var_1877_cast_fp16)[name = string("input_189_cast_fp16")]; + string xt_143_pad_type_0 = const()[name = string("xt_143_pad_type_0"), val = string("custom")]; + tensor xt_143_pad_0 = const()[name = string("xt_143_pad_0"), val = tensor([5, 5])]; + tensor xt_143_strides_0 = const()[name = string("xt_143_strides_0"), val = tensor([1])]; + tensor xt_143_dilations_0 = const()[name = string("xt_143_dilations_0"), val = tensor([1])]; + int32 xt_143_groups_0 = const()[name = string("xt_143_groups_0"), val = int32(1)]; + tensor generator_resblocks_5_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_5_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37930944)))]; + tensor generator_resblocks_5_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_5_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38291456)))]; + tensor xt_143_cast_fp16 = conv(bias = generator_resblocks_5_convs2_2_bias_to_fp16, dilations = xt_143_dilations_0, groups = xt_143_groups_0, pad = xt_143_pad_0, pad_type = xt_143_pad_type_0, strides = xt_143_strides_0, weight = generator_resblocks_5_convs2_2_weight_to_fp16, x = input_189_cast_fp16)[name = string("xt_143_cast_fp16")]; + tensor var_1886_cast_fp16 = add(x = xt_143_cast_fp16, y = input_183_cast_fp16)[name = string("op_1886_cast_fp16")]; + tensor xs_11_cast_fp16 = add(x = xs_9_cast_fp16, y = var_1886_cast_fp16)[name = string("xs_11_cast_fp16")]; + fp16 _inversed_x_7_y_0_to_fp16 = const()[name = string("_inversed_x_7_y_0_to_fp16"), val = fp16(0x1.554p-2)]; + tensor _inversed_x_7_cast_fp16 = mul(x = xs_11_cast_fp16, y = _inversed_x_7_y_0_to_fp16)[name = string("_inversed_x_7_cast_fp16")]; + tensor generator_alphas_2_to_fp16 = const()[name = string("generator_alphas_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38291776)))]; + tensor var_1893_cast_fp16 = mul(x = generator_alphas_2_to_fp16, y = _inversed_x_7_cast_fp16)[name = string("op_1893_cast_fp16")]; + tensor var_1894_cast_fp16 = sin(x = var_1893_cast_fp16)[name = string("op_1894_cast_fp16")]; + fp16 var_24_promoted_50_to_fp16 = const()[name = string("op_24_promoted_50_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1895_cast_fp16 = pow(x = var_1894_cast_fp16, y = var_24_promoted_50_to_fp16)[name = string("op_1895_cast_fp16")]; + tensor var_1890_to_fp16 = const()[name = string("op_1890_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38292096)))]; + tensor var_1896_cast_fp16 = mul(x = var_1890_to_fp16, y = var_1895_cast_fp16)[name = string("op_1896_cast_fp16")]; + tensor input_215_cast_fp16 = add(x = _inversed_x_7_cast_fp16, y = var_1896_cast_fp16)[name = string("input_215_cast_fp16")]; + string input_191_pad_type_0 = const()[name = string("input_191_pad_type_0"), val = string("custom")]; + tensor input_191_pad_0 = const()[name = string("input_191_pad_0"), val = tensor([1, 1])]; + tensor input_191_strides_0 = const()[name = string("input_191_strides_0"), val = tensor([2])]; + tensor input_191_dilations_0 = const()[name = string("input_191_dilations_0"), val = tensor([1])]; + int32 input_191_groups_0 = const()[name = string("input_191_groups_0"), val = int32(1)]; + tensor generator_noise_convs_2_weight_to_fp16 = const()[name = string("generator_noise_convs_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38292416)))]; + tensor generator_noise_convs_2_bias_to_fp16 = const()[name = string("generator_noise_convs_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38292992)))]; + tensor input_191_cast_fp16 = conv(bias = generator_noise_convs_2_bias_to_fp16, dilations = input_191_dilations_0, groups = input_191_groups_0, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = input_191_strides_0, weight = generator_noise_convs_2_weight_to_fp16, x = har_source_to_fp16)[name = string("input_191_cast_fp16")]; + tensor generator_noise_res_2_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_noise_res_2_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38293184)))]; + tensor generator_noise_res_2_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_noise_res_2_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38326016)))]; + tensor linear_48_cast_fp16 = linear(bias = generator_noise_res_2_adain1_0_fc_bias_to_fp16, weight = generator_noise_res_2_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1947 = const()[name = string("op_1947"), val = tensor([1, 128, 1])]; + tensor h_195_cast_fp16 = reshape(shape = var_1947, x = linear_48_cast_fp16)[name = string("h_195_cast_fp16")]; + tensor var_1949_split_sizes_0 = const()[name = string("op_1949_split_sizes_0"), val = tensor([64, 64])]; + int32 var_1949_axis_0 = const()[name = string("op_1949_axis_0"), val = int32(1)]; + tensor var_1949_cast_fp16_0, tensor var_1949_cast_fp16_1 = split(axis = var_1949_axis_0, split_sizes = var_1949_split_sizes_0, x = h_195_cast_fp16)[name = string("op_1949_cast_fp16")]; + fp16 var_1951_promoted_to_fp16 = const()[name = string("op_1951_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1952_cast_fp16 = add(x = var_1949_cast_fp16_0, y = var_1951_promoted_to_fp16)[name = string("op_1952_cast_fp16")]; + tensor var_1953_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_191_cast_fp16)[name = string("op_1953_cast_fp16")]; + tensor var_1954_cast_fp16 = mul(x = var_1952_cast_fp16, y = var_1953_cast_fp16)[name = string("op_1954_cast_fp16")]; + tensor xt_145_cast_fp16 = add(x = var_1954_cast_fp16, y = var_1949_cast_fp16_1)[name = string("xt_145_cast_fp16")]; + tensor generator_noise_res_2_alpha1_0_to_fp16 = const()[name = string("generator_noise_res_2_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38326336)))]; + tensor var_1959_cast_fp16 = mul(x = generator_noise_res_2_alpha1_0_to_fp16, y = xt_145_cast_fp16)[name = string("op_1959_cast_fp16")]; + tensor var_1960_cast_fp16 = sin(x = var_1959_cast_fp16)[name = string("op_1960_cast_fp16")]; + fp16 var_24_promoted_51_to_fp16 = const()[name = string("op_24_promoted_51_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1961_cast_fp16 = pow(x = var_1960_cast_fp16, y = var_24_promoted_51_to_fp16)[name = string("op_1961_cast_fp16")]; + tensor var_1956_to_fp16 = const()[name = string("op_1956_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38326528)))]; + tensor var_1962_cast_fp16 = mul(x = var_1956_to_fp16, y = var_1961_cast_fp16)[name = string("op_1962_cast_fp16")]; + tensor input_193_cast_fp16 = add(x = xt_145_cast_fp16, y = var_1962_cast_fp16)[name = string("input_193_cast_fp16")]; + string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")]; + tensor input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor([3, 3])]; + tensor input_195_strides_0 = const()[name = string("input_195_strides_0"), val = tensor([1])]; + tensor input_195_dilations_0 = const()[name = string("input_195_dilations_0"), val = tensor([1])]; + int32 input_195_groups_0 = const()[name = string("input_195_groups_0"), val = int32(1)]; + tensor generator_noise_res_2_convs1_0_weight_to_fp16 = const()[name = string("generator_noise_res_2_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38326720)))]; + tensor generator_noise_res_2_convs1_0_bias_to_fp16 = const()[name = string("generator_noise_res_2_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38384128)))]; + tensor input_195_cast_fp16 = conv(bias = generator_noise_res_2_convs1_0_bias_to_fp16, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = generator_noise_res_2_convs1_0_weight_to_fp16, x = input_193_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor generator_noise_res_2_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_noise_res_2_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38384320)))]; + tensor generator_noise_res_2_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_noise_res_2_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38417152)))]; + tensor linear_49_cast_fp16 = linear(bias = generator_noise_res_2_adain2_0_fc_bias_to_fp16, weight = generator_noise_res_2_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1977 = const()[name = string("op_1977"), val = tensor([1, 128, 1])]; + tensor h_199_cast_fp16 = reshape(shape = var_1977, x = linear_49_cast_fp16)[name = string("h_199_cast_fp16")]; + tensor var_1979_split_sizes_0 = const()[name = string("op_1979_split_sizes_0"), val = tensor([64, 64])]; + int32 var_1979_axis_0 = const()[name = string("op_1979_axis_0"), val = int32(1)]; + tensor var_1979_cast_fp16_0, tensor var_1979_cast_fp16_1 = split(axis = var_1979_axis_0, split_sizes = var_1979_split_sizes_0, x = h_199_cast_fp16)[name = string("op_1979_cast_fp16")]; + fp16 var_1981_promoted_to_fp16 = const()[name = string("op_1981_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1982_cast_fp16 = add(x = var_1979_cast_fp16_0, y = var_1981_promoted_to_fp16)[name = string("op_1982_cast_fp16")]; + tensor var_1983_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_195_cast_fp16)[name = string("op_1983_cast_fp16")]; + tensor var_1984_cast_fp16 = mul(x = var_1982_cast_fp16, y = var_1983_cast_fp16)[name = string("op_1984_cast_fp16")]; + tensor xt_147_cast_fp16 = add(x = var_1984_cast_fp16, y = var_1979_cast_fp16_1)[name = string("xt_147_cast_fp16")]; + tensor generator_noise_res_2_alpha2_0_to_fp16 = const()[name = string("generator_noise_res_2_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38417472)))]; + tensor var_1989_cast_fp16 = mul(x = generator_noise_res_2_alpha2_0_to_fp16, y = xt_147_cast_fp16)[name = string("op_1989_cast_fp16")]; + tensor var_1990_cast_fp16 = sin(x = var_1989_cast_fp16)[name = string("op_1990_cast_fp16")]; + fp16 var_24_promoted_52_to_fp16 = const()[name = string("op_24_promoted_52_to_fp16"), val = fp16(0x1p+1)]; + tensor var_1991_cast_fp16 = pow(x = var_1990_cast_fp16, y = var_24_promoted_52_to_fp16)[name = string("op_1991_cast_fp16")]; + tensor var_1986_to_fp16 = const()[name = string("op_1986_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38417664)))]; + tensor var_1992_cast_fp16 = mul(x = var_1986_to_fp16, y = var_1991_cast_fp16)[name = string("op_1992_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = xt_147_cast_fp16, y = var_1992_cast_fp16)[name = string("input_197_cast_fp16")]; + string xt_149_pad_type_0 = const()[name = string("xt_149_pad_type_0"), val = string("custom")]; + tensor xt_149_pad_0 = const()[name = string("xt_149_pad_0"), val = tensor([3, 3])]; + tensor xt_149_strides_0 = const()[name = string("xt_149_strides_0"), val = tensor([1])]; + tensor xt_149_dilations_0 = const()[name = string("xt_149_dilations_0"), val = tensor([1])]; + int32 xt_149_groups_0 = const()[name = string("xt_149_groups_0"), val = int32(1)]; + tensor generator_noise_res_2_convs2_0_weight_to_fp16 = const()[name = string("generator_noise_res_2_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38417856)))]; + tensor generator_noise_res_2_convs2_0_bias_to_fp16 = const()[name = string("generator_noise_res_2_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38475264)))]; + tensor xt_149_cast_fp16 = conv(bias = generator_noise_res_2_convs2_0_bias_to_fp16, dilations = xt_149_dilations_0, groups = xt_149_groups_0, pad = xt_149_pad_0, pad_type = xt_149_pad_type_0, strides = xt_149_strides_0, weight = generator_noise_res_2_convs2_0_weight_to_fp16, x = input_197_cast_fp16)[name = string("xt_149_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = xt_149_cast_fp16, y = input_191_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor generator_noise_res_2_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_noise_res_2_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38475456)))]; + tensor generator_noise_res_2_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_noise_res_2_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38508288)))]; + tensor linear_50_cast_fp16 = linear(bias = generator_noise_res_2_adain1_1_fc_bias_to_fp16, weight = generator_noise_res_2_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_2008 = const()[name = string("op_2008"), val = tensor([1, 128, 1])]; + tensor h_203_cast_fp16 = reshape(shape = var_2008, x = linear_50_cast_fp16)[name = string("h_203_cast_fp16")]; + tensor var_2010_split_sizes_0 = const()[name = string("op_2010_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2010_axis_0 = const()[name = string("op_2010_axis_0"), val = int32(1)]; + tensor var_2010_cast_fp16_0, tensor var_2010_cast_fp16_1 = split(axis = var_2010_axis_0, split_sizes = var_2010_split_sizes_0, x = h_203_cast_fp16)[name = string("op_2010_cast_fp16")]; + fp16 var_2012_promoted_to_fp16 = const()[name = string("op_2012_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2013_cast_fp16 = add(x = var_2010_cast_fp16_0, y = var_2012_promoted_to_fp16)[name = string("op_2013_cast_fp16")]; + tensor var_2014_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_199_cast_fp16)[name = string("op_2014_cast_fp16")]; + tensor var_2015_cast_fp16 = mul(x = var_2013_cast_fp16, y = var_2014_cast_fp16)[name = string("op_2015_cast_fp16")]; + tensor xt_151_cast_fp16 = add(x = var_2015_cast_fp16, y = var_2010_cast_fp16_1)[name = string("xt_151_cast_fp16")]; + tensor generator_noise_res_2_alpha1_1_to_fp16 = const()[name = string("generator_noise_res_2_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38508608)))]; + tensor var_2020_cast_fp16 = mul(x = generator_noise_res_2_alpha1_1_to_fp16, y = xt_151_cast_fp16)[name = string("op_2020_cast_fp16")]; + tensor var_2021_cast_fp16 = sin(x = var_2020_cast_fp16)[name = string("op_2021_cast_fp16")]; + fp16 var_24_promoted_53_to_fp16 = const()[name = string("op_24_promoted_53_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2022_cast_fp16 = pow(x = var_2021_cast_fp16, y = var_24_promoted_53_to_fp16)[name = string("op_2022_cast_fp16")]; + tensor var_2017_to_fp16 = const()[name = string("op_2017_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38508800)))]; + tensor var_2023_cast_fp16 = mul(x = var_2017_to_fp16, y = var_2022_cast_fp16)[name = string("op_2023_cast_fp16")]; + tensor input_201_cast_fp16 = add(x = xt_151_cast_fp16, y = var_2023_cast_fp16)[name = string("input_201_cast_fp16")]; + string input_203_pad_type_0 = const()[name = string("input_203_pad_type_0"), val = string("custom")]; + tensor input_203_pad_0 = const()[name = string("input_203_pad_0"), val = tensor([9, 9])]; + tensor input_203_dilations_0 = const()[name = string("input_203_dilations_0"), val = tensor([3])]; + tensor input_203_strides_0 = const()[name = string("input_203_strides_0"), val = tensor([1])]; + int32 input_203_groups_0 = const()[name = string("input_203_groups_0"), val = int32(1)]; + tensor generator_noise_res_2_convs1_1_weight_to_fp16 = const()[name = string("generator_noise_res_2_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38508992)))]; + tensor generator_noise_res_2_convs1_1_bias_to_fp16 = const()[name = string("generator_noise_res_2_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38566400)))]; + tensor input_203_cast_fp16 = conv(bias = generator_noise_res_2_convs1_1_bias_to_fp16, dilations = input_203_dilations_0, groups = input_203_groups_0, pad = input_203_pad_0, pad_type = input_203_pad_type_0, strides = input_203_strides_0, weight = generator_noise_res_2_convs1_1_weight_to_fp16, x = input_201_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor generator_noise_res_2_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_noise_res_2_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38566592)))]; + tensor generator_noise_res_2_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_noise_res_2_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38599424)))]; + tensor linear_51_cast_fp16 = linear(bias = generator_noise_res_2_adain2_1_fc_bias_to_fp16, weight = generator_noise_res_2_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_2038 = const()[name = string("op_2038"), val = tensor([1, 128, 1])]; + tensor h_207_cast_fp16 = reshape(shape = var_2038, x = linear_51_cast_fp16)[name = string("h_207_cast_fp16")]; + tensor var_2040_split_sizes_0 = const()[name = string("op_2040_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2040_axis_0 = const()[name = string("op_2040_axis_0"), val = int32(1)]; + tensor var_2040_cast_fp16_0, tensor var_2040_cast_fp16_1 = split(axis = var_2040_axis_0, split_sizes = var_2040_split_sizes_0, x = h_207_cast_fp16)[name = string("op_2040_cast_fp16")]; + fp16 var_2042_promoted_to_fp16 = const()[name = string("op_2042_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2043_cast_fp16 = add(x = var_2040_cast_fp16_0, y = var_2042_promoted_to_fp16)[name = string("op_2043_cast_fp16")]; + tensor var_2044_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_203_cast_fp16)[name = string("op_2044_cast_fp16")]; + tensor var_2045_cast_fp16 = mul(x = var_2043_cast_fp16, y = var_2044_cast_fp16)[name = string("op_2045_cast_fp16")]; + tensor xt_153_cast_fp16 = add(x = var_2045_cast_fp16, y = var_2040_cast_fp16_1)[name = string("xt_153_cast_fp16")]; + tensor generator_noise_res_2_alpha2_1_to_fp16 = const()[name = string("generator_noise_res_2_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38599744)))]; + tensor var_2050_cast_fp16 = mul(x = generator_noise_res_2_alpha2_1_to_fp16, y = xt_153_cast_fp16)[name = string("op_2050_cast_fp16")]; + tensor var_2051_cast_fp16 = sin(x = var_2050_cast_fp16)[name = string("op_2051_cast_fp16")]; + fp16 var_24_promoted_54_to_fp16 = const()[name = string("op_24_promoted_54_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2052_cast_fp16 = pow(x = var_2051_cast_fp16, y = var_24_promoted_54_to_fp16)[name = string("op_2052_cast_fp16")]; + tensor var_2047_to_fp16 = const()[name = string("op_2047_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38599936)))]; + tensor var_2053_cast_fp16 = mul(x = var_2047_to_fp16, y = var_2052_cast_fp16)[name = string("op_2053_cast_fp16")]; + tensor input_205_cast_fp16 = add(x = xt_153_cast_fp16, y = var_2053_cast_fp16)[name = string("input_205_cast_fp16")]; + string xt_155_pad_type_0 = const()[name = string("xt_155_pad_type_0"), val = string("custom")]; + tensor xt_155_pad_0 = const()[name = string("xt_155_pad_0"), val = tensor([3, 3])]; + tensor xt_155_strides_0 = const()[name = string("xt_155_strides_0"), val = tensor([1])]; + tensor xt_155_dilations_0 = const()[name = string("xt_155_dilations_0"), val = tensor([1])]; + int32 xt_155_groups_0 = const()[name = string("xt_155_groups_0"), val = int32(1)]; + tensor generator_noise_res_2_convs2_1_weight_to_fp16 = const()[name = string("generator_noise_res_2_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38600128)))]; + tensor generator_noise_res_2_convs2_1_bias_to_fp16 = const()[name = string("generator_noise_res_2_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38657536)))]; + tensor xt_155_cast_fp16 = conv(bias = generator_noise_res_2_convs2_1_bias_to_fp16, dilations = xt_155_dilations_0, groups = xt_155_groups_0, pad = xt_155_pad_0, pad_type = xt_155_pad_type_0, strides = xt_155_strides_0, weight = generator_noise_res_2_convs2_1_weight_to_fp16, x = input_205_cast_fp16)[name = string("xt_155_cast_fp16")]; + tensor input_207_cast_fp16 = add(x = xt_155_cast_fp16, y = input_199_cast_fp16)[name = string("input_207_cast_fp16")]; + tensor generator_noise_res_2_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_noise_res_2_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38657728)))]; + tensor generator_noise_res_2_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_noise_res_2_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38690560)))]; + tensor linear_52_cast_fp16 = linear(bias = generator_noise_res_2_adain1_2_fc_bias_to_fp16, weight = generator_noise_res_2_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_2069 = const()[name = string("op_2069"), val = tensor([1, 128, 1])]; + tensor h_211_cast_fp16 = reshape(shape = var_2069, x = linear_52_cast_fp16)[name = string("h_211_cast_fp16")]; + tensor var_2071_split_sizes_0 = const()[name = string("op_2071_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2071_axis_0 = const()[name = string("op_2071_axis_0"), val = int32(1)]; + tensor var_2071_cast_fp16_0, tensor var_2071_cast_fp16_1 = split(axis = var_2071_axis_0, split_sizes = var_2071_split_sizes_0, x = h_211_cast_fp16)[name = string("op_2071_cast_fp16")]; + fp16 var_2073_promoted_to_fp16 = const()[name = string("op_2073_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2074_cast_fp16 = add(x = var_2071_cast_fp16_0, y = var_2073_promoted_to_fp16)[name = string("op_2074_cast_fp16")]; + tensor var_2075_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_207_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor var_2076_cast_fp16 = mul(x = var_2074_cast_fp16, y = var_2075_cast_fp16)[name = string("op_2076_cast_fp16")]; + tensor xt_157_cast_fp16 = add(x = var_2076_cast_fp16, y = var_2071_cast_fp16_1)[name = string("xt_157_cast_fp16")]; + tensor generator_noise_res_2_alpha1_2_to_fp16 = const()[name = string("generator_noise_res_2_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38690880)))]; + tensor var_2081_cast_fp16 = mul(x = generator_noise_res_2_alpha1_2_to_fp16, y = xt_157_cast_fp16)[name = string("op_2081_cast_fp16")]; + tensor var_2082_cast_fp16 = sin(x = var_2081_cast_fp16)[name = string("op_2082_cast_fp16")]; + fp16 var_24_promoted_55_to_fp16 = const()[name = string("op_24_promoted_55_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2083_cast_fp16 = pow(x = var_2082_cast_fp16, y = var_24_promoted_55_to_fp16)[name = string("op_2083_cast_fp16")]; + tensor var_2078_to_fp16 = const()[name = string("op_2078_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38691072)))]; + tensor var_2084_cast_fp16 = mul(x = var_2078_to_fp16, y = var_2083_cast_fp16)[name = string("op_2084_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = xt_157_cast_fp16, y = var_2084_cast_fp16)[name = string("input_209_cast_fp16")]; + string input_211_pad_type_0 = const()[name = string("input_211_pad_type_0"), val = string("custom")]; + tensor input_211_pad_0 = const()[name = string("input_211_pad_0"), val = tensor([15, 15])]; + tensor input_211_dilations_0 = const()[name = string("input_211_dilations_0"), val = tensor([5])]; + tensor input_211_strides_0 = const()[name = string("input_211_strides_0"), val = tensor([1])]; + int32 input_211_groups_0 = const()[name = string("input_211_groups_0"), val = int32(1)]; + tensor generator_noise_res_2_convs1_2_weight_to_fp16 = const()[name = string("generator_noise_res_2_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38691264)))]; + tensor generator_noise_res_2_convs1_2_bias_to_fp16 = const()[name = string("generator_noise_res_2_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38748672)))]; + tensor input_211_cast_fp16 = conv(bias = generator_noise_res_2_convs1_2_bias_to_fp16, dilations = input_211_dilations_0, groups = input_211_groups_0, pad = input_211_pad_0, pad_type = input_211_pad_type_0, strides = input_211_strides_0, weight = generator_noise_res_2_convs1_2_weight_to_fp16, x = input_209_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor generator_noise_res_2_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_noise_res_2_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38748864)))]; + tensor generator_noise_res_2_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_noise_res_2_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38781696)))]; + tensor linear_53_cast_fp16 = linear(bias = generator_noise_res_2_adain2_2_fc_bias_to_fp16, weight = generator_noise_res_2_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_2099 = const()[name = string("op_2099"), val = tensor([1, 128, 1])]; + tensor h_215_cast_fp16 = reshape(shape = var_2099, x = linear_53_cast_fp16)[name = string("h_215_cast_fp16")]; + tensor var_2101_split_sizes_0 = const()[name = string("op_2101_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2101_axis_0 = const()[name = string("op_2101_axis_0"), val = int32(1)]; + tensor var_2101_cast_fp16_0, tensor var_2101_cast_fp16_1 = split(axis = var_2101_axis_0, split_sizes = var_2101_split_sizes_0, x = h_215_cast_fp16)[name = string("op_2101_cast_fp16")]; + fp16 var_2103_promoted_to_fp16 = const()[name = string("op_2103_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2104_cast_fp16 = add(x = var_2101_cast_fp16_0, y = var_2103_promoted_to_fp16)[name = string("op_2104_cast_fp16")]; + tensor var_2105_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_211_cast_fp16)[name = string("op_2105_cast_fp16")]; + tensor var_2106_cast_fp16 = mul(x = var_2104_cast_fp16, y = var_2105_cast_fp16)[name = string("op_2106_cast_fp16")]; + tensor xt_159_cast_fp16 = add(x = var_2106_cast_fp16, y = var_2101_cast_fp16_1)[name = string("xt_159_cast_fp16")]; + tensor generator_noise_res_2_alpha2_2_to_fp16 = const()[name = string("generator_noise_res_2_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38782016)))]; + tensor var_2111_cast_fp16 = mul(x = generator_noise_res_2_alpha2_2_to_fp16, y = xt_159_cast_fp16)[name = string("op_2111_cast_fp16")]; + tensor var_2112_cast_fp16 = sin(x = var_2111_cast_fp16)[name = string("op_2112_cast_fp16")]; + fp16 var_24_promoted_56_to_fp16 = const()[name = string("op_24_promoted_56_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2113_cast_fp16 = pow(x = var_2112_cast_fp16, y = var_24_promoted_56_to_fp16)[name = string("op_2113_cast_fp16")]; + tensor var_2108_to_fp16 = const()[name = string("op_2108_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38782208)))]; + tensor var_2114_cast_fp16 = mul(x = var_2108_to_fp16, y = var_2113_cast_fp16)[name = string("op_2114_cast_fp16")]; + tensor input_213_cast_fp16 = add(x = xt_159_cast_fp16, y = var_2114_cast_fp16)[name = string("input_213_cast_fp16")]; + string xt_161_pad_type_0 = const()[name = string("xt_161_pad_type_0"), val = string("custom")]; + tensor xt_161_pad_0 = const()[name = string("xt_161_pad_0"), val = tensor([3, 3])]; + tensor xt_161_strides_0 = const()[name = string("xt_161_strides_0"), val = tensor([1])]; + tensor xt_161_dilations_0 = const()[name = string("xt_161_dilations_0"), val = tensor([1])]; + int32 xt_161_groups_0 = const()[name = string("xt_161_groups_0"), val = int32(1)]; + tensor generator_noise_res_2_convs2_2_weight_to_fp16 = const()[name = string("generator_noise_res_2_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38782400)))]; + tensor generator_noise_res_2_convs2_2_bias_to_fp16 = const()[name = string("generator_noise_res_2_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38839808)))]; + tensor xt_161_cast_fp16 = conv(bias = generator_noise_res_2_convs2_2_bias_to_fp16, dilations = xt_161_dilations_0, groups = xt_161_groups_0, pad = xt_161_pad_0, pad_type = xt_161_pad_type_0, strides = xt_161_strides_0, weight = generator_noise_res_2_convs2_2_weight_to_fp16, x = input_213_cast_fp16)[name = string("xt_161_cast_fp16")]; + tensor x_source_5_cast_fp16 = add(x = xt_161_cast_fp16, y = input_207_cast_fp16)[name = string("x_source_5_cast_fp16")]; + string conv_transpose_1_pad_type_0 = const()[name = string("conv_transpose_1_pad_type_0"), val = string("custom")]; + tensor conv_transpose_1_pad_0 = const()[name = string("conv_transpose_1_pad_0"), val = tensor([0, 0])]; + tensor conv_transpose_1_strides_0 = const()[name = string("conv_transpose_1_strides_0"), val = tensor([3])]; + tensor conv_transpose_1_dilations_0 = const()[name = string("conv_transpose_1_dilations_0"), val = tensor([1])]; + int32 conv_transpose_1_groups_0 = const()[name = string("conv_transpose_1_groups_0"), val = int32(1)]; + tensor generator_ups_2_weight_to_fp16 = const()[name = string("generator_ups_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38840000)))]; + tensor generator_ups_2_bias_to_fp16 = const()[name = string("generator_ups_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38938368)))]; + tensor conv_transpose_1_cast_fp16 = conv_transpose(bias = generator_ups_2_bias_to_fp16, dilations = conv_transpose_1_dilations_0, groups = conv_transpose_1_groups_0, pad = conv_transpose_1_pad_0, pad_type = conv_transpose_1_pad_type_0, strides = conv_transpose_1_strides_0, weight = generator_ups_2_weight_to_fp16, x = input_215_cast_fp16)[name = string("conv_transpose_1_cast_fp16")]; + tensor x_9_begin_0 = const()[name = string("x_9_begin_0"), val = tensor([0, 0, 2])]; + tensor x_9_end_0 = const()[name = string("x_9_end_0"), val = tensor([0, 0, -1])]; + tensor x_9_begin_mask_0 = const()[name = string("x_9_begin_mask_0"), val = tensor([true, true, false])]; + tensor x_9_end_mask_0 = const()[name = string("x_9_end_mask_0"), val = tensor([true, true, false])]; + tensor x_9_cast_fp16 = slice_by_index(begin = x_9_begin_0, begin_mask = x_9_begin_mask_0, end = x_9_end_0, end_mask = x_9_end_mask_0, x = conv_transpose_1_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_217_cast_fp16 = add(x = x_9_cast_fp16, y = x_source_5_cast_fp16)[name = string("input_217_cast_fp16")]; + tensor generator_resblocks_6_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_6_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38938560)))]; + tensor generator_resblocks_6_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_6_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38971392)))]; + tensor linear_54_cast_fp16 = linear(bias = generator_resblocks_6_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_6_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_54_cast_fp16")]; + tensor var_2174 = const()[name = string("op_2174"), val = tensor([1, 128, 1])]; + tensor h_219_cast_fp16 = reshape(shape = var_2174, x = linear_54_cast_fp16)[name = string("h_219_cast_fp16")]; + tensor var_2176_split_sizes_0 = const()[name = string("op_2176_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2176_axis_0 = const()[name = string("op_2176_axis_0"), val = int32(1)]; + tensor var_2176_cast_fp16_0, tensor var_2176_cast_fp16_1 = split(axis = var_2176_axis_0, split_sizes = var_2176_split_sizes_0, x = h_219_cast_fp16)[name = string("op_2176_cast_fp16")]; + fp16 var_2178_promoted_to_fp16 = const()[name = string("op_2178_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2179_cast_fp16 = add(x = var_2176_cast_fp16_0, y = var_2178_promoted_to_fp16)[name = string("op_2179_cast_fp16")]; + tensor var_2180_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_217_cast_fp16)[name = string("op_2180_cast_fp16")]; + tensor var_2181_cast_fp16 = mul(x = var_2179_cast_fp16, y = var_2180_cast_fp16)[name = string("op_2181_cast_fp16")]; + tensor xt_163_cast_fp16 = add(x = var_2181_cast_fp16, y = var_2176_cast_fp16_1)[name = string("xt_163_cast_fp16")]; + tensor generator_resblocks_6_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_6_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38971712)))]; + tensor var_2186_cast_fp16 = mul(x = generator_resblocks_6_alpha1_0_to_fp16, y = xt_163_cast_fp16)[name = string("op_2186_cast_fp16")]; + tensor var_2187_cast_fp16 = sin(x = var_2186_cast_fp16)[name = string("op_2187_cast_fp16")]; + fp16 var_24_promoted_57_to_fp16 = const()[name = string("op_24_promoted_57_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2188_cast_fp16 = pow(x = var_2187_cast_fp16, y = var_24_promoted_57_to_fp16)[name = string("op_2188_cast_fp16")]; + tensor var_2183_to_fp16 = const()[name = string("op_2183_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38971904)))]; + tensor var_2189_cast_fp16 = mul(x = var_2183_to_fp16, y = var_2188_cast_fp16)[name = string("op_2189_cast_fp16")]; + tensor input_219_cast_fp16 = add(x = xt_163_cast_fp16, y = var_2189_cast_fp16)[name = string("input_219_cast_fp16")]; + string input_221_pad_type_0 = const()[name = string("input_221_pad_type_0"), val = string("custom")]; + tensor input_221_pad_0 = const()[name = string("input_221_pad_0"), val = tensor([1, 1])]; + tensor input_221_strides_0 = const()[name = string("input_221_strides_0"), val = tensor([1])]; + tensor input_221_dilations_0 = const()[name = string("input_221_dilations_0"), val = tensor([1])]; + int32 input_221_groups_0 = const()[name = string("input_221_groups_0"), val = int32(1)]; + tensor generator_resblocks_6_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_6_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38972096)))]; + tensor generator_resblocks_6_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_6_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38996736)))]; + tensor input_221_cast_fp16 = conv(bias = generator_resblocks_6_convs1_0_bias_to_fp16, dilations = input_221_dilations_0, groups = input_221_groups_0, pad = input_221_pad_0, pad_type = input_221_pad_type_0, strides = input_221_strides_0, weight = generator_resblocks_6_convs1_0_weight_to_fp16, x = input_219_cast_fp16)[name = string("input_221_cast_fp16")]; + tensor generator_resblocks_6_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_6_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38996928)))]; + tensor generator_resblocks_6_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_6_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39029760)))]; + tensor linear_55_cast_fp16 = linear(bias = generator_resblocks_6_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_6_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_2204 = const()[name = string("op_2204"), val = tensor([1, 128, 1])]; + tensor h_223_cast_fp16 = reshape(shape = var_2204, x = linear_55_cast_fp16)[name = string("h_223_cast_fp16")]; + tensor var_2206_split_sizes_0 = const()[name = string("op_2206_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2206_axis_0 = const()[name = string("op_2206_axis_0"), val = int32(1)]; + tensor var_2206_cast_fp16_0, tensor var_2206_cast_fp16_1 = split(axis = var_2206_axis_0, split_sizes = var_2206_split_sizes_0, x = h_223_cast_fp16)[name = string("op_2206_cast_fp16")]; + fp16 var_2208_promoted_to_fp16 = const()[name = string("op_2208_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2209_cast_fp16 = add(x = var_2206_cast_fp16_0, y = var_2208_promoted_to_fp16)[name = string("op_2209_cast_fp16")]; + tensor var_2210_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_221_cast_fp16)[name = string("op_2210_cast_fp16")]; + tensor var_2211_cast_fp16 = mul(x = var_2209_cast_fp16, y = var_2210_cast_fp16)[name = string("op_2211_cast_fp16")]; + tensor xt_165_cast_fp16 = add(x = var_2211_cast_fp16, y = var_2206_cast_fp16_1)[name = string("xt_165_cast_fp16")]; + tensor generator_resblocks_6_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_6_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39030080)))]; + tensor var_2216_cast_fp16 = mul(x = generator_resblocks_6_alpha2_0_to_fp16, y = xt_165_cast_fp16)[name = string("op_2216_cast_fp16")]; + tensor var_2217_cast_fp16 = sin(x = var_2216_cast_fp16)[name = string("op_2217_cast_fp16")]; + fp16 var_24_promoted_58_to_fp16 = const()[name = string("op_24_promoted_58_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2218_cast_fp16 = pow(x = var_2217_cast_fp16, y = var_24_promoted_58_to_fp16)[name = string("op_2218_cast_fp16")]; + tensor var_2213_to_fp16 = const()[name = string("op_2213_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39030272)))]; + tensor var_2219_cast_fp16 = mul(x = var_2213_to_fp16, y = var_2218_cast_fp16)[name = string("op_2219_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = xt_165_cast_fp16, y = var_2219_cast_fp16)[name = string("input_223_cast_fp16")]; + string xt_167_pad_type_0 = const()[name = string("xt_167_pad_type_0"), val = string("custom")]; + tensor xt_167_pad_0 = const()[name = string("xt_167_pad_0"), val = tensor([1, 1])]; + tensor xt_167_strides_0 = const()[name = string("xt_167_strides_0"), val = tensor([1])]; + tensor xt_167_dilations_0 = const()[name = string("xt_167_dilations_0"), val = tensor([1])]; + int32 xt_167_groups_0 = const()[name = string("xt_167_groups_0"), val = int32(1)]; + tensor generator_resblocks_6_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_6_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39030464)))]; + tensor generator_resblocks_6_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_6_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39055104)))]; + tensor xt_167_cast_fp16 = conv(bias = generator_resblocks_6_convs2_0_bias_to_fp16, dilations = xt_167_dilations_0, groups = xt_167_groups_0, pad = xt_167_pad_0, pad_type = xt_167_pad_type_0, strides = xt_167_strides_0, weight = generator_resblocks_6_convs2_0_weight_to_fp16, x = input_223_cast_fp16)[name = string("xt_167_cast_fp16")]; + tensor input_225_cast_fp16 = add(x = xt_167_cast_fp16, y = input_217_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor generator_resblocks_6_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_6_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39055296)))]; + tensor generator_resblocks_6_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_6_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39088128)))]; + tensor linear_56_cast_fp16 = linear(bias = generator_resblocks_6_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_6_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_2235 = const()[name = string("op_2235"), val = tensor([1, 128, 1])]; + tensor h_227_cast_fp16 = reshape(shape = var_2235, x = linear_56_cast_fp16)[name = string("h_227_cast_fp16")]; + tensor var_2237_split_sizes_0 = const()[name = string("op_2237_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2237_axis_0 = const()[name = string("op_2237_axis_0"), val = int32(1)]; + tensor var_2237_cast_fp16_0, tensor var_2237_cast_fp16_1 = split(axis = var_2237_axis_0, split_sizes = var_2237_split_sizes_0, x = h_227_cast_fp16)[name = string("op_2237_cast_fp16")]; + fp16 var_2239_promoted_to_fp16 = const()[name = string("op_2239_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2240_cast_fp16 = add(x = var_2237_cast_fp16_0, y = var_2239_promoted_to_fp16)[name = string("op_2240_cast_fp16")]; + tensor var_2241_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_225_cast_fp16)[name = string("op_2241_cast_fp16")]; + tensor var_2242_cast_fp16 = mul(x = var_2240_cast_fp16, y = var_2241_cast_fp16)[name = string("op_2242_cast_fp16")]; + tensor xt_169_cast_fp16 = add(x = var_2242_cast_fp16, y = var_2237_cast_fp16_1)[name = string("xt_169_cast_fp16")]; + tensor generator_resblocks_6_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_6_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39088448)))]; + tensor var_2247_cast_fp16 = mul(x = generator_resblocks_6_alpha1_1_to_fp16, y = xt_169_cast_fp16)[name = string("op_2247_cast_fp16")]; + tensor var_2248_cast_fp16 = sin(x = var_2247_cast_fp16)[name = string("op_2248_cast_fp16")]; + fp16 var_24_promoted_59_to_fp16 = const()[name = string("op_24_promoted_59_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2249_cast_fp16 = pow(x = var_2248_cast_fp16, y = var_24_promoted_59_to_fp16)[name = string("op_2249_cast_fp16")]; + tensor var_2244_to_fp16 = const()[name = string("op_2244_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39088640)))]; + tensor var_2250_cast_fp16 = mul(x = var_2244_to_fp16, y = var_2249_cast_fp16)[name = string("op_2250_cast_fp16")]; + tensor input_227_cast_fp16 = add(x = xt_169_cast_fp16, y = var_2250_cast_fp16)[name = string("input_227_cast_fp16")]; + string input_229_pad_type_0 = const()[name = string("input_229_pad_type_0"), val = string("custom")]; + tensor input_229_pad_0 = const()[name = string("input_229_pad_0"), val = tensor([3, 3])]; + tensor input_229_dilations_0 = const()[name = string("input_229_dilations_0"), val = tensor([3])]; + tensor input_229_strides_0 = const()[name = string("input_229_strides_0"), val = tensor([1])]; + int32 input_229_groups_0 = const()[name = string("input_229_groups_0"), val = int32(1)]; + tensor generator_resblocks_6_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_6_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39088832)))]; + tensor generator_resblocks_6_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_6_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39113472)))]; + tensor input_229_cast_fp16 = conv(bias = generator_resblocks_6_convs1_1_bias_to_fp16, dilations = input_229_dilations_0, groups = input_229_groups_0, pad = input_229_pad_0, pad_type = input_229_pad_type_0, strides = input_229_strides_0, weight = generator_resblocks_6_convs1_1_weight_to_fp16, x = input_227_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor generator_resblocks_6_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_6_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39113664)))]; + tensor generator_resblocks_6_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_6_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39146496)))]; + tensor linear_57_cast_fp16 = linear(bias = generator_resblocks_6_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_6_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_2265 = const()[name = string("op_2265"), val = tensor([1, 128, 1])]; + tensor h_231_cast_fp16 = reshape(shape = var_2265, x = linear_57_cast_fp16)[name = string("h_231_cast_fp16")]; + tensor var_2267_split_sizes_0 = const()[name = string("op_2267_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2267_axis_0 = const()[name = string("op_2267_axis_0"), val = int32(1)]; + tensor var_2267_cast_fp16_0, tensor var_2267_cast_fp16_1 = split(axis = var_2267_axis_0, split_sizes = var_2267_split_sizes_0, x = h_231_cast_fp16)[name = string("op_2267_cast_fp16")]; + fp16 var_2269_promoted_to_fp16 = const()[name = string("op_2269_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2270_cast_fp16 = add(x = var_2267_cast_fp16_0, y = var_2269_promoted_to_fp16)[name = string("op_2270_cast_fp16")]; + tensor var_2271_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_229_cast_fp16)[name = string("op_2271_cast_fp16")]; + tensor var_2272_cast_fp16 = mul(x = var_2270_cast_fp16, y = var_2271_cast_fp16)[name = string("op_2272_cast_fp16")]; + tensor xt_171_cast_fp16 = add(x = var_2272_cast_fp16, y = var_2267_cast_fp16_1)[name = string("xt_171_cast_fp16")]; + tensor generator_resblocks_6_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_6_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39146816)))]; + tensor var_2277_cast_fp16 = mul(x = generator_resblocks_6_alpha2_1_to_fp16, y = xt_171_cast_fp16)[name = string("op_2277_cast_fp16")]; + tensor var_2278_cast_fp16 = sin(x = var_2277_cast_fp16)[name = string("op_2278_cast_fp16")]; + fp16 var_24_promoted_60_to_fp16 = const()[name = string("op_24_promoted_60_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2279_cast_fp16 = pow(x = var_2278_cast_fp16, y = var_24_promoted_60_to_fp16)[name = string("op_2279_cast_fp16")]; + tensor var_2274_to_fp16 = const()[name = string("op_2274_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39147008)))]; + tensor var_2280_cast_fp16 = mul(x = var_2274_to_fp16, y = var_2279_cast_fp16)[name = string("op_2280_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = xt_171_cast_fp16, y = var_2280_cast_fp16)[name = string("input_231_cast_fp16")]; + string xt_173_pad_type_0 = const()[name = string("xt_173_pad_type_0"), val = string("custom")]; + tensor xt_173_pad_0 = const()[name = string("xt_173_pad_0"), val = tensor([1, 1])]; + tensor xt_173_strides_0 = const()[name = string("xt_173_strides_0"), val = tensor([1])]; + tensor xt_173_dilations_0 = const()[name = string("xt_173_dilations_0"), val = tensor([1])]; + int32 xt_173_groups_0 = const()[name = string("xt_173_groups_0"), val = int32(1)]; + tensor generator_resblocks_6_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_6_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39147200)))]; + tensor generator_resblocks_6_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_6_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39171840)))]; + tensor xt_173_cast_fp16 = conv(bias = generator_resblocks_6_convs2_1_bias_to_fp16, dilations = xt_173_dilations_0, groups = xt_173_groups_0, pad = xt_173_pad_0, pad_type = xt_173_pad_type_0, strides = xt_173_strides_0, weight = generator_resblocks_6_convs2_1_weight_to_fp16, x = input_231_cast_fp16)[name = string("xt_173_cast_fp16")]; + tensor input_233_cast_fp16 = add(x = xt_173_cast_fp16, y = input_225_cast_fp16)[name = string("input_233_cast_fp16")]; + tensor generator_resblocks_6_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_6_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39172032)))]; + tensor generator_resblocks_6_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_6_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39204864)))]; + tensor linear_58_cast_fp16 = linear(bias = generator_resblocks_6_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_6_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_2296 = const()[name = string("op_2296"), val = tensor([1, 128, 1])]; + tensor h_235_cast_fp16 = reshape(shape = var_2296, x = linear_58_cast_fp16)[name = string("h_235_cast_fp16")]; + tensor var_2298_split_sizes_0 = const()[name = string("op_2298_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2298_axis_0 = const()[name = string("op_2298_axis_0"), val = int32(1)]; + tensor var_2298_cast_fp16_0, tensor var_2298_cast_fp16_1 = split(axis = var_2298_axis_0, split_sizes = var_2298_split_sizes_0, x = h_235_cast_fp16)[name = string("op_2298_cast_fp16")]; + fp16 var_2300_promoted_to_fp16 = const()[name = string("op_2300_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2301_cast_fp16 = add(x = var_2298_cast_fp16_0, y = var_2300_promoted_to_fp16)[name = string("op_2301_cast_fp16")]; + tensor var_2302_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_233_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_cast_fp16 = mul(x = var_2301_cast_fp16, y = var_2302_cast_fp16)[name = string("op_2303_cast_fp16")]; + tensor xt_175_cast_fp16 = add(x = var_2303_cast_fp16, y = var_2298_cast_fp16_1)[name = string("xt_175_cast_fp16")]; + tensor generator_resblocks_6_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_6_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39205184)))]; + tensor var_2308_cast_fp16 = mul(x = generator_resblocks_6_alpha1_2_to_fp16, y = xt_175_cast_fp16)[name = string("op_2308_cast_fp16")]; + tensor var_2309_cast_fp16 = sin(x = var_2308_cast_fp16)[name = string("op_2309_cast_fp16")]; + fp16 var_24_promoted_61_to_fp16 = const()[name = string("op_24_promoted_61_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2310_cast_fp16 = pow(x = var_2309_cast_fp16, y = var_24_promoted_61_to_fp16)[name = string("op_2310_cast_fp16")]; + tensor var_2305_to_fp16 = const()[name = string("op_2305_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39205376)))]; + tensor var_2311_cast_fp16 = mul(x = var_2305_to_fp16, y = var_2310_cast_fp16)[name = string("op_2311_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = xt_175_cast_fp16, y = var_2311_cast_fp16)[name = string("input_235_cast_fp16")]; + string input_237_pad_type_0 = const()[name = string("input_237_pad_type_0"), val = string("custom")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([5, 5])]; + tensor input_237_dilations_0 = const()[name = string("input_237_dilations_0"), val = tensor([5])]; + tensor input_237_strides_0 = const()[name = string("input_237_strides_0"), val = tensor([1])]; + int32 input_237_groups_0 = const()[name = string("input_237_groups_0"), val = int32(1)]; + tensor generator_resblocks_6_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_6_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39205568)))]; + tensor generator_resblocks_6_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_6_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39230208)))]; + tensor input_237_cast_fp16 = conv(bias = generator_resblocks_6_convs1_2_bias_to_fp16, dilations = input_237_dilations_0, groups = input_237_groups_0, pad = input_237_pad_0, pad_type = input_237_pad_type_0, strides = input_237_strides_0, weight = generator_resblocks_6_convs1_2_weight_to_fp16, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + tensor generator_resblocks_6_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_6_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39230400)))]; + tensor generator_resblocks_6_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_6_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39263232)))]; + tensor linear_59_cast_fp16 = linear(bias = generator_resblocks_6_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_6_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_59_cast_fp16")]; + tensor var_2326 = const()[name = string("op_2326"), val = tensor([1, 128, 1])]; + tensor h_239_cast_fp16 = reshape(shape = var_2326, x = linear_59_cast_fp16)[name = string("h_239_cast_fp16")]; + tensor var_2328_split_sizes_0 = const()[name = string("op_2328_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2328_axis_0 = const()[name = string("op_2328_axis_0"), val = int32(1)]; + tensor var_2328_cast_fp16_0, tensor var_2328_cast_fp16_1 = split(axis = var_2328_axis_0, split_sizes = var_2328_split_sizes_0, x = h_239_cast_fp16)[name = string("op_2328_cast_fp16")]; + fp16 var_2330_promoted_to_fp16 = const()[name = string("op_2330_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2331_cast_fp16 = add(x = var_2328_cast_fp16_0, y = var_2330_promoted_to_fp16)[name = string("op_2331_cast_fp16")]; + tensor var_2332_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_237_cast_fp16)[name = string("op_2332_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = var_2331_cast_fp16, y = var_2332_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor xt_177_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2328_cast_fp16_1)[name = string("xt_177_cast_fp16")]; + tensor generator_resblocks_6_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_6_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39263552)))]; + tensor var_2338_cast_fp16 = mul(x = generator_resblocks_6_alpha2_2_to_fp16, y = xt_177_cast_fp16)[name = string("op_2338_cast_fp16")]; + tensor var_2339_cast_fp16 = sin(x = var_2338_cast_fp16)[name = string("op_2339_cast_fp16")]; + fp16 var_24_promoted_62_to_fp16 = const()[name = string("op_24_promoted_62_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2340_cast_fp16 = pow(x = var_2339_cast_fp16, y = var_24_promoted_62_to_fp16)[name = string("op_2340_cast_fp16")]; + tensor var_2335_to_fp16 = const()[name = string("op_2335_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39263744)))]; + tensor var_2341_cast_fp16 = mul(x = var_2335_to_fp16, y = var_2340_cast_fp16)[name = string("op_2341_cast_fp16")]; + tensor input_239_cast_fp16 = add(x = xt_177_cast_fp16, y = var_2341_cast_fp16)[name = string("input_239_cast_fp16")]; + string xt_179_pad_type_0 = const()[name = string("xt_179_pad_type_0"), val = string("custom")]; + tensor xt_179_pad_0 = const()[name = string("xt_179_pad_0"), val = tensor([1, 1])]; + tensor xt_179_strides_0 = const()[name = string("xt_179_strides_0"), val = tensor([1])]; + tensor xt_179_dilations_0 = const()[name = string("xt_179_dilations_0"), val = tensor([1])]; + int32 xt_179_groups_0 = const()[name = string("xt_179_groups_0"), val = int32(1)]; + tensor generator_resblocks_6_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_6_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39263936)))]; + tensor generator_resblocks_6_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_6_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39288576)))]; + tensor xt_179_cast_fp16 = conv(bias = generator_resblocks_6_convs2_2_bias_to_fp16, dilations = xt_179_dilations_0, groups = xt_179_groups_0, pad = xt_179_pad_0, pad_type = xt_179_pad_type_0, strides = xt_179_strides_0, weight = generator_resblocks_6_convs2_2_weight_to_fp16, x = input_239_cast_fp16)[name = string("xt_179_cast_fp16")]; + tensor xs_13_cast_fp16 = add(x = xt_179_cast_fp16, y = input_233_cast_fp16)[name = string("xs_13_cast_fp16")]; + tensor generator_resblocks_7_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_7_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39288768)))]; + tensor generator_resblocks_7_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_7_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39321600)))]; + tensor linear_60_cast_fp16 = linear(bias = generator_resblocks_7_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_7_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_2393 = const()[name = string("op_2393"), val = tensor([1, 128, 1])]; + tensor h_243_cast_fp16 = reshape(shape = var_2393, x = linear_60_cast_fp16)[name = string("h_243_cast_fp16")]; + tensor var_2395_split_sizes_0 = const()[name = string("op_2395_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2395_axis_0 = const()[name = string("op_2395_axis_0"), val = int32(1)]; + tensor var_2395_cast_fp16_0, tensor var_2395_cast_fp16_1 = split(axis = var_2395_axis_0, split_sizes = var_2395_split_sizes_0, x = h_243_cast_fp16)[name = string("op_2395_cast_fp16")]; + fp16 var_2397_promoted_to_fp16 = const()[name = string("op_2397_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2398_cast_fp16 = add(x = var_2395_cast_fp16_0, y = var_2397_promoted_to_fp16)[name = string("op_2398_cast_fp16")]; + tensor var_2400_cast_fp16 = mul(x = var_2398_cast_fp16, y = var_2180_cast_fp16)[name = string("op_2400_cast_fp16")]; + tensor xt_181_cast_fp16 = add(x = var_2400_cast_fp16, y = var_2395_cast_fp16_1)[name = string("xt_181_cast_fp16")]; + tensor generator_resblocks_7_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_7_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39321920)))]; + tensor var_2405_cast_fp16 = mul(x = generator_resblocks_7_alpha1_0_to_fp16, y = xt_181_cast_fp16)[name = string("op_2405_cast_fp16")]; + tensor var_2406_cast_fp16 = sin(x = var_2405_cast_fp16)[name = string("op_2406_cast_fp16")]; + fp16 var_24_promoted_63_to_fp16 = const()[name = string("op_24_promoted_63_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2407_cast_fp16 = pow(x = var_2406_cast_fp16, y = var_24_promoted_63_to_fp16)[name = string("op_2407_cast_fp16")]; + tensor var_2402_to_fp16 = const()[name = string("op_2402_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39322112)))]; + tensor var_2408_cast_fp16 = mul(x = var_2402_to_fp16, y = var_2407_cast_fp16)[name = string("op_2408_cast_fp16")]; + tensor input_241_cast_fp16 = add(x = xt_181_cast_fp16, y = var_2408_cast_fp16)[name = string("input_241_cast_fp16")]; + string input_243_pad_type_0 = const()[name = string("input_243_pad_type_0"), val = string("custom")]; + tensor input_243_pad_0 = const()[name = string("input_243_pad_0"), val = tensor([3, 3])]; + tensor input_243_strides_0 = const()[name = string("input_243_strides_0"), val = tensor([1])]; + tensor input_243_dilations_0 = const()[name = string("input_243_dilations_0"), val = tensor([1])]; + int32 input_243_groups_0 = const()[name = string("input_243_groups_0"), val = int32(1)]; + tensor generator_resblocks_7_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_7_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39322304)))]; + tensor generator_resblocks_7_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_7_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39379712)))]; + tensor input_243_cast_fp16 = conv(bias = generator_resblocks_7_convs1_0_bias_to_fp16, dilations = input_243_dilations_0, groups = input_243_groups_0, pad = input_243_pad_0, pad_type = input_243_pad_type_0, strides = input_243_strides_0, weight = generator_resblocks_7_convs1_0_weight_to_fp16, x = input_241_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor generator_resblocks_7_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_7_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39379904)))]; + tensor generator_resblocks_7_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_7_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39412736)))]; + tensor linear_61_cast_fp16 = linear(bias = generator_resblocks_7_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_7_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_2423 = const()[name = string("op_2423"), val = tensor([1, 128, 1])]; + tensor h_247_cast_fp16 = reshape(shape = var_2423, x = linear_61_cast_fp16)[name = string("h_247_cast_fp16")]; + tensor var_2425_split_sizes_0 = const()[name = string("op_2425_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2425_axis_0 = const()[name = string("op_2425_axis_0"), val = int32(1)]; + tensor var_2425_cast_fp16_0, tensor var_2425_cast_fp16_1 = split(axis = var_2425_axis_0, split_sizes = var_2425_split_sizes_0, x = h_247_cast_fp16)[name = string("op_2425_cast_fp16")]; + fp16 var_2427_promoted_to_fp16 = const()[name = string("op_2427_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2428_cast_fp16 = add(x = var_2425_cast_fp16_0, y = var_2427_promoted_to_fp16)[name = string("op_2428_cast_fp16")]; + tensor var_2429_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_243_cast_fp16)[name = string("op_2429_cast_fp16")]; + tensor var_2430_cast_fp16 = mul(x = var_2428_cast_fp16, y = var_2429_cast_fp16)[name = string("op_2430_cast_fp16")]; + tensor xt_183_cast_fp16 = add(x = var_2430_cast_fp16, y = var_2425_cast_fp16_1)[name = string("xt_183_cast_fp16")]; + tensor generator_resblocks_7_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_7_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39413056)))]; + tensor var_2435_cast_fp16 = mul(x = generator_resblocks_7_alpha2_0_to_fp16, y = xt_183_cast_fp16)[name = string("op_2435_cast_fp16")]; + tensor var_2436_cast_fp16 = sin(x = var_2435_cast_fp16)[name = string("op_2436_cast_fp16")]; + fp16 var_24_promoted_64_to_fp16 = const()[name = string("op_24_promoted_64_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2437_cast_fp16 = pow(x = var_2436_cast_fp16, y = var_24_promoted_64_to_fp16)[name = string("op_2437_cast_fp16")]; + tensor var_2432_to_fp16 = const()[name = string("op_2432_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39413248)))]; + tensor var_2438_cast_fp16 = mul(x = var_2432_to_fp16, y = var_2437_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor input_245_cast_fp16 = add(x = xt_183_cast_fp16, y = var_2438_cast_fp16)[name = string("input_245_cast_fp16")]; + string xt_185_pad_type_0 = const()[name = string("xt_185_pad_type_0"), val = string("custom")]; + tensor xt_185_pad_0 = const()[name = string("xt_185_pad_0"), val = tensor([3, 3])]; + tensor xt_185_strides_0 = const()[name = string("xt_185_strides_0"), val = tensor([1])]; + tensor xt_185_dilations_0 = const()[name = string("xt_185_dilations_0"), val = tensor([1])]; + int32 xt_185_groups_0 = const()[name = string("xt_185_groups_0"), val = int32(1)]; + tensor generator_resblocks_7_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_7_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39413440)))]; + tensor generator_resblocks_7_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_7_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39470848)))]; + tensor xt_185_cast_fp16 = conv(bias = generator_resblocks_7_convs2_0_bias_to_fp16, dilations = xt_185_dilations_0, groups = xt_185_groups_0, pad = xt_185_pad_0, pad_type = xt_185_pad_type_0, strides = xt_185_strides_0, weight = generator_resblocks_7_convs2_0_weight_to_fp16, x = input_245_cast_fp16)[name = string("xt_185_cast_fp16")]; + tensor input_247_cast_fp16 = add(x = xt_185_cast_fp16, y = input_217_cast_fp16)[name = string("input_247_cast_fp16")]; + tensor generator_resblocks_7_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_7_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39471040)))]; + tensor generator_resblocks_7_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_7_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39503872)))]; + tensor linear_62_cast_fp16 = linear(bias = generator_resblocks_7_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_7_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_2454 = const()[name = string("op_2454"), val = tensor([1, 128, 1])]; + tensor h_251_cast_fp16 = reshape(shape = var_2454, x = linear_62_cast_fp16)[name = string("h_251_cast_fp16")]; + tensor var_2456_split_sizes_0 = const()[name = string("op_2456_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2456_axis_0 = const()[name = string("op_2456_axis_0"), val = int32(1)]; + tensor var_2456_cast_fp16_0, tensor var_2456_cast_fp16_1 = split(axis = var_2456_axis_0, split_sizes = var_2456_split_sizes_0, x = h_251_cast_fp16)[name = string("op_2456_cast_fp16")]; + fp16 var_2458_promoted_to_fp16 = const()[name = string("op_2458_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2459_cast_fp16 = add(x = var_2456_cast_fp16_0, y = var_2458_promoted_to_fp16)[name = string("op_2459_cast_fp16")]; + tensor var_2460_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_247_cast_fp16)[name = string("op_2460_cast_fp16")]; + tensor var_2461_cast_fp16 = mul(x = var_2459_cast_fp16, y = var_2460_cast_fp16)[name = string("op_2461_cast_fp16")]; + tensor xt_187_cast_fp16 = add(x = var_2461_cast_fp16, y = var_2456_cast_fp16_1)[name = string("xt_187_cast_fp16")]; + tensor generator_resblocks_7_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_7_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39504192)))]; + tensor var_2466_cast_fp16 = mul(x = generator_resblocks_7_alpha1_1_to_fp16, y = xt_187_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor var_2467_cast_fp16 = sin(x = var_2466_cast_fp16)[name = string("op_2467_cast_fp16")]; + fp16 var_24_promoted_65_to_fp16 = const()[name = string("op_24_promoted_65_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2468_cast_fp16 = pow(x = var_2467_cast_fp16, y = var_24_promoted_65_to_fp16)[name = string("op_2468_cast_fp16")]; + tensor var_2463_to_fp16 = const()[name = string("op_2463_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39504384)))]; + tensor var_2469_cast_fp16 = mul(x = var_2463_to_fp16, y = var_2468_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = xt_187_cast_fp16, y = var_2469_cast_fp16)[name = string("input_249_cast_fp16")]; + string input_251_pad_type_0 = const()[name = string("input_251_pad_type_0"), val = string("custom")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([9, 9])]; + tensor input_251_dilations_0 = const()[name = string("input_251_dilations_0"), val = tensor([3])]; + tensor input_251_strides_0 = const()[name = string("input_251_strides_0"), val = tensor([1])]; + int32 input_251_groups_0 = const()[name = string("input_251_groups_0"), val = int32(1)]; + tensor generator_resblocks_7_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_7_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39504576)))]; + tensor generator_resblocks_7_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_7_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39561984)))]; + tensor input_251_cast_fp16 = conv(bias = generator_resblocks_7_convs1_1_bias_to_fp16, dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = generator_resblocks_7_convs1_1_weight_to_fp16, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor generator_resblocks_7_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_7_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39562176)))]; + tensor generator_resblocks_7_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_7_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39595008)))]; + tensor linear_63_cast_fp16 = linear(bias = generator_resblocks_7_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_7_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_2484 = const()[name = string("op_2484"), val = tensor([1, 128, 1])]; + tensor h_255_cast_fp16 = reshape(shape = var_2484, x = linear_63_cast_fp16)[name = string("h_255_cast_fp16")]; + tensor var_2486_split_sizes_0 = const()[name = string("op_2486_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2486_axis_0 = const()[name = string("op_2486_axis_0"), val = int32(1)]; + tensor var_2486_cast_fp16_0, tensor var_2486_cast_fp16_1 = split(axis = var_2486_axis_0, split_sizes = var_2486_split_sizes_0, x = h_255_cast_fp16)[name = string("op_2486_cast_fp16")]; + fp16 var_2488_promoted_to_fp16 = const()[name = string("op_2488_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2489_cast_fp16 = add(x = var_2486_cast_fp16_0, y = var_2488_promoted_to_fp16)[name = string("op_2489_cast_fp16")]; + tensor var_2490_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_251_cast_fp16)[name = string("op_2490_cast_fp16")]; + tensor var_2491_cast_fp16 = mul(x = var_2489_cast_fp16, y = var_2490_cast_fp16)[name = string("op_2491_cast_fp16")]; + tensor xt_189_cast_fp16 = add(x = var_2491_cast_fp16, y = var_2486_cast_fp16_1)[name = string("xt_189_cast_fp16")]; + tensor generator_resblocks_7_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_7_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39595328)))]; + tensor var_2496_cast_fp16 = mul(x = generator_resblocks_7_alpha2_1_to_fp16, y = xt_189_cast_fp16)[name = string("op_2496_cast_fp16")]; + tensor var_2497_cast_fp16 = sin(x = var_2496_cast_fp16)[name = string("op_2497_cast_fp16")]; + fp16 var_24_promoted_66_to_fp16 = const()[name = string("op_24_promoted_66_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2498_cast_fp16 = pow(x = var_2497_cast_fp16, y = var_24_promoted_66_to_fp16)[name = string("op_2498_cast_fp16")]; + tensor var_2493_to_fp16 = const()[name = string("op_2493_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39595520)))]; + tensor var_2499_cast_fp16 = mul(x = var_2493_to_fp16, y = var_2498_cast_fp16)[name = string("op_2499_cast_fp16")]; + tensor input_253_cast_fp16 = add(x = xt_189_cast_fp16, y = var_2499_cast_fp16)[name = string("input_253_cast_fp16")]; + string xt_191_pad_type_0 = const()[name = string("xt_191_pad_type_0"), val = string("custom")]; + tensor xt_191_pad_0 = const()[name = string("xt_191_pad_0"), val = tensor([3, 3])]; + tensor xt_191_strides_0 = const()[name = string("xt_191_strides_0"), val = tensor([1])]; + tensor xt_191_dilations_0 = const()[name = string("xt_191_dilations_0"), val = tensor([1])]; + int32 xt_191_groups_0 = const()[name = string("xt_191_groups_0"), val = int32(1)]; + tensor generator_resblocks_7_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_7_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39595712)))]; + tensor generator_resblocks_7_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_7_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39653120)))]; + tensor xt_191_cast_fp16 = conv(bias = generator_resblocks_7_convs2_1_bias_to_fp16, dilations = xt_191_dilations_0, groups = xt_191_groups_0, pad = xt_191_pad_0, pad_type = xt_191_pad_type_0, strides = xt_191_strides_0, weight = generator_resblocks_7_convs2_1_weight_to_fp16, x = input_253_cast_fp16)[name = string("xt_191_cast_fp16")]; + tensor input_255_cast_fp16 = add(x = xt_191_cast_fp16, y = input_247_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor generator_resblocks_7_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_7_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39653312)))]; + tensor generator_resblocks_7_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_7_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39686144)))]; + tensor linear_64_cast_fp16 = linear(bias = generator_resblocks_7_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_7_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_64_cast_fp16")]; + tensor var_2515 = const()[name = string("op_2515"), val = tensor([1, 128, 1])]; + tensor h_259_cast_fp16 = reshape(shape = var_2515, x = linear_64_cast_fp16)[name = string("h_259_cast_fp16")]; + tensor var_2517_split_sizes_0 = const()[name = string("op_2517_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2517_axis_0 = const()[name = string("op_2517_axis_0"), val = int32(1)]; + tensor var_2517_cast_fp16_0, tensor var_2517_cast_fp16_1 = split(axis = var_2517_axis_0, split_sizes = var_2517_split_sizes_0, x = h_259_cast_fp16)[name = string("op_2517_cast_fp16")]; + fp16 var_2519_promoted_to_fp16 = const()[name = string("op_2519_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2520_cast_fp16 = add(x = var_2517_cast_fp16_0, y = var_2519_promoted_to_fp16)[name = string("op_2520_cast_fp16")]; + tensor var_2521_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_255_cast_fp16)[name = string("op_2521_cast_fp16")]; + tensor var_2522_cast_fp16 = mul(x = var_2520_cast_fp16, y = var_2521_cast_fp16)[name = string("op_2522_cast_fp16")]; + tensor xt_193_cast_fp16 = add(x = var_2522_cast_fp16, y = var_2517_cast_fp16_1)[name = string("xt_193_cast_fp16")]; + tensor generator_resblocks_7_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_7_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39686464)))]; + tensor var_2527_cast_fp16 = mul(x = generator_resblocks_7_alpha1_2_to_fp16, y = xt_193_cast_fp16)[name = string("op_2527_cast_fp16")]; + tensor var_2528_cast_fp16 = sin(x = var_2527_cast_fp16)[name = string("op_2528_cast_fp16")]; + fp16 var_24_promoted_67_to_fp16 = const()[name = string("op_24_promoted_67_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2529_cast_fp16 = pow(x = var_2528_cast_fp16, y = var_24_promoted_67_to_fp16)[name = string("op_2529_cast_fp16")]; + tensor var_2524_to_fp16 = const()[name = string("op_2524_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39686656)))]; + tensor var_2530_cast_fp16 = mul(x = var_2524_to_fp16, y = var_2529_cast_fp16)[name = string("op_2530_cast_fp16")]; + tensor input_257_cast_fp16 = add(x = xt_193_cast_fp16, y = var_2530_cast_fp16)[name = string("input_257_cast_fp16")]; + string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("custom")]; + tensor input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor([15, 15])]; + tensor input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor([5])]; + tensor input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor([1])]; + int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)]; + tensor generator_resblocks_7_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_7_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39686848)))]; + tensor generator_resblocks_7_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_7_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39744256)))]; + tensor input_259_cast_fp16 = conv(bias = generator_resblocks_7_convs1_2_bias_to_fp16, dilations = input_259_dilations_0, groups = input_259_groups_0, pad = input_259_pad_0, pad_type = input_259_pad_type_0, strides = input_259_strides_0, weight = generator_resblocks_7_convs1_2_weight_to_fp16, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")]; + tensor generator_resblocks_7_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_7_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39744448)))]; + tensor generator_resblocks_7_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_7_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39777280)))]; + tensor linear_65_cast_fp16 = linear(bias = generator_resblocks_7_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_7_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_2545 = const()[name = string("op_2545"), val = tensor([1, 128, 1])]; + tensor h_263_cast_fp16 = reshape(shape = var_2545, x = linear_65_cast_fp16)[name = string("h_263_cast_fp16")]; + tensor var_2547_split_sizes_0 = const()[name = string("op_2547_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2547_axis_0 = const()[name = string("op_2547_axis_0"), val = int32(1)]; + tensor var_2547_cast_fp16_0, tensor var_2547_cast_fp16_1 = split(axis = var_2547_axis_0, split_sizes = var_2547_split_sizes_0, x = h_263_cast_fp16)[name = string("op_2547_cast_fp16")]; + fp16 var_2549_promoted_to_fp16 = const()[name = string("op_2549_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2550_cast_fp16 = add(x = var_2547_cast_fp16_0, y = var_2549_promoted_to_fp16)[name = string("op_2550_cast_fp16")]; + tensor var_2551_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_259_cast_fp16)[name = string("op_2551_cast_fp16")]; + tensor var_2552_cast_fp16 = mul(x = var_2550_cast_fp16, y = var_2551_cast_fp16)[name = string("op_2552_cast_fp16")]; + tensor xt_195_cast_fp16 = add(x = var_2552_cast_fp16, y = var_2547_cast_fp16_1)[name = string("xt_195_cast_fp16")]; + tensor generator_resblocks_7_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_7_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39777600)))]; + tensor var_2557_cast_fp16 = mul(x = generator_resblocks_7_alpha2_2_to_fp16, y = xt_195_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor var_2558_cast_fp16 = sin(x = var_2557_cast_fp16)[name = string("op_2558_cast_fp16")]; + fp16 var_24_promoted_68_to_fp16 = const()[name = string("op_24_promoted_68_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2559_cast_fp16 = pow(x = var_2558_cast_fp16, y = var_24_promoted_68_to_fp16)[name = string("op_2559_cast_fp16")]; + tensor var_2554_to_fp16 = const()[name = string("op_2554_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39777792)))]; + tensor var_2560_cast_fp16 = mul(x = var_2554_to_fp16, y = var_2559_cast_fp16)[name = string("op_2560_cast_fp16")]; + tensor input_261_cast_fp16 = add(x = xt_195_cast_fp16, y = var_2560_cast_fp16)[name = string("input_261_cast_fp16")]; + string xt_197_pad_type_0 = const()[name = string("xt_197_pad_type_0"), val = string("custom")]; + tensor xt_197_pad_0 = const()[name = string("xt_197_pad_0"), val = tensor([3, 3])]; + tensor xt_197_strides_0 = const()[name = string("xt_197_strides_0"), val = tensor([1])]; + tensor xt_197_dilations_0 = const()[name = string("xt_197_dilations_0"), val = tensor([1])]; + int32 xt_197_groups_0 = const()[name = string("xt_197_groups_0"), val = int32(1)]; + tensor generator_resblocks_7_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_7_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39777984)))]; + tensor generator_resblocks_7_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_7_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39835392)))]; + tensor xt_197_cast_fp16 = conv(bias = generator_resblocks_7_convs2_2_bias_to_fp16, dilations = xt_197_dilations_0, groups = xt_197_groups_0, pad = xt_197_pad_0, pad_type = xt_197_pad_type_0, strides = xt_197_strides_0, weight = generator_resblocks_7_convs2_2_weight_to_fp16, x = input_261_cast_fp16)[name = string("xt_197_cast_fp16")]; + tensor var_2569_cast_fp16 = add(x = xt_197_cast_fp16, y = input_255_cast_fp16)[name = string("op_2569_cast_fp16")]; + tensor xs_15_cast_fp16 = add(x = xs_13_cast_fp16, y = var_2569_cast_fp16)[name = string("xs_15_cast_fp16")]; + tensor generator_resblocks_8_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_8_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39835584)))]; + tensor generator_resblocks_8_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_8_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39868416)))]; + tensor linear_66_cast_fp16 = linear(bias = generator_resblocks_8_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_8_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_2613 = const()[name = string("op_2613"), val = tensor([1, 128, 1])]; + tensor h_267_cast_fp16 = reshape(shape = var_2613, x = linear_66_cast_fp16)[name = string("h_267_cast_fp16")]; + tensor var_2615_split_sizes_0 = const()[name = string("op_2615_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2615_axis_0 = const()[name = string("op_2615_axis_0"), val = int32(1)]; + tensor var_2615_cast_fp16_0, tensor var_2615_cast_fp16_1 = split(axis = var_2615_axis_0, split_sizes = var_2615_split_sizes_0, x = h_267_cast_fp16)[name = string("op_2615_cast_fp16")]; + fp16 var_2617_promoted_to_fp16 = const()[name = string("op_2617_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2618_cast_fp16 = add(x = var_2615_cast_fp16_0, y = var_2617_promoted_to_fp16)[name = string("op_2618_cast_fp16")]; + tensor var_2620_cast_fp16 = mul(x = var_2618_cast_fp16, y = var_2180_cast_fp16)[name = string("op_2620_cast_fp16")]; + tensor xt_199_cast_fp16 = add(x = var_2620_cast_fp16, y = var_2615_cast_fp16_1)[name = string("xt_199_cast_fp16")]; + tensor generator_resblocks_8_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_8_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39868736)))]; + tensor var_2625_cast_fp16 = mul(x = generator_resblocks_8_alpha1_0_to_fp16, y = xt_199_cast_fp16)[name = string("op_2625_cast_fp16")]; + tensor var_2626_cast_fp16 = sin(x = var_2625_cast_fp16)[name = string("op_2626_cast_fp16")]; + fp16 var_24_promoted_69_to_fp16 = const()[name = string("op_24_promoted_69_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2627_cast_fp16 = pow(x = var_2626_cast_fp16, y = var_24_promoted_69_to_fp16)[name = string("op_2627_cast_fp16")]; + tensor var_2622_to_fp16 = const()[name = string("op_2622_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39868928)))]; + tensor var_2628_cast_fp16 = mul(x = var_2622_to_fp16, y = var_2627_cast_fp16)[name = string("op_2628_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = xt_199_cast_fp16, y = var_2628_cast_fp16)[name = string("input_263_cast_fp16")]; + string input_265_pad_type_0 = const()[name = string("input_265_pad_type_0"), val = string("custom")]; + tensor input_265_pad_0 = const()[name = string("input_265_pad_0"), val = tensor([5, 5])]; + tensor input_265_strides_0 = const()[name = string("input_265_strides_0"), val = tensor([1])]; + tensor input_265_dilations_0 = const()[name = string("input_265_dilations_0"), val = tensor([1])]; + int32 input_265_groups_0 = const()[name = string("input_265_groups_0"), val = int32(1)]; + tensor generator_resblocks_8_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_8_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39869120)))]; + tensor generator_resblocks_8_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_8_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39959296)))]; + tensor input_265_cast_fp16 = conv(bias = generator_resblocks_8_convs1_0_bias_to_fp16, dilations = input_265_dilations_0, groups = input_265_groups_0, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = input_265_strides_0, weight = generator_resblocks_8_convs1_0_weight_to_fp16, x = input_263_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor generator_resblocks_8_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_8_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39959488)))]; + tensor generator_resblocks_8_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_8_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39992320)))]; + tensor linear_67_cast_fp16 = linear(bias = generator_resblocks_8_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_8_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_2643 = const()[name = string("op_2643"), val = tensor([1, 128, 1])]; + tensor h_271_cast_fp16 = reshape(shape = var_2643, x = linear_67_cast_fp16)[name = string("h_271_cast_fp16")]; + tensor var_2645_split_sizes_0 = const()[name = string("op_2645_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2645_axis_0 = const()[name = string("op_2645_axis_0"), val = int32(1)]; + tensor var_2645_cast_fp16_0, tensor var_2645_cast_fp16_1 = split(axis = var_2645_axis_0, split_sizes = var_2645_split_sizes_0, x = h_271_cast_fp16)[name = string("op_2645_cast_fp16")]; + fp16 var_2647_promoted_to_fp16 = const()[name = string("op_2647_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2648_cast_fp16 = add(x = var_2645_cast_fp16_0, y = var_2647_promoted_to_fp16)[name = string("op_2648_cast_fp16")]; + tensor var_2649_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_265_cast_fp16)[name = string("op_2649_cast_fp16")]; + tensor var_2650_cast_fp16 = mul(x = var_2648_cast_fp16, y = var_2649_cast_fp16)[name = string("op_2650_cast_fp16")]; + tensor xt_201_cast_fp16 = add(x = var_2650_cast_fp16, y = var_2645_cast_fp16_1)[name = string("xt_201_cast_fp16")]; + tensor generator_resblocks_8_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_8_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39992640)))]; + tensor var_2655_cast_fp16 = mul(x = generator_resblocks_8_alpha2_0_to_fp16, y = xt_201_cast_fp16)[name = string("op_2655_cast_fp16")]; + tensor var_2656_cast_fp16 = sin(x = var_2655_cast_fp16)[name = string("op_2656_cast_fp16")]; + fp16 var_24_promoted_70_to_fp16 = const()[name = string("op_24_promoted_70_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2657_cast_fp16 = pow(x = var_2656_cast_fp16, y = var_24_promoted_70_to_fp16)[name = string("op_2657_cast_fp16")]; + tensor var_2652_to_fp16 = const()[name = string("op_2652_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39992832)))]; + tensor var_2658_cast_fp16 = mul(x = var_2652_to_fp16, y = var_2657_cast_fp16)[name = string("op_2658_cast_fp16")]; + tensor input_267_cast_fp16 = add(x = xt_201_cast_fp16, y = var_2658_cast_fp16)[name = string("input_267_cast_fp16")]; + string xt_203_pad_type_0 = const()[name = string("xt_203_pad_type_0"), val = string("custom")]; + tensor xt_203_pad_0 = const()[name = string("xt_203_pad_0"), val = tensor([5, 5])]; + tensor xt_203_strides_0 = const()[name = string("xt_203_strides_0"), val = tensor([1])]; + tensor xt_203_dilations_0 = const()[name = string("xt_203_dilations_0"), val = tensor([1])]; + int32 xt_203_groups_0 = const()[name = string("xt_203_groups_0"), val = int32(1)]; + tensor generator_resblocks_8_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_8_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39993024)))]; + tensor generator_resblocks_8_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_8_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40083200)))]; + tensor xt_203_cast_fp16 = conv(bias = generator_resblocks_8_convs2_0_bias_to_fp16, dilations = xt_203_dilations_0, groups = xt_203_groups_0, pad = xt_203_pad_0, pad_type = xt_203_pad_type_0, strides = xt_203_strides_0, weight = generator_resblocks_8_convs2_0_weight_to_fp16, x = input_267_cast_fp16)[name = string("xt_203_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = xt_203_cast_fp16, y = input_217_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor generator_resblocks_8_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_8_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40083392)))]; + tensor generator_resblocks_8_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_8_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40116224)))]; + tensor linear_68_cast_fp16 = linear(bias = generator_resblocks_8_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_8_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_2674 = const()[name = string("op_2674"), val = tensor([1, 128, 1])]; + tensor h_275_cast_fp16 = reshape(shape = var_2674, x = linear_68_cast_fp16)[name = string("h_275_cast_fp16")]; + tensor var_2676_split_sizes_0 = const()[name = string("op_2676_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2676_axis_0 = const()[name = string("op_2676_axis_0"), val = int32(1)]; + tensor var_2676_cast_fp16_0, tensor var_2676_cast_fp16_1 = split(axis = var_2676_axis_0, split_sizes = var_2676_split_sizes_0, x = h_275_cast_fp16)[name = string("op_2676_cast_fp16")]; + fp16 var_2678_promoted_to_fp16 = const()[name = string("op_2678_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2679_cast_fp16 = add(x = var_2676_cast_fp16_0, y = var_2678_promoted_to_fp16)[name = string("op_2679_cast_fp16")]; + tensor var_2680_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_269_cast_fp16)[name = string("op_2680_cast_fp16")]; + tensor var_2681_cast_fp16 = mul(x = var_2679_cast_fp16, y = var_2680_cast_fp16)[name = string("op_2681_cast_fp16")]; + tensor xt_205_cast_fp16 = add(x = var_2681_cast_fp16, y = var_2676_cast_fp16_1)[name = string("xt_205_cast_fp16")]; + tensor generator_resblocks_8_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_8_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40116544)))]; + tensor var_2686_cast_fp16 = mul(x = generator_resblocks_8_alpha1_1_to_fp16, y = xt_205_cast_fp16)[name = string("op_2686_cast_fp16")]; + tensor var_2687_cast_fp16 = sin(x = var_2686_cast_fp16)[name = string("op_2687_cast_fp16")]; + fp16 var_24_promoted_71_to_fp16 = const()[name = string("op_24_promoted_71_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2688_cast_fp16 = pow(x = var_2687_cast_fp16, y = var_24_promoted_71_to_fp16)[name = string("op_2688_cast_fp16")]; + tensor var_2683_to_fp16 = const()[name = string("op_2683_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40116736)))]; + tensor var_2689_cast_fp16 = mul(x = var_2683_to_fp16, y = var_2688_cast_fp16)[name = string("op_2689_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = xt_205_cast_fp16, y = var_2689_cast_fp16)[name = string("input_271_cast_fp16")]; + string input_273_pad_type_0 = const()[name = string("input_273_pad_type_0"), val = string("custom")]; + tensor input_273_pad_0 = const()[name = string("input_273_pad_0"), val = tensor([15, 15])]; + tensor input_273_dilations_0 = const()[name = string("input_273_dilations_0"), val = tensor([3])]; + tensor input_273_strides_0 = const()[name = string("input_273_strides_0"), val = tensor([1])]; + int32 input_273_groups_0 = const()[name = string("input_273_groups_0"), val = int32(1)]; + tensor generator_resblocks_8_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_8_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40116928)))]; + tensor generator_resblocks_8_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_8_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40207104)))]; + tensor input_273_cast_fp16 = conv(bias = generator_resblocks_8_convs1_1_bias_to_fp16, dilations = input_273_dilations_0, groups = input_273_groups_0, pad = input_273_pad_0, pad_type = input_273_pad_type_0, strides = input_273_strides_0, weight = generator_resblocks_8_convs1_1_weight_to_fp16, x = input_271_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor generator_resblocks_8_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_8_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40207296)))]; + tensor generator_resblocks_8_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_8_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40240128)))]; + tensor linear_69_cast_fp16 = linear(bias = generator_resblocks_8_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_8_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_69_cast_fp16")]; + tensor var_2704 = const()[name = string("op_2704"), val = tensor([1, 128, 1])]; + tensor h_279_cast_fp16 = reshape(shape = var_2704, x = linear_69_cast_fp16)[name = string("h_279_cast_fp16")]; + tensor var_2706_split_sizes_0 = const()[name = string("op_2706_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2706_axis_0 = const()[name = string("op_2706_axis_0"), val = int32(1)]; + tensor var_2706_cast_fp16_0, tensor var_2706_cast_fp16_1 = split(axis = var_2706_axis_0, split_sizes = var_2706_split_sizes_0, x = h_279_cast_fp16)[name = string("op_2706_cast_fp16")]; + fp16 var_2708_promoted_to_fp16 = const()[name = string("op_2708_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2709_cast_fp16 = add(x = var_2706_cast_fp16_0, y = var_2708_promoted_to_fp16)[name = string("op_2709_cast_fp16")]; + tensor var_2710_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_273_cast_fp16)[name = string("op_2710_cast_fp16")]; + tensor var_2711_cast_fp16 = mul(x = var_2709_cast_fp16, y = var_2710_cast_fp16)[name = string("op_2711_cast_fp16")]; + tensor xt_207_cast_fp16 = add(x = var_2711_cast_fp16, y = var_2706_cast_fp16_1)[name = string("xt_207_cast_fp16")]; + tensor generator_resblocks_8_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_8_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40240448)))]; + tensor var_2716_cast_fp16 = mul(x = generator_resblocks_8_alpha2_1_to_fp16, y = xt_207_cast_fp16)[name = string("op_2716_cast_fp16")]; + tensor var_2717_cast_fp16 = sin(x = var_2716_cast_fp16)[name = string("op_2717_cast_fp16")]; + fp16 var_24_promoted_72_to_fp16 = const()[name = string("op_24_promoted_72_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2718_cast_fp16 = pow(x = var_2717_cast_fp16, y = var_24_promoted_72_to_fp16)[name = string("op_2718_cast_fp16")]; + tensor var_2713_to_fp16 = const()[name = string("op_2713_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40240640)))]; + tensor var_2719_cast_fp16 = mul(x = var_2713_to_fp16, y = var_2718_cast_fp16)[name = string("op_2719_cast_fp16")]; + tensor input_275_cast_fp16 = add(x = xt_207_cast_fp16, y = var_2719_cast_fp16)[name = string("input_275_cast_fp16")]; + string xt_209_pad_type_0 = const()[name = string("xt_209_pad_type_0"), val = string("custom")]; + tensor xt_209_pad_0 = const()[name = string("xt_209_pad_0"), val = tensor([5, 5])]; + tensor xt_209_strides_0 = const()[name = string("xt_209_strides_0"), val = tensor([1])]; + tensor xt_209_dilations_0 = const()[name = string("xt_209_dilations_0"), val = tensor([1])]; + int32 xt_209_groups_0 = const()[name = string("xt_209_groups_0"), val = int32(1)]; + tensor generator_resblocks_8_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_8_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40240832)))]; + tensor generator_resblocks_8_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_8_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40331008)))]; + tensor xt_209_cast_fp16 = conv(bias = generator_resblocks_8_convs2_1_bias_to_fp16, dilations = xt_209_dilations_0, groups = xt_209_groups_0, pad = xt_209_pad_0, pad_type = xt_209_pad_type_0, strides = xt_209_strides_0, weight = generator_resblocks_8_convs2_1_weight_to_fp16, x = input_275_cast_fp16)[name = string("xt_209_cast_fp16")]; + tensor input_277_cast_fp16 = add(x = xt_209_cast_fp16, y = input_269_cast_fp16)[name = string("input_277_cast_fp16")]; + tensor generator_resblocks_8_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_8_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40331200)))]; + tensor generator_resblocks_8_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_8_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40364032)))]; + tensor linear_70_cast_fp16 = linear(bias = generator_resblocks_8_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_8_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_2735 = const()[name = string("op_2735"), val = tensor([1, 128, 1])]; + tensor h_283_cast_fp16 = reshape(shape = var_2735, x = linear_70_cast_fp16)[name = string("h_283_cast_fp16")]; + tensor var_2737_split_sizes_0 = const()[name = string("op_2737_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2737_axis_0 = const()[name = string("op_2737_axis_0"), val = int32(1)]; + tensor var_2737_cast_fp16_0, tensor var_2737_cast_fp16_1 = split(axis = var_2737_axis_0, split_sizes = var_2737_split_sizes_0, x = h_283_cast_fp16)[name = string("op_2737_cast_fp16")]; + fp16 var_2739_promoted_to_fp16 = const()[name = string("op_2739_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2740_cast_fp16 = add(x = var_2737_cast_fp16_0, y = var_2739_promoted_to_fp16)[name = string("op_2740_cast_fp16")]; + tensor var_2741_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_277_cast_fp16)[name = string("op_2741_cast_fp16")]; + tensor var_2742_cast_fp16 = mul(x = var_2740_cast_fp16, y = var_2741_cast_fp16)[name = string("op_2742_cast_fp16")]; + tensor xt_211_cast_fp16 = add(x = var_2742_cast_fp16, y = var_2737_cast_fp16_1)[name = string("xt_211_cast_fp16")]; + tensor generator_resblocks_8_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_8_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40364352)))]; + tensor var_2747_cast_fp16 = mul(x = generator_resblocks_8_alpha1_2_to_fp16, y = xt_211_cast_fp16)[name = string("op_2747_cast_fp16")]; + tensor var_2748_cast_fp16 = sin(x = var_2747_cast_fp16)[name = string("op_2748_cast_fp16")]; + fp16 var_24_promoted_73_to_fp16 = const()[name = string("op_24_promoted_73_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2749_cast_fp16 = pow(x = var_2748_cast_fp16, y = var_24_promoted_73_to_fp16)[name = string("op_2749_cast_fp16")]; + tensor var_2744_to_fp16 = const()[name = string("op_2744_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40364544)))]; + tensor var_2750_cast_fp16 = mul(x = var_2744_to_fp16, y = var_2749_cast_fp16)[name = string("op_2750_cast_fp16")]; + tensor input_279_cast_fp16 = add(x = xt_211_cast_fp16, y = var_2750_cast_fp16)[name = string("input_279_cast_fp16")]; + string input_281_pad_type_0 = const()[name = string("input_281_pad_type_0"), val = string("custom")]; + tensor input_281_pad_0 = const()[name = string("input_281_pad_0"), val = tensor([25, 25])]; + tensor input_281_dilations_0 = const()[name = string("input_281_dilations_0"), val = tensor([5])]; + tensor input_281_strides_0 = const()[name = string("input_281_strides_0"), val = tensor([1])]; + int32 input_281_groups_0 = const()[name = string("input_281_groups_0"), val = int32(1)]; + tensor generator_resblocks_8_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_8_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40364736)))]; + tensor generator_resblocks_8_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_8_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40454912)))]; + tensor input_281_cast_fp16 = conv(bias = generator_resblocks_8_convs1_2_bias_to_fp16, dilations = input_281_dilations_0, groups = input_281_groups_0, pad = input_281_pad_0, pad_type = input_281_pad_type_0, strides = input_281_strides_0, weight = generator_resblocks_8_convs1_2_weight_to_fp16, x = input_279_cast_fp16)[name = string("input_281_cast_fp16")]; + tensor generator_resblocks_8_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_8_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40455104)))]; + tensor generator_resblocks_8_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_8_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40487936)))]; + tensor linear_71_cast_fp16 = linear(bias = generator_resblocks_8_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_8_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_2765 = const()[name = string("op_2765"), val = tensor([1, 128, 1])]; + tensor h_287_cast_fp16 = reshape(shape = var_2765, x = linear_71_cast_fp16)[name = string("h_287_cast_fp16")]; + tensor var_2767_split_sizes_0 = const()[name = string("op_2767_split_sizes_0"), val = tensor([64, 64])]; + int32 var_2767_axis_0 = const()[name = string("op_2767_axis_0"), val = int32(1)]; + tensor var_2767_cast_fp16_0, tensor var_2767_cast_fp16_1 = split(axis = var_2767_axis_0, split_sizes = var_2767_split_sizes_0, x = h_287_cast_fp16)[name = string("op_2767_cast_fp16")]; + fp16 var_2769_promoted_to_fp16 = const()[name = string("op_2769_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2770_cast_fp16 = add(x = var_2767_cast_fp16_0, y = var_2769_promoted_to_fp16)[name = string("op_2770_cast_fp16")]; + tensor var_2771_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_281_cast_fp16)[name = string("op_2771_cast_fp16")]; + tensor var_2772_cast_fp16 = mul(x = var_2770_cast_fp16, y = var_2771_cast_fp16)[name = string("op_2772_cast_fp16")]; + tensor xt_213_cast_fp16 = add(x = var_2772_cast_fp16, y = var_2767_cast_fp16_1)[name = string("xt_213_cast_fp16")]; + tensor generator_resblocks_8_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_8_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40488256)))]; + tensor var_2777_cast_fp16 = mul(x = generator_resblocks_8_alpha2_2_to_fp16, y = xt_213_cast_fp16)[name = string("op_2777_cast_fp16")]; + tensor var_2778_cast_fp16 = sin(x = var_2777_cast_fp16)[name = string("op_2778_cast_fp16")]; + fp16 var_24_promoted_74_to_fp16 = const()[name = string("op_24_promoted_74_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2779_cast_fp16 = pow(x = var_2778_cast_fp16, y = var_24_promoted_74_to_fp16)[name = string("op_2779_cast_fp16")]; + tensor var_2774_to_fp16 = const()[name = string("op_2774_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40488448)))]; + tensor var_2780_cast_fp16 = mul(x = var_2774_to_fp16, y = var_2779_cast_fp16)[name = string("op_2780_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = xt_213_cast_fp16, y = var_2780_cast_fp16)[name = string("input_283_cast_fp16")]; + string xt_215_pad_type_0 = const()[name = string("xt_215_pad_type_0"), val = string("custom")]; + tensor xt_215_pad_0 = const()[name = string("xt_215_pad_0"), val = tensor([5, 5])]; + tensor xt_215_strides_0 = const()[name = string("xt_215_strides_0"), val = tensor([1])]; + tensor xt_215_dilations_0 = const()[name = string("xt_215_dilations_0"), val = tensor([1])]; + int32 xt_215_groups_0 = const()[name = string("xt_215_groups_0"), val = int32(1)]; + tensor generator_resblocks_8_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_8_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40488640)))]; + tensor generator_resblocks_8_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_8_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40578816)))]; + tensor xt_215_cast_fp16 = conv(bias = generator_resblocks_8_convs2_2_bias_to_fp16, dilations = xt_215_dilations_0, groups = xt_215_groups_0, pad = xt_215_pad_0, pad_type = xt_215_pad_type_0, strides = xt_215_strides_0, weight = generator_resblocks_8_convs2_2_weight_to_fp16, x = input_283_cast_fp16)[name = string("xt_215_cast_fp16")]; + tensor var_2789_cast_fp16 = add(x = xt_215_cast_fp16, y = input_277_cast_fp16)[name = string("op_2789_cast_fp16")]; + tensor xs_17_cast_fp16 = add(x = xs_15_cast_fp16, y = var_2789_cast_fp16)[name = string("xs_17_cast_fp16")]; + fp16 _inversed_x_11_y_0_to_fp16 = const()[name = string("_inversed_x_11_y_0_to_fp16"), val = fp16(0x1.554p-2)]; + tensor _inversed_x_11_cast_fp16 = mul(x = xs_17_cast_fp16, y = _inversed_x_11_y_0_to_fp16)[name = string("_inversed_x_11_cast_fp16")]; + tensor generator_alphas_3_to_fp16 = const()[name = string("generator_alphas_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40579008)))]; + tensor var_2796_cast_fp16 = mul(x = generator_alphas_3_to_fp16, y = _inversed_x_11_cast_fp16)[name = string("op_2796_cast_fp16")]; + tensor var_2797_cast_fp16 = sin(x = var_2796_cast_fp16)[name = string("op_2797_cast_fp16")]; + fp16 var_24_promoted_75_to_fp16 = const()[name = string("op_24_promoted_75_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2798_cast_fp16 = pow(x = var_2797_cast_fp16, y = var_24_promoted_75_to_fp16)[name = string("op_2798_cast_fp16")]; + tensor var_2793_to_fp16 = const()[name = string("op_2793_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40579200)))]; + tensor var_2799_cast_fp16 = mul(x = var_2793_to_fp16, y = var_2798_cast_fp16)[name = string("op_2799_cast_fp16")]; + tensor input_309_cast_fp16 = add(x = _inversed_x_11_cast_fp16, y = var_2799_cast_fp16)[name = string("input_309_cast_fp16")]; + string input_285_pad_type_0 = const()[name = string("input_285_pad_type_0"), val = string("valid")]; + tensor input_285_strides_0 = const()[name = string("input_285_strides_0"), val = tensor([1])]; + tensor input_285_pad_0 = const()[name = string("input_285_pad_0"), val = tensor([0, 0])]; + tensor input_285_dilations_0 = const()[name = string("input_285_dilations_0"), val = tensor([1])]; + int32 input_285_groups_0 = const()[name = string("input_285_groups_0"), val = int32(1)]; + tensor generator_noise_convs_3_weight_to_fp16 = const()[name = string("generator_noise_convs_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40579392)))]; + tensor generator_noise_convs_3_bias_to_fp16 = const()[name = string("generator_noise_convs_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40579520)))]; + tensor input_285_cast_fp16 = conv(bias = generator_noise_convs_3_bias_to_fp16, dilations = input_285_dilations_0, groups = input_285_groups_0, pad = input_285_pad_0, pad_type = input_285_pad_type_0, strides = input_285_strides_0, weight = generator_noise_convs_3_weight_to_fp16, x = har_source_to_fp16)[name = string("input_285_cast_fp16")]; + tensor generator_noise_res_3_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_noise_res_3_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40579648)))]; + tensor generator_noise_res_3_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_noise_res_3_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40596096)))]; + tensor linear_72_cast_fp16 = linear(bias = generator_noise_res_3_adain1_0_fc_bias_to_fp16, weight = generator_noise_res_3_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_72_cast_fp16")]; + tensor var_2850 = const()[name = string("op_2850"), val = tensor([1, 64, 1])]; + tensor h_291_cast_fp16 = reshape(shape = var_2850, x = linear_72_cast_fp16)[name = string("h_291_cast_fp16")]; + tensor var_2852_split_sizes_0 = const()[name = string("op_2852_split_sizes_0"), val = tensor([32, 32])]; + int32 var_2852_axis_0 = const()[name = string("op_2852_axis_0"), val = int32(1)]; + tensor var_2852_cast_fp16_0, tensor var_2852_cast_fp16_1 = split(axis = var_2852_axis_0, split_sizes = var_2852_split_sizes_0, x = h_291_cast_fp16)[name = string("op_2852_cast_fp16")]; + fp16 var_2854_promoted_to_fp16 = const()[name = string("op_2854_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2855_cast_fp16 = add(x = var_2852_cast_fp16_0, y = var_2854_promoted_to_fp16)[name = string("op_2855_cast_fp16")]; + tensor var_2856_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_285_cast_fp16)[name = string("op_2856_cast_fp16")]; + tensor var_2857_cast_fp16 = mul(x = var_2855_cast_fp16, y = var_2856_cast_fp16)[name = string("op_2857_cast_fp16")]; + tensor xt_217_cast_fp16 = add(x = var_2857_cast_fp16, y = var_2852_cast_fp16_1)[name = string("xt_217_cast_fp16")]; + tensor generator_noise_res_3_alpha1_0_to_fp16 = const()[name = string("generator_noise_res_3_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40596288)))]; + tensor var_2862_cast_fp16 = mul(x = generator_noise_res_3_alpha1_0_to_fp16, y = xt_217_cast_fp16)[name = string("op_2862_cast_fp16")]; + tensor var_2863_cast_fp16 = sin(x = var_2862_cast_fp16)[name = string("op_2863_cast_fp16")]; + fp16 var_24_promoted_76_to_fp16 = const()[name = string("op_24_promoted_76_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2864_cast_fp16 = pow(x = var_2863_cast_fp16, y = var_24_promoted_76_to_fp16)[name = string("op_2864_cast_fp16")]; + tensor var_2859_to_fp16 = const()[name = string("op_2859_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40596416)))]; + tensor var_2865_cast_fp16 = mul(x = var_2859_to_fp16, y = var_2864_cast_fp16)[name = string("op_2865_cast_fp16")]; + tensor input_287_cast_fp16 = add(x = xt_217_cast_fp16, y = var_2865_cast_fp16)[name = string("input_287_cast_fp16")]; + string input_289_pad_type_0 = const()[name = string("input_289_pad_type_0"), val = string("custom")]; + tensor input_289_pad_0 = const()[name = string("input_289_pad_0"), val = tensor([5, 5])]; + tensor input_289_strides_0 = const()[name = string("input_289_strides_0"), val = tensor([1])]; + tensor input_289_dilations_0 = const()[name = string("input_289_dilations_0"), val = tensor([1])]; + int32 input_289_groups_0 = const()[name = string("input_289_groups_0"), val = int32(1)]; + tensor generator_noise_res_3_convs1_0_weight_to_fp16 = const()[name = string("generator_noise_res_3_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40596544)))]; + tensor generator_noise_res_3_convs1_0_bias_to_fp16 = const()[name = string("generator_noise_res_3_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40619136)))]; + tensor input_289_cast_fp16 = conv(bias = generator_noise_res_3_convs1_0_bias_to_fp16, dilations = input_289_dilations_0, groups = input_289_groups_0, pad = input_289_pad_0, pad_type = input_289_pad_type_0, strides = input_289_strides_0, weight = generator_noise_res_3_convs1_0_weight_to_fp16, x = input_287_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor generator_noise_res_3_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_noise_res_3_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40619264)))]; + tensor generator_noise_res_3_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_noise_res_3_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40635712)))]; + tensor linear_73_cast_fp16 = linear(bias = generator_noise_res_3_adain2_0_fc_bias_to_fp16, weight = generator_noise_res_3_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_2880 = const()[name = string("op_2880"), val = tensor([1, 64, 1])]; + tensor h_295_cast_fp16 = reshape(shape = var_2880, x = linear_73_cast_fp16)[name = string("h_295_cast_fp16")]; + tensor var_2882_split_sizes_0 = const()[name = string("op_2882_split_sizes_0"), val = tensor([32, 32])]; + int32 var_2882_axis_0 = const()[name = string("op_2882_axis_0"), val = int32(1)]; + tensor var_2882_cast_fp16_0, tensor var_2882_cast_fp16_1 = split(axis = var_2882_axis_0, split_sizes = var_2882_split_sizes_0, x = h_295_cast_fp16)[name = string("op_2882_cast_fp16")]; + fp16 var_2884_promoted_to_fp16 = const()[name = string("op_2884_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2885_cast_fp16 = add(x = var_2882_cast_fp16_0, y = var_2884_promoted_to_fp16)[name = string("op_2885_cast_fp16")]; + tensor var_2886_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_289_cast_fp16)[name = string("op_2886_cast_fp16")]; + tensor var_2887_cast_fp16 = mul(x = var_2885_cast_fp16, y = var_2886_cast_fp16)[name = string("op_2887_cast_fp16")]; + tensor xt_219_cast_fp16 = add(x = var_2887_cast_fp16, y = var_2882_cast_fp16_1)[name = string("xt_219_cast_fp16")]; + tensor generator_noise_res_3_alpha2_0_to_fp16 = const()[name = string("generator_noise_res_3_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40635904)))]; + tensor var_2892_cast_fp16 = mul(x = generator_noise_res_3_alpha2_0_to_fp16, y = xt_219_cast_fp16)[name = string("op_2892_cast_fp16")]; + tensor var_2893_cast_fp16 = sin(x = var_2892_cast_fp16)[name = string("op_2893_cast_fp16")]; + fp16 var_24_promoted_77_to_fp16 = const()[name = string("op_24_promoted_77_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2894_cast_fp16 = pow(x = var_2893_cast_fp16, y = var_24_promoted_77_to_fp16)[name = string("op_2894_cast_fp16")]; + tensor var_2889_to_fp16 = const()[name = string("op_2889_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40636032)))]; + tensor var_2895_cast_fp16 = mul(x = var_2889_to_fp16, y = var_2894_cast_fp16)[name = string("op_2895_cast_fp16")]; + tensor input_291_cast_fp16 = add(x = xt_219_cast_fp16, y = var_2895_cast_fp16)[name = string("input_291_cast_fp16")]; + string xt_221_pad_type_0 = const()[name = string("xt_221_pad_type_0"), val = string("custom")]; + tensor xt_221_pad_0 = const()[name = string("xt_221_pad_0"), val = tensor([5, 5])]; + tensor xt_221_strides_0 = const()[name = string("xt_221_strides_0"), val = tensor([1])]; + tensor xt_221_dilations_0 = const()[name = string("xt_221_dilations_0"), val = tensor([1])]; + int32 xt_221_groups_0 = const()[name = string("xt_221_groups_0"), val = int32(1)]; + tensor generator_noise_res_3_convs2_0_weight_to_fp16 = const()[name = string("generator_noise_res_3_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40636160)))]; + tensor generator_noise_res_3_convs2_0_bias_to_fp16 = const()[name = string("generator_noise_res_3_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40658752)))]; + tensor xt_221_cast_fp16 = conv(bias = generator_noise_res_3_convs2_0_bias_to_fp16, dilations = xt_221_dilations_0, groups = xt_221_groups_0, pad = xt_221_pad_0, pad_type = xt_221_pad_type_0, strides = xt_221_strides_0, weight = generator_noise_res_3_convs2_0_weight_to_fp16, x = input_291_cast_fp16)[name = string("xt_221_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = xt_221_cast_fp16, y = input_285_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor generator_noise_res_3_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_noise_res_3_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40658880)))]; + tensor generator_noise_res_3_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_noise_res_3_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40675328)))]; + tensor linear_74_cast_fp16 = linear(bias = generator_noise_res_3_adain1_1_fc_bias_to_fp16, weight = generator_noise_res_3_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_74_cast_fp16")]; + tensor var_2911 = const()[name = string("op_2911"), val = tensor([1, 64, 1])]; + tensor h_299_cast_fp16 = reshape(shape = var_2911, x = linear_74_cast_fp16)[name = string("h_299_cast_fp16")]; + tensor var_2913_split_sizes_0 = const()[name = string("op_2913_split_sizes_0"), val = tensor([32, 32])]; + int32 var_2913_axis_0 = const()[name = string("op_2913_axis_0"), val = int32(1)]; + tensor var_2913_cast_fp16_0, tensor var_2913_cast_fp16_1 = split(axis = var_2913_axis_0, split_sizes = var_2913_split_sizes_0, x = h_299_cast_fp16)[name = string("op_2913_cast_fp16")]; + fp16 var_2915_promoted_to_fp16 = const()[name = string("op_2915_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2916_cast_fp16 = add(x = var_2913_cast_fp16_0, y = var_2915_promoted_to_fp16)[name = string("op_2916_cast_fp16")]; + tensor var_2917_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_293_cast_fp16)[name = string("op_2917_cast_fp16")]; + tensor var_2918_cast_fp16 = mul(x = var_2916_cast_fp16, y = var_2917_cast_fp16)[name = string("op_2918_cast_fp16")]; + tensor xt_223_cast_fp16 = add(x = var_2918_cast_fp16, y = var_2913_cast_fp16_1)[name = string("xt_223_cast_fp16")]; + tensor generator_noise_res_3_alpha1_1_to_fp16 = const()[name = string("generator_noise_res_3_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40675520)))]; + tensor var_2923_cast_fp16 = mul(x = generator_noise_res_3_alpha1_1_to_fp16, y = xt_223_cast_fp16)[name = string("op_2923_cast_fp16")]; + tensor var_2924_cast_fp16 = sin(x = var_2923_cast_fp16)[name = string("op_2924_cast_fp16")]; + fp16 var_24_promoted_78_to_fp16 = const()[name = string("op_24_promoted_78_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2925_cast_fp16 = pow(x = var_2924_cast_fp16, y = var_24_promoted_78_to_fp16)[name = string("op_2925_cast_fp16")]; + tensor var_2920_to_fp16 = const()[name = string("op_2920_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40675648)))]; + tensor var_2926_cast_fp16 = mul(x = var_2920_to_fp16, y = var_2925_cast_fp16)[name = string("op_2926_cast_fp16")]; + tensor input_295_cast_fp16 = add(x = xt_223_cast_fp16, y = var_2926_cast_fp16)[name = string("input_295_cast_fp16")]; + string input_297_pad_type_0 = const()[name = string("input_297_pad_type_0"), val = string("custom")]; + tensor input_297_pad_0 = const()[name = string("input_297_pad_0"), val = tensor([15, 15])]; + tensor input_297_dilations_0 = const()[name = string("input_297_dilations_0"), val = tensor([3])]; + tensor input_297_strides_0 = const()[name = string("input_297_strides_0"), val = tensor([1])]; + int32 input_297_groups_0 = const()[name = string("input_297_groups_0"), val = int32(1)]; + tensor generator_noise_res_3_convs1_1_weight_to_fp16 = const()[name = string("generator_noise_res_3_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40675776)))]; + tensor generator_noise_res_3_convs1_1_bias_to_fp16 = const()[name = string("generator_noise_res_3_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40698368)))]; + tensor input_297_cast_fp16 = conv(bias = generator_noise_res_3_convs1_1_bias_to_fp16, dilations = input_297_dilations_0, groups = input_297_groups_0, pad = input_297_pad_0, pad_type = input_297_pad_type_0, strides = input_297_strides_0, weight = generator_noise_res_3_convs1_1_weight_to_fp16, x = input_295_cast_fp16)[name = string("input_297_cast_fp16")]; + tensor generator_noise_res_3_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_noise_res_3_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40698496)))]; + tensor generator_noise_res_3_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_noise_res_3_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40714944)))]; + tensor linear_75_cast_fp16 = linear(bias = generator_noise_res_3_adain2_1_fc_bias_to_fp16, weight = generator_noise_res_3_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_75_cast_fp16")]; + tensor var_2941 = const()[name = string("op_2941"), val = tensor([1, 64, 1])]; + tensor h_303_cast_fp16 = reshape(shape = var_2941, x = linear_75_cast_fp16)[name = string("h_303_cast_fp16")]; + tensor var_2943_split_sizes_0 = const()[name = string("op_2943_split_sizes_0"), val = tensor([32, 32])]; + int32 var_2943_axis_0 = const()[name = string("op_2943_axis_0"), val = int32(1)]; + tensor var_2943_cast_fp16_0, tensor var_2943_cast_fp16_1 = split(axis = var_2943_axis_0, split_sizes = var_2943_split_sizes_0, x = h_303_cast_fp16)[name = string("op_2943_cast_fp16")]; + fp16 var_2945_promoted_to_fp16 = const()[name = string("op_2945_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2946_cast_fp16 = add(x = var_2943_cast_fp16_0, y = var_2945_promoted_to_fp16)[name = string("op_2946_cast_fp16")]; + tensor var_2947_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_297_cast_fp16)[name = string("op_2947_cast_fp16")]; + tensor var_2948_cast_fp16 = mul(x = var_2946_cast_fp16, y = var_2947_cast_fp16)[name = string("op_2948_cast_fp16")]; + tensor xt_225_cast_fp16 = add(x = var_2948_cast_fp16, y = var_2943_cast_fp16_1)[name = string("xt_225_cast_fp16")]; + tensor generator_noise_res_3_alpha2_1_to_fp16 = const()[name = string("generator_noise_res_3_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40715136)))]; + tensor var_2953_cast_fp16 = mul(x = generator_noise_res_3_alpha2_1_to_fp16, y = xt_225_cast_fp16)[name = string("op_2953_cast_fp16")]; + tensor var_2954_cast_fp16 = sin(x = var_2953_cast_fp16)[name = string("op_2954_cast_fp16")]; + fp16 var_24_promoted_79_to_fp16 = const()[name = string("op_24_promoted_79_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2955_cast_fp16 = pow(x = var_2954_cast_fp16, y = var_24_promoted_79_to_fp16)[name = string("op_2955_cast_fp16")]; + tensor var_2950_to_fp16 = const()[name = string("op_2950_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40715264)))]; + tensor var_2956_cast_fp16 = mul(x = var_2950_to_fp16, y = var_2955_cast_fp16)[name = string("op_2956_cast_fp16")]; + tensor input_299_cast_fp16 = add(x = xt_225_cast_fp16, y = var_2956_cast_fp16)[name = string("input_299_cast_fp16")]; + string xt_227_pad_type_0 = const()[name = string("xt_227_pad_type_0"), val = string("custom")]; + tensor xt_227_pad_0 = const()[name = string("xt_227_pad_0"), val = tensor([5, 5])]; + tensor xt_227_strides_0 = const()[name = string("xt_227_strides_0"), val = tensor([1])]; + tensor xt_227_dilations_0 = const()[name = string("xt_227_dilations_0"), val = tensor([1])]; + int32 xt_227_groups_0 = const()[name = string("xt_227_groups_0"), val = int32(1)]; + tensor generator_noise_res_3_convs2_1_weight_to_fp16 = const()[name = string("generator_noise_res_3_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40715392)))]; + tensor generator_noise_res_3_convs2_1_bias_to_fp16 = const()[name = string("generator_noise_res_3_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40737984)))]; + tensor xt_227_cast_fp16 = conv(bias = generator_noise_res_3_convs2_1_bias_to_fp16, dilations = xt_227_dilations_0, groups = xt_227_groups_0, pad = xt_227_pad_0, pad_type = xt_227_pad_type_0, strides = xt_227_strides_0, weight = generator_noise_res_3_convs2_1_weight_to_fp16, x = input_299_cast_fp16)[name = string("xt_227_cast_fp16")]; + tensor input_301_cast_fp16 = add(x = xt_227_cast_fp16, y = input_293_cast_fp16)[name = string("input_301_cast_fp16")]; + tensor generator_noise_res_3_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_noise_res_3_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40738112)))]; + tensor generator_noise_res_3_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_noise_res_3_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40754560)))]; + tensor linear_76_cast_fp16 = linear(bias = generator_noise_res_3_adain1_2_fc_bias_to_fp16, weight = generator_noise_res_3_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_76_cast_fp16")]; + tensor var_2972 = const()[name = string("op_2972"), val = tensor([1, 64, 1])]; + tensor h_307_cast_fp16 = reshape(shape = var_2972, x = linear_76_cast_fp16)[name = string("h_307_cast_fp16")]; + tensor var_2974_split_sizes_0 = const()[name = string("op_2974_split_sizes_0"), val = tensor([32, 32])]; + int32 var_2974_axis_0 = const()[name = string("op_2974_axis_0"), val = int32(1)]; + tensor var_2974_cast_fp16_0, tensor var_2974_cast_fp16_1 = split(axis = var_2974_axis_0, split_sizes = var_2974_split_sizes_0, x = h_307_cast_fp16)[name = string("op_2974_cast_fp16")]; + fp16 var_2976_promoted_to_fp16 = const()[name = string("op_2976_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_2977_cast_fp16 = add(x = var_2974_cast_fp16_0, y = var_2976_promoted_to_fp16)[name = string("op_2977_cast_fp16")]; + tensor var_2978_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_301_cast_fp16)[name = string("op_2978_cast_fp16")]; + tensor var_2979_cast_fp16 = mul(x = var_2977_cast_fp16, y = var_2978_cast_fp16)[name = string("op_2979_cast_fp16")]; + tensor xt_229_cast_fp16 = add(x = var_2979_cast_fp16, y = var_2974_cast_fp16_1)[name = string("xt_229_cast_fp16")]; + tensor generator_noise_res_3_alpha1_2_to_fp16 = const()[name = string("generator_noise_res_3_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40754752)))]; + tensor var_2984_cast_fp16 = mul(x = generator_noise_res_3_alpha1_2_to_fp16, y = xt_229_cast_fp16)[name = string("op_2984_cast_fp16")]; + tensor var_2985_cast_fp16 = sin(x = var_2984_cast_fp16)[name = string("op_2985_cast_fp16")]; + fp16 var_24_promoted_80_to_fp16 = const()[name = string("op_24_promoted_80_to_fp16"), val = fp16(0x1p+1)]; + tensor var_2986_cast_fp16 = pow(x = var_2985_cast_fp16, y = var_24_promoted_80_to_fp16)[name = string("op_2986_cast_fp16")]; + tensor var_2981_to_fp16 = const()[name = string("op_2981_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40754880)))]; + tensor var_2987_cast_fp16 = mul(x = var_2981_to_fp16, y = var_2986_cast_fp16)[name = string("op_2987_cast_fp16")]; + tensor input_303_cast_fp16 = add(x = xt_229_cast_fp16, y = var_2987_cast_fp16)[name = string("input_303_cast_fp16")]; + string input_305_pad_type_0 = const()[name = string("input_305_pad_type_0"), val = string("custom")]; + tensor input_305_pad_0 = const()[name = string("input_305_pad_0"), val = tensor([25, 25])]; + tensor input_305_dilations_0 = const()[name = string("input_305_dilations_0"), val = tensor([5])]; + tensor input_305_strides_0 = const()[name = string("input_305_strides_0"), val = tensor([1])]; + int32 input_305_groups_0 = const()[name = string("input_305_groups_0"), val = int32(1)]; + tensor generator_noise_res_3_convs1_2_weight_to_fp16 = const()[name = string("generator_noise_res_3_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40755008)))]; + tensor generator_noise_res_3_convs1_2_bias_to_fp16 = const()[name = string("generator_noise_res_3_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40777600)))]; + tensor input_305_cast_fp16 = conv(bias = generator_noise_res_3_convs1_2_bias_to_fp16, dilations = input_305_dilations_0, groups = input_305_groups_0, pad = input_305_pad_0, pad_type = input_305_pad_type_0, strides = input_305_strides_0, weight = generator_noise_res_3_convs1_2_weight_to_fp16, x = input_303_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor generator_noise_res_3_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_noise_res_3_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40777728)))]; + tensor generator_noise_res_3_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_noise_res_3_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40794176)))]; + tensor linear_77_cast_fp16 = linear(bias = generator_noise_res_3_adain2_2_fc_bias_to_fp16, weight = generator_noise_res_3_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_77_cast_fp16")]; + tensor var_3002 = const()[name = string("op_3002"), val = tensor([1, 64, 1])]; + tensor h_311_cast_fp16 = reshape(shape = var_3002, x = linear_77_cast_fp16)[name = string("h_311_cast_fp16")]; + tensor var_3004_split_sizes_0 = const()[name = string("op_3004_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3004_axis_0 = const()[name = string("op_3004_axis_0"), val = int32(1)]; + tensor var_3004_cast_fp16_0, tensor var_3004_cast_fp16_1 = split(axis = var_3004_axis_0, split_sizes = var_3004_split_sizes_0, x = h_311_cast_fp16)[name = string("op_3004_cast_fp16")]; + fp16 var_3006_promoted_to_fp16 = const()[name = string("op_3006_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3007_cast_fp16 = add(x = var_3004_cast_fp16_0, y = var_3006_promoted_to_fp16)[name = string("op_3007_cast_fp16")]; + tensor var_3008_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_305_cast_fp16)[name = string("op_3008_cast_fp16")]; + tensor var_3009_cast_fp16 = mul(x = var_3007_cast_fp16, y = var_3008_cast_fp16)[name = string("op_3009_cast_fp16")]; + tensor xt_231_cast_fp16 = add(x = var_3009_cast_fp16, y = var_3004_cast_fp16_1)[name = string("xt_231_cast_fp16")]; + tensor generator_noise_res_3_alpha2_2_to_fp16 = const()[name = string("generator_noise_res_3_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40794368)))]; + tensor var_3014_cast_fp16 = mul(x = generator_noise_res_3_alpha2_2_to_fp16, y = xt_231_cast_fp16)[name = string("op_3014_cast_fp16")]; + tensor var_3015_cast_fp16 = sin(x = var_3014_cast_fp16)[name = string("op_3015_cast_fp16")]; + fp16 var_24_promoted_81_to_fp16 = const()[name = string("op_24_promoted_81_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3016_cast_fp16 = pow(x = var_3015_cast_fp16, y = var_24_promoted_81_to_fp16)[name = string("op_3016_cast_fp16")]; + tensor var_3011_to_fp16 = const()[name = string("op_3011_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40794496)))]; + tensor var_3017_cast_fp16 = mul(x = var_3011_to_fp16, y = var_3016_cast_fp16)[name = string("op_3017_cast_fp16")]; + tensor input_307_cast_fp16 = add(x = xt_231_cast_fp16, y = var_3017_cast_fp16)[name = string("input_307_cast_fp16")]; + string xt_233_pad_type_0 = const()[name = string("xt_233_pad_type_0"), val = string("custom")]; + tensor xt_233_pad_0 = const()[name = string("xt_233_pad_0"), val = tensor([5, 5])]; + tensor xt_233_strides_0 = const()[name = string("xt_233_strides_0"), val = tensor([1])]; + tensor xt_233_dilations_0 = const()[name = string("xt_233_dilations_0"), val = tensor([1])]; + int32 xt_233_groups_0 = const()[name = string("xt_233_groups_0"), val = int32(1)]; + tensor generator_noise_res_3_convs2_2_weight_to_fp16 = const()[name = string("generator_noise_res_3_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40794624)))]; + tensor generator_noise_res_3_convs2_2_bias_to_fp16 = const()[name = string("generator_noise_res_3_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40817216)))]; + tensor xt_233_cast_fp16 = conv(bias = generator_noise_res_3_convs2_2_bias_to_fp16, dilations = xt_233_dilations_0, groups = xt_233_groups_0, pad = xt_233_pad_0, pad_type = xt_233_pad_type_0, strides = xt_233_strides_0, weight = generator_noise_res_3_convs2_2_weight_to_fp16, x = input_307_cast_fp16)[name = string("xt_233_cast_fp16")]; + tensor x_source_cast_fp16 = add(x = xt_233_cast_fp16, y = input_301_cast_fp16)[name = string("x_source_cast_fp16")]; + string x_13_pad_type_0 = const()[name = string("x_13_pad_type_0"), val = string("custom")]; + tensor x_13_pad_0 = const()[name = string("x_13_pad_0"), val = tensor([1, 1])]; + tensor x_13_strides_0 = const()[name = string("x_13_strides_0"), val = tensor([2])]; + tensor x_13_dilations_0 = const()[name = string("x_13_dilations_0"), val = tensor([1])]; + int32 x_13_groups_0 = const()[name = string("x_13_groups_0"), val = int32(1)]; + tensor generator_ups_3_weight_to_fp16 = const()[name = string("generator_ups_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40817344)))]; + tensor generator_ups_3_bias_to_fp16 = const()[name = string("generator_ups_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40833792)))]; + tensor x_13_cast_fp16 = conv_transpose(bias = generator_ups_3_bias_to_fp16, dilations = x_13_dilations_0, groups = x_13_groups_0, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = x_13_strides_0, weight = generator_ups_3_weight_to_fp16, x = input_309_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_311_cast_fp16 = add(x = x_13_cast_fp16, y = x_source_cast_fp16)[name = string("input_311_cast_fp16")]; + tensor generator_resblocks_9_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_9_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40833920)))]; + tensor generator_resblocks_9_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_9_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40850368)))]; + tensor linear_78_cast_fp16 = linear(bias = generator_resblocks_9_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_9_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_78_cast_fp16")]; + tensor var_3077 = const()[name = string("op_3077"), val = tensor([1, 64, 1])]; + tensor h_315_cast_fp16 = reshape(shape = var_3077, x = linear_78_cast_fp16)[name = string("h_315_cast_fp16")]; + tensor var_3079_split_sizes_0 = const()[name = string("op_3079_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3079_axis_0 = const()[name = string("op_3079_axis_0"), val = int32(1)]; + tensor var_3079_cast_fp16_0, tensor var_3079_cast_fp16_1 = split(axis = var_3079_axis_0, split_sizes = var_3079_split_sizes_0, x = h_315_cast_fp16)[name = string("op_3079_cast_fp16")]; + fp16 var_3081_promoted_to_fp16 = const()[name = string("op_3081_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3082_cast_fp16 = add(x = var_3079_cast_fp16_0, y = var_3081_promoted_to_fp16)[name = string("op_3082_cast_fp16")]; + tensor var_3083_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_311_cast_fp16)[name = string("op_3083_cast_fp16")]; + tensor var_3084_cast_fp16 = mul(x = var_3082_cast_fp16, y = var_3083_cast_fp16)[name = string("op_3084_cast_fp16")]; + tensor xt_235_cast_fp16 = add(x = var_3084_cast_fp16, y = var_3079_cast_fp16_1)[name = string("xt_235_cast_fp16")]; + tensor generator_resblocks_9_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_9_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40850560)))]; + tensor var_3089_cast_fp16 = mul(x = generator_resblocks_9_alpha1_0_to_fp16, y = xt_235_cast_fp16)[name = string("op_3089_cast_fp16")]; + tensor var_3090_cast_fp16 = sin(x = var_3089_cast_fp16)[name = string("op_3090_cast_fp16")]; + fp16 var_24_promoted_82_to_fp16 = const()[name = string("op_24_promoted_82_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3091_cast_fp16 = pow(x = var_3090_cast_fp16, y = var_24_promoted_82_to_fp16)[name = string("op_3091_cast_fp16")]; + tensor var_3086_to_fp16 = const()[name = string("op_3086_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40850688)))]; + tensor var_3092_cast_fp16 = mul(x = var_3086_to_fp16, y = var_3091_cast_fp16)[name = string("op_3092_cast_fp16")]; + tensor input_313_cast_fp16 = add(x = xt_235_cast_fp16, y = var_3092_cast_fp16)[name = string("input_313_cast_fp16")]; + string input_315_pad_type_0 = const()[name = string("input_315_pad_type_0"), val = string("custom")]; + tensor input_315_pad_0 = const()[name = string("input_315_pad_0"), val = tensor([1, 1])]; + tensor input_315_strides_0 = const()[name = string("input_315_strides_0"), val = tensor([1])]; + tensor input_315_dilations_0 = const()[name = string("input_315_dilations_0"), val = tensor([1])]; + int32 input_315_groups_0 = const()[name = string("input_315_groups_0"), val = int32(1)]; + tensor generator_resblocks_9_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_9_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40850816)))]; + tensor generator_resblocks_9_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_9_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40857024)))]; + tensor input_315_cast_fp16 = conv(bias = generator_resblocks_9_convs1_0_bias_to_fp16, dilations = input_315_dilations_0, groups = input_315_groups_0, pad = input_315_pad_0, pad_type = input_315_pad_type_0, strides = input_315_strides_0, weight = generator_resblocks_9_convs1_0_weight_to_fp16, x = input_313_cast_fp16)[name = string("input_315_cast_fp16")]; + tensor generator_resblocks_9_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_9_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40857152)))]; + tensor generator_resblocks_9_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_9_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40873600)))]; + tensor linear_79_cast_fp16 = linear(bias = generator_resblocks_9_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_9_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_79_cast_fp16")]; + tensor var_3107 = const()[name = string("op_3107"), val = tensor([1, 64, 1])]; + tensor h_319_cast_fp16 = reshape(shape = var_3107, x = linear_79_cast_fp16)[name = string("h_319_cast_fp16")]; + tensor var_3109_split_sizes_0 = const()[name = string("op_3109_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3109_axis_0 = const()[name = string("op_3109_axis_0"), val = int32(1)]; + tensor var_3109_cast_fp16_0, tensor var_3109_cast_fp16_1 = split(axis = var_3109_axis_0, split_sizes = var_3109_split_sizes_0, x = h_319_cast_fp16)[name = string("op_3109_cast_fp16")]; + fp16 var_3111_promoted_to_fp16 = const()[name = string("op_3111_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3112_cast_fp16 = add(x = var_3109_cast_fp16_0, y = var_3111_promoted_to_fp16)[name = string("op_3112_cast_fp16")]; + tensor var_3113_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_315_cast_fp16)[name = string("op_3113_cast_fp16")]; + tensor var_3114_cast_fp16 = mul(x = var_3112_cast_fp16, y = var_3113_cast_fp16)[name = string("op_3114_cast_fp16")]; + tensor xt_237_cast_fp16 = add(x = var_3114_cast_fp16, y = var_3109_cast_fp16_1)[name = string("xt_237_cast_fp16")]; + tensor generator_resblocks_9_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_9_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40873792)))]; + tensor var_3119_cast_fp16 = mul(x = generator_resblocks_9_alpha2_0_to_fp16, y = xt_237_cast_fp16)[name = string("op_3119_cast_fp16")]; + tensor var_3120_cast_fp16 = sin(x = var_3119_cast_fp16)[name = string("op_3120_cast_fp16")]; + fp16 var_24_promoted_83_to_fp16 = const()[name = string("op_24_promoted_83_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3121_cast_fp16 = pow(x = var_3120_cast_fp16, y = var_24_promoted_83_to_fp16)[name = string("op_3121_cast_fp16")]; + tensor var_3116_to_fp16 = const()[name = string("op_3116_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40873920)))]; + tensor var_3122_cast_fp16 = mul(x = var_3116_to_fp16, y = var_3121_cast_fp16)[name = string("op_3122_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = xt_237_cast_fp16, y = var_3122_cast_fp16)[name = string("input_317_cast_fp16")]; + string xt_239_pad_type_0 = const()[name = string("xt_239_pad_type_0"), val = string("custom")]; + tensor xt_239_pad_0 = const()[name = string("xt_239_pad_0"), val = tensor([1, 1])]; + tensor xt_239_strides_0 = const()[name = string("xt_239_strides_0"), val = tensor([1])]; + tensor xt_239_dilations_0 = const()[name = string("xt_239_dilations_0"), val = tensor([1])]; + int32 xt_239_groups_0 = const()[name = string("xt_239_groups_0"), val = int32(1)]; + tensor generator_resblocks_9_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_9_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40874048)))]; + tensor generator_resblocks_9_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_9_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40880256)))]; + tensor xt_239_cast_fp16 = conv(bias = generator_resblocks_9_convs2_0_bias_to_fp16, dilations = xt_239_dilations_0, groups = xt_239_groups_0, pad = xt_239_pad_0, pad_type = xt_239_pad_type_0, strides = xt_239_strides_0, weight = generator_resblocks_9_convs2_0_weight_to_fp16, x = input_317_cast_fp16)[name = string("xt_239_cast_fp16")]; + tensor input_319_cast_fp16 = add(x = xt_239_cast_fp16, y = input_311_cast_fp16)[name = string("input_319_cast_fp16")]; + tensor generator_resblocks_9_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_9_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40880384)))]; + tensor generator_resblocks_9_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_9_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40896832)))]; + tensor linear_80_cast_fp16 = linear(bias = generator_resblocks_9_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_9_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_80_cast_fp16")]; + tensor var_3138 = const()[name = string("op_3138"), val = tensor([1, 64, 1])]; + tensor h_323_cast_fp16 = reshape(shape = var_3138, x = linear_80_cast_fp16)[name = string("h_323_cast_fp16")]; + tensor var_3140_split_sizes_0 = const()[name = string("op_3140_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3140_axis_0 = const()[name = string("op_3140_axis_0"), val = int32(1)]; + tensor var_3140_cast_fp16_0, tensor var_3140_cast_fp16_1 = split(axis = var_3140_axis_0, split_sizes = var_3140_split_sizes_0, x = h_323_cast_fp16)[name = string("op_3140_cast_fp16")]; + fp16 var_3142_promoted_to_fp16 = const()[name = string("op_3142_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3143_cast_fp16 = add(x = var_3140_cast_fp16_0, y = var_3142_promoted_to_fp16)[name = string("op_3143_cast_fp16")]; + tensor var_3144_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_319_cast_fp16)[name = string("op_3144_cast_fp16")]; + tensor var_3145_cast_fp16 = mul(x = var_3143_cast_fp16, y = var_3144_cast_fp16)[name = string("op_3145_cast_fp16")]; + tensor xt_241_cast_fp16 = add(x = var_3145_cast_fp16, y = var_3140_cast_fp16_1)[name = string("xt_241_cast_fp16")]; + tensor generator_resblocks_9_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_9_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40897024)))]; + tensor var_3150_cast_fp16 = mul(x = generator_resblocks_9_alpha1_1_to_fp16, y = xt_241_cast_fp16)[name = string("op_3150_cast_fp16")]; + tensor var_3151_cast_fp16 = sin(x = var_3150_cast_fp16)[name = string("op_3151_cast_fp16")]; + fp16 var_24_promoted_84_to_fp16 = const()[name = string("op_24_promoted_84_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3152_cast_fp16 = pow(x = var_3151_cast_fp16, y = var_24_promoted_84_to_fp16)[name = string("op_3152_cast_fp16")]; + tensor var_3147_to_fp16 = const()[name = string("op_3147_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40897152)))]; + tensor var_3153_cast_fp16 = mul(x = var_3147_to_fp16, y = var_3152_cast_fp16)[name = string("op_3153_cast_fp16")]; + tensor input_321_cast_fp16 = add(x = xt_241_cast_fp16, y = var_3153_cast_fp16)[name = string("input_321_cast_fp16")]; + string input_323_pad_type_0 = const()[name = string("input_323_pad_type_0"), val = string("custom")]; + tensor input_323_pad_0 = const()[name = string("input_323_pad_0"), val = tensor([3, 3])]; + tensor input_323_dilations_0 = const()[name = string("input_323_dilations_0"), val = tensor([3])]; + tensor input_323_strides_0 = const()[name = string("input_323_strides_0"), val = tensor([1])]; + int32 input_323_groups_0 = const()[name = string("input_323_groups_0"), val = int32(1)]; + tensor generator_resblocks_9_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_9_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40897280)))]; + tensor generator_resblocks_9_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_9_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40903488)))]; + tensor input_323_cast_fp16 = conv(bias = generator_resblocks_9_convs1_1_bias_to_fp16, dilations = input_323_dilations_0, groups = input_323_groups_0, pad = input_323_pad_0, pad_type = input_323_pad_type_0, strides = input_323_strides_0, weight = generator_resblocks_9_convs1_1_weight_to_fp16, x = input_321_cast_fp16)[name = string("input_323_cast_fp16")]; + tensor generator_resblocks_9_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_9_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40903616)))]; + tensor generator_resblocks_9_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_9_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40920064)))]; + tensor linear_81_cast_fp16 = linear(bias = generator_resblocks_9_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_9_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_81_cast_fp16")]; + tensor var_3168 = const()[name = string("op_3168"), val = tensor([1, 64, 1])]; + tensor h_327_cast_fp16 = reshape(shape = var_3168, x = linear_81_cast_fp16)[name = string("h_327_cast_fp16")]; + tensor var_3170_split_sizes_0 = const()[name = string("op_3170_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3170_axis_0 = const()[name = string("op_3170_axis_0"), val = int32(1)]; + tensor var_3170_cast_fp16_0, tensor var_3170_cast_fp16_1 = split(axis = var_3170_axis_0, split_sizes = var_3170_split_sizes_0, x = h_327_cast_fp16)[name = string("op_3170_cast_fp16")]; + fp16 var_3172_promoted_to_fp16 = const()[name = string("op_3172_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3173_cast_fp16 = add(x = var_3170_cast_fp16_0, y = var_3172_promoted_to_fp16)[name = string("op_3173_cast_fp16")]; + tensor var_3174_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_323_cast_fp16)[name = string("op_3174_cast_fp16")]; + tensor var_3175_cast_fp16 = mul(x = var_3173_cast_fp16, y = var_3174_cast_fp16)[name = string("op_3175_cast_fp16")]; + tensor xt_243_cast_fp16 = add(x = var_3175_cast_fp16, y = var_3170_cast_fp16_1)[name = string("xt_243_cast_fp16")]; + tensor generator_resblocks_9_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_9_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40920256)))]; + tensor var_3180_cast_fp16 = mul(x = generator_resblocks_9_alpha2_1_to_fp16, y = xt_243_cast_fp16)[name = string("op_3180_cast_fp16")]; + tensor var_3181_cast_fp16 = sin(x = var_3180_cast_fp16)[name = string("op_3181_cast_fp16")]; + fp16 var_24_promoted_85_to_fp16 = const()[name = string("op_24_promoted_85_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3182_cast_fp16 = pow(x = var_3181_cast_fp16, y = var_24_promoted_85_to_fp16)[name = string("op_3182_cast_fp16")]; + tensor var_3177_to_fp16 = const()[name = string("op_3177_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40920384)))]; + tensor var_3183_cast_fp16 = mul(x = var_3177_to_fp16, y = var_3182_cast_fp16)[name = string("op_3183_cast_fp16")]; + tensor input_325_cast_fp16 = add(x = xt_243_cast_fp16, y = var_3183_cast_fp16)[name = string("input_325_cast_fp16")]; + string xt_245_pad_type_0 = const()[name = string("xt_245_pad_type_0"), val = string("custom")]; + tensor xt_245_pad_0 = const()[name = string("xt_245_pad_0"), val = tensor([1, 1])]; + tensor xt_245_strides_0 = const()[name = string("xt_245_strides_0"), val = tensor([1])]; + tensor xt_245_dilations_0 = const()[name = string("xt_245_dilations_0"), val = tensor([1])]; + int32 xt_245_groups_0 = const()[name = string("xt_245_groups_0"), val = int32(1)]; + tensor generator_resblocks_9_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_9_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40920512)))]; + tensor generator_resblocks_9_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_9_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40926720)))]; + tensor xt_245_cast_fp16 = conv(bias = generator_resblocks_9_convs2_1_bias_to_fp16, dilations = xt_245_dilations_0, groups = xt_245_groups_0, pad = xt_245_pad_0, pad_type = xt_245_pad_type_0, strides = xt_245_strides_0, weight = generator_resblocks_9_convs2_1_weight_to_fp16, x = input_325_cast_fp16)[name = string("xt_245_cast_fp16")]; + tensor input_327_cast_fp16 = add(x = xt_245_cast_fp16, y = input_319_cast_fp16)[name = string("input_327_cast_fp16")]; + tensor generator_resblocks_9_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_9_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40926848)))]; + tensor generator_resblocks_9_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_9_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40943296)))]; + tensor linear_82_cast_fp16 = linear(bias = generator_resblocks_9_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_9_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_82_cast_fp16")]; + tensor var_3199 = const()[name = string("op_3199"), val = tensor([1, 64, 1])]; + tensor h_331_cast_fp16 = reshape(shape = var_3199, x = linear_82_cast_fp16)[name = string("h_331_cast_fp16")]; + tensor var_3201_split_sizes_0 = const()[name = string("op_3201_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3201_axis_0 = const()[name = string("op_3201_axis_0"), val = int32(1)]; + tensor var_3201_cast_fp16_0, tensor var_3201_cast_fp16_1 = split(axis = var_3201_axis_0, split_sizes = var_3201_split_sizes_0, x = h_331_cast_fp16)[name = string("op_3201_cast_fp16")]; + fp16 var_3203_promoted_to_fp16 = const()[name = string("op_3203_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3204_cast_fp16 = add(x = var_3201_cast_fp16_0, y = var_3203_promoted_to_fp16)[name = string("op_3204_cast_fp16")]; + tensor var_3205_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_327_cast_fp16)[name = string("op_3205_cast_fp16")]; + tensor var_3206_cast_fp16 = mul(x = var_3204_cast_fp16, y = var_3205_cast_fp16)[name = string("op_3206_cast_fp16")]; + tensor xt_247_cast_fp16 = add(x = var_3206_cast_fp16, y = var_3201_cast_fp16_1)[name = string("xt_247_cast_fp16")]; + tensor generator_resblocks_9_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_9_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40943488)))]; + tensor var_3211_cast_fp16 = mul(x = generator_resblocks_9_alpha1_2_to_fp16, y = xt_247_cast_fp16)[name = string("op_3211_cast_fp16")]; + tensor var_3212_cast_fp16 = sin(x = var_3211_cast_fp16)[name = string("op_3212_cast_fp16")]; + fp16 var_24_promoted_86_to_fp16 = const()[name = string("op_24_promoted_86_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3213_cast_fp16 = pow(x = var_3212_cast_fp16, y = var_24_promoted_86_to_fp16)[name = string("op_3213_cast_fp16")]; + tensor var_3208_to_fp16 = const()[name = string("op_3208_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40943616)))]; + tensor var_3214_cast_fp16 = mul(x = var_3208_to_fp16, y = var_3213_cast_fp16)[name = string("op_3214_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = xt_247_cast_fp16, y = var_3214_cast_fp16)[name = string("input_329_cast_fp16")]; + string input_331_pad_type_0 = const()[name = string("input_331_pad_type_0"), val = string("custom")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([5, 5])]; + tensor input_331_dilations_0 = const()[name = string("input_331_dilations_0"), val = tensor([5])]; + tensor input_331_strides_0 = const()[name = string("input_331_strides_0"), val = tensor([1])]; + int32 input_331_groups_0 = const()[name = string("input_331_groups_0"), val = int32(1)]; + tensor generator_resblocks_9_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_9_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40943744)))]; + tensor generator_resblocks_9_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_9_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40949952)))]; + tensor input_331_cast_fp16 = conv(bias = generator_resblocks_9_convs1_2_bias_to_fp16, dilations = input_331_dilations_0, groups = input_331_groups_0, pad = input_331_pad_0, pad_type = input_331_pad_type_0, strides = input_331_strides_0, weight = generator_resblocks_9_convs1_2_weight_to_fp16, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + tensor generator_resblocks_9_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_9_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40950080)))]; + tensor generator_resblocks_9_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_9_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40966528)))]; + tensor linear_83_cast_fp16 = linear(bias = generator_resblocks_9_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_9_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_83_cast_fp16")]; + tensor var_3229 = const()[name = string("op_3229"), val = tensor([1, 64, 1])]; + tensor h_335_cast_fp16 = reshape(shape = var_3229, x = linear_83_cast_fp16)[name = string("h_335_cast_fp16")]; + tensor var_3231_split_sizes_0 = const()[name = string("op_3231_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3231_axis_0 = const()[name = string("op_3231_axis_0"), val = int32(1)]; + tensor var_3231_cast_fp16_0, tensor var_3231_cast_fp16_1 = split(axis = var_3231_axis_0, split_sizes = var_3231_split_sizes_0, x = h_335_cast_fp16)[name = string("op_3231_cast_fp16")]; + fp16 var_3233_promoted_to_fp16 = const()[name = string("op_3233_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3234_cast_fp16 = add(x = var_3231_cast_fp16_0, y = var_3233_promoted_to_fp16)[name = string("op_3234_cast_fp16")]; + tensor var_3235_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_331_cast_fp16)[name = string("op_3235_cast_fp16")]; + tensor var_3236_cast_fp16 = mul(x = var_3234_cast_fp16, y = var_3235_cast_fp16)[name = string("op_3236_cast_fp16")]; + tensor xt_249_cast_fp16 = add(x = var_3236_cast_fp16, y = var_3231_cast_fp16_1)[name = string("xt_249_cast_fp16")]; + tensor generator_resblocks_9_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_9_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40966720)))]; + tensor var_3241_cast_fp16 = mul(x = generator_resblocks_9_alpha2_2_to_fp16, y = xt_249_cast_fp16)[name = string("op_3241_cast_fp16")]; + tensor var_3242_cast_fp16 = sin(x = var_3241_cast_fp16)[name = string("op_3242_cast_fp16")]; + fp16 var_24_promoted_87_to_fp16 = const()[name = string("op_24_promoted_87_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3243_cast_fp16 = pow(x = var_3242_cast_fp16, y = var_24_promoted_87_to_fp16)[name = string("op_3243_cast_fp16")]; + tensor var_3238_to_fp16 = const()[name = string("op_3238_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40966848)))]; + tensor var_3244_cast_fp16 = mul(x = var_3238_to_fp16, y = var_3243_cast_fp16)[name = string("op_3244_cast_fp16")]; + tensor input_333_cast_fp16 = add(x = xt_249_cast_fp16, y = var_3244_cast_fp16)[name = string("input_333_cast_fp16")]; + string xt_251_pad_type_0 = const()[name = string("xt_251_pad_type_0"), val = string("custom")]; + tensor xt_251_pad_0 = const()[name = string("xt_251_pad_0"), val = tensor([1, 1])]; + tensor xt_251_strides_0 = const()[name = string("xt_251_strides_0"), val = tensor([1])]; + tensor xt_251_dilations_0 = const()[name = string("xt_251_dilations_0"), val = tensor([1])]; + int32 xt_251_groups_0 = const()[name = string("xt_251_groups_0"), val = int32(1)]; + tensor generator_resblocks_9_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_9_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40966976)))]; + tensor generator_resblocks_9_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_9_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40973184)))]; + tensor xt_251_cast_fp16 = conv(bias = generator_resblocks_9_convs2_2_bias_to_fp16, dilations = xt_251_dilations_0, groups = xt_251_groups_0, pad = xt_251_pad_0, pad_type = xt_251_pad_type_0, strides = xt_251_strides_0, weight = generator_resblocks_9_convs2_2_weight_to_fp16, x = input_333_cast_fp16)[name = string("xt_251_cast_fp16")]; + tensor xs_19_cast_fp16 = add(x = xt_251_cast_fp16, y = input_327_cast_fp16)[name = string("xs_19_cast_fp16")]; + tensor generator_resblocks_10_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_10_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40973312)))]; + tensor generator_resblocks_10_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_10_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40989760)))]; + tensor linear_84_cast_fp16 = linear(bias = generator_resblocks_10_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_10_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_84_cast_fp16")]; + tensor var_3296 = const()[name = string("op_3296"), val = tensor([1, 64, 1])]; + tensor h_339_cast_fp16 = reshape(shape = var_3296, x = linear_84_cast_fp16)[name = string("h_339_cast_fp16")]; + tensor var_3298_split_sizes_0 = const()[name = string("op_3298_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3298_axis_0 = const()[name = string("op_3298_axis_0"), val = int32(1)]; + tensor var_3298_cast_fp16_0, tensor var_3298_cast_fp16_1 = split(axis = var_3298_axis_0, split_sizes = var_3298_split_sizes_0, x = h_339_cast_fp16)[name = string("op_3298_cast_fp16")]; + fp16 var_3300_promoted_to_fp16 = const()[name = string("op_3300_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3301_cast_fp16 = add(x = var_3298_cast_fp16_0, y = var_3300_promoted_to_fp16)[name = string("op_3301_cast_fp16")]; + tensor var_3303_cast_fp16 = mul(x = var_3301_cast_fp16, y = var_3083_cast_fp16)[name = string("op_3303_cast_fp16")]; + tensor xt_253_cast_fp16 = add(x = var_3303_cast_fp16, y = var_3298_cast_fp16_1)[name = string("xt_253_cast_fp16")]; + tensor generator_resblocks_10_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_10_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40989952)))]; + tensor var_3308_cast_fp16 = mul(x = generator_resblocks_10_alpha1_0_to_fp16, y = xt_253_cast_fp16)[name = string("op_3308_cast_fp16")]; + tensor var_3309_cast_fp16 = sin(x = var_3308_cast_fp16)[name = string("op_3309_cast_fp16")]; + fp16 var_24_promoted_88_to_fp16 = const()[name = string("op_24_promoted_88_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3310_cast_fp16 = pow(x = var_3309_cast_fp16, y = var_24_promoted_88_to_fp16)[name = string("op_3310_cast_fp16")]; + tensor var_3305_to_fp16 = const()[name = string("op_3305_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40990080)))]; + tensor var_3311_cast_fp16 = mul(x = var_3305_to_fp16, y = var_3310_cast_fp16)[name = string("op_3311_cast_fp16")]; + tensor input_335_cast_fp16 = add(x = xt_253_cast_fp16, y = var_3311_cast_fp16)[name = string("input_335_cast_fp16")]; + string input_337_pad_type_0 = const()[name = string("input_337_pad_type_0"), val = string("custom")]; + tensor input_337_pad_0 = const()[name = string("input_337_pad_0"), val = tensor([3, 3])]; + tensor input_337_strides_0 = const()[name = string("input_337_strides_0"), val = tensor([1])]; + tensor input_337_dilations_0 = const()[name = string("input_337_dilations_0"), val = tensor([1])]; + int32 input_337_groups_0 = const()[name = string("input_337_groups_0"), val = int32(1)]; + tensor generator_resblocks_10_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_10_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40990208)))]; + tensor generator_resblocks_10_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_10_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41004608)))]; + tensor input_337_cast_fp16 = conv(bias = generator_resblocks_10_convs1_0_bias_to_fp16, dilations = input_337_dilations_0, groups = input_337_groups_0, pad = input_337_pad_0, pad_type = input_337_pad_type_0, strides = input_337_strides_0, weight = generator_resblocks_10_convs1_0_weight_to_fp16, x = input_335_cast_fp16)[name = string("input_337_cast_fp16")]; + tensor generator_resblocks_10_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_10_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41004736)))]; + tensor generator_resblocks_10_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_10_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41021184)))]; + tensor linear_85_cast_fp16 = linear(bias = generator_resblocks_10_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_10_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_85_cast_fp16")]; + tensor var_3326 = const()[name = string("op_3326"), val = tensor([1, 64, 1])]; + tensor h_343_cast_fp16 = reshape(shape = var_3326, x = linear_85_cast_fp16)[name = string("h_343_cast_fp16")]; + tensor var_3328_split_sizes_0 = const()[name = string("op_3328_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3328_axis_0 = const()[name = string("op_3328_axis_0"), val = int32(1)]; + tensor var_3328_cast_fp16_0, tensor var_3328_cast_fp16_1 = split(axis = var_3328_axis_0, split_sizes = var_3328_split_sizes_0, x = h_343_cast_fp16)[name = string("op_3328_cast_fp16")]; + fp16 var_3330_promoted_to_fp16 = const()[name = string("op_3330_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3331_cast_fp16 = add(x = var_3328_cast_fp16_0, y = var_3330_promoted_to_fp16)[name = string("op_3331_cast_fp16")]; + tensor var_3332_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_337_cast_fp16)[name = string("op_3332_cast_fp16")]; + tensor var_3333_cast_fp16 = mul(x = var_3331_cast_fp16, y = var_3332_cast_fp16)[name = string("op_3333_cast_fp16")]; + tensor xt_255_cast_fp16 = add(x = var_3333_cast_fp16, y = var_3328_cast_fp16_1)[name = string("xt_255_cast_fp16")]; + tensor generator_resblocks_10_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_10_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41021376)))]; + tensor var_3338_cast_fp16 = mul(x = generator_resblocks_10_alpha2_0_to_fp16, y = xt_255_cast_fp16)[name = string("op_3338_cast_fp16")]; + tensor var_3339_cast_fp16 = sin(x = var_3338_cast_fp16)[name = string("op_3339_cast_fp16")]; + fp16 var_24_promoted_89_to_fp16 = const()[name = string("op_24_promoted_89_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3340_cast_fp16 = pow(x = var_3339_cast_fp16, y = var_24_promoted_89_to_fp16)[name = string("op_3340_cast_fp16")]; + tensor var_3335_to_fp16 = const()[name = string("op_3335_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41021504)))]; + tensor var_3341_cast_fp16 = mul(x = var_3335_to_fp16, y = var_3340_cast_fp16)[name = string("op_3341_cast_fp16")]; + tensor input_339_cast_fp16 = add(x = xt_255_cast_fp16, y = var_3341_cast_fp16)[name = string("input_339_cast_fp16")]; + string xt_257_pad_type_0 = const()[name = string("xt_257_pad_type_0"), val = string("custom")]; + tensor xt_257_pad_0 = const()[name = string("xt_257_pad_0"), val = tensor([3, 3])]; + tensor xt_257_strides_0 = const()[name = string("xt_257_strides_0"), val = tensor([1])]; + tensor xt_257_dilations_0 = const()[name = string("xt_257_dilations_0"), val = tensor([1])]; + int32 xt_257_groups_0 = const()[name = string("xt_257_groups_0"), val = int32(1)]; + tensor generator_resblocks_10_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_10_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41021632)))]; + tensor generator_resblocks_10_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_10_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41036032)))]; + tensor xt_257_cast_fp16 = conv(bias = generator_resblocks_10_convs2_0_bias_to_fp16, dilations = xt_257_dilations_0, groups = xt_257_groups_0, pad = xt_257_pad_0, pad_type = xt_257_pad_type_0, strides = xt_257_strides_0, weight = generator_resblocks_10_convs2_0_weight_to_fp16, x = input_339_cast_fp16)[name = string("xt_257_cast_fp16")]; + tensor input_341_cast_fp16 = add(x = xt_257_cast_fp16, y = input_311_cast_fp16)[name = string("input_341_cast_fp16")]; + tensor generator_resblocks_10_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_10_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41036160)))]; + tensor generator_resblocks_10_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_10_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41052608)))]; + tensor linear_86_cast_fp16 = linear(bias = generator_resblocks_10_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_10_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_86_cast_fp16")]; + tensor var_3357 = const()[name = string("op_3357"), val = tensor([1, 64, 1])]; + tensor h_347_cast_fp16 = reshape(shape = var_3357, x = linear_86_cast_fp16)[name = string("h_347_cast_fp16")]; + tensor var_3359_split_sizes_0 = const()[name = string("op_3359_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3359_axis_0 = const()[name = string("op_3359_axis_0"), val = int32(1)]; + tensor var_3359_cast_fp16_0, tensor var_3359_cast_fp16_1 = split(axis = var_3359_axis_0, split_sizes = var_3359_split_sizes_0, x = h_347_cast_fp16)[name = string("op_3359_cast_fp16")]; + fp16 var_3361_promoted_to_fp16 = const()[name = string("op_3361_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3362_cast_fp16 = add(x = var_3359_cast_fp16_0, y = var_3361_promoted_to_fp16)[name = string("op_3362_cast_fp16")]; + tensor var_3363_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_341_cast_fp16)[name = string("op_3363_cast_fp16")]; + tensor var_3364_cast_fp16 = mul(x = var_3362_cast_fp16, y = var_3363_cast_fp16)[name = string("op_3364_cast_fp16")]; + tensor xt_259_cast_fp16 = add(x = var_3364_cast_fp16, y = var_3359_cast_fp16_1)[name = string("xt_259_cast_fp16")]; + tensor generator_resblocks_10_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_10_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41052800)))]; + tensor var_3369_cast_fp16 = mul(x = generator_resblocks_10_alpha1_1_to_fp16, y = xt_259_cast_fp16)[name = string("op_3369_cast_fp16")]; + tensor var_3370_cast_fp16 = sin(x = var_3369_cast_fp16)[name = string("op_3370_cast_fp16")]; + fp16 var_24_promoted_90_to_fp16 = const()[name = string("op_24_promoted_90_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3371_cast_fp16 = pow(x = var_3370_cast_fp16, y = var_24_promoted_90_to_fp16)[name = string("op_3371_cast_fp16")]; + tensor var_3366_to_fp16 = const()[name = string("op_3366_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41052928)))]; + tensor var_3372_cast_fp16 = mul(x = var_3366_to_fp16, y = var_3371_cast_fp16)[name = string("op_3372_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = xt_259_cast_fp16, y = var_3372_cast_fp16)[name = string("input_343_cast_fp16")]; + string input_345_pad_type_0 = const()[name = string("input_345_pad_type_0"), val = string("custom")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([9, 9])]; + tensor input_345_dilations_0 = const()[name = string("input_345_dilations_0"), val = tensor([3])]; + tensor input_345_strides_0 = const()[name = string("input_345_strides_0"), val = tensor([1])]; + int32 input_345_groups_0 = const()[name = string("input_345_groups_0"), val = int32(1)]; + tensor generator_resblocks_10_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_10_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41053056)))]; + tensor generator_resblocks_10_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_10_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41067456)))]; + tensor input_345_cast_fp16 = conv(bias = generator_resblocks_10_convs1_1_bias_to_fp16, dilations = input_345_dilations_0, groups = input_345_groups_0, pad = input_345_pad_0, pad_type = input_345_pad_type_0, strides = input_345_strides_0, weight = generator_resblocks_10_convs1_1_weight_to_fp16, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + tensor generator_resblocks_10_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_10_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41067584)))]; + tensor generator_resblocks_10_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_10_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41084032)))]; + tensor linear_87_cast_fp16 = linear(bias = generator_resblocks_10_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_10_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_87_cast_fp16")]; + tensor var_3387 = const()[name = string("op_3387"), val = tensor([1, 64, 1])]; + tensor h_351_cast_fp16 = reshape(shape = var_3387, x = linear_87_cast_fp16)[name = string("h_351_cast_fp16")]; + tensor var_3389_split_sizes_0 = const()[name = string("op_3389_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3389_axis_0 = const()[name = string("op_3389_axis_0"), val = int32(1)]; + tensor var_3389_cast_fp16_0, tensor var_3389_cast_fp16_1 = split(axis = var_3389_axis_0, split_sizes = var_3389_split_sizes_0, x = h_351_cast_fp16)[name = string("op_3389_cast_fp16")]; + fp16 var_3391_promoted_to_fp16 = const()[name = string("op_3391_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3392_cast_fp16 = add(x = var_3389_cast_fp16_0, y = var_3391_promoted_to_fp16)[name = string("op_3392_cast_fp16")]; + tensor var_3393_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_345_cast_fp16)[name = string("op_3393_cast_fp16")]; + tensor var_3394_cast_fp16 = mul(x = var_3392_cast_fp16, y = var_3393_cast_fp16)[name = string("op_3394_cast_fp16")]; + tensor xt_261_cast_fp16 = add(x = var_3394_cast_fp16, y = var_3389_cast_fp16_1)[name = string("xt_261_cast_fp16")]; + tensor generator_resblocks_10_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_10_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41084224)))]; + tensor var_3399_cast_fp16 = mul(x = generator_resblocks_10_alpha2_1_to_fp16, y = xt_261_cast_fp16)[name = string("op_3399_cast_fp16")]; + tensor var_3400_cast_fp16 = sin(x = var_3399_cast_fp16)[name = string("op_3400_cast_fp16")]; + fp16 var_24_promoted_91_to_fp16 = const()[name = string("op_24_promoted_91_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3401_cast_fp16 = pow(x = var_3400_cast_fp16, y = var_24_promoted_91_to_fp16)[name = string("op_3401_cast_fp16")]; + tensor var_3396_to_fp16 = const()[name = string("op_3396_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41084352)))]; + tensor var_3402_cast_fp16 = mul(x = var_3396_to_fp16, y = var_3401_cast_fp16)[name = string("op_3402_cast_fp16")]; + tensor input_347_cast_fp16 = add(x = xt_261_cast_fp16, y = var_3402_cast_fp16)[name = string("input_347_cast_fp16")]; + string xt_263_pad_type_0 = const()[name = string("xt_263_pad_type_0"), val = string("custom")]; + tensor xt_263_pad_0 = const()[name = string("xt_263_pad_0"), val = tensor([3, 3])]; + tensor xt_263_strides_0 = const()[name = string("xt_263_strides_0"), val = tensor([1])]; + tensor xt_263_dilations_0 = const()[name = string("xt_263_dilations_0"), val = tensor([1])]; + int32 xt_263_groups_0 = const()[name = string("xt_263_groups_0"), val = int32(1)]; + tensor generator_resblocks_10_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_10_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41084480)))]; + tensor generator_resblocks_10_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_10_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41098880)))]; + tensor xt_263_cast_fp16 = conv(bias = generator_resblocks_10_convs2_1_bias_to_fp16, dilations = xt_263_dilations_0, groups = xt_263_groups_0, pad = xt_263_pad_0, pad_type = xt_263_pad_type_0, strides = xt_263_strides_0, weight = generator_resblocks_10_convs2_1_weight_to_fp16, x = input_347_cast_fp16)[name = string("xt_263_cast_fp16")]; + tensor input_349_cast_fp16 = add(x = xt_263_cast_fp16, y = input_341_cast_fp16)[name = string("input_349_cast_fp16")]; + tensor generator_resblocks_10_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_10_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41099008)))]; + tensor generator_resblocks_10_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_10_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41115456)))]; + tensor linear_88_cast_fp16 = linear(bias = generator_resblocks_10_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_10_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_88_cast_fp16")]; + tensor var_3418 = const()[name = string("op_3418"), val = tensor([1, 64, 1])]; + tensor h_355_cast_fp16 = reshape(shape = var_3418, x = linear_88_cast_fp16)[name = string("h_355_cast_fp16")]; + tensor var_3420_split_sizes_0 = const()[name = string("op_3420_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3420_axis_0 = const()[name = string("op_3420_axis_0"), val = int32(1)]; + tensor var_3420_cast_fp16_0, tensor var_3420_cast_fp16_1 = split(axis = var_3420_axis_0, split_sizes = var_3420_split_sizes_0, x = h_355_cast_fp16)[name = string("op_3420_cast_fp16")]; + fp16 var_3422_promoted_to_fp16 = const()[name = string("op_3422_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3423_cast_fp16 = add(x = var_3420_cast_fp16_0, y = var_3422_promoted_to_fp16)[name = string("op_3423_cast_fp16")]; + tensor var_3424_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_349_cast_fp16)[name = string("op_3424_cast_fp16")]; + tensor var_3425_cast_fp16 = mul(x = var_3423_cast_fp16, y = var_3424_cast_fp16)[name = string("op_3425_cast_fp16")]; + tensor xt_265_cast_fp16 = add(x = var_3425_cast_fp16, y = var_3420_cast_fp16_1)[name = string("xt_265_cast_fp16")]; + tensor generator_resblocks_10_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_10_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41115648)))]; + tensor var_3430_cast_fp16 = mul(x = generator_resblocks_10_alpha1_2_to_fp16, y = xt_265_cast_fp16)[name = string("op_3430_cast_fp16")]; + tensor var_3431_cast_fp16 = sin(x = var_3430_cast_fp16)[name = string("op_3431_cast_fp16")]; + fp16 var_24_promoted_92_to_fp16 = const()[name = string("op_24_promoted_92_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3432_cast_fp16 = pow(x = var_3431_cast_fp16, y = var_24_promoted_92_to_fp16)[name = string("op_3432_cast_fp16")]; + tensor var_3427_to_fp16 = const()[name = string("op_3427_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41115776)))]; + tensor var_3433_cast_fp16 = mul(x = var_3427_to_fp16, y = var_3432_cast_fp16)[name = string("op_3433_cast_fp16")]; + tensor input_351_cast_fp16 = add(x = xt_265_cast_fp16, y = var_3433_cast_fp16)[name = string("input_351_cast_fp16")]; + string input_353_pad_type_0 = const()[name = string("input_353_pad_type_0"), val = string("custom")]; + tensor input_353_pad_0 = const()[name = string("input_353_pad_0"), val = tensor([15, 15])]; + tensor input_353_dilations_0 = const()[name = string("input_353_dilations_0"), val = tensor([5])]; + tensor input_353_strides_0 = const()[name = string("input_353_strides_0"), val = tensor([1])]; + int32 input_353_groups_0 = const()[name = string("input_353_groups_0"), val = int32(1)]; + tensor generator_resblocks_10_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_10_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41115904)))]; + tensor generator_resblocks_10_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_10_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41130304)))]; + tensor input_353_cast_fp16 = conv(bias = generator_resblocks_10_convs1_2_bias_to_fp16, dilations = input_353_dilations_0, groups = input_353_groups_0, pad = input_353_pad_0, pad_type = input_353_pad_type_0, strides = input_353_strides_0, weight = generator_resblocks_10_convs1_2_weight_to_fp16, x = input_351_cast_fp16)[name = string("input_353_cast_fp16")]; + tensor generator_resblocks_10_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_10_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41130432)))]; + tensor generator_resblocks_10_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_10_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41146880)))]; + tensor linear_89_cast_fp16 = linear(bias = generator_resblocks_10_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_10_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_89_cast_fp16")]; + tensor var_3448 = const()[name = string("op_3448"), val = tensor([1, 64, 1])]; + tensor h_359_cast_fp16 = reshape(shape = var_3448, x = linear_89_cast_fp16)[name = string("h_359_cast_fp16")]; + tensor var_3450_split_sizes_0 = const()[name = string("op_3450_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3450_axis_0 = const()[name = string("op_3450_axis_0"), val = int32(1)]; + tensor var_3450_cast_fp16_0, tensor var_3450_cast_fp16_1 = split(axis = var_3450_axis_0, split_sizes = var_3450_split_sizes_0, x = h_359_cast_fp16)[name = string("op_3450_cast_fp16")]; + fp16 var_3452_promoted_to_fp16 = const()[name = string("op_3452_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3453_cast_fp16 = add(x = var_3450_cast_fp16_0, y = var_3452_promoted_to_fp16)[name = string("op_3453_cast_fp16")]; + tensor var_3454_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_353_cast_fp16)[name = string("op_3454_cast_fp16")]; + tensor var_3455_cast_fp16 = mul(x = var_3453_cast_fp16, y = var_3454_cast_fp16)[name = string("op_3455_cast_fp16")]; + tensor xt_267_cast_fp16 = add(x = var_3455_cast_fp16, y = var_3450_cast_fp16_1)[name = string("xt_267_cast_fp16")]; + tensor generator_resblocks_10_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_10_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41147072)))]; + tensor var_3460_cast_fp16 = mul(x = generator_resblocks_10_alpha2_2_to_fp16, y = xt_267_cast_fp16)[name = string("op_3460_cast_fp16")]; + tensor var_3461_cast_fp16 = sin(x = var_3460_cast_fp16)[name = string("op_3461_cast_fp16")]; + fp16 var_24_promoted_93_to_fp16 = const()[name = string("op_24_promoted_93_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3462_cast_fp16 = pow(x = var_3461_cast_fp16, y = var_24_promoted_93_to_fp16)[name = string("op_3462_cast_fp16")]; + tensor var_3457_to_fp16 = const()[name = string("op_3457_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41147200)))]; + tensor var_3463_cast_fp16 = mul(x = var_3457_to_fp16, y = var_3462_cast_fp16)[name = string("op_3463_cast_fp16")]; + tensor input_355_cast_fp16 = add(x = xt_267_cast_fp16, y = var_3463_cast_fp16)[name = string("input_355_cast_fp16")]; + string xt_269_pad_type_0 = const()[name = string("xt_269_pad_type_0"), val = string("custom")]; + tensor xt_269_pad_0 = const()[name = string("xt_269_pad_0"), val = tensor([3, 3])]; + tensor xt_269_strides_0 = const()[name = string("xt_269_strides_0"), val = tensor([1])]; + tensor xt_269_dilations_0 = const()[name = string("xt_269_dilations_0"), val = tensor([1])]; + int32 xt_269_groups_0 = const()[name = string("xt_269_groups_0"), val = int32(1)]; + tensor generator_resblocks_10_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_10_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41147328)))]; + tensor generator_resblocks_10_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_10_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41161728)))]; + tensor xt_269_cast_fp16 = conv(bias = generator_resblocks_10_convs2_2_bias_to_fp16, dilations = xt_269_dilations_0, groups = xt_269_groups_0, pad = xt_269_pad_0, pad_type = xt_269_pad_type_0, strides = xt_269_strides_0, weight = generator_resblocks_10_convs2_2_weight_to_fp16, x = input_355_cast_fp16)[name = string("xt_269_cast_fp16")]; + tensor var_3472_cast_fp16 = add(x = xt_269_cast_fp16, y = input_349_cast_fp16)[name = string("op_3472_cast_fp16")]; + tensor xs_21_cast_fp16 = add(x = xs_19_cast_fp16, y = var_3472_cast_fp16)[name = string("xs_21_cast_fp16")]; + tensor generator_resblocks_11_adain1_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_11_adain1_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41161856)))]; + tensor generator_resblocks_11_adain1_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_11_adain1_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41178304)))]; + tensor linear_90_cast_fp16 = linear(bias = generator_resblocks_11_adain1_0_fc_bias_to_fp16, weight = generator_resblocks_11_adain1_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_90_cast_fp16")]; + tensor var_3516 = const()[name = string("op_3516"), val = tensor([1, 64, 1])]; + tensor h_363_cast_fp16 = reshape(shape = var_3516, x = linear_90_cast_fp16)[name = string("h_363_cast_fp16")]; + tensor var_3518_split_sizes_0 = const()[name = string("op_3518_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3518_axis_0 = const()[name = string("op_3518_axis_0"), val = int32(1)]; + tensor var_3518_cast_fp16_0, tensor var_3518_cast_fp16_1 = split(axis = var_3518_axis_0, split_sizes = var_3518_split_sizes_0, x = h_363_cast_fp16)[name = string("op_3518_cast_fp16")]; + fp16 var_3520_promoted_to_fp16 = const()[name = string("op_3520_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3521_cast_fp16 = add(x = var_3518_cast_fp16_0, y = var_3520_promoted_to_fp16)[name = string("op_3521_cast_fp16")]; + tensor var_3523_cast_fp16 = mul(x = var_3521_cast_fp16, y = var_3083_cast_fp16)[name = string("op_3523_cast_fp16")]; + tensor xt_271_cast_fp16 = add(x = var_3523_cast_fp16, y = var_3518_cast_fp16_1)[name = string("xt_271_cast_fp16")]; + tensor generator_resblocks_11_alpha1_0_to_fp16 = const()[name = string("generator_resblocks_11_alpha1_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41178496)))]; + tensor var_3528_cast_fp16 = mul(x = generator_resblocks_11_alpha1_0_to_fp16, y = xt_271_cast_fp16)[name = string("op_3528_cast_fp16")]; + tensor var_3529_cast_fp16 = sin(x = var_3528_cast_fp16)[name = string("op_3529_cast_fp16")]; + fp16 var_24_promoted_94_to_fp16 = const()[name = string("op_24_promoted_94_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3530_cast_fp16 = pow(x = var_3529_cast_fp16, y = var_24_promoted_94_to_fp16)[name = string("op_3530_cast_fp16")]; + tensor var_3525_to_fp16 = const()[name = string("op_3525_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41178624)))]; + tensor var_3531_cast_fp16 = mul(x = var_3525_to_fp16, y = var_3530_cast_fp16)[name = string("op_3531_cast_fp16")]; + tensor input_357_cast_fp16 = add(x = xt_271_cast_fp16, y = var_3531_cast_fp16)[name = string("input_357_cast_fp16")]; + string input_359_pad_type_0 = const()[name = string("input_359_pad_type_0"), val = string("custom")]; + tensor input_359_pad_0 = const()[name = string("input_359_pad_0"), val = tensor([5, 5])]; + tensor input_359_strides_0 = const()[name = string("input_359_strides_0"), val = tensor([1])]; + tensor input_359_dilations_0 = const()[name = string("input_359_dilations_0"), val = tensor([1])]; + int32 input_359_groups_0 = const()[name = string("input_359_groups_0"), val = int32(1)]; + tensor generator_resblocks_11_convs1_0_weight_to_fp16 = const()[name = string("generator_resblocks_11_convs1_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41178752)))]; + tensor generator_resblocks_11_convs1_0_bias_to_fp16 = const()[name = string("generator_resblocks_11_convs1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41201344)))]; + tensor input_359_cast_fp16 = conv(bias = generator_resblocks_11_convs1_0_bias_to_fp16, dilations = input_359_dilations_0, groups = input_359_groups_0, pad = input_359_pad_0, pad_type = input_359_pad_type_0, strides = input_359_strides_0, weight = generator_resblocks_11_convs1_0_weight_to_fp16, x = input_357_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor generator_resblocks_11_adain2_0_fc_weight_to_fp16 = const()[name = string("generator_resblocks_11_adain2_0_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41201472)))]; + tensor generator_resblocks_11_adain2_0_fc_bias_to_fp16 = const()[name = string("generator_resblocks_11_adain2_0_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41217920)))]; + tensor linear_91_cast_fp16 = linear(bias = generator_resblocks_11_adain2_0_fc_bias_to_fp16, weight = generator_resblocks_11_adain2_0_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_91_cast_fp16")]; + tensor var_3546 = const()[name = string("op_3546"), val = tensor([1, 64, 1])]; + tensor h_367_cast_fp16 = reshape(shape = var_3546, x = linear_91_cast_fp16)[name = string("h_367_cast_fp16")]; + tensor var_3548_split_sizes_0 = const()[name = string("op_3548_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3548_axis_0 = const()[name = string("op_3548_axis_0"), val = int32(1)]; + tensor var_3548_cast_fp16_0, tensor var_3548_cast_fp16_1 = split(axis = var_3548_axis_0, split_sizes = var_3548_split_sizes_0, x = h_367_cast_fp16)[name = string("op_3548_cast_fp16")]; + fp16 var_3550_promoted_to_fp16 = const()[name = string("op_3550_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3551_cast_fp16 = add(x = var_3548_cast_fp16_0, y = var_3550_promoted_to_fp16)[name = string("op_3551_cast_fp16")]; + tensor var_3552_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_359_cast_fp16)[name = string("op_3552_cast_fp16")]; + tensor var_3553_cast_fp16 = mul(x = var_3551_cast_fp16, y = var_3552_cast_fp16)[name = string("op_3553_cast_fp16")]; + tensor xt_273_cast_fp16 = add(x = var_3553_cast_fp16, y = var_3548_cast_fp16_1)[name = string("xt_273_cast_fp16")]; + tensor generator_resblocks_11_alpha2_0_to_fp16 = const()[name = string("generator_resblocks_11_alpha2_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41218112)))]; + tensor var_3558_cast_fp16 = mul(x = generator_resblocks_11_alpha2_0_to_fp16, y = xt_273_cast_fp16)[name = string("op_3558_cast_fp16")]; + tensor var_3559_cast_fp16 = sin(x = var_3558_cast_fp16)[name = string("op_3559_cast_fp16")]; + fp16 var_24_promoted_95_to_fp16 = const()[name = string("op_24_promoted_95_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3560_cast_fp16 = pow(x = var_3559_cast_fp16, y = var_24_promoted_95_to_fp16)[name = string("op_3560_cast_fp16")]; + tensor var_3555_to_fp16 = const()[name = string("op_3555_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41218240)))]; + tensor var_3561_cast_fp16 = mul(x = var_3555_to_fp16, y = var_3560_cast_fp16)[name = string("op_3561_cast_fp16")]; + tensor input_361_cast_fp16 = add(x = xt_273_cast_fp16, y = var_3561_cast_fp16)[name = string("input_361_cast_fp16")]; + string xt_275_pad_type_0 = const()[name = string("xt_275_pad_type_0"), val = string("custom")]; + tensor xt_275_pad_0 = const()[name = string("xt_275_pad_0"), val = tensor([5, 5])]; + tensor xt_275_strides_0 = const()[name = string("xt_275_strides_0"), val = tensor([1])]; + tensor xt_275_dilations_0 = const()[name = string("xt_275_dilations_0"), val = tensor([1])]; + int32 xt_275_groups_0 = const()[name = string("xt_275_groups_0"), val = int32(1)]; + tensor generator_resblocks_11_convs2_0_weight_to_fp16 = const()[name = string("generator_resblocks_11_convs2_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41218368)))]; + tensor generator_resblocks_11_convs2_0_bias_to_fp16 = const()[name = string("generator_resblocks_11_convs2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41240960)))]; + tensor xt_275_cast_fp16 = conv(bias = generator_resblocks_11_convs2_0_bias_to_fp16, dilations = xt_275_dilations_0, groups = xt_275_groups_0, pad = xt_275_pad_0, pad_type = xt_275_pad_type_0, strides = xt_275_strides_0, weight = generator_resblocks_11_convs2_0_weight_to_fp16, x = input_361_cast_fp16)[name = string("xt_275_cast_fp16")]; + tensor input_363_cast_fp16 = add(x = xt_275_cast_fp16, y = input_311_cast_fp16)[name = string("input_363_cast_fp16")]; + tensor generator_resblocks_11_adain1_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_11_adain1_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41241088)))]; + tensor generator_resblocks_11_adain1_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_11_adain1_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41257536)))]; + tensor linear_92_cast_fp16 = linear(bias = generator_resblocks_11_adain1_1_fc_bias_to_fp16, weight = generator_resblocks_11_adain1_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_92_cast_fp16")]; + tensor var_3577 = const()[name = string("op_3577"), val = tensor([1, 64, 1])]; + tensor h_371_cast_fp16 = reshape(shape = var_3577, x = linear_92_cast_fp16)[name = string("h_371_cast_fp16")]; + tensor var_3579_split_sizes_0 = const()[name = string("op_3579_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3579_axis_0 = const()[name = string("op_3579_axis_0"), val = int32(1)]; + tensor var_3579_cast_fp16_0, tensor var_3579_cast_fp16_1 = split(axis = var_3579_axis_0, split_sizes = var_3579_split_sizes_0, x = h_371_cast_fp16)[name = string("op_3579_cast_fp16")]; + fp16 var_3581_promoted_to_fp16 = const()[name = string("op_3581_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3582_cast_fp16 = add(x = var_3579_cast_fp16_0, y = var_3581_promoted_to_fp16)[name = string("op_3582_cast_fp16")]; + tensor var_3583_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_363_cast_fp16)[name = string("op_3583_cast_fp16")]; + tensor var_3584_cast_fp16 = mul(x = var_3582_cast_fp16, y = var_3583_cast_fp16)[name = string("op_3584_cast_fp16")]; + tensor xt_277_cast_fp16 = add(x = var_3584_cast_fp16, y = var_3579_cast_fp16_1)[name = string("xt_277_cast_fp16")]; + tensor generator_resblocks_11_alpha1_1_to_fp16 = const()[name = string("generator_resblocks_11_alpha1_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41257728)))]; + tensor var_3589_cast_fp16 = mul(x = generator_resblocks_11_alpha1_1_to_fp16, y = xt_277_cast_fp16)[name = string("op_3589_cast_fp16")]; + tensor var_3590_cast_fp16 = sin(x = var_3589_cast_fp16)[name = string("op_3590_cast_fp16")]; + fp16 var_24_promoted_96_to_fp16 = const()[name = string("op_24_promoted_96_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3591_cast_fp16 = pow(x = var_3590_cast_fp16, y = var_24_promoted_96_to_fp16)[name = string("op_3591_cast_fp16")]; + tensor var_3586_to_fp16 = const()[name = string("op_3586_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41257856)))]; + tensor var_3592_cast_fp16 = mul(x = var_3586_to_fp16, y = var_3591_cast_fp16)[name = string("op_3592_cast_fp16")]; + tensor input_365_cast_fp16 = add(x = xt_277_cast_fp16, y = var_3592_cast_fp16)[name = string("input_365_cast_fp16")]; + string input_367_pad_type_0 = const()[name = string("input_367_pad_type_0"), val = string("custom")]; + tensor input_367_pad_0 = const()[name = string("input_367_pad_0"), val = tensor([15, 15])]; + tensor input_367_dilations_0 = const()[name = string("input_367_dilations_0"), val = tensor([3])]; + tensor input_367_strides_0 = const()[name = string("input_367_strides_0"), val = tensor([1])]; + int32 input_367_groups_0 = const()[name = string("input_367_groups_0"), val = int32(1)]; + tensor generator_resblocks_11_convs1_1_weight_to_fp16 = const()[name = string("generator_resblocks_11_convs1_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41257984)))]; + tensor generator_resblocks_11_convs1_1_bias_to_fp16 = const()[name = string("generator_resblocks_11_convs1_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41280576)))]; + tensor input_367_cast_fp16 = conv(bias = generator_resblocks_11_convs1_1_bias_to_fp16, dilations = input_367_dilations_0, groups = input_367_groups_0, pad = input_367_pad_0, pad_type = input_367_pad_type_0, strides = input_367_strides_0, weight = generator_resblocks_11_convs1_1_weight_to_fp16, x = input_365_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor generator_resblocks_11_adain2_1_fc_weight_to_fp16 = const()[name = string("generator_resblocks_11_adain2_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41280704)))]; + tensor generator_resblocks_11_adain2_1_fc_bias_to_fp16 = const()[name = string("generator_resblocks_11_adain2_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41297152)))]; + tensor linear_93_cast_fp16 = linear(bias = generator_resblocks_11_adain2_1_fc_bias_to_fp16, weight = generator_resblocks_11_adain2_1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_93_cast_fp16")]; + tensor var_3607 = const()[name = string("op_3607"), val = tensor([1, 64, 1])]; + tensor h_375_cast_fp16 = reshape(shape = var_3607, x = linear_93_cast_fp16)[name = string("h_375_cast_fp16")]; + tensor var_3609_split_sizes_0 = const()[name = string("op_3609_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3609_axis_0 = const()[name = string("op_3609_axis_0"), val = int32(1)]; + tensor var_3609_cast_fp16_0, tensor var_3609_cast_fp16_1 = split(axis = var_3609_axis_0, split_sizes = var_3609_split_sizes_0, x = h_375_cast_fp16)[name = string("op_3609_cast_fp16")]; + fp16 var_3611_promoted_to_fp16 = const()[name = string("op_3611_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3612_cast_fp16 = add(x = var_3609_cast_fp16_0, y = var_3611_promoted_to_fp16)[name = string("op_3612_cast_fp16")]; + tensor var_3613_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_367_cast_fp16)[name = string("op_3613_cast_fp16")]; + tensor var_3614_cast_fp16 = mul(x = var_3612_cast_fp16, y = var_3613_cast_fp16)[name = string("op_3614_cast_fp16")]; + tensor xt_279_cast_fp16 = add(x = var_3614_cast_fp16, y = var_3609_cast_fp16_1)[name = string("xt_279_cast_fp16")]; + tensor generator_resblocks_11_alpha2_1_to_fp16 = const()[name = string("generator_resblocks_11_alpha2_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41297344)))]; + tensor var_3619_cast_fp16 = mul(x = generator_resblocks_11_alpha2_1_to_fp16, y = xt_279_cast_fp16)[name = string("op_3619_cast_fp16")]; + tensor var_3620_cast_fp16 = sin(x = var_3619_cast_fp16)[name = string("op_3620_cast_fp16")]; + fp16 var_24_promoted_97_to_fp16 = const()[name = string("op_24_promoted_97_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3621_cast_fp16 = pow(x = var_3620_cast_fp16, y = var_24_promoted_97_to_fp16)[name = string("op_3621_cast_fp16")]; + tensor var_3616_to_fp16 = const()[name = string("op_3616_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41297472)))]; + tensor var_3622_cast_fp16 = mul(x = var_3616_to_fp16, y = var_3621_cast_fp16)[name = string("op_3622_cast_fp16")]; + tensor input_369_cast_fp16 = add(x = xt_279_cast_fp16, y = var_3622_cast_fp16)[name = string("input_369_cast_fp16")]; + string xt_281_pad_type_0 = const()[name = string("xt_281_pad_type_0"), val = string("custom")]; + tensor xt_281_pad_0 = const()[name = string("xt_281_pad_0"), val = tensor([5, 5])]; + tensor xt_281_strides_0 = const()[name = string("xt_281_strides_0"), val = tensor([1])]; + tensor xt_281_dilations_0 = const()[name = string("xt_281_dilations_0"), val = tensor([1])]; + int32 xt_281_groups_0 = const()[name = string("xt_281_groups_0"), val = int32(1)]; + tensor generator_resblocks_11_convs2_1_weight_to_fp16 = const()[name = string("generator_resblocks_11_convs2_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41297600)))]; + tensor generator_resblocks_11_convs2_1_bias_to_fp16 = const()[name = string("generator_resblocks_11_convs2_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41320192)))]; + tensor xt_281_cast_fp16 = conv(bias = generator_resblocks_11_convs2_1_bias_to_fp16, dilations = xt_281_dilations_0, groups = xt_281_groups_0, pad = xt_281_pad_0, pad_type = xt_281_pad_type_0, strides = xt_281_strides_0, weight = generator_resblocks_11_convs2_1_weight_to_fp16, x = input_369_cast_fp16)[name = string("xt_281_cast_fp16")]; + tensor input_371_cast_fp16 = add(x = xt_281_cast_fp16, y = input_363_cast_fp16)[name = string("input_371_cast_fp16")]; + tensor generator_resblocks_11_adain1_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_11_adain1_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41320320)))]; + tensor generator_resblocks_11_adain1_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_11_adain1_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41336768)))]; + tensor linear_94_cast_fp16 = linear(bias = generator_resblocks_11_adain1_2_fc_bias_to_fp16, weight = generator_resblocks_11_adain1_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_94_cast_fp16")]; + tensor var_3638 = const()[name = string("op_3638"), val = tensor([1, 64, 1])]; + tensor h_379_cast_fp16 = reshape(shape = var_3638, x = linear_94_cast_fp16)[name = string("h_379_cast_fp16")]; + tensor var_3640_split_sizes_0 = const()[name = string("op_3640_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3640_axis_0 = const()[name = string("op_3640_axis_0"), val = int32(1)]; + tensor var_3640_cast_fp16_0, tensor var_3640_cast_fp16_1 = split(axis = var_3640_axis_0, split_sizes = var_3640_split_sizes_0, x = h_379_cast_fp16)[name = string("op_3640_cast_fp16")]; + fp16 var_3642_promoted_to_fp16 = const()[name = string("op_3642_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3643_cast_fp16 = add(x = var_3640_cast_fp16_0, y = var_3642_promoted_to_fp16)[name = string("op_3643_cast_fp16")]; + tensor var_3644_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_371_cast_fp16)[name = string("op_3644_cast_fp16")]; + tensor var_3645_cast_fp16 = mul(x = var_3643_cast_fp16, y = var_3644_cast_fp16)[name = string("op_3645_cast_fp16")]; + tensor xt_283_cast_fp16 = add(x = var_3645_cast_fp16, y = var_3640_cast_fp16_1)[name = string("xt_283_cast_fp16")]; + tensor generator_resblocks_11_alpha1_2_to_fp16 = const()[name = string("generator_resblocks_11_alpha1_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41336960)))]; + tensor var_3650_cast_fp16 = mul(x = generator_resblocks_11_alpha1_2_to_fp16, y = xt_283_cast_fp16)[name = string("op_3650_cast_fp16")]; + tensor var_3651_cast_fp16 = sin(x = var_3650_cast_fp16)[name = string("op_3651_cast_fp16")]; + fp16 var_24_promoted_98_to_fp16 = const()[name = string("op_24_promoted_98_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3652_cast_fp16 = pow(x = var_3651_cast_fp16, y = var_24_promoted_98_to_fp16)[name = string("op_3652_cast_fp16")]; + tensor var_3647_to_fp16 = const()[name = string("op_3647_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41337088)))]; + tensor var_3653_cast_fp16 = mul(x = var_3647_to_fp16, y = var_3652_cast_fp16)[name = string("op_3653_cast_fp16")]; + tensor input_373_cast_fp16 = add(x = xt_283_cast_fp16, y = var_3653_cast_fp16)[name = string("input_373_cast_fp16")]; + string input_375_pad_type_0 = const()[name = string("input_375_pad_type_0"), val = string("custom")]; + tensor input_375_pad_0 = const()[name = string("input_375_pad_0"), val = tensor([25, 25])]; + tensor input_375_dilations_0 = const()[name = string("input_375_dilations_0"), val = tensor([5])]; + tensor input_375_strides_0 = const()[name = string("input_375_strides_0"), val = tensor([1])]; + int32 input_375_groups_0 = const()[name = string("input_375_groups_0"), val = int32(1)]; + tensor generator_resblocks_11_convs1_2_weight_to_fp16 = const()[name = string("generator_resblocks_11_convs1_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41337216)))]; + tensor generator_resblocks_11_convs1_2_bias_to_fp16 = const()[name = string("generator_resblocks_11_convs1_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41359808)))]; + tensor input_375_cast_fp16 = conv(bias = generator_resblocks_11_convs1_2_bias_to_fp16, dilations = input_375_dilations_0, groups = input_375_groups_0, pad = input_375_pad_0, pad_type = input_375_pad_type_0, strides = input_375_strides_0, weight = generator_resblocks_11_convs1_2_weight_to_fp16, x = input_373_cast_fp16)[name = string("input_375_cast_fp16")]; + tensor generator_resblocks_11_adain2_2_fc_weight_to_fp16 = const()[name = string("generator_resblocks_11_adain2_2_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41359936)))]; + tensor generator_resblocks_11_adain2_2_fc_bias_to_fp16 = const()[name = string("generator_resblocks_11_adain2_2_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41376384)))]; + tensor linear_95_cast_fp16 = linear(bias = generator_resblocks_11_adain2_2_fc_bias_to_fp16, weight = generator_resblocks_11_adain2_2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_95_cast_fp16")]; + tensor var_3668 = const()[name = string("op_3668"), val = tensor([1, 64, 1])]; + tensor h_cast_fp16 = reshape(shape = var_3668, x = linear_95_cast_fp16)[name = string("h_cast_fp16")]; + tensor var_3670_split_sizes_0 = const()[name = string("op_3670_split_sizes_0"), val = tensor([32, 32])]; + int32 var_3670_axis_0 = const()[name = string("op_3670_axis_0"), val = int32(1)]; + tensor var_3670_cast_fp16_0, tensor var_3670_cast_fp16_1 = split(axis = var_3670_axis_0, split_sizes = var_3670_split_sizes_0, x = h_cast_fp16)[name = string("op_3670_cast_fp16")]; + fp16 var_3672_promoted_to_fp16 = const()[name = string("op_3672_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_3673_cast_fp16 = add(x = var_3670_cast_fp16_0, y = var_3672_promoted_to_fp16)[name = string("op_3673_cast_fp16")]; + tensor var_3674_cast_fp16 = instance_norm(epsilon = var_14_to_fp16, x = input_375_cast_fp16)[name = string("op_3674_cast_fp16")]; + tensor var_3675_cast_fp16 = mul(x = var_3673_cast_fp16, y = var_3674_cast_fp16)[name = string("op_3675_cast_fp16")]; + tensor xt_285_cast_fp16 = add(x = var_3675_cast_fp16, y = var_3670_cast_fp16_1)[name = string("xt_285_cast_fp16")]; + tensor generator_resblocks_11_alpha2_2_to_fp16 = const()[name = string("generator_resblocks_11_alpha2_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41376576)))]; + tensor var_3680_cast_fp16 = mul(x = generator_resblocks_11_alpha2_2_to_fp16, y = xt_285_cast_fp16)[name = string("op_3680_cast_fp16")]; + tensor var_3681_cast_fp16 = sin(x = var_3680_cast_fp16)[name = string("op_3681_cast_fp16")]; + fp16 var_24_promoted_99_to_fp16 = const()[name = string("op_24_promoted_99_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3682_cast_fp16 = pow(x = var_3681_cast_fp16, y = var_24_promoted_99_to_fp16)[name = string("op_3682_cast_fp16")]; + tensor var_3677_to_fp16 = const()[name = string("op_3677_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41376704)))]; + tensor var_3683_cast_fp16 = mul(x = var_3677_to_fp16, y = var_3682_cast_fp16)[name = string("op_3683_cast_fp16")]; + tensor input_377_cast_fp16 = add(x = xt_285_cast_fp16, y = var_3683_cast_fp16)[name = string("input_377_cast_fp16")]; + string xt_pad_type_0 = const()[name = string("xt_pad_type_0"), val = string("custom")]; + tensor xt_pad_0 = const()[name = string("xt_pad_0"), val = tensor([5, 5])]; + tensor xt_strides_0 = const()[name = string("xt_strides_0"), val = tensor([1])]; + tensor xt_dilations_0 = const()[name = string("xt_dilations_0"), val = tensor([1])]; + int32 xt_groups_0 = const()[name = string("xt_groups_0"), val = int32(1)]; + tensor generator_resblocks_11_convs2_2_weight_to_fp16 = const()[name = string("generator_resblocks_11_convs2_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41376832)))]; + tensor generator_resblocks_11_convs2_2_bias_to_fp16 = const()[name = string("generator_resblocks_11_convs2_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41399424)))]; + tensor xt_cast_fp16 = conv(bias = generator_resblocks_11_convs2_2_bias_to_fp16, dilations = xt_dilations_0, groups = xt_groups_0, pad = xt_pad_0, pad_type = xt_pad_type_0, strides = xt_strides_0, weight = generator_resblocks_11_convs2_2_weight_to_fp16, x = input_377_cast_fp16)[name = string("xt_cast_fp16")]; + tensor var_3692_cast_fp16 = add(x = xt_cast_fp16, y = input_371_cast_fp16)[name = string("op_3692_cast_fp16")]; + tensor xs_cast_fp16 = add(x = xs_21_cast_fp16, y = var_3692_cast_fp16)[name = string("xs_cast_fp16")]; + fp16 _inversed_x_15_y_0_to_fp16 = const()[name = string("_inversed_x_15_y_0_to_fp16"), val = fp16(0x1.554p-2)]; + tensor _inversed_x_15_cast_fp16 = mul(x = xs_cast_fp16, y = _inversed_x_15_y_0_to_fp16)[name = string("_inversed_x_15_cast_fp16")]; + tensor generator_alphas_4_to_fp16 = const()[name = string("generator_alphas_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41399552)))]; + tensor var_3699_cast_fp16 = mul(x = generator_alphas_4_to_fp16, y = _inversed_x_15_cast_fp16)[name = string("op_3699_cast_fp16")]; + tensor var_3700_cast_fp16 = sin(x = var_3699_cast_fp16)[name = string("op_3700_cast_fp16")]; + fp16 var_24_promoted_100_to_fp16 = const()[name = string("op_24_promoted_100_to_fp16"), val = fp16(0x1p+1)]; + tensor var_3701_cast_fp16 = pow(x = var_3700_cast_fp16, y = var_24_promoted_100_to_fp16)[name = string("op_3701_cast_fp16")]; + tensor var_3696_to_fp16 = const()[name = string("op_3696_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41399680)))]; + tensor var_3702_cast_fp16 = mul(x = var_3696_to_fp16, y = var_3701_cast_fp16)[name = string("op_3702_cast_fp16")]; + tensor input_cast_fp16 = add(x = _inversed_x_15_cast_fp16, y = var_3702_cast_fp16)[name = string("input_cast_fp16")]; + string x_pad_type_0 = const()[name = string("x_pad_type_0"), val = string("custom")]; + tensor x_pad_0 = const()[name = string("x_pad_0"), val = tensor([3, 3])]; + tensor x_strides_0 = const()[name = string("x_strides_0"), val = tensor([1])]; + tensor x_dilations_0 = const()[name = string("x_dilations_0"), val = tensor([1])]; + int32 x_groups_0 = const()[name = string("x_groups_0"), val = int32(1)]; + tensor generator_conv_post_weight_to_fp16 = const()[name = string("generator_conv_post_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41399808)))]; + tensor generator_conv_post_bias_to_fp16 = const()[name = string("generator_conv_post_bias_to_fp16"), val = tensor([0x1.26p-16])]; + tensor x_cast_fp16 = conv(bias = generator_conv_post_bias_to_fp16, dilations = x_dilations_0, groups = x_groups_0, pad = x_pad_0, pad_type = x_pad_type_0, strides = x_strides_0, weight = generator_conv_post_weight_to_fp16, x = input_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_3711 = tanh(x = x_cast_fp16)[name = string("op_3711_cast_fp16")]; + } -> (var_3711); +} \ No newline at end of file diff --git a/iteration_3/compiled/decoder_upsample_fp16.mlmodelc/weights/weight.bin b/iteration_3/compiled/decoder_upsample_fp16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..f9a8dc1e94045ee471746015f2aafc76fa4cb215 --- /dev/null +++ b/iteration_3/compiled/decoder_upsample_fp16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:43161151f001bb951c34952465adfc3c4f5fb8ab2845f31903be09ea9f1a6bc5 +size 41400320 diff --git a/iteration_3/compiled/duration_predictor_fp16.mlmodelc/analytics/coremldata.bin b/iteration_3/compiled/duration_predictor_fp16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..4c869a7d175b6eb080a485ddf34b4ec018cce6c4 --- /dev/null +++ b/iteration_3/compiled/duration_predictor_fp16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ba0441a674538a9fcf54834f20487d807286cb7b9b26b0d5187d4fab9abcd03 +size 243 diff --git a/iteration_3/compiled/duration_predictor_fp16.mlmodelc/coremldata.bin b/iteration_3/compiled/duration_predictor_fp16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..5e7ef17d138d9d9fbfb301fe120fb93299b34df8 --- /dev/null +++ b/iteration_3/compiled/duration_predictor_fp16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b1de14b8aeaf322f76418d3455ffe54d926f74f638130431b68a42b109b5f9c +size 470 diff --git a/iteration_3/compiled/duration_predictor_fp16.mlmodelc/metadata.json b/iteration_3/compiled/duration_predictor_fp16.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c8bc5ffc23a008a3195b38e47a24ed1f29b3790d --- /dev/null +++ b/iteration_3/compiled/duration_predictor_fp16.mlmodelc/metadata.json @@ -0,0 +1,112 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Float16", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16)", + "shortDescription" : "", + "shape" : "[]", + "name" : "input_13", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16)", + "shortDescription" : "", + "shape" : "[]", + "name" : "var_308", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 9, + "mlProgramOperationTypeHistogram" : { + "Ios18.linear" : 4, + "Ios18.sub" : 1, + "Select" : 1, + "Ios18.expandDims" : 2, + "Ios18.gather" : 1, + "Ios18.concat" : 5, + "Shape" : 1, + "Ios18.lstm" : 4, + "Ios18.realDiv" : 1, + "Ios18.add" : 6, + "Ios18.transpose" : 18, + "Ios18.cast" : 5, + "Tile" : 1, + "Ios18.layerNorm" : 3, + "Ios18.equal" : 1, + "Ios18.reshape" : 3, + "Split" : 3, + "Ios18.mul" : 7 + }, + "computePrecision" : "Mixed (Float16, Int16, Int32, UInt16)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "15.0", + "tvOS" : "18.0", + "visionOS" : "2.0", + "watchOS" : "11.0", + "iOS" : "18.0", + "macCatalyst" : "18.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-05-08", + "com.github.apple.coremltools.source" : "torch==2.11.0", + "com.github.apple.coremltools.version" : "9.0", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "inputSchema" : [ + { + "dataType" : "Float32", + "hasShapeFlexibility" : "1", + "isOptional" : "0", + "shapeFlexibility" : "1 × 512 × 1...512", + "shapeRange" : "[[1, 1], [512, 512], [1, 512]]", + "formattedType" : "MultiArray (Float32 1 × 512 × 57)", + "type" : "MultiArray", + "shape" : "[1, 512, 57]", + "name" : "d_en", + "shortDescription" : "" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 128)", + "shortDescription" : "", + "shape" : "[1, 128]", + "name" : "s", + "type" : "MultiArray" + }, + { + "dataType" : "Float32", + "hasShapeFlexibility" : "1", + "isOptional" : "0", + "shapeFlexibility" : "1 × 1...512", + "shapeRange" : "[[1, 1], [1, 512]]", + "formattedType" : "MultiArray (Float32 1 × 57)", + "type" : "MultiArray", + "shape" : "[1, 57]", + "name" : "text_mask", + "shortDescription" : "" + } + ], + "generatedClassName" : "duration_predictor_fp16", + "method" : "predict" + } +] \ No newline at end of file diff --git a/iteration_3/compiled/duration_predictor_fp16.mlmodelc/model.mil b/iteration_3/compiled/duration_predictor_fp16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a8162114af00cb954b5648ad529e95a40c7b86f4 --- /dev/null +++ b/iteration_3/compiled/duration_predictor_fp16.mlmodelc/model.mil @@ -0,0 +1,188 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor d_en, tensor s, tensor text_mask) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"d_en", [1, 512, 57]}, {"text_mask", [1, 57]}}), ("RangeDims", {{"d_en", [[1, 1], [512, 512], [1, 512]]}, {"text_mask", [[1, 1], [1, 512]]}})))] { + string d_en_to_fp16_dtype_0 = const()[name = string("d_en_to_fp16_dtype_0"), val = string("fp16")]; + tensor d_en_to_fp16 = cast(dtype = d_en_to_fp16_dtype_0, x = d_en)[name = string("cast_10")]; + tensor var_25_shape_cast_fp16 = shape(x = d_en_to_fp16)[name = string("op_25_shape_cast_fp16")]; + int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)]; + int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)]; + bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)]; + string var_25_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_25_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_0_indices_0_to_uint16 = const()[name = string("gather_0_indices_0_to_uint16"), val = uint16(2)]; + tensor var_25_shape_cast_fp16_to_int16 = cast(dtype = var_25_shape_cast_fp16_to_int16_dtype_0, x = var_25_shape_cast_fp16)[name = string("cast_9")]; + int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = gather_0_indices_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_25_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")]; + string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + fp16 var_33_to_fp16 = const()[name = string("op_33_to_fp16"), val = fp16(0x1p+0)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_7")]; + tensor var_35_cast_fp16 = sub(x = var_33_to_fp16, y = text_mask_to_fp16)[name = string("op_35_cast_fp16")]; + tensor keep_axes_0 = const()[name = string("keep_axes_0"), val = tensor([1])]; + tensor keep_cast_fp16 = expand_dims(axes = keep_axes_0, x = var_35_cast_fp16)[name = string("keep_cast_fp16")]; + tensor var_39_axes_0 = const()[name = string("op_39_axes_0"), val = tensor([-1])]; + string s_to_fp16_dtype_0 = const()[name = string("s_to_fp16_dtype_0"), val = string("fp16")]; + tensor s_to_fp16 = cast(dtype = s_to_fp16_dtype_0, x = s)[name = string("cast_6")]; + tensor var_39_cast_fp16 = expand_dims(axes = var_39_axes_0, x = s_to_fp16)[name = string("op_39_cast_fp16")]; + int32 var_43 = const()[name = string("op_43"), val = int32(128)]; + int32 var_44 = const()[name = string("op_44"), val = int32(-1)]; + int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)]; + bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)]; + int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_8")]; + tensor concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_44, var_43, gather_0_cast_uint16_to_int32))[name = string("concat_0")]; + tensor shape_0 = const()[name = string("shape_0"), val = tensor([1, 128, 1])]; + int32 equal_0_y_0 = const()[name = string("equal_0_y_0"), val = int32(-1)]; + tensor equal_0 = equal(x = concat_0, y = equal_0_y_0)[name = string("equal_0")]; + tensor select_0 = select(a = shape_0, b = concat_0, cond = equal_0)[name = string("select_0")]; + tensor real_div_0 = real_div(x = select_0, y = shape_0)[name = string("real_div_0")]; + tensor s_exp_cast_fp16 = tile(reps = real_div_0, x = var_39_cast_fp16)[name = string("s_exp_cast_fp16")]; + int32 var_49 = const()[name = string("op_49"), val = int32(1)]; + bool var_50_interleave_0 = const()[name = string("op_50_interleave_0"), val = bool(false)]; + tensor var_50_cast_fp16 = concat(axis = var_49, interleave = var_50_interleave_0, values = (d_en_to_fp16, s_exp_cast_fp16))[name = string("op_50_cast_fp16")]; + tensor x_1_cast_fp16 = mul(x = var_50_cast_fp16, y = keep_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([-1, 0, -2])]; + string x_t_1_batch_first_direction_0 = const()[name = string("x_t_1_batch_first_direction_0"), val = string("bidirectional")]; + bool x_t_1_batch_first_output_sequence_0 = const()[name = string("x_t_1_batch_first_output_sequence_0"), val = bool(true)]; + string x_t_1_batch_first_recurrent_activation_0 = const()[name = string("x_t_1_batch_first_recurrent_activation_0"), val = string("sigmoid")]; + string x_t_1_batch_first_cell_activation_0 = const()[name = string("x_t_1_batch_first_cell_activation_0"), val = string("tanh")]; + string x_t_1_batch_first_activation_0 = const()[name = string("x_t_1_batch_first_activation_0"), val = string("tanh")]; + tensor x_t_1_batch_first_lstm_h0_reshaped_to_fp16 = const()[name = string("x_t_1_batch_first_lstm_h0_reshaped_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152)))]; + tensor concat_6_to_fp16 = const()[name = string("concat_6_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor add_0_to_fp16 = const()[name = string("add_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1836288)))]; + tensor concat_7_to_fp16 = const()[name = string("concat_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1838400)))]; + tensor concat_8_to_fp16 = const()[name = string("concat_8_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3149184)))]; + tensor add_1_to_fp16 = const()[name = string("add_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3673536)))]; + tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = x_1_cast_fp16)[name = string("transpose_26")]; + tensor x_t_1_batch_first_cast_fp16_0, tensor x_t_1_batch_first_cast_fp16_1, tensor x_t_1_batch_first_cast_fp16_2 = lstm(activation = x_t_1_batch_first_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, cell_activation = x_t_1_batch_first_cell_activation_0, direction = x_t_1_batch_first_direction_0, initial_c = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_t_1_batch_first_output_sequence_0, recurrent_activation = x_t_1_batch_first_recurrent_activation_0, weight_hh = concat_6_to_fp16, weight_hh_back = concat_8_to_fp16, weight_ih = concat_5_to_fp16, weight_ih_back = concat_7_to_fp16, x = transpose_0_cast_fp16)[name = string("x_t_1_batch_first_cast_fp16")]; + tensor transpose_6_perm_0 = const()[name = string("transpose_6_perm_0"), val = tensor([1, 0, 2])]; + tensor text_encoder_lstms_1_fc_weight_to_fp16 = const()[name = string("text_encoder_lstms_1_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3675648)))]; + tensor text_encoder_lstms_1_fc_bias_to_fp16 = const()[name = string("text_encoder_lstms_1_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3937856)))]; + tensor linear_0_cast_fp16 = linear(bias = text_encoder_lstms_1_fc_bias_to_fp16, weight = text_encoder_lstms_1_fc_weight_to_fp16, x = s_to_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_105 = const()[name = string("op_105"), val = tensor([1, 1024, 1])]; + tensor h_3_cast_fp16 = reshape(shape = var_105, x = linear_0_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor var_107_split_sizes_0 = const()[name = string("op_107_split_sizes_0"), val = tensor([512, 512])]; + int32 var_107_axis_0 = const()[name = string("op_107_axis_0"), val = int32(1)]; + tensor var_107_cast_fp16_0, tensor var_107_cast_fp16_1 = split(axis = var_107_axis_0, split_sizes = var_107_split_sizes_0, x = h_3_cast_fp16)[name = string("op_107_cast_fp16")]; + tensor gamma_3_perm_0 = const()[name = string("gamma_3_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_3_perm_0 = const()[name = string("beta_3_perm_0"), val = tensor([0, -1, 1])]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + fp16 var_90_to_fp16 = const()[name = string("op_90_to_fp16"), val = fp16(0x1.5p-17)]; + tensor transpose_6_cast_fp16 = transpose(perm = transpose_6_perm_0, x = x_t_1_batch_first_cast_fp16_0)[name = string("transpose_25")]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_90_to_fp16, x = transpose_6_cast_fp16)[name = string("x_9_cast_fp16")]; + fp16 var_113_promoted_to_fp16 = const()[name = string("op_113_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_3_cast_fp16 = transpose(perm = gamma_3_perm_0, x = var_107_cast_fp16_0)[name = string("transpose_24")]; + tensor var_114_cast_fp16 = add(x = gamma_3_cast_fp16, y = var_113_promoted_to_fp16)[name = string("op_114_cast_fp16")]; + tensor var_115_cast_fp16 = mul(x = var_114_cast_fp16, y = x_9_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor beta_3_cast_fp16 = transpose(perm = beta_3_perm_0, x = var_107_cast_fp16_1)[name = string("transpose_23")]; + tensor x_11_cast_fp16 = add(x = var_115_cast_fp16, y = beta_3_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor x_13_perm_0 = const()[name = string("x_13_perm_0"), val = tensor([0, -1, -2])]; + int32 var_123 = const()[name = string("op_123"), val = int32(1)]; + bool var_124_interleave_0 = const()[name = string("op_124_interleave_0"), val = bool(false)]; + tensor x_13_cast_fp16 = transpose(perm = x_13_perm_0, x = x_11_cast_fp16)[name = string("transpose_22")]; + tensor var_124_cast_fp16 = concat(axis = var_123, interleave = var_124_interleave_0, values = (x_13_cast_fp16, s_exp_cast_fp16))[name = string("op_124_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = var_124_cast_fp16, y = keep_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor transpose_2_perm_0 = const()[name = string("transpose_2_perm_0"), val = tensor([-1, 0, -2])]; + string x_t_3_batch_first_direction_0 = const()[name = string("x_t_3_batch_first_direction_0"), val = string("bidirectional")]; + bool x_t_3_batch_first_output_sequence_0 = const()[name = string("x_t_3_batch_first_output_sequence_0"), val = bool(true)]; + string x_t_3_batch_first_recurrent_activation_0 = const()[name = string("x_t_3_batch_first_recurrent_activation_0"), val = string("sigmoid")]; + string x_t_3_batch_first_cell_activation_0 = const()[name = string("x_t_3_batch_first_cell_activation_0"), val = string("tanh")]; + string x_t_3_batch_first_activation_0 = const()[name = string("x_t_3_batch_first_activation_0"), val = string("tanh")]; + tensor concat_15_to_fp16 = const()[name = string("concat_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3939968)))]; + tensor concat_16_to_fp16 = const()[name = string("concat_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5250752)))]; + tensor add_2_to_fp16 = const()[name = string("add_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5775104)))]; + tensor concat_17_to_fp16 = const()[name = string("concat_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5777216)))]; + tensor concat_18_to_fp16 = const()[name = string("concat_18_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7088000)))]; + tensor add_3_to_fp16 = const()[name = string("add_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612352)))]; + tensor transpose_2_cast_fp16 = transpose(perm = transpose_2_perm_0, x = x_15_cast_fp16)[name = string("transpose_21")]; + tensor x_t_3_batch_first_cast_fp16_0, tensor x_t_3_batch_first_cast_fp16_1, tensor x_t_3_batch_first_cast_fp16_2 = lstm(activation = x_t_3_batch_first_activation_0, bias = add_2_to_fp16, bias_back = add_3_to_fp16, cell_activation = x_t_3_batch_first_cell_activation_0, direction = x_t_3_batch_first_direction_0, initial_c = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_t_3_batch_first_output_sequence_0, recurrent_activation = x_t_3_batch_first_recurrent_activation_0, weight_hh = concat_16_to_fp16, weight_hh_back = concat_18_to_fp16, weight_ih = concat_15_to_fp16, weight_ih_back = concat_17_to_fp16, x = transpose_2_cast_fp16)[name = string("x_t_3_batch_first_cast_fp16")]; + tensor transpose_7_perm_0 = const()[name = string("transpose_7_perm_0"), val = tensor([1, 0, 2])]; + tensor text_encoder_lstms_3_fc_weight_to_fp16 = const()[name = string("text_encoder_lstms_3_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7614464)))]; + tensor text_encoder_lstms_3_fc_bias_to_fp16 = const()[name = string("text_encoder_lstms_3_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7876672)))]; + tensor linear_1_cast_fp16 = linear(bias = text_encoder_lstms_3_fc_bias_to_fp16, weight = text_encoder_lstms_3_fc_weight_to_fp16, x = s_to_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_179 = const()[name = string("op_179"), val = tensor([1, 1024, 1])]; + tensor h_7_cast_fp16 = reshape(shape = var_179, x = linear_1_cast_fp16)[name = string("h_7_cast_fp16")]; + tensor var_181_split_sizes_0 = const()[name = string("op_181_split_sizes_0"), val = tensor([512, 512])]; + int32 var_181_axis_0 = const()[name = string("op_181_axis_0"), val = int32(1)]; + tensor var_181_cast_fp16_0, tensor var_181_cast_fp16_1 = split(axis = var_181_axis_0, split_sizes = var_181_split_sizes_0, x = h_7_cast_fp16)[name = string("op_181_cast_fp16")]; + tensor gamma_7_perm_0 = const()[name = string("gamma_7_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_7_perm_0 = const()[name = string("beta_7_perm_0"), val = tensor([0, -1, 1])]; + tensor x_23_axes_0 = const()[name = string("x_23_axes_0"), val = tensor([-1])]; + fp16 var_164_to_fp16 = const()[name = string("op_164_to_fp16"), val = fp16(0x1.5p-17)]; + tensor transpose_7_cast_fp16 = transpose(perm = transpose_7_perm_0, x = x_t_3_batch_first_cast_fp16_0)[name = string("transpose_20")]; + tensor x_23_cast_fp16 = layer_norm(axes = x_23_axes_0, epsilon = var_164_to_fp16, x = transpose_7_cast_fp16)[name = string("x_23_cast_fp16")]; + fp16 var_187_promoted_to_fp16 = const()[name = string("op_187_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_7_cast_fp16 = transpose(perm = gamma_7_perm_0, x = var_181_cast_fp16_0)[name = string("transpose_19")]; + tensor var_188_cast_fp16 = add(x = gamma_7_cast_fp16, y = var_187_promoted_to_fp16)[name = string("op_188_cast_fp16")]; + tensor var_189_cast_fp16 = mul(x = var_188_cast_fp16, y = x_23_cast_fp16)[name = string("op_189_cast_fp16")]; + tensor beta_7_cast_fp16 = transpose(perm = beta_7_perm_0, x = var_181_cast_fp16_1)[name = string("transpose_18")]; + tensor x_25_cast_fp16 = add(x = var_189_cast_fp16, y = beta_7_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_perm_0 = const()[name = string("x_27_perm_0"), val = tensor([0, -1, -2])]; + int32 var_197 = const()[name = string("op_197"), val = int32(1)]; + bool var_198_interleave_0 = const()[name = string("op_198_interleave_0"), val = bool(false)]; + tensor x_27_cast_fp16 = transpose(perm = x_27_perm_0, x = x_25_cast_fp16)[name = string("transpose_17")]; + tensor var_198_cast_fp16 = concat(axis = var_197, interleave = var_198_interleave_0, values = (x_27_cast_fp16, s_exp_cast_fp16))[name = string("op_198_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_198_cast_fp16, y = keep_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor transpose_4_perm_0 = const()[name = string("transpose_4_perm_0"), val = tensor([-1, 0, -2])]; + string x_t_batch_first_direction_0 = const()[name = string("x_t_batch_first_direction_0"), val = string("bidirectional")]; + bool x_t_batch_first_output_sequence_0 = const()[name = string("x_t_batch_first_output_sequence_0"), val = bool(true)]; + string x_t_batch_first_recurrent_activation_0 = const()[name = string("x_t_batch_first_recurrent_activation_0"), val = string("sigmoid")]; + string x_t_batch_first_cell_activation_0 = const()[name = string("x_t_batch_first_cell_activation_0"), val = string("tanh")]; + string x_t_batch_first_activation_0 = const()[name = string("x_t_batch_first_activation_0"), val = string("tanh")]; + tensor concat_25_to_fp16 = const()[name = string("concat_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7878784)))]; + tensor concat_26_to_fp16 = const()[name = string("concat_26_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9189568)))]; + tensor add_4_to_fp16 = const()[name = string("add_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9713920)))]; + tensor concat_27_to_fp16 = const()[name = string("concat_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9716032)))]; + tensor concat_28_to_fp16 = const()[name = string("concat_28_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11026816)))]; + tensor add_5_to_fp16 = const()[name = string("add_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11551168)))]; + tensor transpose_4_cast_fp16 = transpose(perm = transpose_4_perm_0, x = x_29_cast_fp16)[name = string("transpose_16")]; + tensor x_t_batch_first_cast_fp16_0, tensor x_t_batch_first_cast_fp16_1, tensor x_t_batch_first_cast_fp16_2 = lstm(activation = x_t_batch_first_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, cell_activation = x_t_batch_first_cell_activation_0, direction = x_t_batch_first_direction_0, initial_c = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_t_batch_first_output_sequence_0, recurrent_activation = x_t_batch_first_recurrent_activation_0, weight_hh = concat_26_to_fp16, weight_hh_back = concat_28_to_fp16, weight_ih = concat_25_to_fp16, weight_ih_back = concat_27_to_fp16, x = transpose_4_cast_fp16)[name = string("x_t_batch_first_cast_fp16")]; + tensor transpose_8_perm_0 = const()[name = string("transpose_8_perm_0"), val = tensor([1, 0, 2])]; + tensor text_encoder_lstms_5_fc_weight_to_fp16 = const()[name = string("text_encoder_lstms_5_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11553280)))]; + tensor text_encoder_lstms_5_fc_bias_to_fp16 = const()[name = string("text_encoder_lstms_5_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11815488)))]; + tensor linear_2_cast_fp16 = linear(bias = text_encoder_lstms_5_fc_bias_to_fp16, weight = text_encoder_lstms_5_fc_weight_to_fp16, x = s_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_253 = const()[name = string("op_253"), val = tensor([1, 1024, 1])]; + tensor h_cast_fp16 = reshape(shape = var_253, x = linear_2_cast_fp16)[name = string("h_cast_fp16")]; + tensor var_255_split_sizes_0 = const()[name = string("op_255_split_sizes_0"), val = tensor([512, 512])]; + int32 var_255_axis_0 = const()[name = string("op_255_axis_0"), val = int32(1)]; + tensor var_255_cast_fp16_0, tensor var_255_cast_fp16_1 = split(axis = var_255_axis_0, split_sizes = var_255_split_sizes_0, x = h_cast_fp16)[name = string("op_255_cast_fp16")]; + tensor gamma_perm_0 = const()[name = string("gamma_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_perm_0 = const()[name = string("beta_perm_0"), val = tensor([0, -1, 1])]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + fp16 var_238_to_fp16 = const()[name = string("op_238_to_fp16"), val = fp16(0x1.5p-17)]; + tensor transpose_8_cast_fp16 = transpose(perm = transpose_8_perm_0, x = x_t_batch_first_cast_fp16_0)[name = string("transpose_15")]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_238_to_fp16, x = transpose_8_cast_fp16)[name = string("x_37_cast_fp16")]; + fp16 var_261_promoted_to_fp16 = const()[name = string("op_261_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_cast_fp16 = transpose(perm = gamma_perm_0, x = var_255_cast_fp16_0)[name = string("transpose_14")]; + tensor var_262_cast_fp16 = add(x = gamma_cast_fp16, y = var_261_promoted_to_fp16)[name = string("op_262_cast_fp16")]; + tensor var_263_cast_fp16 = mul(x = var_262_cast_fp16, y = x_37_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor beta_cast_fp16 = transpose(perm = beta_perm_0, x = var_255_cast_fp16_1)[name = string("transpose_13")]; + tensor x_39_cast_fp16 = add(x = var_263_cast_fp16, y = beta_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor x_41_perm_0 = const()[name = string("x_41_perm_0"), val = tensor([0, -1, -2])]; + int32 var_271 = const()[name = string("op_271"), val = int32(1)]; + bool var_272_interleave_0 = const()[name = string("op_272_interleave_0"), val = bool(false)]; + tensor x_41_cast_fp16 = transpose(perm = x_41_perm_0, x = x_39_cast_fp16)[name = string("transpose_12")]; + tensor var_272_cast_fp16 = concat(axis = var_271, interleave = var_272_interleave_0, values = (x_41_cast_fp16, s_exp_cast_fp16))[name = string("op_272_cast_fp16")]; + tensor x_cast_fp16 = mul(x = var_272_cast_fp16, y = keep_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, -1, -2])]; + tensor input_13_batch_first_transpose_perm_0 = const()[name = string("input_13_batch_first_transpose_perm_0"), val = tensor([1, 0, 2])]; + string input_batch_first_direction_0 = const()[name = string("input_batch_first_direction_0"), val = string("bidirectional")]; + bool input_batch_first_output_sequence_0 = const()[name = string("input_batch_first_output_sequence_0"), val = bool(true)]; + string input_batch_first_recurrent_activation_0 = const()[name = string("input_batch_first_recurrent_activation_0"), val = string("sigmoid")]; + string input_batch_first_cell_activation_0 = const()[name = string("input_batch_first_cell_activation_0"), val = string("tanh")]; + string input_batch_first_activation_0 = const()[name = string("input_batch_first_activation_0"), val = string("tanh")]; + tensor concat_35_to_fp16 = const()[name = string("concat_35_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11817600)))]; + tensor concat_36_to_fp16 = const()[name = string("concat_36_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13128384)))]; + tensor add_6_to_fp16 = const()[name = string("add_6_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13652736)))]; + tensor concat_37_to_fp16 = const()[name = string("concat_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13654848)))]; + tensor concat_38_to_fp16 = const()[name = string("concat_38_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14965632)))]; + tensor add_7_to_fp16 = const()[name = string("add_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15489984)))]; + tensor input_13 = transpose(perm = input_13_perm_0, x = x_cast_fp16)[name = string("transpose_11")]; + tensor input_13_batch_first_transpose_cast_fp16 = transpose(perm = input_13_batch_first_transpose_perm_0, x = input_13)[name = string("transpose_10")]; + tensor input_batch_first_cast_fp16_0, tensor input_batch_first_cast_fp16_1, tensor input_batch_first_cast_fp16_2 = lstm(activation = input_batch_first_activation_0, bias = add_6_to_fp16, bias_back = add_7_to_fp16, cell_activation = input_batch_first_cell_activation_0, direction = input_batch_first_direction_0, initial_c = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_batch_first_output_sequence_0, recurrent_activation = input_batch_first_recurrent_activation_0, weight_hh = concat_36_to_fp16, weight_hh_back = concat_38_to_fp16, weight_ih = concat_35_to_fp16, weight_ih_back = concat_37_to_fp16, x = input_13_batch_first_transpose_cast_fp16)[name = string("input_batch_first_cast_fp16")]; + tensor input_perm_0 = const()[name = string("input_perm_0"), val = tensor([1, 0, 2])]; + tensor duration_proj_linear_layer_weight_to_fp16 = const()[name = string("duration_proj_linear_layer_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15492096)))]; + tensor duration_proj_linear_layer_bias_to_fp16 = const()[name = string("duration_proj_linear_layer_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15543360)))]; + tensor input_cast_fp16 = transpose(perm = input_perm_0, x = input_batch_first_cast_fp16_0)[name = string("transpose_9")]; + tensor var_308 = linear(bias = duration_proj_linear_layer_bias_to_fp16, weight = duration_proj_linear_layer_weight_to_fp16, x = input_cast_fp16)[name = string("linear_3_cast_fp16")]; + } -> (input_13, var_308); +} \ No newline at end of file diff --git a/iteration_3/compiled/duration_predictor_fp16.mlmodelc/weights/weight.bin b/iteration_3/compiled/duration_predictor_fp16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..86cee48688324f08a613657903236319bb4179af --- /dev/null +++ b/iteration_3/compiled/duration_predictor_fp16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75ba0b7b2f7dc6a687e9ec01d226c300b09f07832d8e4aac2705a16b5079910c +size 15543524 diff --git a/iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/analytics/coremldata.bin b/iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..5fb77c7ecb56e0acb826f019fedbd08655401e7a --- /dev/null +++ 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0000000000000000000000000000000000000000..b17a11d93a5bd0ab284cb830922cfb7fd833218b --- /dev/null +++ b/iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/metadata.json @@ -0,0 +1,110 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Float16", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 1 × 256)", + "shortDescription" : "", + "shape" : "[1, 1, 256]", + "name" : "var_6189", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 9, + "mlProgramOperationTypeHistogram" : { + "Ios18.expandDims" : 16, + "Ios18.softmax" : 24, + "Ios18.mul" : 117, + "Ios18.matmul" : 48, + "Ios16.reduceMean" : 8, + "Split" : 72, + "Tile" : 16, + "Ios18.add" : 188, + "Ios16.reduceSum" : 8, + "Ios18.layerNorm" : 24, + "Ios18.reshape" : 102, + "Ios18.linear" : 143, + "Ios18.conv" : 8, + "Ios18.gelu" : 41, + "Ios18.sub" : 8, + "Ios18.concat" : 8, + "Stack" : 8, + "Ios18.transpose" : 216, + "Ios18.cast" : 4, + "Ios18.sliceByIndex" : 4 + }, + "computePrecision" : "Mixed (Float16, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "15.0", + "tvOS" : "18.0", + "visionOS" : "2.0", + "watchOS" : "11.0", + "iOS" : "18.0", + "macCatalyst" : "18.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-05-08", + "com.github.apple.coremltools.source" : "torch==2.11.0", + "com.github.apple.coremltools.version" : "9.0", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1 × 256)", + "shortDescription" : "", + "shape" : "[1, 1, 256]", + "name" : "noise_init", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 4 × 1 × 1 × 256)", + "shortDescription" : "", + "shape" : "[4, 1, 1, 256]", + "name" : "noises_aux", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 57 × 768)", + "shortDescription" : "", + "shape" : "[1, 57, 768]", + "name" : "embedding", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 256)", + "shortDescription" : "", + "shape" : "[1, 256]", + "name" : "features", + "type" : "MultiArray" + } + ], + "generatedClassName" : "fused_diffusion_sampler_fp16", + "method" : "predict" + } +] \ No newline at end of file diff --git a/iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/model.mil b/iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..1d2c4d27f6f2a5423e725890061d2ca9b6df23b7 --- /dev/null +++ b/iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/model.mil @@ -0,0 +1,2019 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor embedding, tensor features, tensor noise_init, tensor noises_aux) { + fp16 var_12_to_fp16 = const()[name = string("op_12_to_fp16"), val = fp16(0x1.8p+1)]; + string noise_init_to_fp16_dtype_0 = const()[name = string("noise_init_to_fp16_dtype_0"), val = string("fp16")]; + tensor noise_init_to_fp16 = cast(dtype = noise_init_to_fp16_dtype_0, x = noise_init)[name = string("cast_196")]; + tensor x_noisy_1_cast_fp16 = mul(x = var_12_to_fp16, y = noise_init_to_fp16)[name = string("x_noisy_1_cast_fp16")]; + int32 var_35 = const()[name = string("op_35"), val = int32(-1)]; + tensor c_in_1_to_fp16 = const()[name = string("c_in_1_to_fp16"), val = tensor([[[0x1.548p-2]]])]; + tensor x_11_cast_fp16 = mul(x = c_in_1_to_fp16, y = x_noisy_1_cast_fp16)[name = string("x_11_cast_fp16")]; + string features_to_fp16_dtype_0 = const()[name = string("features_to_fp16_dtype_0"), val = string("fp16")]; + tensor unet_wrap_kdiffusion_net_to_features_0_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_to_features_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor unet_wrap_kdiffusion_net_to_features_0_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_to_features_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416)))]; + tensor features_to_fp16 = cast(dtype = features_to_fp16_dtype_0, x = features)[name = string("cast_195")]; + tensor linear_1_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_features_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_features_0_weight_to_fp16, x = features_to_fp16)[name = string("linear_1_cast_fp16")]; + string var_423_mode_0 = const()[name = string("op_423_mode_0"), val = string("EXACT")]; + tensor var_423_cast_fp16 = gelu(mode = var_423_mode_0, x = linear_1_cast_fp16)[name = string("op_423_cast_fp16")]; + int32 x_7_axis_0 = const()[name = string("x_7_axis_0"), val = int32(0)]; + tensor var_421_to_fp16 = const()[name = string("op_421_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526528)))]; + tensor x_7_cast_fp16 = stack(axis = x_7_axis_0, values = (var_421_to_fp16, var_423_cast_fp16))[name = string("x_7_cast_fp16")]; + tensor var_426 = const()[name = string("op_426"), val = tensor([1, 2, 0])]; + tensor input_7_axes_0 = const()[name = string("input_7_axes_0"), val = tensor([2])]; + bool input_7_keep_dims_0 = const()[name = string("input_7_keep_dims_0"), val = bool(false)]; + tensor x_9_cast_fp16 = transpose(perm = var_426, x = x_7_cast_fp16)[name = string("transpose_335")]; + tensor input_7_cast_fp16 = reduce_sum(axes = input_7_axes_0, keep_dims = input_7_keep_dims_0, x = x_9_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_to_mapping_0_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_to_mapping_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(528640)))]; + tensor unet_wrap_kdiffusion_net_to_mapping_0_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_to_mapping_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2625856)))]; + tensor linear_2_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_0_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_2_cast_fp16")]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("EXACT")]; + tensor input_11_cast_fp16 = gelu(mode = input_11_mode_0, x = linear_2_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_to_mapping_2_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_to_mapping_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2627968)))]; + tensor unet_wrap_kdiffusion_net_to_mapping_2_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_to_mapping_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4725184)))]; + tensor linear_3_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_2_weight_to_fp16, x = input_11_cast_fp16)[name = string("linear_3_cast_fp16")]; + string mapping_1_mode_0 = const()[name = string("mapping_1_mode_0"), val = string("EXACT")]; + tensor mapping_1_cast_fp16 = gelu(mode = mapping_1_mode_0, x = linear_3_cast_fp16)[name = string("mapping_1_cast_fp16")]; + tensor var_436_reps_0 = const()[name = string("op_436_reps_0"), val = tensor([1, 57, 1])]; + tensor var_436_cast_fp16 = tile(reps = var_436_reps_0, x = x_11_cast_fp16)[name = string("op_436_cast_fp16")]; + bool x_13_interleave_0 = const()[name = string("x_13_interleave_0"), val = bool(false)]; + string embedding_to_fp16_dtype_0 = const()[name = string("embedding_to_fp16_dtype_0"), val = string("fp16")]; + tensor embedding_to_fp16 = cast(dtype = embedding_to_fp16_dtype_0, x = embedding)[name = string("cast_194")]; + tensor x_13_cast_fp16 = concat(axis = var_35, interleave = x_13_interleave_0, values = (var_436_cast_fp16, embedding_to_fp16))[name = string("x_13_cast_fp16")]; + tensor var_439_axes_0 = const()[name = string("op_439_axes_0"), val = tensor([1])]; + tensor var_439_cast_fp16 = expand_dims(axes = var_439_axes_0, x = mapping_1_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor mapping_3_reps_0 = const()[name = string("mapping_3_reps_0"), val = tensor([1, 57, 1])]; + tensor mapping_3_cast_fp16 = tile(reps = mapping_3_reps_0, x = var_439_cast_fp16)[name = string("mapping_3_cast_fp16")]; + tensor x_15_cast_fp16 = add(x = x_13_cast_fp16, y = mapping_3_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_0_attention_norm_fc_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_attention_norm_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4727296)))]; + tensor unet_wrap_kdiffusion_net_blocks_0_attention_norm_fc_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_attention_norm_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5775936)))]; + tensor linear_4_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_attention_norm_fc_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_norm_fc_weight_to_fp16, x = features_to_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_449 = const()[name = string("op_449"), val = tensor([1, 2048, 1])]; + tensor h_3_cast_fp16 = reshape(shape = var_449, x = linear_4_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor var_451_split_sizes_0 = const()[name = string("op_451_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_451_axis_0 = const()[name = string("op_451_axis_0"), val = int32(1)]; + tensor var_451_cast_fp16_0, tensor var_451_cast_fp16_1 = split(axis = var_451_axis_0, split_sizes = var_451_split_sizes_0, x = h_3_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor gamma_3_perm_0 = const()[name = string("gamma_3_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_3_perm_0 = const()[name = string("beta_3_perm_0"), val = tensor([0, -1, 1])]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + fp16 var_31_to_fp16 = const()[name = string("op_31_to_fp16"), val = fp16(0x1.5p-17)]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_31_to_fp16, x = x_15_cast_fp16)[name = string("x_19_cast_fp16")]; + fp16 var_457_promoted_to_fp16 = const()[name = string("op_457_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_3_cast_fp16 = transpose(perm = gamma_3_perm_0, x = var_451_cast_fp16_0)[name = string("transpose_334")]; + tensor var_458_cast_fp16 = add(x = gamma_3_cast_fp16, y = var_457_promoted_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = var_458_cast_fp16, y = x_19_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor beta_3_cast_fp16 = transpose(perm = beta_3_perm_0, x = var_451_cast_fp16_1)[name = string("transpose_333")]; + tensor x_21_cast_fp16 = add(x = var_459_cast_fp16, y = beta_3_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_0_attention_norm_context_fc_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_attention_norm_context_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5780096)))]; + tensor unet_wrap_kdiffusion_net_blocks_0_attention_norm_context_fc_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_attention_norm_context_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6828736)))]; + tensor linear_5_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_attention_norm_context_fc_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_norm_context_fc_weight_to_fp16, x = features_to_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([1, 2048, 1])]; + tensor h_7_cast_fp16 = reshape(shape = var_468, x = linear_5_cast_fp16)[name = string("h_7_cast_fp16")]; + tensor var_470_split_sizes_0 = const()[name = string("op_470_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_470_axis_0 = const()[name = string("op_470_axis_0"), val = int32(1)]; + tensor var_470_cast_fp16_0, tensor var_470_cast_fp16_1 = split(axis = var_470_axis_0, split_sizes = var_470_split_sizes_0, x = h_7_cast_fp16)[name = string("op_470_cast_fp16")]; + tensor gamma_7_perm_0 = const()[name = string("gamma_7_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_7_perm_0 = const()[name = string("beta_7_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_476_promoted_to_fp16 = const()[name = string("op_476_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_7_cast_fp16 = transpose(perm = gamma_7_perm_0, x = var_470_cast_fp16_0)[name = string("transpose_332")]; + tensor var_477_cast_fp16 = add(x = gamma_7_cast_fp16, y = var_476_promoted_to_fp16)[name = string("op_477_cast_fp16")]; + tensor var_478_cast_fp16 = mul(x = var_477_cast_fp16, y = x_19_cast_fp16)[name = string("op_478_cast_fp16")]; + tensor beta_7_cast_fp16 = transpose(perm = beta_7_perm_0, x = var_470_cast_fp16_1)[name = string("transpose_331")]; + tensor x_27_cast_fp16 = add(x = var_478_cast_fp16, y = beta_7_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_0_attention_to_q_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_attention_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6832896)))]; + tensor linear_6_bias_0_to_fp16 = const()[name = string("linear_6_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7881536)))]; + tensor linear_6_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_q_weight_to_fp16, x = x_21_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_0_attention_to_kv_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_attention_to_kv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7882624)))]; + tensor linear_7_bias_0_to_fp16 = const()[name = string("linear_7_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9979840)))]; + tensor linear_7_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_kv_weight_to_fp16, x = x_27_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_484_split_sizes_0 = const()[name = string("op_484_split_sizes_0"), val = tensor([512, 512])]; + int32 var_484_axis_0 = const()[name = string("op_484_axis_0"), val = int32(-1)]; + tensor var_484_cast_fp16_0, tensor var_484_cast_fp16_1 = split(axis = var_484_axis_0, split_sizes = var_484_split_sizes_0, x = linear_7_cast_fp16)[name = string("op_484_cast_fp16")]; + tensor var_492 = const()[name = string("op_492"), val = tensor([1, 57, 8, 64])]; + tensor x_31_cast_fp16 = reshape(shape = var_492, x = linear_6_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_502 = const()[name = string("op_502"), val = tensor([1, 57, 8, 64])]; + tensor x_35_cast_fp16 = reshape(shape = var_502, x = var_484_cast_fp16_0)[name = string("x_35_cast_fp16")]; + tensor var_512 = const()[name = string("op_512"), val = tensor([1, 57, 8, 64])]; + tensor x_39_cast_fp16 = reshape(shape = var_512, x = var_484_cast_fp16_1)[name = string("x_39_cast_fp16")]; + tensor var_514 = const()[name = string("op_514"), val = tensor([0, 2, 1, 3])]; + bool sim_1_transpose_x_0 = const()[name = string("sim_1_transpose_x_0"), val = bool(false)]; + bool sim_1_transpose_y_0 = const()[name = string("sim_1_transpose_y_0"), val = bool(false)]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = x_35_cast_fp16)[name = string("transpose_328")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = x_31_cast_fp16)[name = string("transpose_329")]; + tensor sim_1_cast_fp16 = matmul(transpose_x = sim_1_transpose_x_0, transpose_y = sim_1_transpose_y_0, x = transpose_72, y = transpose_73)[name = string("sim_1_cast_fp16")]; + fp16 var_518_to_fp16 = const()[name = string("op_518_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_3_cast_fp16 = mul(x = sim_1_cast_fp16, y = var_518_to_fp16)[name = string("sim_3_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_35, x = sim_3_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool x_41_transpose_x_0 = const()[name = string("x_41_transpose_x_0"), val = bool(false)]; + bool x_41_transpose_y_0 = const()[name = string("x_41_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = var_514, x = x_39_cast_fp16)[name = string("transpose_330")]; + tensor x_41_cast_fp16 = matmul(transpose_x = x_41_transpose_x_0, transpose_y = x_41_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor var_540 = const()[name = string("op_540"), val = tensor([0, 2, 1, 3])]; + tensor var_542 = const()[name = string("op_542"), val = tensor([1, 57, 512])]; + tensor x_43_cast_fp16 = transpose(perm = var_540, x = x_41_cast_fp16)[name = string("transpose_327")]; + tensor input_23_cast_fp16 = reshape(shape = var_542, x = x_43_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9981952)))]; + tensor unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11030592)))]; + tensor linear_8_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_weight_to_fp16, x = input_23_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_25_cast_fp16 = add(x = linear_8_cast_fp16, y = x_15_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032704)))]; + tensor unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15227072)))]; + tensor linear_9_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_weight_to_fp16, x = input_25_cast_fp16)[name = string("linear_9_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = linear_9_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15231232)))]; + tensor unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19425600)))]; + tensor linear_10_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_weight_to_fp16, x = input_29_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor x_45_cast_fp16 = add(x = linear_10_cast_fp16, y = input_25_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = x_45_cast_fp16, y = mapping_3_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_1_attention_norm_fc_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_attention_norm_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19427712)))]; + tensor unet_wrap_kdiffusion_net_blocks_1_attention_norm_fc_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_attention_norm_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20476352)))]; + tensor linear_11_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_attention_norm_fc_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_norm_fc_weight_to_fp16, x = features_to_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_556 = const()[name = string("op_556"), val = tensor([1, 2048, 1])]; + tensor h_11_cast_fp16 = reshape(shape = var_556, x = linear_11_cast_fp16)[name = string("h_11_cast_fp16")]; + tensor var_558_split_sizes_0 = const()[name = string("op_558_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_558_axis_0 = const()[name = string("op_558_axis_0"), val = int32(1)]; + tensor var_558_cast_fp16_0, tensor var_558_cast_fp16_1 = split(axis = var_558_axis_0, split_sizes = var_558_split_sizes_0, x = h_11_cast_fp16)[name = string("op_558_cast_fp16")]; + tensor gamma_11_perm_0 = const()[name = string("gamma_11_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_11_perm_0 = const()[name = string("beta_11_perm_0"), val = tensor([0, -1, 1])]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_31_to_fp16, x = x_47_cast_fp16)[name = string("x_51_cast_fp16")]; + fp16 var_564_promoted_to_fp16 = const()[name = string("op_564_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_11_cast_fp16 = transpose(perm = gamma_11_perm_0, x = var_558_cast_fp16_0)[name = string("transpose_326")]; + tensor var_565_cast_fp16 = add(x = gamma_11_cast_fp16, y = var_564_promoted_to_fp16)[name = string("op_565_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = var_565_cast_fp16, y = x_51_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor beta_11_cast_fp16 = transpose(perm = beta_11_perm_0, x = var_558_cast_fp16_1)[name = string("transpose_325")]; + tensor x_53_cast_fp16 = add(x = var_566_cast_fp16, y = beta_11_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_1_attention_norm_context_fc_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_attention_norm_context_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20480512)))]; + tensor unet_wrap_kdiffusion_net_blocks_1_attention_norm_context_fc_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_attention_norm_context_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21529152)))]; + tensor linear_12_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_attention_norm_context_fc_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_norm_context_fc_weight_to_fp16, x = features_to_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_575 = const()[name = string("op_575"), val = tensor([1, 2048, 1])]; + tensor h_15_cast_fp16 = reshape(shape = var_575, x = linear_12_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor var_577_split_sizes_0 = const()[name = string("op_577_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_577_axis_0 = const()[name = string("op_577_axis_0"), val = int32(1)]; + tensor var_577_cast_fp16_0, tensor var_577_cast_fp16_1 = split(axis = var_577_axis_0, split_sizes = var_577_split_sizes_0, x = h_15_cast_fp16)[name = string("op_577_cast_fp16")]; + tensor gamma_15_perm_0 = const()[name = string("gamma_15_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_15_perm_0 = const()[name = string("beta_15_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_583_promoted_to_fp16 = const()[name = string("op_583_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_15_cast_fp16 = transpose(perm = gamma_15_perm_0, x = var_577_cast_fp16_0)[name = string("transpose_324")]; + tensor var_584_cast_fp16 = add(x = gamma_15_cast_fp16, y = var_583_promoted_to_fp16)[name = string("op_584_cast_fp16")]; + tensor var_585_cast_fp16 = mul(x = var_584_cast_fp16, y = x_51_cast_fp16)[name = string("op_585_cast_fp16")]; + tensor beta_15_cast_fp16 = transpose(perm = beta_15_perm_0, x = var_577_cast_fp16_1)[name = string("transpose_323")]; + tensor x_59_cast_fp16 = add(x = var_585_cast_fp16, y = beta_15_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_1_attention_to_q_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_attention_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533312)))]; + tensor linear_13_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_q_weight_to_fp16, x = x_53_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_1_attention_to_kv_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_attention_to_kv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22581952)))]; + tensor linear_14_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_kv_weight_to_fp16, x = x_59_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_591_split_sizes_0 = const()[name = string("op_591_split_sizes_0"), val = tensor([512, 512])]; + int32 var_591_axis_0 = const()[name = string("op_591_axis_0"), val = int32(-1)]; + tensor var_591_cast_fp16_0, tensor var_591_cast_fp16_1 = split(axis = var_591_axis_0, split_sizes = var_591_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_591_cast_fp16")]; + tensor var_599 = const()[name = string("op_599"), val = tensor([1, 57, 8, 64])]; + tensor x_63_cast_fp16 = reshape(shape = var_599, x = linear_13_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_609 = const()[name = string("op_609"), val = tensor([1, 57, 8, 64])]; + tensor x_67_cast_fp16 = reshape(shape = var_609, x = var_591_cast_fp16_0)[name = string("x_67_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([1, 57, 8, 64])]; + tensor x_71_cast_fp16 = reshape(shape = var_619, x = var_591_cast_fp16_1)[name = string("x_71_cast_fp16")]; + tensor var_621 = const()[name = string("op_621"), val = tensor([0, 2, 1, 3])]; + bool sim_5_transpose_x_0 = const()[name = string("sim_5_transpose_x_0"), val = bool(false)]; + bool sim_5_transpose_y_0 = const()[name = string("sim_5_transpose_y_0"), val = bool(false)]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = x_67_cast_fp16)[name = string("transpose_320")]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = x_63_cast_fp16)[name = string("transpose_321")]; + tensor sim_5_cast_fp16 = matmul(transpose_x = sim_5_transpose_x_0, transpose_y = sim_5_transpose_y_0, x = transpose_74, y = transpose_75)[name = string("sim_5_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_7_cast_fp16 = mul(x = sim_5_cast_fp16, y = var_625_to_fp16)[name = string("sim_7_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_35, x = sim_7_cast_fp16)[name = string("attn_3_cast_fp16")]; + bool x_73_transpose_x_0 = const()[name = string("x_73_transpose_x_0"), val = bool(false)]; + bool x_73_transpose_y_0 = const()[name = string("x_73_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = var_621, x = x_71_cast_fp16)[name = string("transpose_322")]; + tensor x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = attn_3_cast_fp16, y = v_3_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_647 = const()[name = string("op_647"), val = tensor([0, 2, 1, 3])]; + tensor var_649 = const()[name = string("op_649"), val = tensor([1, 57, 512])]; + tensor x_75_cast_fp16 = transpose(perm = var_647, x = x_73_cast_fp16)[name = string("transpose_319")]; + tensor input_39_cast_fp16 = reshape(shape = var_649, x = x_75_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24679168)))]; + tensor unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25727808)))]; + tensor linear_15_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_weight_to_fp16, x = input_39_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = linear_15_cast_fp16, y = x_47_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25729920)))]; + tensor unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29924288)))]; + tensor linear_16_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_weight_to_fp16, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + string input_45_mode_0 = const()[name = string("input_45_mode_0"), val = string("EXACT")]; + tensor input_45_cast_fp16 = gelu(mode = input_45_mode_0, x = linear_16_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29928448)))]; + tensor unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34122816)))]; + tensor linear_17_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_weight_to_fp16, x = input_45_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor x_77_cast_fp16 = add(x = linear_17_cast_fp16, y = input_41_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor x_79_cast_fp16 = add(x = x_77_cast_fp16, y = mapping_3_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_2_attention_norm_fc_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_attention_norm_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34124928)))]; + tensor unet_wrap_kdiffusion_net_blocks_2_attention_norm_fc_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_attention_norm_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35173568)))]; + tensor linear_18_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_attention_norm_fc_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_norm_fc_weight_to_fp16, x = features_to_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_663 = const()[name = string("op_663"), val = tensor([1, 2048, 1])]; + tensor h_19_cast_fp16 = reshape(shape = var_663, x = linear_18_cast_fp16)[name = string("h_19_cast_fp16")]; + tensor var_665_split_sizes_0 = const()[name = string("op_665_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_665_axis_0 = const()[name = string("op_665_axis_0"), val = int32(1)]; + tensor var_665_cast_fp16_0, tensor var_665_cast_fp16_1 = split(axis = var_665_axis_0, split_sizes = var_665_split_sizes_0, x = h_19_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor gamma_19_perm_0 = const()[name = string("gamma_19_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_19_perm_0 = const()[name = string("beta_19_perm_0"), val = tensor([0, -1, 1])]; + tensor x_83_axes_0 = const()[name = string("x_83_axes_0"), val = tensor([-1])]; + tensor x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, epsilon = var_31_to_fp16, x = x_79_cast_fp16)[name = string("x_83_cast_fp16")]; + fp16 var_671_promoted_to_fp16 = const()[name = string("op_671_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_19_cast_fp16 = transpose(perm = gamma_19_perm_0, x = var_665_cast_fp16_0)[name = string("transpose_318")]; + tensor var_672_cast_fp16 = add(x = gamma_19_cast_fp16, y = var_671_promoted_to_fp16)[name = string("op_672_cast_fp16")]; + tensor var_673_cast_fp16 = mul(x = var_672_cast_fp16, y = x_83_cast_fp16)[name = string("op_673_cast_fp16")]; + tensor beta_19_cast_fp16 = transpose(perm = beta_19_perm_0, x = var_665_cast_fp16_1)[name = string("transpose_317")]; + tensor x_85_cast_fp16 = add(x = var_673_cast_fp16, y = beta_19_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_2_attention_norm_context_fc_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_attention_norm_context_fc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35177728)))]; + tensor unet_wrap_kdiffusion_net_blocks_2_attention_norm_context_fc_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_attention_norm_context_fc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36226368)))]; + tensor linear_19_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_attention_norm_context_fc_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_norm_context_fc_weight_to_fp16, x = features_to_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_682 = const()[name = string("op_682"), val = tensor([1, 2048, 1])]; + tensor h_23_cast_fp16 = reshape(shape = var_682, x = linear_19_cast_fp16)[name = string("h_23_cast_fp16")]; + tensor var_684_split_sizes_0 = const()[name = string("op_684_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_684_axis_0 = const()[name = string("op_684_axis_0"), val = int32(1)]; + tensor var_684_cast_fp16_0, tensor var_684_cast_fp16_1 = split(axis = var_684_axis_0, split_sizes = var_684_split_sizes_0, x = h_23_cast_fp16)[name = string("op_684_cast_fp16")]; + tensor gamma_23_perm_0 = const()[name = string("gamma_23_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_23_perm_0 = const()[name = string("beta_23_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_690_promoted_to_fp16 = const()[name = string("op_690_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_23_cast_fp16 = transpose(perm = gamma_23_perm_0, x = var_684_cast_fp16_0)[name = string("transpose_316")]; + tensor var_691_cast_fp16 = add(x = gamma_23_cast_fp16, y = var_690_promoted_to_fp16)[name = string("op_691_cast_fp16")]; + tensor var_692_cast_fp16 = mul(x = var_691_cast_fp16, y = x_83_cast_fp16)[name = string("op_692_cast_fp16")]; + tensor beta_23_cast_fp16 = transpose(perm = beta_23_perm_0, x = var_684_cast_fp16_1)[name = string("transpose_315")]; + tensor x_91_cast_fp16 = add(x = var_692_cast_fp16, y = beta_23_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_2_attention_to_q_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_attention_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36230528)))]; + tensor linear_20_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_q_weight_to_fp16, x = x_85_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_2_attention_to_kv_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_attention_to_kv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37279168)))]; + tensor linear_21_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_kv_weight_to_fp16, x = x_91_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_698_split_sizes_0 = const()[name = string("op_698_split_sizes_0"), val = tensor([512, 512])]; + int32 var_698_axis_0 = const()[name = string("op_698_axis_0"), val = int32(-1)]; + tensor var_698_cast_fp16_0, tensor var_698_cast_fp16_1 = split(axis = var_698_axis_0, split_sizes = var_698_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_706 = const()[name = string("op_706"), val = tensor([1, 57, 8, 64])]; + tensor x_95_cast_fp16 = reshape(shape = var_706, x = linear_20_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_716 = const()[name = string("op_716"), val = tensor([1, 57, 8, 64])]; + tensor x_99_cast_fp16 = reshape(shape = var_716, x = var_698_cast_fp16_0)[name = string("x_99_cast_fp16")]; + tensor var_726 = const()[name = string("op_726"), val = tensor([1, 57, 8, 64])]; + tensor x_103_cast_fp16 = reshape(shape = var_726, x = var_698_cast_fp16_1)[name = string("x_103_cast_fp16")]; + tensor var_728 = const()[name = string("op_728"), val = tensor([0, 2, 1, 3])]; + bool sim_9_transpose_x_0 = const()[name = string("sim_9_transpose_x_0"), val = bool(false)]; + bool sim_9_transpose_y_0 = const()[name = string("sim_9_transpose_y_0"), val = bool(false)]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = x_99_cast_fp16)[name = string("transpose_312")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = x_95_cast_fp16)[name = string("transpose_313")]; + tensor sim_9_cast_fp16 = matmul(transpose_x = sim_9_transpose_x_0, transpose_y = sim_9_transpose_y_0, x = transpose_76, y = transpose_77)[name = string("sim_9_cast_fp16")]; + fp16 var_732_to_fp16 = const()[name = string("op_732_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_11_cast_fp16 = mul(x = sim_9_cast_fp16, y = var_732_to_fp16)[name = string("sim_11_cast_fp16")]; + tensor attn_5_cast_fp16 = softmax(axis = var_35, x = sim_11_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool x_105_transpose_x_0 = const()[name = string("x_105_transpose_x_0"), val = bool(false)]; + bool x_105_transpose_y_0 = const()[name = string("x_105_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = var_728, x = x_103_cast_fp16)[name = string("transpose_314")]; + tensor x_105_cast_fp16 = matmul(transpose_x = x_105_transpose_x_0, transpose_y = x_105_transpose_y_0, x = attn_5_cast_fp16, y = v_5_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_754 = const()[name = string("op_754"), val = tensor([0, 2, 1, 3])]; + tensor var_756 = const()[name = string("op_756"), val = tensor([1, 57, 512])]; + tensor x_107_cast_fp16 = transpose(perm = var_754, x = x_105_cast_fp16)[name = string("transpose_311")]; + tensor input_55_cast_fp16 = reshape(shape = var_756, x = x_107_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39376384)))]; + tensor unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40425024)))]; + tensor linear_22_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_weight_to_fp16, x = input_55_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = linear_22_cast_fp16, y = x_79_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40427136)))]; + tensor unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44621504)))]; + tensor linear_23_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_weight_to_fp16, x = input_57_cast_fp16)[name = string("linear_23_cast_fp16")]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("EXACT")]; + tensor input_61_cast_fp16 = gelu(mode = input_61_mode_0, x = linear_23_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44625664)))]; + tensor unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48820032)))]; + tensor linear_24_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_weight_to_fp16, x = input_61_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor x_109_cast_fp16 = add(x = linear_24_cast_fp16, y = input_57_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(false)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = x_109_cast_fp16)[name = string("op_765_cast_fp16")]; + tensor x_111_axes_0 = const()[name = string("x_111_axes_0"), val = tensor([1])]; + tensor x_111_cast_fp16 = expand_dims(axes = x_111_axes_0, x = var_765_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_767 = const()[name = string("op_767"), val = tensor([0, 2, 1])]; + string x_113_pad_type_0 = const()[name = string("x_113_pad_type_0"), val = string("valid")]; + tensor x_113_strides_0 = const()[name = string("x_113_strides_0"), val = tensor([1])]; + tensor x_113_pad_0 = const()[name = string("x_113_pad_0"), val = tensor([0, 0])]; + tensor x_113_dilations_0 = const()[name = string("x_113_dilations_0"), val = tensor([1])]; + int32 x_113_groups_0 = const()[name = string("x_113_groups_0"), val = int32(1)]; + tensor unet_wrap_kdiffusion_net_to_out_1_weight_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_to_out_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48822144)))]; + tensor unet_wrap_kdiffusion_net_to_out_1_bias_to_fp16 = const()[name = string("unet_wrap_kdiffusion_net_to_out_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49346496)))]; + tensor input_63_cast_fp16 = transpose(perm = var_767, x = x_111_cast_fp16)[name = string("transpose_310")]; + tensor x_113_cast_fp16 = conv(bias = unet_wrap_kdiffusion_net_to_out_1_bias_to_fp16, dilations = x_113_dilations_0, groups = x_113_groups_0, pad = x_113_pad_0, pad_type = x_113_pad_type_0, strides = x_113_strides_0, weight = unet_wrap_kdiffusion_net_to_out_1_weight_to_fp16, x = input_63_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor x_pred_1_perm_0 = const()[name = string("x_pred_1_perm_0"), val = tensor([0, -1, -2])]; + tensor c_skip_1_to_fp16 = const()[name = string("c_skip_1_to_fp16"), val = tensor([[[0x1.1fcp-8]]])]; + tensor var_775_cast_fp16 = mul(x = c_skip_1_to_fp16, y = x_noisy_1_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor c_out_1_to_fp16 = const()[name = string("c_out_1_to_fp16"), val = tensor([[[0x1.974p-3]]])]; + tensor x_pred_1_cast_fp16 = transpose(perm = x_pred_1_perm_0, x = x_113_cast_fp16)[name = string("transpose_309")]; + tensor var_776_cast_fp16 = mul(x = c_out_1_to_fp16, y = x_pred_1_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor x_dn_1_cast_fp16 = add(x = var_775_cast_fp16, y = var_776_cast_fp16)[name = string("x_dn_1_cast_fp16")]; + tensor var_779_cast_fp16 = sub(x = x_noisy_1_cast_fp16, y = x_dn_1_cast_fp16)[name = string("op_779_cast_fp16")]; + tensor _inversed_d_1_y_0_to_fp16 = const()[name = string("_inversed_d_1_y_0_to_fp16"), val = tensor([0x1.554p-2])]; + tensor _inversed_d_1_cast_fp16 = mul(x = var_779_cast_fp16, y = _inversed_d_1_y_0_to_fp16)[name = string("_inversed_d_1_cast_fp16")]; + tensor var_782_to_fp16 = const()[name = string("op_782_to_fp16"), val = tensor([-0x1.72cp+0])]; + tensor var_783_cast_fp16 = mul(x = _inversed_d_1_cast_fp16, y = var_782_to_fp16)[name = string("op_783_cast_fp16")]; + tensor x_noisy_3_cast_fp16 = add(x = x_noisy_1_cast_fp16, y = var_783_cast_fp16)[name = string("x_noisy_3_cast_fp16")]; + int32 var_795 = const()[name = string("op_795"), val = int32(-1)]; + tensor c_in_3_to_fp16 = const()[name = string("c_in_3_to_fp16"), val = tensor([[[0x1.474p-1]]])]; + tensor x_123_cast_fp16 = mul(x = c_in_3_to_fp16, y = x_noisy_3_cast_fp16)[name = string("x_123_cast_fp16")]; + int32 x_119_axis_0 = const()[name = string("x_119_axis_0"), val = int32(0)]; + tensor var_1181_to_fp16 = const()[name = string("op_1181_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49347072)))]; + tensor x_119_cast_fp16 = stack(axis = x_119_axis_0, values = (var_1181_to_fp16, var_423_cast_fp16))[name = string("x_119_cast_fp16")]; + tensor var_1186 = const()[name = string("op_1186"), val = tensor([1, 2, 0])]; + tensor input_71_axes_0 = const()[name = string("input_71_axes_0"), val = tensor([2])]; + bool input_71_keep_dims_0 = const()[name = string("input_71_keep_dims_0"), val = bool(false)]; + tensor x_121_cast_fp16 = transpose(perm = var_1186, x = x_119_cast_fp16)[name = string("transpose_308")]; + tensor input_71_cast_fp16 = reduce_sum(axes = input_71_axes_0, keep_dims = input_71_keep_dims_0, x = x_121_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor linear_27_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_0_weight_to_fp16, x = input_71_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_27_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor linear_28_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_2_weight_to_fp16, x = input_75_cast_fp16)[name = string("linear_28_cast_fp16")]; + string mapping_5_mode_0 = const()[name = string("mapping_5_mode_0"), val = string("EXACT")]; + tensor mapping_5_cast_fp16 = gelu(mode = mapping_5_mode_0, x = linear_28_cast_fp16)[name = string("mapping_5_cast_fp16")]; + tensor var_1196_reps_0 = const()[name = string("op_1196_reps_0"), val = tensor([1, 57, 1])]; + tensor var_1196_cast_fp16 = tile(reps = var_1196_reps_0, x = x_123_cast_fp16)[name = string("op_1196_cast_fp16")]; + bool x_125_interleave_0 = const()[name = string("x_125_interleave_0"), val = bool(false)]; + tensor x_125_cast_fp16 = concat(axis = var_795, interleave = x_125_interleave_0, values = (var_1196_cast_fp16, embedding_to_fp16))[name = string("x_125_cast_fp16")]; + tensor var_1199_axes_0 = const()[name = string("op_1199_axes_0"), val = tensor([1])]; + tensor var_1199_cast_fp16 = expand_dims(axes = var_1199_axes_0, x = mapping_5_cast_fp16)[name = string("op_1199_cast_fp16")]; + tensor mapping_7_reps_0 = const()[name = string("mapping_7_reps_0"), val = tensor([1, 57, 1])]; + tensor mapping_7_cast_fp16 = tile(reps = mapping_7_reps_0, x = var_1199_cast_fp16)[name = string("mapping_7_cast_fp16")]; + tensor x_127_cast_fp16 = add(x = x_125_cast_fp16, y = mapping_7_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = h_3_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor gamma_27_perm_0 = const()[name = string("gamma_27_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_27_perm_0 = const()[name = string("beta_27_perm_0"), val = tensor([0, -1, 1])]; + tensor x_131_axes_0 = const()[name = string("x_131_axes_0"), val = tensor([-1])]; + fp16 var_791_to_fp16 = const()[name = string("op_791_to_fp16"), val = fp16(0x1.5p-17)]; + tensor x_131_cast_fp16 = layer_norm(axes = x_131_axes_0, epsilon = var_791_to_fp16, x = x_127_cast_fp16)[name = string("x_131_cast_fp16")]; + fp16 var_1217_promoted_to_fp16 = const()[name = string("op_1217_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_27_cast_fp16 = transpose(perm = gamma_27_perm_0, x = var_1211_cast_fp16_0)[name = string("transpose_307")]; + tensor var_1218_cast_fp16 = add(x = gamma_27_cast_fp16, y = var_1217_promoted_to_fp16)[name = string("op_1218_cast_fp16")]; + tensor var_1219_cast_fp16 = mul(x = var_1218_cast_fp16, y = x_131_cast_fp16)[name = string("op_1219_cast_fp16")]; + tensor beta_27_cast_fp16 = transpose(perm = beta_27_perm_0, x = var_1211_cast_fp16_1)[name = string("transpose_306")]; + tensor x_133_cast_fp16 = add(x = var_1219_cast_fp16, y = beta_27_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor var_1230_split_sizes_0 = const()[name = string("op_1230_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_1230_axis_0 = const()[name = string("op_1230_axis_0"), val = int32(1)]; + tensor var_1230_cast_fp16_0, tensor var_1230_cast_fp16_1 = split(axis = var_1230_axis_0, split_sizes = var_1230_split_sizes_0, x = h_7_cast_fp16)[name = string("op_1230_cast_fp16")]; + tensor gamma_31_perm_0 = const()[name = string("gamma_31_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_31_perm_0 = const()[name = string("beta_31_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_1236_promoted_to_fp16 = const()[name = string("op_1236_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_31_cast_fp16 = transpose(perm = gamma_31_perm_0, x = var_1230_cast_fp16_0)[name = string("transpose_305")]; + tensor var_1237_cast_fp16 = add(x = gamma_31_cast_fp16, y = var_1236_promoted_to_fp16)[name = string("op_1237_cast_fp16")]; + tensor var_1238_cast_fp16 = mul(x = var_1237_cast_fp16, y = x_131_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor beta_31_cast_fp16 = transpose(perm = beta_31_perm_0, x = var_1230_cast_fp16_1)[name = string("transpose_304")]; + tensor x_139_cast_fp16 = add(x = var_1238_cast_fp16, y = beta_31_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor linear_31_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_q_weight_to_fp16, x = x_133_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor linear_32_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_kv_weight_to_fp16, x = x_139_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_1244_split_sizes_0 = const()[name = string("op_1244_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1244_axis_0 = const()[name = string("op_1244_axis_0"), val = int32(-1)]; + tensor var_1244_cast_fp16_0, tensor var_1244_cast_fp16_1 = split(axis = var_1244_axis_0, split_sizes = var_1244_split_sizes_0, x = linear_32_cast_fp16)[name = string("op_1244_cast_fp16")]; + tensor var_1252 = const()[name = string("op_1252"), val = tensor([1, 57, 8, 64])]; + tensor x_143_cast_fp16 = reshape(shape = var_1252, x = linear_31_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor var_1262 = const()[name = string("op_1262"), val = tensor([1, 57, 8, 64])]; + tensor x_147_cast_fp16 = reshape(shape = var_1262, x = var_1244_cast_fp16_0)[name = string("x_147_cast_fp16")]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 57, 8, 64])]; + tensor x_151_cast_fp16 = reshape(shape = var_1272, x = var_1244_cast_fp16_1)[name = string("x_151_cast_fp16")]; + tensor var_1274 = const()[name = string("op_1274"), val = tensor([0, 2, 1, 3])]; + bool sim_13_transpose_x_0 = const()[name = string("sim_13_transpose_x_0"), val = bool(false)]; + bool sim_13_transpose_y_0 = const()[name = string("sim_13_transpose_y_0"), val = bool(false)]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = x_147_cast_fp16)[name = string("transpose_301")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = x_143_cast_fp16)[name = string("transpose_302")]; + tensor sim_13_cast_fp16 = matmul(transpose_x = sim_13_transpose_x_0, transpose_y = sim_13_transpose_y_0, x = transpose_78, y = transpose_79)[name = string("sim_13_cast_fp16")]; + fp16 var_1278_to_fp16 = const()[name = string("op_1278_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_15_cast_fp16 = mul(x = sim_13_cast_fp16, y = var_1278_to_fp16)[name = string("sim_15_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_795, x = sim_15_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool x_153_transpose_x_0 = const()[name = string("x_153_transpose_x_0"), val = bool(false)]; + bool x_153_transpose_y_0 = const()[name = string("x_153_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = var_1274, x = x_151_cast_fp16)[name = string("transpose_303")]; + tensor x_153_cast_fp16 = matmul(transpose_x = x_153_transpose_x_0, transpose_y = x_153_transpose_y_0, x = attn_7_cast_fp16, y = v_7_cast_fp16)[name = string("x_153_cast_fp16")]; + tensor var_1300 = const()[name = string("op_1300"), val = tensor([0, 2, 1, 3])]; + tensor var_1302 = const()[name = string("op_1302"), val = tensor([1, 57, 512])]; + tensor x_155_cast_fp16 = transpose(perm = var_1300, x = x_153_cast_fp16)[name = string("transpose_300")]; + tensor input_87_cast_fp16 = reshape(shape = var_1302, x = x_155_cast_fp16)[name = string("input_87_cast_fp16")]; + tensor linear_33_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_weight_to_fp16, x = input_87_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = linear_33_cast_fp16, y = x_127_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor linear_34_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_weight_to_fp16, x = input_89_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_93_mode_0 = const()[name = string("input_93_mode_0"), val = string("EXACT")]; + tensor input_93_cast_fp16 = gelu(mode = input_93_mode_0, x = linear_34_cast_fp16)[name = string("input_93_cast_fp16")]; + tensor linear_35_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_weight_to_fp16, x = input_93_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor x_157_cast_fp16 = add(x = linear_35_cast_fp16, y = input_89_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor x_159_cast_fp16 = add(x = x_157_cast_fp16, y = mapping_7_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor var_1318_split_sizes_0 = const()[name = string("op_1318_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_1318_axis_0 = const()[name = string("op_1318_axis_0"), val = int32(1)]; + tensor var_1318_cast_fp16_0, tensor var_1318_cast_fp16_1 = split(axis = var_1318_axis_0, split_sizes = var_1318_split_sizes_0, x = h_11_cast_fp16)[name = string("op_1318_cast_fp16")]; + tensor gamma_35_perm_0 = const()[name = string("gamma_35_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_35_perm_0 = const()[name = string("beta_35_perm_0"), val = tensor([0, -1, 1])]; + tensor x_163_axes_0 = const()[name = string("x_163_axes_0"), val = tensor([-1])]; + tensor x_163_cast_fp16 = layer_norm(axes = x_163_axes_0, epsilon = var_791_to_fp16, x = x_159_cast_fp16)[name = string("x_163_cast_fp16")]; + fp16 var_1324_promoted_to_fp16 = const()[name = string("op_1324_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_35_cast_fp16 = transpose(perm = gamma_35_perm_0, x = var_1318_cast_fp16_0)[name = string("transpose_299")]; + tensor var_1325_cast_fp16 = add(x = gamma_35_cast_fp16, y = var_1324_promoted_to_fp16)[name = string("op_1325_cast_fp16")]; + tensor var_1326_cast_fp16 = mul(x = var_1325_cast_fp16, y = x_163_cast_fp16)[name = string("op_1326_cast_fp16")]; + tensor beta_35_cast_fp16 = transpose(perm = beta_35_perm_0, x = var_1318_cast_fp16_1)[name = string("transpose_298")]; + tensor x_165_cast_fp16 = add(x = var_1326_cast_fp16, y = beta_35_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor var_1337_split_sizes_0 = const()[name = string("op_1337_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_1337_axis_0 = const()[name = string("op_1337_axis_0"), val = int32(1)]; + tensor var_1337_cast_fp16_0, tensor var_1337_cast_fp16_1 = split(axis = var_1337_axis_0, split_sizes = var_1337_split_sizes_0, x = h_15_cast_fp16)[name = string("op_1337_cast_fp16")]; + tensor gamma_39_perm_0 = const()[name = string("gamma_39_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_39_perm_0 = const()[name = string("beta_39_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_1343_promoted_to_fp16 = const()[name = string("op_1343_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_39_cast_fp16 = transpose(perm = gamma_39_perm_0, x = var_1337_cast_fp16_0)[name = string("transpose_297")]; + tensor var_1344_cast_fp16 = add(x = gamma_39_cast_fp16, y = var_1343_promoted_to_fp16)[name = string("op_1344_cast_fp16")]; + tensor var_1345_cast_fp16 = mul(x = var_1344_cast_fp16, y = x_163_cast_fp16)[name = string("op_1345_cast_fp16")]; + tensor beta_39_cast_fp16 = transpose(perm = beta_39_perm_0, x = var_1337_cast_fp16_1)[name = string("transpose_296")]; + tensor x_171_cast_fp16 = add(x = var_1345_cast_fp16, y = beta_39_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor linear_38_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_q_weight_to_fp16, x = x_165_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor linear_39_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_kv_weight_to_fp16, x = x_171_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_1351_split_sizes_0 = const()[name = string("op_1351_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1351_axis_0 = const()[name = string("op_1351_axis_0"), val = int32(-1)]; + tensor var_1351_cast_fp16_0, tensor var_1351_cast_fp16_1 = split(axis = var_1351_axis_0, split_sizes = var_1351_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1359 = const()[name = string("op_1359"), val = tensor([1, 57, 8, 64])]; + tensor x_175_cast_fp16 = reshape(shape = var_1359, x = linear_38_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor var_1369 = const()[name = string("op_1369"), val = tensor([1, 57, 8, 64])]; + tensor x_179_cast_fp16 = reshape(shape = var_1369, x = var_1351_cast_fp16_0)[name = string("x_179_cast_fp16")]; + tensor var_1379 = const()[name = string("op_1379"), val = tensor([1, 57, 8, 64])]; + tensor x_183_cast_fp16 = reshape(shape = var_1379, x = var_1351_cast_fp16_1)[name = string("x_183_cast_fp16")]; + tensor var_1381 = const()[name = string("op_1381"), val = tensor([0, 2, 1, 3])]; + bool sim_17_transpose_x_0 = const()[name = string("sim_17_transpose_x_0"), val = bool(false)]; + bool sim_17_transpose_y_0 = const()[name = string("sim_17_transpose_y_0"), val = bool(false)]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = x_179_cast_fp16)[name = string("transpose_293")]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = x_175_cast_fp16)[name = string("transpose_294")]; + tensor sim_17_cast_fp16 = matmul(transpose_x = sim_17_transpose_x_0, transpose_y = sim_17_transpose_y_0, x = transpose_80, y = transpose_81)[name = string("sim_17_cast_fp16")]; + fp16 var_1385_to_fp16 = const()[name = string("op_1385_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_19_cast_fp16 = mul(x = sim_17_cast_fp16, y = var_1385_to_fp16)[name = string("sim_19_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_795, x = sim_19_cast_fp16)[name = string("attn_9_cast_fp16")]; + bool x_185_transpose_x_0 = const()[name = string("x_185_transpose_x_0"), val = bool(false)]; + bool x_185_transpose_y_0 = const()[name = string("x_185_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = var_1381, x = x_183_cast_fp16)[name = string("transpose_295")]; + tensor x_185_cast_fp16 = matmul(transpose_x = x_185_transpose_x_0, transpose_y = x_185_transpose_y_0, x = attn_9_cast_fp16, y = v_9_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor var_1407 = const()[name = string("op_1407"), val = tensor([0, 2, 1, 3])]; + tensor var_1409 = const()[name = string("op_1409"), val = tensor([1, 57, 512])]; + tensor x_187_cast_fp16 = transpose(perm = var_1407, x = x_185_cast_fp16)[name = string("transpose_292")]; + tensor input_103_cast_fp16 = reshape(shape = var_1409, x = x_187_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor linear_40_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_weight_to_fp16, x = input_103_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = linear_40_cast_fp16, y = x_159_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor linear_41_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_weight_to_fp16, x = input_105_cast_fp16)[name = string("linear_41_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_41_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor linear_42_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_weight_to_fp16, x = input_109_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor x_189_cast_fp16 = add(x = linear_42_cast_fp16, y = input_105_cast_fp16)[name = string("x_189_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_189_cast_fp16, y = mapping_7_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor var_1425_split_sizes_0 = const()[name = string("op_1425_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_1425_axis_0 = const()[name = string("op_1425_axis_0"), val = int32(1)]; + tensor var_1425_cast_fp16_0, tensor var_1425_cast_fp16_1 = split(axis = var_1425_axis_0, split_sizes = var_1425_split_sizes_0, x = h_19_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor gamma_43_perm_0 = const()[name = string("gamma_43_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_43_perm_0 = const()[name = string("beta_43_perm_0"), val = tensor([0, -1, 1])]; + tensor x_195_axes_0 = const()[name = string("x_195_axes_0"), val = tensor([-1])]; + tensor x_195_cast_fp16 = layer_norm(axes = x_195_axes_0, epsilon = var_791_to_fp16, x = x_191_cast_fp16)[name = string("x_195_cast_fp16")]; + fp16 var_1431_promoted_to_fp16 = const()[name = string("op_1431_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_43_cast_fp16 = transpose(perm = gamma_43_perm_0, x = var_1425_cast_fp16_0)[name = string("transpose_291")]; + tensor var_1432_cast_fp16 = add(x = gamma_43_cast_fp16, y = var_1431_promoted_to_fp16)[name = string("op_1432_cast_fp16")]; + tensor var_1433_cast_fp16 = mul(x = var_1432_cast_fp16, y = x_195_cast_fp16)[name = string("op_1433_cast_fp16")]; + tensor beta_43_cast_fp16 = transpose(perm = beta_43_perm_0, x = var_1425_cast_fp16_1)[name = string("transpose_290")]; + tensor x_197_cast_fp16 = add(x = var_1433_cast_fp16, y = beta_43_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor var_1444_split_sizes_0 = const()[name = string("op_1444_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_1444_axis_0 = const()[name = string("op_1444_axis_0"), val = int32(1)]; + tensor var_1444_cast_fp16_0, tensor var_1444_cast_fp16_1 = split(axis = var_1444_axis_0, split_sizes = var_1444_split_sizes_0, x = h_23_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor gamma_47_perm_0 = const()[name = string("gamma_47_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_47_perm_0 = const()[name = string("beta_47_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_1450_promoted_to_fp16 = const()[name = string("op_1450_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_47_cast_fp16 = transpose(perm = gamma_47_perm_0, x = var_1444_cast_fp16_0)[name = string("transpose_289")]; + tensor var_1451_cast_fp16 = add(x = gamma_47_cast_fp16, y = var_1450_promoted_to_fp16)[name = string("op_1451_cast_fp16")]; + tensor var_1452_cast_fp16 = mul(x = var_1451_cast_fp16, y = x_195_cast_fp16)[name = string("op_1452_cast_fp16")]; + tensor beta_47_cast_fp16 = transpose(perm = beta_47_perm_0, x = var_1444_cast_fp16_1)[name = string("transpose_288")]; + tensor x_203_cast_fp16 = add(x = var_1452_cast_fp16, y = beta_47_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor linear_45_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_q_weight_to_fp16, x = x_197_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor linear_46_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_kv_weight_to_fp16, x = x_203_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1458_split_sizes_0 = const()[name = string("op_1458_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1458_axis_0 = const()[name = string("op_1458_axis_0"), val = int32(-1)]; + tensor var_1458_cast_fp16_0, tensor var_1458_cast_fp16_1 = split(axis = var_1458_axis_0, split_sizes = var_1458_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1458_cast_fp16")]; + tensor var_1466 = const()[name = string("op_1466"), val = tensor([1, 57, 8, 64])]; + tensor x_207_cast_fp16 = reshape(shape = var_1466, x = linear_45_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor var_1476 = const()[name = string("op_1476"), val = tensor([1, 57, 8, 64])]; + tensor x_211_cast_fp16 = reshape(shape = var_1476, x = var_1458_cast_fp16_0)[name = string("x_211_cast_fp16")]; + tensor var_1486 = const()[name = string("op_1486"), val = tensor([1, 57, 8, 64])]; + tensor x_215_cast_fp16 = reshape(shape = var_1486, x = var_1458_cast_fp16_1)[name = string("x_215_cast_fp16")]; + tensor var_1488 = const()[name = string("op_1488"), val = tensor([0, 2, 1, 3])]; + bool sim_21_transpose_x_0 = const()[name = string("sim_21_transpose_x_0"), val = bool(false)]; + bool sim_21_transpose_y_0 = const()[name = string("sim_21_transpose_y_0"), val = bool(false)]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = x_211_cast_fp16)[name = string("transpose_285")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = x_207_cast_fp16)[name = string("transpose_286")]; + tensor sim_21_cast_fp16 = matmul(transpose_x = sim_21_transpose_x_0, transpose_y = sim_21_transpose_y_0, x = transpose_82, y = transpose_83)[name = string("sim_21_cast_fp16")]; + fp16 var_1492_to_fp16 = const()[name = string("op_1492_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_23_cast_fp16 = mul(x = sim_21_cast_fp16, y = var_1492_to_fp16)[name = string("sim_23_cast_fp16")]; + tensor attn_11_cast_fp16 = softmax(axis = var_795, x = sim_23_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool x_217_transpose_x_0 = const()[name = string("x_217_transpose_x_0"), val = bool(false)]; + bool x_217_transpose_y_0 = const()[name = string("x_217_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = var_1488, x = x_215_cast_fp16)[name = string("transpose_287")]; + tensor x_217_cast_fp16 = matmul(transpose_x = x_217_transpose_x_0, transpose_y = x_217_transpose_y_0, x = attn_11_cast_fp16, y = v_11_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_1514 = const()[name = string("op_1514"), val = tensor([0, 2, 1, 3])]; + tensor var_1516 = const()[name = string("op_1516"), val = tensor([1, 57, 512])]; + tensor x_219_cast_fp16 = transpose(perm = var_1514, x = x_217_cast_fp16)[name = string("transpose_284")]; + tensor input_119_cast_fp16 = reshape(shape = var_1516, x = x_219_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor linear_47_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_weight_to_fp16, x = input_119_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor input_121_cast_fp16 = add(x = linear_47_cast_fp16, y = x_191_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor linear_48_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_weight_to_fp16, x = input_121_cast_fp16)[name = string("linear_48_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = linear_48_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor linear_49_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_weight_to_fp16, x = input_125_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor x_221_cast_fp16 = add(x = linear_49_cast_fp16, y = input_121_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor var_1525_axes_0 = const()[name = string("op_1525_axes_0"), val = tensor([1])]; + bool var_1525_keep_dims_0 = const()[name = string("op_1525_keep_dims_0"), val = bool(false)]; + tensor var_1525_cast_fp16 = reduce_mean(axes = var_1525_axes_0, keep_dims = var_1525_keep_dims_0, x = x_221_cast_fp16)[name = string("op_1525_cast_fp16")]; + tensor x_223_axes_0 = const()[name = string("x_223_axes_0"), val = tensor([1])]; + tensor x_223_cast_fp16 = expand_dims(axes = x_223_axes_0, x = var_1525_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_1527 = const()[name = string("op_1527"), val = tensor([0, 2, 1])]; + string x_225_pad_type_0 = const()[name = string("x_225_pad_type_0"), val = string("valid")]; + tensor x_225_strides_0 = const()[name = string("x_225_strides_0"), val = tensor([1])]; + tensor x_225_pad_0 = const()[name = string("x_225_pad_0"), val = tensor([0, 0])]; + tensor x_225_dilations_0 = const()[name = string("x_225_dilations_0"), val = tensor([1])]; + int32 x_225_groups_0 = const()[name = string("x_225_groups_0"), val = int32(1)]; + tensor input_127_cast_fp16 = transpose(perm = var_1527, x = x_223_cast_fp16)[name = string("transpose_283")]; + tensor x_225_cast_fp16 = conv(bias = unet_wrap_kdiffusion_net_to_out_1_bias_to_fp16, dilations = x_225_dilations_0, groups = x_225_groups_0, pad = x_225_pad_0, pad_type = x_225_pad_type_0, strides = x_225_strides_0, weight = unet_wrap_kdiffusion_net_to_out_1_weight_to_fp16, x = input_127_cast_fp16)[name = string("x_225_cast_fp16")]; + tensor x_pred_3_perm_0 = const()[name = string("x_pred_3_perm_0"), val = tensor([0, -1, -2])]; + tensor c_skip_3_to_fp16 = const()[name = string("c_skip_3_to_fp16"), val = tensor([[[0x1.09cp-6]]])]; + tensor var_1535_cast_fp16 = mul(x = c_skip_3_to_fp16, y = x_noisy_3_cast_fp16)[name = string("op_1535_cast_fp16")]; + tensor c_out_3_to_fp16 = const()[name = string("c_out_3_to_fp16"), val = tensor([[[0x1.94cp-3]]])]; + tensor x_pred_3_cast_fp16 = transpose(perm = x_pred_3_perm_0, x = x_225_cast_fp16)[name = string("transpose_282")]; + tensor var_1536_cast_fp16 = mul(x = c_out_3_to_fp16, y = x_pred_3_cast_fp16)[name = string("op_1536_cast_fp16")]; + tensor x_mid_dn_1_cast_fp16 = add(x = var_1535_cast_fp16, y = var_1536_cast_fp16)[name = string("x_mid_dn_1_cast_fp16")]; + tensor var_1539_cast_fp16 = sub(x = x_noisy_3_cast_fp16, y = x_mid_dn_1_cast_fp16)[name = string("op_1539_cast_fp16")]; + tensor _inversed_d_mid_1_y_0_to_fp16 = const()[name = string("_inversed_d_mid_1_y_0_to_fp16"), val = tensor([0x1.4ap-1])]; + tensor _inversed_d_mid_1_cast_fp16 = mul(x = var_1539_cast_fp16, y = _inversed_d_mid_1_y_0_to_fp16)[name = string("_inversed_d_mid_1_cast_fp16")]; + tensor var_1545_to_fp16 = const()[name = string("op_1545_to_fp16"), val = tensor([-0x1.72cp+1])]; + tensor var_1546_cast_fp16 = mul(x = _inversed_d_mid_1_cast_fp16, y = var_1545_to_fp16)[name = string("op_1546_cast_fp16")]; + tensor x_227_cast_fp16 = add(x = x_noisy_1_cast_fp16, y = var_1546_cast_fp16)[name = string("x_227_cast_fp16")]; + tensor var_1551_begin_0 = const()[name = string("op_1551_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1551_end_0 = const()[name = string("op_1551_end_0"), val = tensor([1, 1, 1, 256])]; + tensor var_1551_end_mask_0 = const()[name = string("op_1551_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_1551_squeeze_mask_0 = const()[name = string("op_1551_squeeze_mask_0"), val = tensor([true, false, false, false])]; + string noises_aux_to_fp16_dtype_0 = const()[name = string("noises_aux_to_fp16_dtype_0"), val = string("fp16")]; + tensor noises_aux_to_fp16 = cast(dtype = noises_aux_to_fp16_dtype_0, x = noises_aux)[name = string("cast_193")]; + tensor var_1551_cast_fp16 = slice_by_index(begin = var_1551_begin_0, end = var_1551_end_0, end_mask = var_1551_end_mask_0, squeeze_mask = var_1551_squeeze_mask_0, x = noises_aux_to_fp16)[name = string("op_1551_cast_fp16")]; + fp16 var_1554_to_fp16 = const()[name = string("op_1554_to_fp16"), val = fp16(0x1.18cp-1)]; + tensor var_1555_cast_fp16 = mul(x = var_1551_cast_fp16, y = var_1554_to_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_noisy_5_cast_fp16 = add(x = x_227_cast_fp16, y = var_1555_cast_fp16)[name = string("x_noisy_5_cast_fp16")]; + int32 var_1579 = const()[name = string("op_1579"), val = int32(-1)]; + tensor c_in_5_to_fp16 = const()[name = string("c_in_5_to_fp16"), val = tensor([[[0x1.bp+0]]])]; + tensor x_237_cast_fp16 = mul(x = c_in_5_to_fp16, y = x_noisy_5_cast_fp16)[name = string("x_237_cast_fp16")]; + int32 x_233_axis_0 = const()[name = string("x_233_axis_0"), val = int32(0)]; + tensor var_1965_to_fp16 = const()[name = string("op_1965_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49349184)))]; + tensor x_233_cast_fp16 = stack(axis = x_233_axis_0, values = (var_1965_to_fp16, var_423_cast_fp16))[name = string("x_233_cast_fp16")]; + tensor var_1970 = const()[name = string("op_1970"), val = tensor([1, 2, 0])]; + tensor input_135_axes_0 = const()[name = string("input_135_axes_0"), val = tensor([2])]; + bool input_135_keep_dims_0 = const()[name = string("input_135_keep_dims_0"), val = bool(false)]; + tensor x_235_cast_fp16 = transpose(perm = var_1970, x = x_233_cast_fp16)[name = string("transpose_281")]; + tensor input_135_cast_fp16 = reduce_sum(axes = input_135_axes_0, keep_dims = input_135_keep_dims_0, x = x_235_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor linear_52_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_0_weight_to_fp16, x = input_135_cast_fp16)[name = string("linear_52_cast_fp16")]; + string input_139_mode_0 = const()[name = string("input_139_mode_0"), val = string("EXACT")]; + tensor input_139_cast_fp16 = gelu(mode = input_139_mode_0, x = linear_52_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor linear_53_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_2_weight_to_fp16, x = input_139_cast_fp16)[name = string("linear_53_cast_fp16")]; + string mapping_9_mode_0 = const()[name = string("mapping_9_mode_0"), val = string("EXACT")]; + tensor mapping_9_cast_fp16 = gelu(mode = mapping_9_mode_0, x = linear_53_cast_fp16)[name = string("mapping_9_cast_fp16")]; + tensor var_1980_reps_0 = const()[name = string("op_1980_reps_0"), val = tensor([1, 57, 1])]; + tensor var_1980_cast_fp16 = tile(reps = var_1980_reps_0, x = x_237_cast_fp16)[name = string("op_1980_cast_fp16")]; + bool x_239_interleave_0 = const()[name = string("x_239_interleave_0"), val = bool(false)]; + tensor x_239_cast_fp16 = concat(axis = var_1579, interleave = x_239_interleave_0, values = (var_1980_cast_fp16, embedding_to_fp16))[name = string("x_239_cast_fp16")]; + tensor var_1983_axes_0 = const()[name = string("op_1983_axes_0"), val = tensor([1])]; + tensor var_1983_cast_fp16 = expand_dims(axes = var_1983_axes_0, x = mapping_9_cast_fp16)[name = string("op_1983_cast_fp16")]; + tensor mapping_11_reps_0 = const()[name = string("mapping_11_reps_0"), val = tensor([1, 57, 1])]; + tensor mapping_11_cast_fp16 = tile(reps = mapping_11_reps_0, x = var_1983_cast_fp16)[name = string("mapping_11_cast_fp16")]; + tensor x_241_cast_fp16 = add(x = x_239_cast_fp16, y = mapping_11_cast_fp16)[name = string("x_241_cast_fp16")]; + tensor var_1995_split_sizes_0 = const()[name = string("op_1995_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_1995_axis_0 = const()[name = string("op_1995_axis_0"), val = int32(1)]; + tensor var_1995_cast_fp16_0, tensor var_1995_cast_fp16_1 = split(axis = var_1995_axis_0, split_sizes = var_1995_split_sizes_0, x = h_3_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor gamma_51_perm_0 = const()[name = string("gamma_51_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_51_perm_0 = const()[name = string("beta_51_perm_0"), val = tensor([0, -1, 1])]; + tensor x_245_axes_0 = const()[name = string("x_245_axes_0"), val = tensor([-1])]; + fp16 var_1575_to_fp16 = const()[name = string("op_1575_to_fp16"), val = fp16(0x1.5p-17)]; + tensor x_245_cast_fp16 = layer_norm(axes = x_245_axes_0, epsilon = var_1575_to_fp16, x = x_241_cast_fp16)[name = string("x_245_cast_fp16")]; + fp16 var_2001_promoted_to_fp16 = const()[name = string("op_2001_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_51_cast_fp16 = transpose(perm = gamma_51_perm_0, x = var_1995_cast_fp16_0)[name = string("transpose_280")]; + tensor var_2002_cast_fp16 = add(x = gamma_51_cast_fp16, y = var_2001_promoted_to_fp16)[name = string("op_2002_cast_fp16")]; + tensor var_2003_cast_fp16 = mul(x = var_2002_cast_fp16, y = x_245_cast_fp16)[name = string("op_2003_cast_fp16")]; + tensor beta_51_cast_fp16 = transpose(perm = beta_51_perm_0, x = var_1995_cast_fp16_1)[name = string("transpose_279")]; + tensor x_247_cast_fp16 = add(x = var_2003_cast_fp16, y = beta_51_cast_fp16)[name = string("x_247_cast_fp16")]; + tensor var_2014_split_sizes_0 = const()[name = string("op_2014_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2014_axis_0 = const()[name = string("op_2014_axis_0"), val = int32(1)]; + tensor var_2014_cast_fp16_0, tensor var_2014_cast_fp16_1 = split(axis = var_2014_axis_0, split_sizes = var_2014_split_sizes_0, x = h_7_cast_fp16)[name = string("op_2014_cast_fp16")]; + tensor gamma_55_perm_0 = const()[name = string("gamma_55_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_55_perm_0 = const()[name = string("beta_55_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_2020_promoted_to_fp16 = const()[name = string("op_2020_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_55_cast_fp16 = transpose(perm = gamma_55_perm_0, x = var_2014_cast_fp16_0)[name = string("transpose_278")]; + tensor var_2021_cast_fp16 = add(x = gamma_55_cast_fp16, y = var_2020_promoted_to_fp16)[name = string("op_2021_cast_fp16")]; + tensor var_2022_cast_fp16 = mul(x = var_2021_cast_fp16, y = x_245_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor beta_55_cast_fp16 = transpose(perm = beta_55_perm_0, x = var_2014_cast_fp16_1)[name = string("transpose_277")]; + tensor x_253_cast_fp16 = add(x = var_2022_cast_fp16, y = beta_55_cast_fp16)[name = string("x_253_cast_fp16")]; + tensor linear_56_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_q_weight_to_fp16, x = x_247_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor linear_57_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_kv_weight_to_fp16, x = x_253_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_2028_split_sizes_0 = const()[name = string("op_2028_split_sizes_0"), val = tensor([512, 512])]; + int32 var_2028_axis_0 = const()[name = string("op_2028_axis_0"), val = int32(-1)]; + tensor var_2028_cast_fp16_0, tensor var_2028_cast_fp16_1 = split(axis = var_2028_axis_0, split_sizes = var_2028_split_sizes_0, x = linear_57_cast_fp16)[name = string("op_2028_cast_fp16")]; + tensor var_2036 = const()[name = string("op_2036"), val = tensor([1, 57, 8, 64])]; + tensor x_257_cast_fp16 = reshape(shape = var_2036, x = linear_56_cast_fp16)[name = string("x_257_cast_fp16")]; + tensor var_2046 = const()[name = string("op_2046"), val = tensor([1, 57, 8, 64])]; + tensor x_261_cast_fp16 = reshape(shape = var_2046, x = var_2028_cast_fp16_0)[name = string("x_261_cast_fp16")]; + tensor var_2056 = const()[name = string("op_2056"), val = tensor([1, 57, 8, 64])]; + tensor x_265_cast_fp16 = reshape(shape = var_2056, x = var_2028_cast_fp16_1)[name = string("x_265_cast_fp16")]; + tensor var_2058 = const()[name = string("op_2058"), val = tensor([0, 2, 1, 3])]; + bool sim_25_transpose_x_0 = const()[name = string("sim_25_transpose_x_0"), val = bool(false)]; + bool sim_25_transpose_y_0 = const()[name = string("sim_25_transpose_y_0"), val = bool(false)]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = x_261_cast_fp16)[name = string("transpose_274")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = x_257_cast_fp16)[name = string("transpose_275")]; + tensor sim_25_cast_fp16 = matmul(transpose_x = sim_25_transpose_x_0, transpose_y = sim_25_transpose_y_0, x = transpose_84, y = transpose_85)[name = string("sim_25_cast_fp16")]; + fp16 var_2062_to_fp16 = const()[name = string("op_2062_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_27_cast_fp16 = mul(x = sim_25_cast_fp16, y = var_2062_to_fp16)[name = string("sim_27_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1579, x = sim_27_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool x_267_transpose_x_0 = const()[name = string("x_267_transpose_x_0"), val = bool(false)]; + bool x_267_transpose_y_0 = const()[name = string("x_267_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = var_2058, x = x_265_cast_fp16)[name = string("transpose_276")]; + tensor x_267_cast_fp16 = matmul(transpose_x = x_267_transpose_x_0, transpose_y = x_267_transpose_y_0, x = attn_13_cast_fp16, y = v_13_cast_fp16)[name = string("x_267_cast_fp16")]; + tensor var_2084 = const()[name = string("op_2084"), val = tensor([0, 2, 1, 3])]; + tensor var_2086 = const()[name = string("op_2086"), val = tensor([1, 57, 512])]; + tensor x_269_cast_fp16 = transpose(perm = var_2084, x = x_267_cast_fp16)[name = string("transpose_273")]; + tensor input_151_cast_fp16 = reshape(shape = var_2086, x = x_269_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor linear_58_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_weight_to_fp16, x = input_151_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor input_153_cast_fp16 = add(x = linear_58_cast_fp16, y = x_241_cast_fp16)[name = string("input_153_cast_fp16")]; + tensor linear_59_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_weight_to_fp16, x = input_153_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("EXACT")]; + tensor input_157_cast_fp16 = gelu(mode = input_157_mode_0, x = linear_59_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor linear_60_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_weight_to_fp16, x = input_157_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor x_271_cast_fp16 = add(x = linear_60_cast_fp16, y = input_153_cast_fp16)[name = string("x_271_cast_fp16")]; + tensor x_273_cast_fp16 = add(x = x_271_cast_fp16, y = mapping_11_cast_fp16)[name = string("x_273_cast_fp16")]; + tensor var_2102_split_sizes_0 = const()[name = string("op_2102_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2102_axis_0 = const()[name = string("op_2102_axis_0"), val = int32(1)]; + tensor var_2102_cast_fp16_0, tensor var_2102_cast_fp16_1 = split(axis = var_2102_axis_0, split_sizes = var_2102_split_sizes_0, x = h_11_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor gamma_59_perm_0 = const()[name = string("gamma_59_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_59_perm_0 = const()[name = string("beta_59_perm_0"), val = tensor([0, -1, 1])]; + tensor x_277_axes_0 = const()[name = string("x_277_axes_0"), val = tensor([-1])]; + tensor x_277_cast_fp16 = layer_norm(axes = x_277_axes_0, epsilon = var_1575_to_fp16, x = x_273_cast_fp16)[name = string("x_277_cast_fp16")]; + fp16 var_2108_promoted_to_fp16 = const()[name = string("op_2108_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_59_cast_fp16 = transpose(perm = gamma_59_perm_0, x = var_2102_cast_fp16_0)[name = string("transpose_272")]; + tensor var_2109_cast_fp16 = add(x = gamma_59_cast_fp16, y = var_2108_promoted_to_fp16)[name = string("op_2109_cast_fp16")]; + tensor var_2110_cast_fp16 = mul(x = var_2109_cast_fp16, y = x_277_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor beta_59_cast_fp16 = transpose(perm = beta_59_perm_0, x = var_2102_cast_fp16_1)[name = string("transpose_271")]; + tensor x_279_cast_fp16 = add(x = var_2110_cast_fp16, y = beta_59_cast_fp16)[name = string("x_279_cast_fp16")]; + tensor var_2121_split_sizes_0 = const()[name = string("op_2121_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2121_axis_0 = const()[name = string("op_2121_axis_0"), val = int32(1)]; + tensor var_2121_cast_fp16_0, tensor var_2121_cast_fp16_1 = split(axis = var_2121_axis_0, split_sizes = var_2121_split_sizes_0, x = h_15_cast_fp16)[name = string("op_2121_cast_fp16")]; + tensor gamma_63_perm_0 = const()[name = string("gamma_63_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_63_perm_0 = const()[name = string("beta_63_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_2127_promoted_to_fp16 = const()[name = string("op_2127_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_63_cast_fp16 = transpose(perm = gamma_63_perm_0, x = var_2121_cast_fp16_0)[name = string("transpose_270")]; + tensor var_2128_cast_fp16 = add(x = gamma_63_cast_fp16, y = var_2127_promoted_to_fp16)[name = string("op_2128_cast_fp16")]; + tensor var_2129_cast_fp16 = mul(x = var_2128_cast_fp16, y = x_277_cast_fp16)[name = string("op_2129_cast_fp16")]; + tensor beta_63_cast_fp16 = transpose(perm = beta_63_perm_0, x = var_2121_cast_fp16_1)[name = string("transpose_269")]; + tensor x_285_cast_fp16 = add(x = var_2129_cast_fp16, y = beta_63_cast_fp16)[name = string("x_285_cast_fp16")]; + tensor linear_63_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_q_weight_to_fp16, x = x_279_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor linear_64_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_kv_weight_to_fp16, x = x_285_cast_fp16)[name = string("linear_64_cast_fp16")]; + tensor var_2135_split_sizes_0 = const()[name = string("op_2135_split_sizes_0"), val = tensor([512, 512])]; + int32 var_2135_axis_0 = const()[name = string("op_2135_axis_0"), val = int32(-1)]; + tensor var_2135_cast_fp16_0, tensor var_2135_cast_fp16_1 = split(axis = var_2135_axis_0, split_sizes = var_2135_split_sizes_0, x = linear_64_cast_fp16)[name = string("op_2135_cast_fp16")]; + tensor var_2143 = const()[name = string("op_2143"), val = tensor([1, 57, 8, 64])]; + tensor x_289_cast_fp16 = reshape(shape = var_2143, x = linear_63_cast_fp16)[name = string("x_289_cast_fp16")]; + tensor var_2153 = const()[name = string("op_2153"), val = tensor([1, 57, 8, 64])]; + tensor x_293_cast_fp16 = reshape(shape = var_2153, x = var_2135_cast_fp16_0)[name = string("x_293_cast_fp16")]; + tensor var_2163 = const()[name = string("op_2163"), val = tensor([1, 57, 8, 64])]; + tensor x_297_cast_fp16 = reshape(shape = var_2163, x = var_2135_cast_fp16_1)[name = string("x_297_cast_fp16")]; + tensor var_2165 = const()[name = string("op_2165"), val = tensor([0, 2, 1, 3])]; + bool sim_29_transpose_x_0 = const()[name = string("sim_29_transpose_x_0"), val = bool(false)]; + bool sim_29_transpose_y_0 = const()[name = string("sim_29_transpose_y_0"), val = bool(false)]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = x_293_cast_fp16)[name = string("transpose_266")]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = x_289_cast_fp16)[name = string("transpose_267")]; + tensor sim_29_cast_fp16 = matmul(transpose_x = sim_29_transpose_x_0, transpose_y = sim_29_transpose_y_0, x = transpose_86, y = transpose_87)[name = string("sim_29_cast_fp16")]; + fp16 var_2169_to_fp16 = const()[name = string("op_2169_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_31_cast_fp16 = mul(x = sim_29_cast_fp16, y = var_2169_to_fp16)[name = string("sim_31_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1579, x = sim_31_cast_fp16)[name = string("attn_15_cast_fp16")]; + bool x_299_transpose_x_0 = const()[name = string("x_299_transpose_x_0"), val = bool(false)]; + bool x_299_transpose_y_0 = const()[name = string("x_299_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = var_2165, x = x_297_cast_fp16)[name = string("transpose_268")]; + tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = attn_15_cast_fp16, y = v_15_cast_fp16)[name = string("x_299_cast_fp16")]; + tensor var_2191 = const()[name = string("op_2191"), val = tensor([0, 2, 1, 3])]; + tensor var_2193 = const()[name = string("op_2193"), val = tensor([1, 57, 512])]; + tensor x_301_cast_fp16 = transpose(perm = var_2191, x = x_299_cast_fp16)[name = string("transpose_265")]; + tensor input_167_cast_fp16 = reshape(shape = var_2193, x = x_301_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor linear_65_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_weight_to_fp16, x = input_167_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = linear_65_cast_fp16, y = x_273_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor linear_66_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_weight_to_fp16, x = input_169_cast_fp16)[name = string("linear_66_cast_fp16")]; + string input_173_mode_0 = const()[name = string("input_173_mode_0"), val = string("EXACT")]; + tensor input_173_cast_fp16 = gelu(mode = input_173_mode_0, x = linear_66_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor linear_67_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_weight_to_fp16, x = input_173_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor x_303_cast_fp16 = add(x = linear_67_cast_fp16, y = input_169_cast_fp16)[name = string("x_303_cast_fp16")]; + tensor x_305_cast_fp16 = add(x = x_303_cast_fp16, y = mapping_11_cast_fp16)[name = string("x_305_cast_fp16")]; + tensor var_2209_split_sizes_0 = const()[name = string("op_2209_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2209_axis_0 = const()[name = string("op_2209_axis_0"), val = int32(1)]; + tensor var_2209_cast_fp16_0, tensor var_2209_cast_fp16_1 = split(axis = var_2209_axis_0, split_sizes = var_2209_split_sizes_0, x = h_19_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor gamma_67_perm_0 = const()[name = string("gamma_67_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_67_perm_0 = const()[name = string("beta_67_perm_0"), val = tensor([0, -1, 1])]; + tensor x_309_axes_0 = const()[name = string("x_309_axes_0"), val = tensor([-1])]; + tensor x_309_cast_fp16 = layer_norm(axes = x_309_axes_0, epsilon = var_1575_to_fp16, x = x_305_cast_fp16)[name = string("x_309_cast_fp16")]; + fp16 var_2215_promoted_to_fp16 = const()[name = string("op_2215_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_67_cast_fp16 = transpose(perm = gamma_67_perm_0, x = var_2209_cast_fp16_0)[name = string("transpose_264")]; + tensor var_2216_cast_fp16 = add(x = gamma_67_cast_fp16, y = var_2215_promoted_to_fp16)[name = string("op_2216_cast_fp16")]; + tensor var_2217_cast_fp16 = mul(x = var_2216_cast_fp16, y = x_309_cast_fp16)[name = string("op_2217_cast_fp16")]; + tensor beta_67_cast_fp16 = transpose(perm = beta_67_perm_0, x = var_2209_cast_fp16_1)[name = string("transpose_263")]; + tensor x_311_cast_fp16 = add(x = var_2217_cast_fp16, y = beta_67_cast_fp16)[name = string("x_311_cast_fp16")]; + tensor var_2228_split_sizes_0 = const()[name = string("op_2228_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2228_axis_0 = const()[name = string("op_2228_axis_0"), val = int32(1)]; + tensor var_2228_cast_fp16_0, tensor var_2228_cast_fp16_1 = split(axis = var_2228_axis_0, split_sizes = var_2228_split_sizes_0, x = h_23_cast_fp16)[name = string("op_2228_cast_fp16")]; + tensor gamma_71_perm_0 = const()[name = string("gamma_71_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_71_perm_0 = const()[name = string("beta_71_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_2234_promoted_to_fp16 = const()[name = string("op_2234_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_71_cast_fp16 = transpose(perm = gamma_71_perm_0, x = var_2228_cast_fp16_0)[name = string("transpose_262")]; + tensor var_2235_cast_fp16 = add(x = gamma_71_cast_fp16, y = var_2234_promoted_to_fp16)[name = string("op_2235_cast_fp16")]; + tensor var_2236_cast_fp16 = mul(x = var_2235_cast_fp16, y = x_309_cast_fp16)[name = string("op_2236_cast_fp16")]; + tensor beta_71_cast_fp16 = transpose(perm = beta_71_perm_0, x = var_2228_cast_fp16_1)[name = string("transpose_261")]; + tensor x_317_cast_fp16 = add(x = var_2236_cast_fp16, y = beta_71_cast_fp16)[name = string("x_317_cast_fp16")]; + tensor linear_70_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_q_weight_to_fp16, x = x_311_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor linear_71_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_kv_weight_to_fp16, x = x_317_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_2242_split_sizes_0 = const()[name = string("op_2242_split_sizes_0"), val = tensor([512, 512])]; + int32 var_2242_axis_0 = const()[name = string("op_2242_axis_0"), val = int32(-1)]; + tensor var_2242_cast_fp16_0, tensor var_2242_cast_fp16_1 = split(axis = var_2242_axis_0, split_sizes = var_2242_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_2242_cast_fp16")]; + tensor var_2250 = const()[name = string("op_2250"), val = tensor([1, 57, 8, 64])]; + tensor x_321_cast_fp16 = reshape(shape = var_2250, x = linear_70_cast_fp16)[name = string("x_321_cast_fp16")]; + tensor var_2260 = const()[name = string("op_2260"), val = tensor([1, 57, 8, 64])]; + tensor x_325_cast_fp16 = reshape(shape = var_2260, x = var_2242_cast_fp16_0)[name = string("x_325_cast_fp16")]; + tensor var_2270 = const()[name = string("op_2270"), val = tensor([1, 57, 8, 64])]; + tensor x_329_cast_fp16 = reshape(shape = var_2270, x = var_2242_cast_fp16_1)[name = string("x_329_cast_fp16")]; + tensor var_2272 = const()[name = string("op_2272"), val = tensor([0, 2, 1, 3])]; + bool sim_33_transpose_x_0 = const()[name = string("sim_33_transpose_x_0"), val = bool(false)]; + bool sim_33_transpose_y_0 = const()[name = string("sim_33_transpose_y_0"), val = bool(false)]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = x_325_cast_fp16)[name = string("transpose_258")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = x_321_cast_fp16)[name = string("transpose_259")]; + tensor sim_33_cast_fp16 = matmul(transpose_x = sim_33_transpose_x_0, transpose_y = sim_33_transpose_y_0, x = transpose_88, y = transpose_89)[name = string("sim_33_cast_fp16")]; + fp16 var_2276_to_fp16 = const()[name = string("op_2276_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_35_cast_fp16 = mul(x = sim_33_cast_fp16, y = var_2276_to_fp16)[name = string("sim_35_cast_fp16")]; + tensor attn_17_cast_fp16 = softmax(axis = var_1579, x = sim_35_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool x_331_transpose_x_0 = const()[name = string("x_331_transpose_x_0"), val = bool(false)]; + bool x_331_transpose_y_0 = const()[name = string("x_331_transpose_y_0"), val = bool(false)]; + tensor v_17_cast_fp16 = transpose(perm = var_2272, x = x_329_cast_fp16)[name = string("transpose_260")]; + tensor x_331_cast_fp16 = matmul(transpose_x = x_331_transpose_x_0, transpose_y = x_331_transpose_y_0, x = attn_17_cast_fp16, y = v_17_cast_fp16)[name = string("x_331_cast_fp16")]; + tensor var_2298 = const()[name = string("op_2298"), val = tensor([0, 2, 1, 3])]; + tensor var_2300 = const()[name = string("op_2300"), val = tensor([1, 57, 512])]; + tensor x_333_cast_fp16 = transpose(perm = var_2298, x = x_331_cast_fp16)[name = string("transpose_257")]; + tensor input_183_cast_fp16 = reshape(shape = var_2300, x = x_333_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor linear_72_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_weight_to_fp16, x = input_183_cast_fp16)[name = string("linear_72_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = linear_72_cast_fp16, y = x_305_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor linear_73_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_weight_to_fp16, x = input_185_cast_fp16)[name = string("linear_73_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_73_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor linear_74_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_weight_to_fp16, x = input_189_cast_fp16)[name = string("linear_74_cast_fp16")]; + tensor x_335_cast_fp16 = add(x = linear_74_cast_fp16, y = input_185_cast_fp16)[name = string("x_335_cast_fp16")]; + tensor var_2309_axes_0 = const()[name = string("op_2309_axes_0"), val = tensor([1])]; + bool var_2309_keep_dims_0 = const()[name = string("op_2309_keep_dims_0"), val = bool(false)]; + tensor var_2309_cast_fp16 = reduce_mean(axes = var_2309_axes_0, keep_dims = var_2309_keep_dims_0, x = x_335_cast_fp16)[name = string("op_2309_cast_fp16")]; + tensor x_337_axes_0 = const()[name = string("x_337_axes_0"), val = tensor([1])]; + tensor x_337_cast_fp16 = expand_dims(axes = x_337_axes_0, x = var_2309_cast_fp16)[name = string("x_337_cast_fp16")]; + tensor var_2311 = const()[name = string("op_2311"), val = tensor([0, 2, 1])]; + string x_339_pad_type_0 = const()[name = string("x_339_pad_type_0"), val = string("valid")]; + tensor x_339_strides_0 = const()[name = string("x_339_strides_0"), val = tensor([1])]; + tensor x_339_pad_0 = const()[name = string("x_339_pad_0"), val = tensor([0, 0])]; + tensor x_339_dilations_0 = const()[name = string("x_339_dilations_0"), val = tensor([1])]; + int32 x_339_groups_0 = const()[name = string("x_339_groups_0"), val = int32(1)]; + tensor input_191_cast_fp16 = transpose(perm = var_2311, x = x_337_cast_fp16)[name = string("transpose_256")]; + tensor x_339_cast_fp16 = conv(bias = unet_wrap_kdiffusion_net_to_out_1_bias_to_fp16, dilations = x_339_dilations_0, groups = x_339_groups_0, pad = x_339_pad_0, pad_type = x_339_pad_type_0, strides = x_339_strides_0, weight = unet_wrap_kdiffusion_net_to_out_1_weight_to_fp16, x = input_191_cast_fp16)[name = string("x_339_cast_fp16")]; + tensor x_pred_5_perm_0 = const()[name = string("x_pred_5_perm_0"), val = tensor([0, -1, -2])]; + tensor c_skip_5_to_fp16 = const()[name = string("c_skip_5_to_fp16"), val = tensor([[[0x1.cf4p-4]]])]; + tensor var_2319_cast_fp16 = mul(x = c_skip_5_to_fp16, y = x_noisy_5_cast_fp16)[name = string("op_2319_cast_fp16")]; + tensor c_out_5_to_fp16 = const()[name = string("c_out_5_to_fp16"), val = tensor([[[0x1.804p-3]]])]; + tensor x_pred_5_cast_fp16 = transpose(perm = x_pred_5_perm_0, x = x_339_cast_fp16)[name = string("transpose_255")]; + tensor var_2320_cast_fp16 = mul(x = c_out_5_to_fp16, y = x_pred_5_cast_fp16)[name = string("op_2320_cast_fp16")]; + tensor x_dn_3_cast_fp16 = add(x = var_2319_cast_fp16, y = var_2320_cast_fp16)[name = string("x_dn_3_cast_fp16")]; + tensor var_2323_cast_fp16 = sub(x = x_noisy_5_cast_fp16, y = x_dn_3_cast_fp16)[name = string("op_2323_cast_fp16")]; + tensor _inversed_d_3_y_0_to_fp16 = const()[name = string("_inversed_d_3_y_0_to_fp16"), val = tensor([0x1.cacp+0])]; + tensor _inversed_d_3_cast_fp16 = mul(x = var_2323_cast_fp16, y = _inversed_d_3_y_0_to_fp16)[name = string("_inversed_d_3_cast_fp16")]; + tensor var_2326_to_fp16 = const()[name = string("op_2326_to_fp16"), val = tensor([-0x1.19p-2])]; + tensor var_2327_cast_fp16 = mul(x = _inversed_d_3_cast_fp16, y = var_2326_to_fp16)[name = string("op_2327_cast_fp16")]; + tensor x_noisy_7_cast_fp16 = add(x = x_noisy_5_cast_fp16, y = var_2327_cast_fp16)[name = string("x_noisy_7_cast_fp16")]; + int32 var_2339 = const()[name = string("op_2339"), val = int32(-1)]; + tensor c_in_7_to_fp16 = const()[name = string("c_in_7_to_fp16"), val = tensor([[[0x1.718p+1]]])]; + tensor x_349_cast_fp16 = mul(x = c_in_7_to_fp16, y = x_noisy_7_cast_fp16)[name = string("x_349_cast_fp16")]; + int32 x_345_axis_0 = const()[name = string("x_345_axis_0"), val = int32(0)]; + tensor var_2725_to_fp16 = const()[name = string("op_2725_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49351296)))]; + tensor x_345_cast_fp16 = stack(axis = x_345_axis_0, values = (var_2725_to_fp16, var_423_cast_fp16))[name = string("x_345_cast_fp16")]; + tensor var_2730 = const()[name = string("op_2730"), val = tensor([1, 2, 0])]; + tensor input_199_axes_0 = const()[name = string("input_199_axes_0"), val = tensor([2])]; + bool input_199_keep_dims_0 = const()[name = string("input_199_keep_dims_0"), val = bool(false)]; + tensor x_347_cast_fp16 = transpose(perm = var_2730, x = x_345_cast_fp16)[name = string("transpose_254")]; + tensor input_199_cast_fp16 = reduce_sum(axes = input_199_axes_0, keep_dims = input_199_keep_dims_0, x = x_347_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor linear_77_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_0_weight_to_fp16, x = input_199_cast_fp16)[name = string("linear_77_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_77_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor linear_78_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_2_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_78_cast_fp16")]; + string mapping_13_mode_0 = const()[name = string("mapping_13_mode_0"), val = string("EXACT")]; + tensor mapping_13_cast_fp16 = gelu(mode = mapping_13_mode_0, x = linear_78_cast_fp16)[name = string("mapping_13_cast_fp16")]; + tensor var_2740_reps_0 = const()[name = string("op_2740_reps_0"), val = tensor([1, 57, 1])]; + tensor var_2740_cast_fp16 = tile(reps = var_2740_reps_0, x = x_349_cast_fp16)[name = string("op_2740_cast_fp16")]; + bool x_351_interleave_0 = const()[name = string("x_351_interleave_0"), val = bool(false)]; + tensor x_351_cast_fp16 = concat(axis = var_2339, interleave = x_351_interleave_0, values = (var_2740_cast_fp16, embedding_to_fp16))[name = string("x_351_cast_fp16")]; + tensor var_2743_axes_0 = const()[name = string("op_2743_axes_0"), val = tensor([1])]; + tensor var_2743_cast_fp16 = expand_dims(axes = var_2743_axes_0, x = mapping_13_cast_fp16)[name = string("op_2743_cast_fp16")]; + tensor mapping_15_reps_0 = const()[name = string("mapping_15_reps_0"), val = tensor([1, 57, 1])]; + tensor mapping_15_cast_fp16 = tile(reps = mapping_15_reps_0, x = var_2743_cast_fp16)[name = string("mapping_15_cast_fp16")]; + tensor x_353_cast_fp16 = add(x = x_351_cast_fp16, y = mapping_15_cast_fp16)[name = string("x_353_cast_fp16")]; + tensor var_2755_split_sizes_0 = const()[name = string("op_2755_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2755_axis_0 = const()[name = string("op_2755_axis_0"), val = int32(1)]; + tensor var_2755_cast_fp16_0, tensor var_2755_cast_fp16_1 = split(axis = var_2755_axis_0, split_sizes = var_2755_split_sizes_0, x = h_3_cast_fp16)[name = string("op_2755_cast_fp16")]; + tensor gamma_75_perm_0 = const()[name = string("gamma_75_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_75_perm_0 = const()[name = string("beta_75_perm_0"), val = tensor([0, -1, 1])]; + tensor x_357_axes_0 = const()[name = string("x_357_axes_0"), val = tensor([-1])]; + fp16 var_2335_to_fp16 = const()[name = string("op_2335_to_fp16"), val = fp16(0x1.5p-17)]; + tensor x_357_cast_fp16 = layer_norm(axes = x_357_axes_0, epsilon = var_2335_to_fp16, x = x_353_cast_fp16)[name = string("x_357_cast_fp16")]; + fp16 var_2761_promoted_to_fp16 = const()[name = string("op_2761_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_75_cast_fp16 = transpose(perm = gamma_75_perm_0, x = var_2755_cast_fp16_0)[name = string("transpose_253")]; + tensor var_2762_cast_fp16 = add(x = gamma_75_cast_fp16, y = var_2761_promoted_to_fp16)[name = string("op_2762_cast_fp16")]; + tensor var_2763_cast_fp16 = mul(x = var_2762_cast_fp16, y = x_357_cast_fp16)[name = string("op_2763_cast_fp16")]; + tensor beta_75_cast_fp16 = transpose(perm = beta_75_perm_0, x = var_2755_cast_fp16_1)[name = string("transpose_252")]; + tensor x_359_cast_fp16 = add(x = var_2763_cast_fp16, y = beta_75_cast_fp16)[name = string("x_359_cast_fp16")]; + tensor var_2774_split_sizes_0 = const()[name = string("op_2774_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2774_axis_0 = const()[name = string("op_2774_axis_0"), val = int32(1)]; + tensor var_2774_cast_fp16_0, tensor var_2774_cast_fp16_1 = split(axis = var_2774_axis_0, split_sizes = var_2774_split_sizes_0, x = h_7_cast_fp16)[name = string("op_2774_cast_fp16")]; + tensor gamma_79_perm_0 = const()[name = string("gamma_79_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_79_perm_0 = const()[name = string("beta_79_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_2780_promoted_to_fp16 = const()[name = string("op_2780_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_79_cast_fp16 = transpose(perm = gamma_79_perm_0, x = var_2774_cast_fp16_0)[name = string("transpose_251")]; + tensor var_2781_cast_fp16 = add(x = gamma_79_cast_fp16, y = var_2780_promoted_to_fp16)[name = string("op_2781_cast_fp16")]; + tensor var_2782_cast_fp16 = mul(x = var_2781_cast_fp16, y = x_357_cast_fp16)[name = string("op_2782_cast_fp16")]; + tensor beta_79_cast_fp16 = transpose(perm = beta_79_perm_0, x = var_2774_cast_fp16_1)[name = string("transpose_250")]; + tensor x_365_cast_fp16 = add(x = var_2782_cast_fp16, y = beta_79_cast_fp16)[name = string("x_365_cast_fp16")]; + tensor linear_81_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_q_weight_to_fp16, x = x_359_cast_fp16)[name = string("linear_81_cast_fp16")]; + tensor linear_82_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_kv_weight_to_fp16, x = x_365_cast_fp16)[name = string("linear_82_cast_fp16")]; + tensor var_2788_split_sizes_0 = const()[name = string("op_2788_split_sizes_0"), val = tensor([512, 512])]; + int32 var_2788_axis_0 = const()[name = string("op_2788_axis_0"), val = int32(-1)]; + tensor var_2788_cast_fp16_0, tensor var_2788_cast_fp16_1 = split(axis = var_2788_axis_0, split_sizes = var_2788_split_sizes_0, x = linear_82_cast_fp16)[name = string("op_2788_cast_fp16")]; + tensor var_2796 = const()[name = string("op_2796"), val = tensor([1, 57, 8, 64])]; + tensor x_369_cast_fp16 = reshape(shape = var_2796, x = linear_81_cast_fp16)[name = string("x_369_cast_fp16")]; + tensor var_2806 = const()[name = string("op_2806"), val = tensor([1, 57, 8, 64])]; + tensor x_373_cast_fp16 = reshape(shape = var_2806, x = var_2788_cast_fp16_0)[name = string("x_373_cast_fp16")]; + tensor var_2816 = const()[name = string("op_2816"), val = tensor([1, 57, 8, 64])]; + tensor x_377_cast_fp16 = reshape(shape = var_2816, x = var_2788_cast_fp16_1)[name = string("x_377_cast_fp16")]; + tensor var_2818 = const()[name = string("op_2818"), val = tensor([0, 2, 1, 3])]; + bool sim_37_transpose_x_0 = const()[name = string("sim_37_transpose_x_0"), val = bool(false)]; + bool sim_37_transpose_y_0 = const()[name = string("sim_37_transpose_y_0"), val = bool(false)]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = x_373_cast_fp16)[name = string("transpose_247")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = x_369_cast_fp16)[name = string("transpose_248")]; + tensor sim_37_cast_fp16 = matmul(transpose_x = sim_37_transpose_x_0, transpose_y = sim_37_transpose_y_0, x = transpose_90, y = transpose_91)[name = string("sim_37_cast_fp16")]; + fp16 var_2822_to_fp16 = const()[name = string("op_2822_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_39_cast_fp16 = mul(x = sim_37_cast_fp16, y = var_2822_to_fp16)[name = string("sim_39_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_2339, x = sim_39_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool x_379_transpose_x_0 = const()[name = string("x_379_transpose_x_0"), val = bool(false)]; + bool x_379_transpose_y_0 = const()[name = string("x_379_transpose_y_0"), val = bool(false)]; + tensor v_19_cast_fp16 = transpose(perm = var_2818, x = x_377_cast_fp16)[name = string("transpose_249")]; + tensor x_379_cast_fp16 = matmul(transpose_x = x_379_transpose_x_0, transpose_y = x_379_transpose_y_0, x = attn_19_cast_fp16, y = v_19_cast_fp16)[name = string("x_379_cast_fp16")]; + tensor var_2844 = const()[name = string("op_2844"), val = tensor([0, 2, 1, 3])]; + tensor var_2846 = const()[name = string("op_2846"), val = tensor([1, 57, 512])]; + tensor x_381_cast_fp16 = transpose(perm = var_2844, x = x_379_cast_fp16)[name = string("transpose_246")]; + tensor input_215_cast_fp16 = reshape(shape = var_2846, x = x_381_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor linear_83_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_weight_to_fp16, x = input_215_cast_fp16)[name = string("linear_83_cast_fp16")]; + tensor input_217_cast_fp16 = add(x = linear_83_cast_fp16, y = x_353_cast_fp16)[name = string("input_217_cast_fp16")]; + tensor linear_84_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_weight_to_fp16, x = input_217_cast_fp16)[name = string("linear_84_cast_fp16")]; + string input_221_mode_0 = const()[name = string("input_221_mode_0"), val = string("EXACT")]; + tensor input_221_cast_fp16 = gelu(mode = input_221_mode_0, x = linear_84_cast_fp16)[name = string("input_221_cast_fp16")]; + tensor linear_85_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_weight_to_fp16, x = input_221_cast_fp16)[name = string("linear_85_cast_fp16")]; + tensor x_383_cast_fp16 = add(x = linear_85_cast_fp16, y = input_217_cast_fp16)[name = string("x_383_cast_fp16")]; + tensor x_385_cast_fp16 = add(x = x_383_cast_fp16, y = mapping_15_cast_fp16)[name = string("x_385_cast_fp16")]; + tensor var_2862_split_sizes_0 = const()[name = string("op_2862_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2862_axis_0 = const()[name = string("op_2862_axis_0"), val = int32(1)]; + tensor var_2862_cast_fp16_0, tensor var_2862_cast_fp16_1 = split(axis = var_2862_axis_0, split_sizes = var_2862_split_sizes_0, x = h_11_cast_fp16)[name = string("op_2862_cast_fp16")]; + tensor gamma_83_perm_0 = const()[name = string("gamma_83_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_83_perm_0 = const()[name = string("beta_83_perm_0"), val = tensor([0, -1, 1])]; + tensor x_389_axes_0 = const()[name = string("x_389_axes_0"), val = tensor([-1])]; + tensor x_389_cast_fp16 = layer_norm(axes = x_389_axes_0, epsilon = var_2335_to_fp16, x = x_385_cast_fp16)[name = string("x_389_cast_fp16")]; + fp16 var_2868_promoted_to_fp16 = const()[name = string("op_2868_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_83_cast_fp16 = transpose(perm = gamma_83_perm_0, x = var_2862_cast_fp16_0)[name = string("transpose_245")]; + tensor var_2869_cast_fp16 = add(x = gamma_83_cast_fp16, y = var_2868_promoted_to_fp16)[name = string("op_2869_cast_fp16")]; + tensor var_2870_cast_fp16 = mul(x = var_2869_cast_fp16, y = x_389_cast_fp16)[name = string("op_2870_cast_fp16")]; + tensor beta_83_cast_fp16 = transpose(perm = beta_83_perm_0, x = var_2862_cast_fp16_1)[name = string("transpose_244")]; + tensor x_391_cast_fp16 = add(x = var_2870_cast_fp16, y = beta_83_cast_fp16)[name = string("x_391_cast_fp16")]; + tensor var_2881_split_sizes_0 = const()[name = string("op_2881_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2881_axis_0 = const()[name = string("op_2881_axis_0"), val = int32(1)]; + tensor var_2881_cast_fp16_0, tensor var_2881_cast_fp16_1 = split(axis = var_2881_axis_0, split_sizes = var_2881_split_sizes_0, x = h_15_cast_fp16)[name = string("op_2881_cast_fp16")]; + tensor gamma_87_perm_0 = const()[name = string("gamma_87_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_87_perm_0 = const()[name = string("beta_87_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_2887_promoted_to_fp16 = const()[name = string("op_2887_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_87_cast_fp16 = transpose(perm = gamma_87_perm_0, x = var_2881_cast_fp16_0)[name = string("transpose_243")]; + tensor var_2888_cast_fp16 = add(x = gamma_87_cast_fp16, y = var_2887_promoted_to_fp16)[name = string("op_2888_cast_fp16")]; + tensor var_2889_cast_fp16 = mul(x = var_2888_cast_fp16, y = x_389_cast_fp16)[name = string("op_2889_cast_fp16")]; + tensor beta_87_cast_fp16 = transpose(perm = beta_87_perm_0, x = var_2881_cast_fp16_1)[name = string("transpose_242")]; + tensor x_397_cast_fp16 = add(x = var_2889_cast_fp16, y = beta_87_cast_fp16)[name = string("x_397_cast_fp16")]; + tensor linear_88_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_q_weight_to_fp16, x = x_391_cast_fp16)[name = string("linear_88_cast_fp16")]; + tensor linear_89_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_kv_weight_to_fp16, x = x_397_cast_fp16)[name = string("linear_89_cast_fp16")]; + tensor var_2895_split_sizes_0 = const()[name = string("op_2895_split_sizes_0"), val = tensor([512, 512])]; + int32 var_2895_axis_0 = const()[name = string("op_2895_axis_0"), val = int32(-1)]; + tensor var_2895_cast_fp16_0, tensor var_2895_cast_fp16_1 = split(axis = var_2895_axis_0, split_sizes = var_2895_split_sizes_0, x = linear_89_cast_fp16)[name = string("op_2895_cast_fp16")]; + tensor var_2903 = const()[name = string("op_2903"), val = tensor([1, 57, 8, 64])]; + tensor x_401_cast_fp16 = reshape(shape = var_2903, x = linear_88_cast_fp16)[name = string("x_401_cast_fp16")]; + tensor var_2913 = const()[name = string("op_2913"), val = tensor([1, 57, 8, 64])]; + tensor x_405_cast_fp16 = reshape(shape = var_2913, x = var_2895_cast_fp16_0)[name = string("x_405_cast_fp16")]; + tensor var_2923 = const()[name = string("op_2923"), val = tensor([1, 57, 8, 64])]; + tensor x_409_cast_fp16 = reshape(shape = var_2923, x = var_2895_cast_fp16_1)[name = string("x_409_cast_fp16")]; + tensor var_2925 = const()[name = string("op_2925"), val = tensor([0, 2, 1, 3])]; + bool sim_41_transpose_x_0 = const()[name = string("sim_41_transpose_x_0"), val = bool(false)]; + bool sim_41_transpose_y_0 = const()[name = string("sim_41_transpose_y_0"), val = bool(false)]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = x_405_cast_fp16)[name = string("transpose_239")]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = x_401_cast_fp16)[name = string("transpose_240")]; + tensor sim_41_cast_fp16 = matmul(transpose_x = sim_41_transpose_x_0, transpose_y = sim_41_transpose_y_0, x = transpose_92, y = transpose_93)[name = string("sim_41_cast_fp16")]; + fp16 var_2929_to_fp16 = const()[name = string("op_2929_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_43_cast_fp16 = mul(x = sim_41_cast_fp16, y = var_2929_to_fp16)[name = string("sim_43_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_2339, x = sim_43_cast_fp16)[name = string("attn_21_cast_fp16")]; + bool x_411_transpose_x_0 = const()[name = string("x_411_transpose_x_0"), val = bool(false)]; + bool x_411_transpose_y_0 = const()[name = string("x_411_transpose_y_0"), val = bool(false)]; + tensor v_21_cast_fp16 = transpose(perm = var_2925, x = x_409_cast_fp16)[name = string("transpose_241")]; + tensor x_411_cast_fp16 = matmul(transpose_x = x_411_transpose_x_0, transpose_y = x_411_transpose_y_0, x = attn_21_cast_fp16, y = v_21_cast_fp16)[name = string("x_411_cast_fp16")]; + tensor var_2951 = const()[name = string("op_2951"), val = tensor([0, 2, 1, 3])]; + tensor var_2953 = const()[name = string("op_2953"), val = tensor([1, 57, 512])]; + tensor x_413_cast_fp16 = transpose(perm = var_2951, x = x_411_cast_fp16)[name = string("transpose_238")]; + tensor input_231_cast_fp16 = reshape(shape = var_2953, x = x_413_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor linear_90_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_weight_to_fp16, x = input_231_cast_fp16)[name = string("linear_90_cast_fp16")]; + tensor input_233_cast_fp16 = add(x = linear_90_cast_fp16, y = x_385_cast_fp16)[name = string("input_233_cast_fp16")]; + tensor linear_91_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_weight_to_fp16, x = input_233_cast_fp16)[name = string("linear_91_cast_fp16")]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("EXACT")]; + tensor input_237_cast_fp16 = gelu(mode = input_237_mode_0, x = linear_91_cast_fp16)[name = string("input_237_cast_fp16")]; + tensor linear_92_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_weight_to_fp16, x = input_237_cast_fp16)[name = string("linear_92_cast_fp16")]; + tensor x_415_cast_fp16 = add(x = linear_92_cast_fp16, y = input_233_cast_fp16)[name = string("x_415_cast_fp16")]; + tensor x_417_cast_fp16 = add(x = x_415_cast_fp16, y = mapping_15_cast_fp16)[name = string("x_417_cast_fp16")]; + tensor var_2969_split_sizes_0 = const()[name = string("op_2969_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2969_axis_0 = const()[name = string("op_2969_axis_0"), val = int32(1)]; + tensor var_2969_cast_fp16_0, tensor var_2969_cast_fp16_1 = split(axis = var_2969_axis_0, split_sizes = var_2969_split_sizes_0, x = h_19_cast_fp16)[name = string("op_2969_cast_fp16")]; + tensor gamma_91_perm_0 = const()[name = string("gamma_91_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_91_perm_0 = const()[name = string("beta_91_perm_0"), val = tensor([0, -1, 1])]; + tensor x_421_axes_0 = const()[name = string("x_421_axes_0"), val = tensor([-1])]; + tensor x_421_cast_fp16 = layer_norm(axes = x_421_axes_0, epsilon = var_2335_to_fp16, x = x_417_cast_fp16)[name = string("x_421_cast_fp16")]; + fp16 var_2975_promoted_to_fp16 = const()[name = string("op_2975_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_91_cast_fp16 = transpose(perm = gamma_91_perm_0, x = var_2969_cast_fp16_0)[name = string("transpose_237")]; + tensor var_2976_cast_fp16 = add(x = gamma_91_cast_fp16, y = var_2975_promoted_to_fp16)[name = string("op_2976_cast_fp16")]; + tensor var_2977_cast_fp16 = mul(x = var_2976_cast_fp16, y = x_421_cast_fp16)[name = string("op_2977_cast_fp16")]; + tensor beta_91_cast_fp16 = transpose(perm = beta_91_perm_0, x = var_2969_cast_fp16_1)[name = string("transpose_236")]; + tensor x_423_cast_fp16 = add(x = var_2977_cast_fp16, y = beta_91_cast_fp16)[name = string("x_423_cast_fp16")]; + tensor var_2988_split_sizes_0 = const()[name = string("op_2988_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_2988_axis_0 = const()[name = string("op_2988_axis_0"), val = int32(1)]; + tensor var_2988_cast_fp16_0, tensor var_2988_cast_fp16_1 = split(axis = var_2988_axis_0, split_sizes = var_2988_split_sizes_0, x = h_23_cast_fp16)[name = string("op_2988_cast_fp16")]; + tensor gamma_95_perm_0 = const()[name = string("gamma_95_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_95_perm_0 = const()[name = string("beta_95_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_2994_promoted_to_fp16 = const()[name = string("op_2994_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_95_cast_fp16 = transpose(perm = gamma_95_perm_0, x = var_2988_cast_fp16_0)[name = string("transpose_235")]; + tensor var_2995_cast_fp16 = add(x = gamma_95_cast_fp16, y = var_2994_promoted_to_fp16)[name = string("op_2995_cast_fp16")]; + tensor var_2996_cast_fp16 = mul(x = var_2995_cast_fp16, y = x_421_cast_fp16)[name = string("op_2996_cast_fp16")]; + tensor beta_95_cast_fp16 = transpose(perm = beta_95_perm_0, x = var_2988_cast_fp16_1)[name = string("transpose_234")]; + tensor x_429_cast_fp16 = add(x = var_2996_cast_fp16, y = beta_95_cast_fp16)[name = string("x_429_cast_fp16")]; + tensor linear_95_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_q_weight_to_fp16, x = x_423_cast_fp16)[name = string("linear_95_cast_fp16")]; + tensor linear_96_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_kv_weight_to_fp16, x = x_429_cast_fp16)[name = string("linear_96_cast_fp16")]; + tensor var_3002_split_sizes_0 = const()[name = string("op_3002_split_sizes_0"), val = tensor([512, 512])]; + int32 var_3002_axis_0 = const()[name = string("op_3002_axis_0"), val = int32(-1)]; + tensor var_3002_cast_fp16_0, tensor var_3002_cast_fp16_1 = split(axis = var_3002_axis_0, split_sizes = var_3002_split_sizes_0, x = linear_96_cast_fp16)[name = string("op_3002_cast_fp16")]; + tensor var_3010 = const()[name = string("op_3010"), val = tensor([1, 57, 8, 64])]; + tensor x_433_cast_fp16 = reshape(shape = var_3010, x = linear_95_cast_fp16)[name = string("x_433_cast_fp16")]; + tensor var_3020 = const()[name = string("op_3020"), val = tensor([1, 57, 8, 64])]; + tensor x_437_cast_fp16 = reshape(shape = var_3020, x = var_3002_cast_fp16_0)[name = string("x_437_cast_fp16")]; + tensor var_3030 = const()[name = string("op_3030"), val = tensor([1, 57, 8, 64])]; + tensor x_441_cast_fp16 = reshape(shape = var_3030, x = var_3002_cast_fp16_1)[name = string("x_441_cast_fp16")]; + tensor var_3032 = const()[name = string("op_3032"), val = tensor([0, 2, 1, 3])]; + bool sim_45_transpose_x_0 = const()[name = string("sim_45_transpose_x_0"), val = bool(false)]; + bool sim_45_transpose_y_0 = const()[name = string("sim_45_transpose_y_0"), val = bool(false)]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = x_437_cast_fp16)[name = string("transpose_231")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = x_433_cast_fp16)[name = string("transpose_232")]; + tensor sim_45_cast_fp16 = matmul(transpose_x = sim_45_transpose_x_0, transpose_y = sim_45_transpose_y_0, x = transpose_94, y = transpose_95)[name = string("sim_45_cast_fp16")]; + fp16 var_3036_to_fp16 = const()[name = string("op_3036_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_47_cast_fp16 = mul(x = sim_45_cast_fp16, y = var_3036_to_fp16)[name = string("sim_47_cast_fp16")]; + tensor attn_23_cast_fp16 = softmax(axis = var_2339, x = sim_47_cast_fp16)[name = string("attn_23_cast_fp16")]; + bool x_443_transpose_x_0 = const()[name = string("x_443_transpose_x_0"), val = bool(false)]; + bool x_443_transpose_y_0 = const()[name = string("x_443_transpose_y_0"), val = bool(false)]; + tensor v_23_cast_fp16 = transpose(perm = var_3032, x = x_441_cast_fp16)[name = string("transpose_233")]; + tensor x_443_cast_fp16 = matmul(transpose_x = x_443_transpose_x_0, transpose_y = x_443_transpose_y_0, x = attn_23_cast_fp16, y = v_23_cast_fp16)[name = string("x_443_cast_fp16")]; + tensor var_3058 = const()[name = string("op_3058"), val = tensor([0, 2, 1, 3])]; + tensor var_3060 = const()[name = string("op_3060"), val = tensor([1, 57, 512])]; + tensor x_445_cast_fp16 = transpose(perm = var_3058, x = x_443_cast_fp16)[name = string("transpose_230")]; + tensor input_247_cast_fp16 = reshape(shape = var_3060, x = x_445_cast_fp16)[name = string("input_247_cast_fp16")]; + tensor linear_97_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_weight_to_fp16, x = input_247_cast_fp16)[name = string("linear_97_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = linear_97_cast_fp16, y = x_417_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor linear_98_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_weight_to_fp16, x = input_249_cast_fp16)[name = string("linear_98_cast_fp16")]; + string input_253_mode_0 = const()[name = string("input_253_mode_0"), val = string("EXACT")]; + tensor input_253_cast_fp16 = gelu(mode = input_253_mode_0, x = linear_98_cast_fp16)[name = string("input_253_cast_fp16")]; + tensor linear_99_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_weight_to_fp16, x = input_253_cast_fp16)[name = string("linear_99_cast_fp16")]; + tensor x_447_cast_fp16 = add(x = linear_99_cast_fp16, y = input_249_cast_fp16)[name = string("x_447_cast_fp16")]; + tensor var_3069_axes_0 = const()[name = string("op_3069_axes_0"), val = tensor([1])]; + bool var_3069_keep_dims_0 = const()[name = string("op_3069_keep_dims_0"), val = bool(false)]; + tensor var_3069_cast_fp16 = reduce_mean(axes = var_3069_axes_0, keep_dims = var_3069_keep_dims_0, x = x_447_cast_fp16)[name = string("op_3069_cast_fp16")]; + tensor x_449_axes_0 = const()[name = string("x_449_axes_0"), val = tensor([1])]; + tensor x_449_cast_fp16 = expand_dims(axes = x_449_axes_0, x = var_3069_cast_fp16)[name = string("x_449_cast_fp16")]; + tensor var_3071 = const()[name = string("op_3071"), val = tensor([0, 2, 1])]; + string x_451_pad_type_0 = const()[name = string("x_451_pad_type_0"), val = string("valid")]; + tensor x_451_strides_0 = const()[name = string("x_451_strides_0"), val = tensor([1])]; + tensor x_451_pad_0 = const()[name = string("x_451_pad_0"), val = tensor([0, 0])]; + tensor x_451_dilations_0 = const()[name = string("x_451_dilations_0"), val = tensor([1])]; + int32 x_451_groups_0 = const()[name = string("x_451_groups_0"), val = int32(1)]; + tensor input_255_cast_fp16 = transpose(perm = var_3071, x = x_449_cast_fp16)[name = string("transpose_229")]; + tensor x_451_cast_fp16 = conv(bias = unet_wrap_kdiffusion_net_to_out_1_bias_to_fp16, dilations = x_451_dilations_0, groups = x_451_groups_0, pad = x_451_pad_0, pad_type = x_451_pad_type_0, strides = x_451_strides_0, weight = unet_wrap_kdiffusion_net_to_out_1_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_451_cast_fp16")]; + tensor x_pred_7_perm_0 = const()[name = string("x_pred_7_perm_0"), val = tensor([0, -1, -2])]; + tensor c_skip_7_to_fp16 = const()[name = string("c_skip_7_to_fp16"), val = tensor([[[0x1.528p-2]]])]; + tensor var_3079_cast_fp16 = mul(x = c_skip_7_to_fp16, y = x_noisy_7_cast_fp16)[name = string("op_3079_cast_fp16")]; + tensor c_out_7_to_fp16 = const()[name = string("c_out_7_to_fp16"), val = tensor([[[0x1.4dcp-3]]])]; + tensor x_pred_7_cast_fp16 = transpose(perm = x_pred_7_perm_0, x = x_451_cast_fp16)[name = string("transpose_228")]; + tensor var_3080_cast_fp16 = mul(x = c_out_7_to_fp16, y = x_pred_7_cast_fp16)[name = string("op_3080_cast_fp16")]; + tensor x_mid_dn_3_cast_fp16 = add(x = var_3079_cast_fp16, y = var_3080_cast_fp16)[name = string("x_mid_dn_3_cast_fp16")]; + tensor var_3083_cast_fp16 = sub(x = x_noisy_7_cast_fp16, y = x_mid_dn_3_cast_fp16)[name = string("op_3083_cast_fp16")]; + tensor _inversed_d_mid_3_y_0_to_fp16 = const()[name = string("_inversed_d_mid_3_y_0_to_fp16"), val = tensor([0x1.c3cp+1])]; + tensor _inversed_d_mid_3_cast_fp16 = mul(x = var_3083_cast_fp16, y = _inversed_d_mid_3_y_0_to_fp16)[name = string("_inversed_d_mid_3_cast_fp16")]; + tensor var_3089_to_fp16 = const()[name = string("op_3089_to_fp16"), val = tensor([-0x1.19p-1])]; + tensor var_3090_cast_fp16 = mul(x = _inversed_d_mid_3_cast_fp16, y = var_3089_to_fp16)[name = string("op_3090_cast_fp16")]; + tensor x_453_cast_fp16 = add(x = x_noisy_5_cast_fp16, y = var_3090_cast_fp16)[name = string("x_453_cast_fp16")]; + tensor var_3095_begin_0 = const()[name = string("op_3095_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor var_3095_end_0 = const()[name = string("op_3095_end_0"), val = tensor([2, 1, 1, 256])]; + tensor var_3095_end_mask_0 = const()[name = string("op_3095_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_3095_squeeze_mask_0 = const()[name = string("op_3095_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor var_3095_cast_fp16 = slice_by_index(begin = var_3095_begin_0, end = var_3095_end_0, end_mask = var_3095_end_mask_0, squeeze_mask = var_3095_squeeze_mask_0, x = noises_aux_to_fp16)[name = string("op_3095_cast_fp16")]; + fp16 var_3098_to_fp16 = const()[name = string("op_3098_to_fp16"), val = fp16(0x1.1ep-4)]; + tensor var_3099_cast_fp16 = mul(x = var_3095_cast_fp16, y = var_3098_to_fp16)[name = string("op_3099_cast_fp16")]; + tensor x_noisy_9_cast_fp16 = add(x = x_453_cast_fp16, y = var_3099_cast_fp16)[name = string("x_noisy_9_cast_fp16")]; + int32 var_3123 = const()[name = string("op_3123"), val = int32(-1)]; + tensor c_in_9_to_fp16 = const()[name = string("c_in_9_to_fp16"), val = tensor([[[0x1.2ecp+2]]])]; + tensor x_463_cast_fp16 = mul(x = c_in_9_to_fp16, y = x_noisy_9_cast_fp16)[name = string("x_463_cast_fp16")]; + int32 x_459_axis_0 = const()[name = string("x_459_axis_0"), val = int32(0)]; + tensor var_3509_to_fp16 = const()[name = string("op_3509_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49353408)))]; + tensor x_459_cast_fp16 = stack(axis = x_459_axis_0, values = (var_3509_to_fp16, var_423_cast_fp16))[name = string("x_459_cast_fp16")]; + tensor var_3514 = const()[name = string("op_3514"), val = tensor([1, 2, 0])]; + tensor input_263_axes_0 = const()[name = string("input_263_axes_0"), val = tensor([2])]; + bool input_263_keep_dims_0 = const()[name = string("input_263_keep_dims_0"), val = bool(false)]; + tensor x_461_cast_fp16 = transpose(perm = var_3514, x = x_459_cast_fp16)[name = string("transpose_227")]; + tensor input_263_cast_fp16 = reduce_sum(axes = input_263_axes_0, keep_dims = input_263_keep_dims_0, x = x_461_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor linear_102_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_0_weight_to_fp16, x = input_263_cast_fp16)[name = string("linear_102_cast_fp16")]; + string input_267_mode_0 = const()[name = string("input_267_mode_0"), val = string("EXACT")]; + tensor input_267_cast_fp16 = gelu(mode = input_267_mode_0, x = linear_102_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor linear_103_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_2_weight_to_fp16, x = input_267_cast_fp16)[name = string("linear_103_cast_fp16")]; + string mapping_17_mode_0 = const()[name = string("mapping_17_mode_0"), val = string("EXACT")]; + tensor mapping_17_cast_fp16 = gelu(mode = mapping_17_mode_0, x = linear_103_cast_fp16)[name = string("mapping_17_cast_fp16")]; + tensor var_3524_reps_0 = const()[name = string("op_3524_reps_0"), val = tensor([1, 57, 1])]; + tensor var_3524_cast_fp16 = tile(reps = var_3524_reps_0, x = x_463_cast_fp16)[name = string("op_3524_cast_fp16")]; + bool x_465_interleave_0 = const()[name = string("x_465_interleave_0"), val = bool(false)]; + tensor x_465_cast_fp16 = concat(axis = var_3123, interleave = x_465_interleave_0, values = (var_3524_cast_fp16, embedding_to_fp16))[name = string("x_465_cast_fp16")]; + tensor var_3527_axes_0 = const()[name = string("op_3527_axes_0"), val = tensor([1])]; + tensor var_3527_cast_fp16 = expand_dims(axes = var_3527_axes_0, x = mapping_17_cast_fp16)[name = string("op_3527_cast_fp16")]; + tensor mapping_19_reps_0 = const()[name = string("mapping_19_reps_0"), val = tensor([1, 57, 1])]; + tensor mapping_19_cast_fp16 = tile(reps = mapping_19_reps_0, x = var_3527_cast_fp16)[name = string("mapping_19_cast_fp16")]; + tensor x_467_cast_fp16 = add(x = x_465_cast_fp16, y = mapping_19_cast_fp16)[name = string("x_467_cast_fp16")]; + tensor var_3539_split_sizes_0 = const()[name = string("op_3539_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_3539_axis_0 = const()[name = string("op_3539_axis_0"), val = int32(1)]; + tensor var_3539_cast_fp16_0, tensor var_3539_cast_fp16_1 = split(axis = var_3539_axis_0, split_sizes = var_3539_split_sizes_0, x = h_3_cast_fp16)[name = string("op_3539_cast_fp16")]; + tensor gamma_99_perm_0 = const()[name = string("gamma_99_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_99_perm_0 = const()[name = string("beta_99_perm_0"), val = tensor([0, -1, 1])]; + tensor x_471_axes_0 = const()[name = string("x_471_axes_0"), val = tensor([-1])]; + fp16 var_3119_to_fp16 = const()[name = string("op_3119_to_fp16"), val = fp16(0x1.5p-17)]; + tensor x_471_cast_fp16 = layer_norm(axes = x_471_axes_0, epsilon = var_3119_to_fp16, x = x_467_cast_fp16)[name = string("x_471_cast_fp16")]; + fp16 var_3545_promoted_to_fp16 = const()[name = string("op_3545_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_99_cast_fp16 = transpose(perm = gamma_99_perm_0, x = var_3539_cast_fp16_0)[name = string("transpose_226")]; + tensor var_3546_cast_fp16 = add(x = gamma_99_cast_fp16, y = var_3545_promoted_to_fp16)[name = string("op_3546_cast_fp16")]; + tensor var_3547_cast_fp16 = mul(x = var_3546_cast_fp16, y = x_471_cast_fp16)[name = string("op_3547_cast_fp16")]; + tensor beta_99_cast_fp16 = transpose(perm = beta_99_perm_0, x = var_3539_cast_fp16_1)[name = string("transpose_225")]; + tensor x_473_cast_fp16 = add(x = var_3547_cast_fp16, y = beta_99_cast_fp16)[name = string("x_473_cast_fp16")]; + tensor var_3558_split_sizes_0 = const()[name = string("op_3558_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_3558_axis_0 = const()[name = string("op_3558_axis_0"), val = int32(1)]; + tensor var_3558_cast_fp16_0, tensor var_3558_cast_fp16_1 = split(axis = var_3558_axis_0, split_sizes = var_3558_split_sizes_0, x = h_7_cast_fp16)[name = string("op_3558_cast_fp16")]; + tensor gamma_103_perm_0 = const()[name = string("gamma_103_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_103_perm_0 = const()[name = string("beta_103_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_3564_promoted_to_fp16 = const()[name = string("op_3564_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_103_cast_fp16 = transpose(perm = gamma_103_perm_0, x = var_3558_cast_fp16_0)[name = string("transpose_224")]; + tensor var_3565_cast_fp16 = add(x = gamma_103_cast_fp16, y = var_3564_promoted_to_fp16)[name = string("op_3565_cast_fp16")]; + tensor var_3566_cast_fp16 = mul(x = var_3565_cast_fp16, y = x_471_cast_fp16)[name = string("op_3566_cast_fp16")]; + tensor beta_103_cast_fp16 = transpose(perm = beta_103_perm_0, x = var_3558_cast_fp16_1)[name = string("transpose_223")]; + tensor x_479_cast_fp16 = add(x = var_3566_cast_fp16, y = beta_103_cast_fp16)[name = string("x_479_cast_fp16")]; + tensor linear_106_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_q_weight_to_fp16, x = x_473_cast_fp16)[name = string("linear_106_cast_fp16")]; + tensor linear_107_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_kv_weight_to_fp16, x = x_479_cast_fp16)[name = string("linear_107_cast_fp16")]; + tensor var_3572_split_sizes_0 = const()[name = string("op_3572_split_sizes_0"), val = tensor([512, 512])]; + int32 var_3572_axis_0 = const()[name = string("op_3572_axis_0"), val = int32(-1)]; + tensor var_3572_cast_fp16_0, tensor var_3572_cast_fp16_1 = split(axis = var_3572_axis_0, split_sizes = var_3572_split_sizes_0, x = linear_107_cast_fp16)[name = string("op_3572_cast_fp16")]; + tensor var_3580 = const()[name = string("op_3580"), val = tensor([1, 57, 8, 64])]; + tensor x_483_cast_fp16 = reshape(shape = var_3580, x = linear_106_cast_fp16)[name = string("x_483_cast_fp16")]; + tensor var_3590 = const()[name = string("op_3590"), val = tensor([1, 57, 8, 64])]; + tensor x_487_cast_fp16 = reshape(shape = var_3590, x = var_3572_cast_fp16_0)[name = string("x_487_cast_fp16")]; + tensor var_3600 = const()[name = string("op_3600"), val = tensor([1, 57, 8, 64])]; + tensor x_491_cast_fp16 = reshape(shape = var_3600, x = var_3572_cast_fp16_1)[name = string("x_491_cast_fp16")]; + tensor var_3602 = const()[name = string("op_3602"), val = tensor([0, 2, 1, 3])]; + bool sim_49_transpose_x_0 = const()[name = string("sim_49_transpose_x_0"), val = bool(false)]; + bool sim_49_transpose_y_0 = const()[name = string("sim_49_transpose_y_0"), val = bool(false)]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = x_487_cast_fp16)[name = string("transpose_220")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = x_483_cast_fp16)[name = string("transpose_221")]; + tensor sim_49_cast_fp16 = matmul(transpose_x = sim_49_transpose_x_0, transpose_y = sim_49_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("sim_49_cast_fp16")]; + fp16 var_3606_to_fp16 = const()[name = string("op_3606_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_51_cast_fp16 = mul(x = sim_49_cast_fp16, y = var_3606_to_fp16)[name = string("sim_51_cast_fp16")]; + tensor attn_25_cast_fp16 = softmax(axis = var_3123, x = sim_51_cast_fp16)[name = string("attn_25_cast_fp16")]; + bool x_493_transpose_x_0 = const()[name = string("x_493_transpose_x_0"), val = bool(false)]; + bool x_493_transpose_y_0 = const()[name = string("x_493_transpose_y_0"), val = bool(false)]; + tensor v_25_cast_fp16 = transpose(perm = var_3602, x = x_491_cast_fp16)[name = string("transpose_222")]; + tensor x_493_cast_fp16 = matmul(transpose_x = x_493_transpose_x_0, transpose_y = x_493_transpose_y_0, x = attn_25_cast_fp16, y = v_25_cast_fp16)[name = string("x_493_cast_fp16")]; + tensor var_3628 = const()[name = string("op_3628"), val = tensor([0, 2, 1, 3])]; + tensor var_3630 = const()[name = string("op_3630"), val = tensor([1, 57, 512])]; + tensor x_495_cast_fp16 = transpose(perm = var_3628, x = x_493_cast_fp16)[name = string("transpose_219")]; + tensor input_279_cast_fp16 = reshape(shape = var_3630, x = x_495_cast_fp16)[name = string("input_279_cast_fp16")]; + tensor linear_108_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_weight_to_fp16, x = input_279_cast_fp16)[name = string("linear_108_cast_fp16")]; + tensor input_281_cast_fp16 = add(x = linear_108_cast_fp16, y = x_467_cast_fp16)[name = string("input_281_cast_fp16")]; + tensor linear_109_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_weight_to_fp16, x = input_281_cast_fp16)[name = string("linear_109_cast_fp16")]; + string input_285_mode_0 = const()[name = string("input_285_mode_0"), val = string("EXACT")]; + tensor input_285_cast_fp16 = gelu(mode = input_285_mode_0, x = linear_109_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor linear_110_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_weight_to_fp16, x = input_285_cast_fp16)[name = string("linear_110_cast_fp16")]; + tensor x_497_cast_fp16 = add(x = linear_110_cast_fp16, y = input_281_cast_fp16)[name = string("x_497_cast_fp16")]; + tensor x_499_cast_fp16 = add(x = x_497_cast_fp16, y = mapping_19_cast_fp16)[name = string("x_499_cast_fp16")]; + tensor var_3646_split_sizes_0 = const()[name = string("op_3646_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_3646_axis_0 = const()[name = string("op_3646_axis_0"), val = int32(1)]; + tensor var_3646_cast_fp16_0, tensor var_3646_cast_fp16_1 = split(axis = var_3646_axis_0, split_sizes = var_3646_split_sizes_0, x = h_11_cast_fp16)[name = string("op_3646_cast_fp16")]; + tensor gamma_107_perm_0 = const()[name = string("gamma_107_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_107_perm_0 = const()[name = string("beta_107_perm_0"), val = tensor([0, -1, 1])]; + tensor x_503_axes_0 = const()[name = string("x_503_axes_0"), val = tensor([-1])]; + tensor x_503_cast_fp16 = layer_norm(axes = x_503_axes_0, epsilon = var_3119_to_fp16, x = x_499_cast_fp16)[name = string("x_503_cast_fp16")]; + fp16 var_3652_promoted_to_fp16 = const()[name = string("op_3652_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_107_cast_fp16 = transpose(perm = gamma_107_perm_0, x = var_3646_cast_fp16_0)[name = string("transpose_218")]; + tensor var_3653_cast_fp16 = add(x = gamma_107_cast_fp16, y = var_3652_promoted_to_fp16)[name = string("op_3653_cast_fp16")]; + tensor var_3654_cast_fp16 = mul(x = var_3653_cast_fp16, y = x_503_cast_fp16)[name = string("op_3654_cast_fp16")]; + tensor beta_107_cast_fp16 = transpose(perm = beta_107_perm_0, x = var_3646_cast_fp16_1)[name = string("transpose_217")]; + tensor x_505_cast_fp16 = add(x = var_3654_cast_fp16, y = beta_107_cast_fp16)[name = string("x_505_cast_fp16")]; + tensor var_3665_split_sizes_0 = const()[name = string("op_3665_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_3665_axis_0 = const()[name = string("op_3665_axis_0"), val = int32(1)]; + tensor var_3665_cast_fp16_0, tensor var_3665_cast_fp16_1 = split(axis = var_3665_axis_0, split_sizes = var_3665_split_sizes_0, x = h_15_cast_fp16)[name = string("op_3665_cast_fp16")]; + tensor gamma_111_perm_0 = const()[name = string("gamma_111_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_111_perm_0 = const()[name = string("beta_111_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_3671_promoted_to_fp16 = const()[name = string("op_3671_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_111_cast_fp16 = transpose(perm = gamma_111_perm_0, x = var_3665_cast_fp16_0)[name = string("transpose_216")]; + tensor var_3672_cast_fp16 = add(x = gamma_111_cast_fp16, y = var_3671_promoted_to_fp16)[name = string("op_3672_cast_fp16")]; + tensor var_3673_cast_fp16 = mul(x = var_3672_cast_fp16, y = x_503_cast_fp16)[name = string("op_3673_cast_fp16")]; + tensor beta_111_cast_fp16 = transpose(perm = beta_111_perm_0, x = var_3665_cast_fp16_1)[name = string("transpose_215")]; + tensor x_511_cast_fp16 = add(x = var_3673_cast_fp16, y = beta_111_cast_fp16)[name = string("x_511_cast_fp16")]; + tensor linear_113_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_q_weight_to_fp16, x = x_505_cast_fp16)[name = string("linear_113_cast_fp16")]; + tensor linear_114_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_kv_weight_to_fp16, x = x_511_cast_fp16)[name = string("linear_114_cast_fp16")]; + tensor var_3679_split_sizes_0 = const()[name = string("op_3679_split_sizes_0"), val = tensor([512, 512])]; + int32 var_3679_axis_0 = const()[name = string("op_3679_axis_0"), val = int32(-1)]; + tensor var_3679_cast_fp16_0, tensor var_3679_cast_fp16_1 = split(axis = var_3679_axis_0, split_sizes = var_3679_split_sizes_0, x = linear_114_cast_fp16)[name = string("op_3679_cast_fp16")]; + tensor var_3687 = const()[name = string("op_3687"), val = tensor([1, 57, 8, 64])]; + tensor x_515_cast_fp16 = reshape(shape = var_3687, x = linear_113_cast_fp16)[name = string("x_515_cast_fp16")]; + tensor var_3697 = const()[name = string("op_3697"), val = tensor([1, 57, 8, 64])]; + tensor x_519_cast_fp16 = reshape(shape = var_3697, x = var_3679_cast_fp16_0)[name = string("x_519_cast_fp16")]; + tensor var_3707 = const()[name = string("op_3707"), val = tensor([1, 57, 8, 64])]; + tensor x_523_cast_fp16 = reshape(shape = var_3707, x = var_3679_cast_fp16_1)[name = string("x_523_cast_fp16")]; + tensor var_3709 = const()[name = string("op_3709"), val = tensor([0, 2, 1, 3])]; + bool sim_53_transpose_x_0 = const()[name = string("sim_53_transpose_x_0"), val = bool(false)]; + bool sim_53_transpose_y_0 = const()[name = string("sim_53_transpose_y_0"), val = bool(false)]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = x_519_cast_fp16)[name = string("transpose_212")]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = x_515_cast_fp16)[name = string("transpose_213")]; + tensor sim_53_cast_fp16 = matmul(transpose_x = sim_53_transpose_x_0, transpose_y = sim_53_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("sim_53_cast_fp16")]; + fp16 var_3713_to_fp16 = const()[name = string("op_3713_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_55_cast_fp16 = mul(x = sim_53_cast_fp16, y = var_3713_to_fp16)[name = string("sim_55_cast_fp16")]; + tensor attn_27_cast_fp16 = softmax(axis = var_3123, x = sim_55_cast_fp16)[name = string("attn_27_cast_fp16")]; + bool x_525_transpose_x_0 = const()[name = string("x_525_transpose_x_0"), val = bool(false)]; + bool x_525_transpose_y_0 = const()[name = string("x_525_transpose_y_0"), val = bool(false)]; + tensor v_27_cast_fp16 = transpose(perm = var_3709, x = x_523_cast_fp16)[name = string("transpose_214")]; + tensor x_525_cast_fp16 = matmul(transpose_x = x_525_transpose_x_0, transpose_y = x_525_transpose_y_0, x = attn_27_cast_fp16, y = v_27_cast_fp16)[name = string("x_525_cast_fp16")]; + tensor var_3735 = const()[name = string("op_3735"), val = tensor([0, 2, 1, 3])]; + tensor var_3737 = const()[name = string("op_3737"), val = tensor([1, 57, 512])]; + tensor x_527_cast_fp16 = transpose(perm = var_3735, x = x_525_cast_fp16)[name = string("transpose_211")]; + tensor input_295_cast_fp16 = reshape(shape = var_3737, x = x_527_cast_fp16)[name = string("input_295_cast_fp16")]; + tensor linear_115_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_weight_to_fp16, x = input_295_cast_fp16)[name = string("linear_115_cast_fp16")]; + tensor input_297_cast_fp16 = add(x = linear_115_cast_fp16, y = x_499_cast_fp16)[name = string("input_297_cast_fp16")]; + tensor linear_116_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_weight_to_fp16, x = input_297_cast_fp16)[name = string("linear_116_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = linear_116_cast_fp16)[name = string("input_301_cast_fp16")]; + tensor linear_117_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_weight_to_fp16, x = input_301_cast_fp16)[name = string("linear_117_cast_fp16")]; + tensor x_529_cast_fp16 = add(x = linear_117_cast_fp16, y = input_297_cast_fp16)[name = string("x_529_cast_fp16")]; + tensor x_531_cast_fp16 = add(x = x_529_cast_fp16, y = mapping_19_cast_fp16)[name = string("x_531_cast_fp16")]; + tensor var_3753_split_sizes_0 = const()[name = string("op_3753_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_3753_axis_0 = const()[name = string("op_3753_axis_0"), val = int32(1)]; + tensor var_3753_cast_fp16_0, tensor var_3753_cast_fp16_1 = split(axis = var_3753_axis_0, split_sizes = var_3753_split_sizes_0, x = h_19_cast_fp16)[name = string("op_3753_cast_fp16")]; + tensor gamma_115_perm_0 = const()[name = string("gamma_115_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_115_perm_0 = const()[name = string("beta_115_perm_0"), val = tensor([0, -1, 1])]; + tensor x_535_axes_0 = const()[name = string("x_535_axes_0"), val = tensor([-1])]; + tensor x_535_cast_fp16 = layer_norm(axes = x_535_axes_0, epsilon = var_3119_to_fp16, x = x_531_cast_fp16)[name = string("x_535_cast_fp16")]; + fp16 var_3759_promoted_to_fp16 = const()[name = string("op_3759_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_115_cast_fp16 = transpose(perm = gamma_115_perm_0, x = var_3753_cast_fp16_0)[name = string("transpose_210")]; + tensor var_3760_cast_fp16 = add(x = gamma_115_cast_fp16, y = var_3759_promoted_to_fp16)[name = string("op_3760_cast_fp16")]; + tensor var_3761_cast_fp16 = mul(x = var_3760_cast_fp16, y = x_535_cast_fp16)[name = string("op_3761_cast_fp16")]; + tensor beta_115_cast_fp16 = transpose(perm = beta_115_perm_0, x = var_3753_cast_fp16_1)[name = string("transpose_209")]; + tensor x_537_cast_fp16 = add(x = var_3761_cast_fp16, y = beta_115_cast_fp16)[name = string("x_537_cast_fp16")]; + tensor var_3772_split_sizes_0 = const()[name = string("op_3772_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_3772_axis_0 = const()[name = string("op_3772_axis_0"), val = int32(1)]; + tensor var_3772_cast_fp16_0, tensor var_3772_cast_fp16_1 = split(axis = var_3772_axis_0, split_sizes = var_3772_split_sizes_0, x = h_23_cast_fp16)[name = string("op_3772_cast_fp16")]; + tensor gamma_119_perm_0 = const()[name = string("gamma_119_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_119_perm_0 = const()[name = string("beta_119_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_3778_promoted_to_fp16 = const()[name = string("op_3778_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_119_cast_fp16 = transpose(perm = gamma_119_perm_0, x = var_3772_cast_fp16_0)[name = string("transpose_208")]; + tensor var_3779_cast_fp16 = add(x = gamma_119_cast_fp16, y = var_3778_promoted_to_fp16)[name = string("op_3779_cast_fp16")]; + tensor var_3780_cast_fp16 = mul(x = var_3779_cast_fp16, y = x_535_cast_fp16)[name = string("op_3780_cast_fp16")]; + tensor beta_119_cast_fp16 = transpose(perm = beta_119_perm_0, x = var_3772_cast_fp16_1)[name = string("transpose_207")]; + tensor x_543_cast_fp16 = add(x = var_3780_cast_fp16, y = beta_119_cast_fp16)[name = string("x_543_cast_fp16")]; + tensor linear_120_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_q_weight_to_fp16, x = x_537_cast_fp16)[name = string("linear_120_cast_fp16")]; + tensor linear_121_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_kv_weight_to_fp16, x = x_543_cast_fp16)[name = string("linear_121_cast_fp16")]; + tensor var_3786_split_sizes_0 = const()[name = string("op_3786_split_sizes_0"), val = tensor([512, 512])]; + int32 var_3786_axis_0 = const()[name = string("op_3786_axis_0"), val = int32(-1)]; + tensor var_3786_cast_fp16_0, tensor var_3786_cast_fp16_1 = split(axis = var_3786_axis_0, split_sizes = var_3786_split_sizes_0, x = linear_121_cast_fp16)[name = string("op_3786_cast_fp16")]; + tensor var_3794 = const()[name = string("op_3794"), val = tensor([1, 57, 8, 64])]; + tensor x_547_cast_fp16 = reshape(shape = var_3794, x = linear_120_cast_fp16)[name = string("x_547_cast_fp16")]; + tensor var_3804 = const()[name = string("op_3804"), val = tensor([1, 57, 8, 64])]; + tensor x_551_cast_fp16 = reshape(shape = var_3804, x = var_3786_cast_fp16_0)[name = string("x_551_cast_fp16")]; + tensor var_3814 = const()[name = string("op_3814"), val = tensor([1, 57, 8, 64])]; + tensor x_555_cast_fp16 = reshape(shape = var_3814, x = var_3786_cast_fp16_1)[name = string("x_555_cast_fp16")]; + tensor var_3816 = const()[name = string("op_3816"), val = tensor([0, 2, 1, 3])]; + bool sim_57_transpose_x_0 = const()[name = string("sim_57_transpose_x_0"), val = bool(false)]; + bool sim_57_transpose_y_0 = const()[name = string("sim_57_transpose_y_0"), val = bool(false)]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = x_551_cast_fp16)[name = string("transpose_204")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = x_547_cast_fp16)[name = string("transpose_205")]; + tensor sim_57_cast_fp16 = matmul(transpose_x = sim_57_transpose_x_0, transpose_y = sim_57_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("sim_57_cast_fp16")]; + fp16 var_3820_to_fp16 = const()[name = string("op_3820_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_59_cast_fp16 = mul(x = sim_57_cast_fp16, y = var_3820_to_fp16)[name = string("sim_59_cast_fp16")]; + tensor attn_29_cast_fp16 = softmax(axis = var_3123, x = sim_59_cast_fp16)[name = string("attn_29_cast_fp16")]; + bool x_557_transpose_x_0 = const()[name = string("x_557_transpose_x_0"), val = bool(false)]; + bool x_557_transpose_y_0 = const()[name = string("x_557_transpose_y_0"), val = bool(false)]; + tensor v_29_cast_fp16 = transpose(perm = var_3816, x = x_555_cast_fp16)[name = string("transpose_206")]; + tensor x_557_cast_fp16 = matmul(transpose_x = x_557_transpose_x_0, transpose_y = x_557_transpose_y_0, x = attn_29_cast_fp16, y = v_29_cast_fp16)[name = string("x_557_cast_fp16")]; + tensor var_3842 = const()[name = string("op_3842"), val = tensor([0, 2, 1, 3])]; + tensor var_3844 = const()[name = string("op_3844"), val = tensor([1, 57, 512])]; + tensor x_559_cast_fp16 = transpose(perm = var_3842, x = x_557_cast_fp16)[name = string("transpose_203")]; + tensor input_311_cast_fp16 = reshape(shape = var_3844, x = x_559_cast_fp16)[name = string("input_311_cast_fp16")]; + tensor linear_122_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_weight_to_fp16, x = input_311_cast_fp16)[name = string("linear_122_cast_fp16")]; + tensor input_313_cast_fp16 = add(x = linear_122_cast_fp16, y = x_531_cast_fp16)[name = string("input_313_cast_fp16")]; + tensor linear_123_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_weight_to_fp16, x = input_313_cast_fp16)[name = string("linear_123_cast_fp16")]; + string input_317_mode_0 = const()[name = string("input_317_mode_0"), val = string("EXACT")]; + tensor input_317_cast_fp16 = gelu(mode = input_317_mode_0, x = linear_123_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor linear_124_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_weight_to_fp16, x = input_317_cast_fp16)[name = string("linear_124_cast_fp16")]; + tensor x_561_cast_fp16 = add(x = linear_124_cast_fp16, y = input_313_cast_fp16)[name = string("x_561_cast_fp16")]; + tensor var_3853_axes_0 = const()[name = string("op_3853_axes_0"), val = tensor([1])]; + bool var_3853_keep_dims_0 = const()[name = string("op_3853_keep_dims_0"), val = bool(false)]; + tensor var_3853_cast_fp16 = reduce_mean(axes = var_3853_axes_0, keep_dims = var_3853_keep_dims_0, x = x_561_cast_fp16)[name = string("op_3853_cast_fp16")]; + tensor x_563_axes_0 = const()[name = string("x_563_axes_0"), val = tensor([1])]; + tensor x_563_cast_fp16 = expand_dims(axes = x_563_axes_0, x = var_3853_cast_fp16)[name = string("x_563_cast_fp16")]; + tensor var_3855 = const()[name = string("op_3855"), val = tensor([0, 2, 1])]; + string x_565_pad_type_0 = const()[name = string("x_565_pad_type_0"), val = string("valid")]; + tensor x_565_strides_0 = const()[name = string("x_565_strides_0"), val = tensor([1])]; + tensor x_565_pad_0 = const()[name = string("x_565_pad_0"), val = tensor([0, 0])]; + tensor x_565_dilations_0 = const()[name = string("x_565_dilations_0"), val = tensor([1])]; + int32 x_565_groups_0 = const()[name = string("x_565_groups_0"), val = int32(1)]; + tensor input_319_cast_fp16 = transpose(perm = var_3855, x = x_563_cast_fp16)[name = string("transpose_202")]; + tensor x_565_cast_fp16 = conv(bias = unet_wrap_kdiffusion_net_to_out_1_bias_to_fp16, dilations = x_565_dilations_0, groups = x_565_groups_0, pad = x_565_pad_0, pad_type = x_565_pad_type_0, strides = x_565_strides_0, weight = unet_wrap_kdiffusion_net_to_out_1_weight_to_fp16, x = input_319_cast_fp16)[name = string("x_565_cast_fp16")]; + tensor x_pred_9_perm_0 = const()[name = string("x_pred_9_perm_0"), val = tensor([0, -1, -2])]; + tensor c_skip_9_to_fp16 = const()[name = string("c_skip_9_to_fp16"), val = tensor([[[0x1.c64p-1]]])]; + tensor var_3863_cast_fp16 = mul(x = c_skip_9_to_fp16, y = x_noisy_9_cast_fp16)[name = string("op_3863_cast_fp16")]; + tensor c_out_9_to_fp16 = const()[name = string("c_out_9_to_fp16"), val = tensor([[[0x1.0fcp-4]]])]; + tensor x_pred_9_cast_fp16 = transpose(perm = x_pred_9_perm_0, x = x_565_cast_fp16)[name = string("transpose_201")]; + tensor var_3864_cast_fp16 = mul(x = c_out_9_to_fp16, y = x_pred_9_cast_fp16)[name = string("op_3864_cast_fp16")]; + tensor x_dn_5_cast_fp16 = add(x = var_3863_cast_fp16, y = var_3864_cast_fp16)[name = string("x_dn_5_cast_fp16")]; + tensor var_3867_cast_fp16 = sub(x = x_noisy_9_cast_fp16, y = x_dn_5_cast_fp16)[name = string("op_3867_cast_fp16")]; + tensor _inversed_d_5_y_0_to_fp16 = const()[name = string("_inversed_d_5_y_0_to_fp16"), val = tensor([0x1.c6cp+3])]; + tensor _inversed_d_5_cast_fp16 = mul(x = var_3867_cast_fp16, y = _inversed_d_5_y_0_to_fp16)[name = string("_inversed_d_5_cast_fp16")]; + tensor var_3870_to_fp16 = const()[name = string("op_3870_to_fp16"), val = tensor([-0x1.1fp-5])]; + tensor var_3871_cast_fp16 = mul(x = _inversed_d_5_cast_fp16, y = var_3870_to_fp16)[name = string("op_3871_cast_fp16")]; + tensor x_noisy_11_cast_fp16 = add(x = x_noisy_9_cast_fp16, y = var_3871_cast_fp16)[name = string("x_noisy_11_cast_fp16")]; + int32 var_3883 = const()[name = string("op_3883"), val = int32(-1)]; + tensor c_in_11_to_fp16 = const()[name = string("c_in_11_to_fp16"), val = tensor([[[0x1.3c4p+2]]])]; + tensor x_575_cast_fp16 = mul(x = c_in_11_to_fp16, y = x_noisy_11_cast_fp16)[name = string("x_575_cast_fp16")]; + int32 x_571_axis_0 = const()[name = string("x_571_axis_0"), val = int32(0)]; + tensor var_4269_to_fp16 = const()[name = string("op_4269_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49355520)))]; + tensor x_571_cast_fp16 = stack(axis = x_571_axis_0, values = (var_4269_to_fp16, var_423_cast_fp16))[name = string("x_571_cast_fp16")]; + tensor var_4274 = const()[name = string("op_4274"), val = tensor([1, 2, 0])]; + tensor input_327_axes_0 = const()[name = string("input_327_axes_0"), val = tensor([2])]; + bool input_327_keep_dims_0 = const()[name = string("input_327_keep_dims_0"), val = bool(false)]; + tensor x_573_cast_fp16 = transpose(perm = var_4274, x = x_571_cast_fp16)[name = string("transpose_200")]; + tensor input_327_cast_fp16 = reduce_sum(axes = input_327_axes_0, keep_dims = input_327_keep_dims_0, x = x_573_cast_fp16)[name = string("input_327_cast_fp16")]; + tensor linear_127_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_0_weight_to_fp16, x = input_327_cast_fp16)[name = string("linear_127_cast_fp16")]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("EXACT")]; + tensor input_331_cast_fp16 = gelu(mode = input_331_mode_0, x = linear_127_cast_fp16)[name = string("input_331_cast_fp16")]; + tensor linear_128_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_2_weight_to_fp16, x = input_331_cast_fp16)[name = string("linear_128_cast_fp16")]; + string mapping_21_mode_0 = const()[name = string("mapping_21_mode_0"), val = string("EXACT")]; + tensor mapping_21_cast_fp16 = gelu(mode = mapping_21_mode_0, x = linear_128_cast_fp16)[name = string("mapping_21_cast_fp16")]; + tensor var_4284_reps_0 = const()[name = string("op_4284_reps_0"), val = tensor([1, 57, 1])]; + tensor var_4284_cast_fp16 = tile(reps = var_4284_reps_0, x = x_575_cast_fp16)[name = string("op_4284_cast_fp16")]; + bool x_577_interleave_0 = const()[name = string("x_577_interleave_0"), val = bool(false)]; + tensor x_577_cast_fp16 = concat(axis = var_3883, interleave = x_577_interleave_0, values = (var_4284_cast_fp16, embedding_to_fp16))[name = string("x_577_cast_fp16")]; + tensor var_4287_axes_0 = const()[name = string("op_4287_axes_0"), val = tensor([1])]; + tensor var_4287_cast_fp16 = expand_dims(axes = var_4287_axes_0, x = mapping_21_cast_fp16)[name = string("op_4287_cast_fp16")]; + tensor mapping_23_reps_0 = const()[name = string("mapping_23_reps_0"), val = tensor([1, 57, 1])]; + tensor mapping_23_cast_fp16 = tile(reps = mapping_23_reps_0, x = var_4287_cast_fp16)[name = string("mapping_23_cast_fp16")]; + tensor x_579_cast_fp16 = add(x = x_577_cast_fp16, y = mapping_23_cast_fp16)[name = string("x_579_cast_fp16")]; + tensor var_4299_split_sizes_0 = const()[name = string("op_4299_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_4299_axis_0 = const()[name = string("op_4299_axis_0"), val = int32(1)]; + tensor var_4299_cast_fp16_0, tensor var_4299_cast_fp16_1 = split(axis = var_4299_axis_0, split_sizes = var_4299_split_sizes_0, x = h_3_cast_fp16)[name = string("op_4299_cast_fp16")]; + tensor gamma_123_perm_0 = const()[name = string("gamma_123_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_123_perm_0 = const()[name = string("beta_123_perm_0"), val = tensor([0, -1, 1])]; + tensor x_583_axes_0 = const()[name = string("x_583_axes_0"), val = tensor([-1])]; + fp16 var_3879_to_fp16 = const()[name = string("op_3879_to_fp16"), val = fp16(0x1.5p-17)]; + tensor x_583_cast_fp16 = layer_norm(axes = x_583_axes_0, epsilon = var_3879_to_fp16, x = x_579_cast_fp16)[name = string("x_583_cast_fp16")]; + fp16 var_4305_promoted_to_fp16 = const()[name = string("op_4305_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_123_cast_fp16 = transpose(perm = gamma_123_perm_0, x = var_4299_cast_fp16_0)[name = string("transpose_199")]; + tensor var_4306_cast_fp16 = add(x = gamma_123_cast_fp16, y = var_4305_promoted_to_fp16)[name = string("op_4306_cast_fp16")]; + tensor var_4307_cast_fp16 = mul(x = var_4306_cast_fp16, y = x_583_cast_fp16)[name = string("op_4307_cast_fp16")]; + tensor beta_123_cast_fp16 = transpose(perm = beta_123_perm_0, x = var_4299_cast_fp16_1)[name = string("transpose_198")]; + tensor x_585_cast_fp16 = add(x = var_4307_cast_fp16, y = beta_123_cast_fp16)[name = string("x_585_cast_fp16")]; + tensor var_4318_split_sizes_0 = const()[name = string("op_4318_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_4318_axis_0 = const()[name = string("op_4318_axis_0"), val = int32(1)]; + tensor var_4318_cast_fp16_0, tensor var_4318_cast_fp16_1 = split(axis = var_4318_axis_0, split_sizes = var_4318_split_sizes_0, x = h_7_cast_fp16)[name = string("op_4318_cast_fp16")]; + tensor gamma_127_perm_0 = const()[name = string("gamma_127_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_127_perm_0 = const()[name = string("beta_127_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_4324_promoted_to_fp16 = const()[name = string("op_4324_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_127_cast_fp16 = transpose(perm = gamma_127_perm_0, x = var_4318_cast_fp16_0)[name = string("transpose_197")]; + tensor var_4325_cast_fp16 = add(x = gamma_127_cast_fp16, y = var_4324_promoted_to_fp16)[name = string("op_4325_cast_fp16")]; + tensor var_4326_cast_fp16 = mul(x = var_4325_cast_fp16, y = x_583_cast_fp16)[name = string("op_4326_cast_fp16")]; + tensor beta_127_cast_fp16 = transpose(perm = beta_127_perm_0, x = var_4318_cast_fp16_1)[name = string("transpose_196")]; + tensor x_591_cast_fp16 = add(x = var_4326_cast_fp16, y = beta_127_cast_fp16)[name = string("x_591_cast_fp16")]; + tensor linear_131_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_q_weight_to_fp16, x = x_585_cast_fp16)[name = string("linear_131_cast_fp16")]; + tensor linear_132_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_kv_weight_to_fp16, x = x_591_cast_fp16)[name = string("linear_132_cast_fp16")]; + tensor var_4332_split_sizes_0 = const()[name = string("op_4332_split_sizes_0"), val = tensor([512, 512])]; + int32 var_4332_axis_0 = const()[name = string("op_4332_axis_0"), val = int32(-1)]; + tensor var_4332_cast_fp16_0, tensor var_4332_cast_fp16_1 = split(axis = var_4332_axis_0, split_sizes = var_4332_split_sizes_0, x = linear_132_cast_fp16)[name = string("op_4332_cast_fp16")]; + tensor var_4340 = const()[name = string("op_4340"), val = tensor([1, 57, 8, 64])]; + tensor x_595_cast_fp16 = reshape(shape = var_4340, x = linear_131_cast_fp16)[name = string("x_595_cast_fp16")]; + tensor var_4350 = const()[name = string("op_4350"), val = tensor([1, 57, 8, 64])]; + tensor x_599_cast_fp16 = reshape(shape = var_4350, x = var_4332_cast_fp16_0)[name = string("x_599_cast_fp16")]; + tensor var_4360 = const()[name = string("op_4360"), val = tensor([1, 57, 8, 64])]; + tensor x_603_cast_fp16 = reshape(shape = var_4360, x = var_4332_cast_fp16_1)[name = string("x_603_cast_fp16")]; + tensor var_4362 = const()[name = string("op_4362"), val = tensor([0, 2, 1, 3])]; + bool sim_61_transpose_x_0 = const()[name = string("sim_61_transpose_x_0"), val = bool(false)]; + bool sim_61_transpose_y_0 = const()[name = string("sim_61_transpose_y_0"), val = bool(false)]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = x_599_cast_fp16)[name = string("transpose_193")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = x_595_cast_fp16)[name = string("transpose_194")]; + tensor sim_61_cast_fp16 = matmul(transpose_x = sim_61_transpose_x_0, transpose_y = sim_61_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("sim_61_cast_fp16")]; + fp16 var_4366_to_fp16 = const()[name = string("op_4366_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_63_cast_fp16 = mul(x = sim_61_cast_fp16, y = var_4366_to_fp16)[name = string("sim_63_cast_fp16")]; + tensor attn_31_cast_fp16 = softmax(axis = var_3883, x = sim_63_cast_fp16)[name = string("attn_31_cast_fp16")]; + bool x_605_transpose_x_0 = const()[name = string("x_605_transpose_x_0"), val = bool(false)]; + bool x_605_transpose_y_0 = const()[name = string("x_605_transpose_y_0"), val = bool(false)]; + tensor v_31_cast_fp16 = transpose(perm = var_4362, x = x_603_cast_fp16)[name = string("transpose_195")]; + tensor x_605_cast_fp16 = matmul(transpose_x = x_605_transpose_x_0, transpose_y = x_605_transpose_y_0, x = attn_31_cast_fp16, y = v_31_cast_fp16)[name = string("x_605_cast_fp16")]; + tensor var_4388 = const()[name = string("op_4388"), val = tensor([0, 2, 1, 3])]; + tensor var_4390 = const()[name = string("op_4390"), val = tensor([1, 57, 512])]; + tensor x_607_cast_fp16 = transpose(perm = var_4388, x = x_605_cast_fp16)[name = string("transpose_192")]; + tensor input_343_cast_fp16 = reshape(shape = var_4390, x = x_607_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor linear_133_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_weight_to_fp16, x = input_343_cast_fp16)[name = string("linear_133_cast_fp16")]; + tensor input_345_cast_fp16 = add(x = linear_133_cast_fp16, y = x_579_cast_fp16)[name = string("input_345_cast_fp16")]; + tensor linear_134_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_weight_to_fp16, x = input_345_cast_fp16)[name = string("linear_134_cast_fp16")]; + string input_349_mode_0 = const()[name = string("input_349_mode_0"), val = string("EXACT")]; + tensor input_349_cast_fp16 = gelu(mode = input_349_mode_0, x = linear_134_cast_fp16)[name = string("input_349_cast_fp16")]; + tensor linear_135_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_weight_to_fp16, x = input_349_cast_fp16)[name = string("linear_135_cast_fp16")]; + tensor x_609_cast_fp16 = add(x = linear_135_cast_fp16, y = input_345_cast_fp16)[name = string("x_609_cast_fp16")]; + tensor x_611_cast_fp16 = add(x = x_609_cast_fp16, y = mapping_23_cast_fp16)[name = string("x_611_cast_fp16")]; + tensor var_4406_split_sizes_0 = const()[name = string("op_4406_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_4406_axis_0 = const()[name = string("op_4406_axis_0"), val = int32(1)]; + tensor var_4406_cast_fp16_0, tensor var_4406_cast_fp16_1 = split(axis = var_4406_axis_0, split_sizes = var_4406_split_sizes_0, x = h_11_cast_fp16)[name = string("op_4406_cast_fp16")]; + tensor gamma_131_perm_0 = const()[name = string("gamma_131_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_131_perm_0 = const()[name = string("beta_131_perm_0"), val = tensor([0, -1, 1])]; + tensor x_615_axes_0 = const()[name = string("x_615_axes_0"), val = tensor([-1])]; + tensor x_615_cast_fp16 = layer_norm(axes = x_615_axes_0, epsilon = var_3879_to_fp16, x = x_611_cast_fp16)[name = string("x_615_cast_fp16")]; + fp16 var_4412_promoted_to_fp16 = const()[name = string("op_4412_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_131_cast_fp16 = transpose(perm = gamma_131_perm_0, x = var_4406_cast_fp16_0)[name = string("transpose_191")]; + tensor var_4413_cast_fp16 = add(x = gamma_131_cast_fp16, y = var_4412_promoted_to_fp16)[name = string("op_4413_cast_fp16")]; + tensor var_4414_cast_fp16 = mul(x = var_4413_cast_fp16, y = x_615_cast_fp16)[name = string("op_4414_cast_fp16")]; + tensor beta_131_cast_fp16 = transpose(perm = beta_131_perm_0, x = var_4406_cast_fp16_1)[name = string("transpose_190")]; + tensor x_617_cast_fp16 = add(x = var_4414_cast_fp16, y = beta_131_cast_fp16)[name = string("x_617_cast_fp16")]; + tensor var_4425_split_sizes_0 = const()[name = string("op_4425_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_4425_axis_0 = const()[name = string("op_4425_axis_0"), val = int32(1)]; + tensor var_4425_cast_fp16_0, tensor var_4425_cast_fp16_1 = split(axis = var_4425_axis_0, split_sizes = var_4425_split_sizes_0, x = h_15_cast_fp16)[name = string("op_4425_cast_fp16")]; + tensor gamma_135_perm_0 = const()[name = string("gamma_135_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_135_perm_0 = const()[name = string("beta_135_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_4431_promoted_to_fp16 = const()[name = string("op_4431_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_135_cast_fp16 = transpose(perm = gamma_135_perm_0, x = var_4425_cast_fp16_0)[name = string("transpose_189")]; + tensor var_4432_cast_fp16 = add(x = gamma_135_cast_fp16, y = var_4431_promoted_to_fp16)[name = string("op_4432_cast_fp16")]; + tensor var_4433_cast_fp16 = mul(x = var_4432_cast_fp16, y = x_615_cast_fp16)[name = string("op_4433_cast_fp16")]; + tensor beta_135_cast_fp16 = transpose(perm = beta_135_perm_0, x = var_4425_cast_fp16_1)[name = string("transpose_188")]; + tensor x_623_cast_fp16 = add(x = var_4433_cast_fp16, y = beta_135_cast_fp16)[name = string("x_623_cast_fp16")]; + tensor linear_138_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_q_weight_to_fp16, x = x_617_cast_fp16)[name = string("linear_138_cast_fp16")]; + tensor linear_139_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_kv_weight_to_fp16, x = x_623_cast_fp16)[name = string("linear_139_cast_fp16")]; + tensor var_4439_split_sizes_0 = const()[name = string("op_4439_split_sizes_0"), val = tensor([512, 512])]; + int32 var_4439_axis_0 = const()[name = string("op_4439_axis_0"), val = int32(-1)]; + tensor var_4439_cast_fp16_0, tensor var_4439_cast_fp16_1 = split(axis = var_4439_axis_0, split_sizes = var_4439_split_sizes_0, x = linear_139_cast_fp16)[name = string("op_4439_cast_fp16")]; + tensor var_4447 = const()[name = string("op_4447"), val = tensor([1, 57, 8, 64])]; + tensor x_627_cast_fp16 = reshape(shape = var_4447, x = linear_138_cast_fp16)[name = string("x_627_cast_fp16")]; + tensor var_4457 = const()[name = string("op_4457"), val = tensor([1, 57, 8, 64])]; + tensor x_631_cast_fp16 = reshape(shape = var_4457, x = var_4439_cast_fp16_0)[name = string("x_631_cast_fp16")]; + tensor var_4467 = const()[name = string("op_4467"), val = tensor([1, 57, 8, 64])]; + tensor x_635_cast_fp16 = reshape(shape = var_4467, x = var_4439_cast_fp16_1)[name = string("x_635_cast_fp16")]; + tensor var_4469 = const()[name = string("op_4469"), val = tensor([0, 2, 1, 3])]; + bool sim_65_transpose_x_0 = const()[name = string("sim_65_transpose_x_0"), val = bool(false)]; + bool sim_65_transpose_y_0 = const()[name = string("sim_65_transpose_y_0"), val = bool(false)]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = x_631_cast_fp16)[name = string("transpose_185")]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = x_627_cast_fp16)[name = string("transpose_186")]; + tensor sim_65_cast_fp16 = matmul(transpose_x = sim_65_transpose_x_0, transpose_y = sim_65_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("sim_65_cast_fp16")]; + fp16 var_4473_to_fp16 = const()[name = string("op_4473_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_67_cast_fp16 = mul(x = sim_65_cast_fp16, y = var_4473_to_fp16)[name = string("sim_67_cast_fp16")]; + tensor attn_33_cast_fp16 = softmax(axis = var_3883, x = sim_67_cast_fp16)[name = string("attn_33_cast_fp16")]; + bool x_637_transpose_x_0 = const()[name = string("x_637_transpose_x_0"), val = bool(false)]; + bool x_637_transpose_y_0 = const()[name = string("x_637_transpose_y_0"), val = bool(false)]; + tensor v_33_cast_fp16 = transpose(perm = var_4469, x = x_635_cast_fp16)[name = string("transpose_187")]; + tensor x_637_cast_fp16 = matmul(transpose_x = x_637_transpose_x_0, transpose_y = x_637_transpose_y_0, x = attn_33_cast_fp16, y = v_33_cast_fp16)[name = string("x_637_cast_fp16")]; + tensor var_4495 = const()[name = string("op_4495"), val = tensor([0, 2, 1, 3])]; + tensor var_4497 = const()[name = string("op_4497"), val = tensor([1, 57, 512])]; + tensor x_639_cast_fp16 = transpose(perm = var_4495, x = x_637_cast_fp16)[name = string("transpose_184")]; + tensor input_359_cast_fp16 = reshape(shape = var_4497, x = x_639_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor linear_140_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_weight_to_fp16, x = input_359_cast_fp16)[name = string("linear_140_cast_fp16")]; + tensor input_361_cast_fp16 = add(x = linear_140_cast_fp16, y = x_611_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor linear_141_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_weight_to_fp16, x = input_361_cast_fp16)[name = string("linear_141_cast_fp16")]; + string input_365_mode_0 = const()[name = string("input_365_mode_0"), val = string("EXACT")]; + tensor input_365_cast_fp16 = gelu(mode = input_365_mode_0, x = linear_141_cast_fp16)[name = string("input_365_cast_fp16")]; + tensor linear_142_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_weight_to_fp16, x = input_365_cast_fp16)[name = string("linear_142_cast_fp16")]; + tensor x_641_cast_fp16 = add(x = linear_142_cast_fp16, y = input_361_cast_fp16)[name = string("x_641_cast_fp16")]; + tensor x_643_cast_fp16 = add(x = x_641_cast_fp16, y = mapping_23_cast_fp16)[name = string("x_643_cast_fp16")]; + tensor var_4513_split_sizes_0 = const()[name = string("op_4513_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_4513_axis_0 = const()[name = string("op_4513_axis_0"), val = int32(1)]; + tensor var_4513_cast_fp16_0, tensor var_4513_cast_fp16_1 = split(axis = var_4513_axis_0, split_sizes = var_4513_split_sizes_0, x = h_19_cast_fp16)[name = string("op_4513_cast_fp16")]; + tensor gamma_139_perm_0 = const()[name = string("gamma_139_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_139_perm_0 = const()[name = string("beta_139_perm_0"), val = tensor([0, -1, 1])]; + tensor x_647_axes_0 = const()[name = string("x_647_axes_0"), val = tensor([-1])]; + tensor x_647_cast_fp16 = layer_norm(axes = x_647_axes_0, epsilon = var_3879_to_fp16, x = x_643_cast_fp16)[name = string("x_647_cast_fp16")]; + fp16 var_4519_promoted_to_fp16 = const()[name = string("op_4519_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_139_cast_fp16 = transpose(perm = gamma_139_perm_0, x = var_4513_cast_fp16_0)[name = string("transpose_183")]; + tensor var_4520_cast_fp16 = add(x = gamma_139_cast_fp16, y = var_4519_promoted_to_fp16)[name = string("op_4520_cast_fp16")]; + tensor var_4521_cast_fp16 = mul(x = var_4520_cast_fp16, y = x_647_cast_fp16)[name = string("op_4521_cast_fp16")]; + tensor beta_139_cast_fp16 = transpose(perm = beta_139_perm_0, x = var_4513_cast_fp16_1)[name = string("transpose_182")]; + tensor x_649_cast_fp16 = add(x = var_4521_cast_fp16, y = beta_139_cast_fp16)[name = string("x_649_cast_fp16")]; + tensor var_4532_split_sizes_0 = const()[name = string("op_4532_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_4532_axis_0 = const()[name = string("op_4532_axis_0"), val = int32(1)]; + tensor var_4532_cast_fp16_0, tensor var_4532_cast_fp16_1 = split(axis = var_4532_axis_0, split_sizes = var_4532_split_sizes_0, x = h_23_cast_fp16)[name = string("op_4532_cast_fp16")]; + tensor gamma_143_perm_0 = const()[name = string("gamma_143_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_143_perm_0 = const()[name = string("beta_143_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_4538_promoted_to_fp16 = const()[name = string("op_4538_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_143_cast_fp16 = transpose(perm = gamma_143_perm_0, x = var_4532_cast_fp16_0)[name = string("transpose_181")]; + tensor var_4539_cast_fp16 = add(x = gamma_143_cast_fp16, y = var_4538_promoted_to_fp16)[name = string("op_4539_cast_fp16")]; + tensor var_4540_cast_fp16 = mul(x = var_4539_cast_fp16, y = x_647_cast_fp16)[name = string("op_4540_cast_fp16")]; + tensor beta_143_cast_fp16 = transpose(perm = beta_143_perm_0, x = var_4532_cast_fp16_1)[name = string("transpose_180")]; + tensor x_655_cast_fp16 = add(x = var_4540_cast_fp16, y = beta_143_cast_fp16)[name = string("x_655_cast_fp16")]; + tensor linear_145_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_q_weight_to_fp16, x = x_649_cast_fp16)[name = string("linear_145_cast_fp16")]; + tensor linear_146_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_kv_weight_to_fp16, x = x_655_cast_fp16)[name = string("linear_146_cast_fp16")]; + tensor var_4546_split_sizes_0 = const()[name = string("op_4546_split_sizes_0"), val = tensor([512, 512])]; + int32 var_4546_axis_0 = const()[name = string("op_4546_axis_0"), val = int32(-1)]; + tensor var_4546_cast_fp16_0, tensor var_4546_cast_fp16_1 = split(axis = var_4546_axis_0, split_sizes = var_4546_split_sizes_0, x = linear_146_cast_fp16)[name = string("op_4546_cast_fp16")]; + tensor var_4554 = const()[name = string("op_4554"), val = tensor([1, 57, 8, 64])]; + tensor x_659_cast_fp16 = reshape(shape = var_4554, x = linear_145_cast_fp16)[name = string("x_659_cast_fp16")]; + tensor var_4564 = const()[name = string("op_4564"), val = tensor([1, 57, 8, 64])]; + tensor x_663_cast_fp16 = reshape(shape = var_4564, x = var_4546_cast_fp16_0)[name = string("x_663_cast_fp16")]; + tensor var_4574 = const()[name = string("op_4574"), val = tensor([1, 57, 8, 64])]; + tensor x_667_cast_fp16 = reshape(shape = var_4574, x = var_4546_cast_fp16_1)[name = string("x_667_cast_fp16")]; + tensor var_4576 = const()[name = string("op_4576"), val = tensor([0, 2, 1, 3])]; + bool sim_69_transpose_x_0 = const()[name = string("sim_69_transpose_x_0"), val = bool(false)]; + bool sim_69_transpose_y_0 = const()[name = string("sim_69_transpose_y_0"), val = bool(false)]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = x_663_cast_fp16)[name = string("transpose_177")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = x_659_cast_fp16)[name = string("transpose_178")]; + tensor sim_69_cast_fp16 = matmul(transpose_x = sim_69_transpose_x_0, transpose_y = sim_69_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("sim_69_cast_fp16")]; + fp16 var_4580_to_fp16 = const()[name = string("op_4580_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_71_cast_fp16 = mul(x = sim_69_cast_fp16, y = var_4580_to_fp16)[name = string("sim_71_cast_fp16")]; + tensor attn_35_cast_fp16 = softmax(axis = var_3883, x = sim_71_cast_fp16)[name = string("attn_35_cast_fp16")]; + bool x_669_transpose_x_0 = const()[name = string("x_669_transpose_x_0"), val = bool(false)]; + bool x_669_transpose_y_0 = const()[name = string("x_669_transpose_y_0"), val = bool(false)]; + tensor v_35_cast_fp16 = transpose(perm = var_4576, x = x_667_cast_fp16)[name = string("transpose_179")]; + tensor x_669_cast_fp16 = matmul(transpose_x = x_669_transpose_x_0, transpose_y = x_669_transpose_y_0, x = attn_35_cast_fp16, y = v_35_cast_fp16)[name = string("x_669_cast_fp16")]; + tensor var_4602 = const()[name = string("op_4602"), val = tensor([0, 2, 1, 3])]; + tensor var_4604 = const()[name = string("op_4604"), val = tensor([1, 57, 512])]; + tensor x_671_cast_fp16 = transpose(perm = var_4602, x = x_669_cast_fp16)[name = string("transpose_176")]; + tensor input_375_cast_fp16 = reshape(shape = var_4604, x = x_671_cast_fp16)[name = string("input_375_cast_fp16")]; + tensor linear_147_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_weight_to_fp16, x = input_375_cast_fp16)[name = string("linear_147_cast_fp16")]; + tensor input_377_cast_fp16 = add(x = linear_147_cast_fp16, y = x_643_cast_fp16)[name = string("input_377_cast_fp16")]; + tensor linear_148_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_weight_to_fp16, x = input_377_cast_fp16)[name = string("linear_148_cast_fp16")]; + string input_381_mode_0 = const()[name = string("input_381_mode_0"), val = string("EXACT")]; + tensor input_381_cast_fp16 = gelu(mode = input_381_mode_0, x = linear_148_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor linear_149_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_weight_to_fp16, x = input_381_cast_fp16)[name = string("linear_149_cast_fp16")]; + tensor x_673_cast_fp16 = add(x = linear_149_cast_fp16, y = input_377_cast_fp16)[name = string("x_673_cast_fp16")]; + tensor var_4613_axes_0 = const()[name = string("op_4613_axes_0"), val = tensor([1])]; + bool var_4613_keep_dims_0 = const()[name = string("op_4613_keep_dims_0"), val = bool(false)]; + tensor var_4613_cast_fp16 = reduce_mean(axes = var_4613_axes_0, keep_dims = var_4613_keep_dims_0, x = x_673_cast_fp16)[name = string("op_4613_cast_fp16")]; + tensor x_675_axes_0 = const()[name = string("x_675_axes_0"), val = tensor([1])]; + tensor x_675_cast_fp16 = expand_dims(axes = x_675_axes_0, x = var_4613_cast_fp16)[name = string("x_675_cast_fp16")]; + tensor var_4615 = const()[name = string("op_4615"), val = tensor([0, 2, 1])]; + string x_677_pad_type_0 = const()[name = string("x_677_pad_type_0"), val = string("valid")]; + tensor x_677_strides_0 = const()[name = string("x_677_strides_0"), val = tensor([1])]; + tensor x_677_pad_0 = const()[name = string("x_677_pad_0"), val = tensor([0, 0])]; + tensor x_677_dilations_0 = const()[name = string("x_677_dilations_0"), val = tensor([1])]; + int32 x_677_groups_0 = const()[name = string("x_677_groups_0"), val = int32(1)]; + tensor input_383_cast_fp16 = transpose(perm = var_4615, x = x_675_cast_fp16)[name = string("transpose_175")]; + tensor x_677_cast_fp16 = conv(bias = unet_wrap_kdiffusion_net_to_out_1_bias_to_fp16, dilations = x_677_dilations_0, groups = x_677_groups_0, pad = x_677_pad_0, pad_type = x_677_pad_type_0, strides = x_677_strides_0, weight = unet_wrap_kdiffusion_net_to_out_1_weight_to_fp16, x = input_383_cast_fp16)[name = string("x_677_cast_fp16")]; + tensor x_pred_11_perm_0 = const()[name = string("x_pred_11_perm_0"), val = tensor([0, -1, -2])]; + tensor c_skip_11_to_fp16 = const()[name = string("c_skip_11_to_fp16"), val = tensor([[[0x1.ef4p-1]]])]; + tensor var_4623_cast_fp16 = mul(x = c_skip_11_to_fp16, y = x_noisy_11_cast_fp16)[name = string("op_4623_cast_fp16")]; + tensor c_out_11_to_fp16 = const()[name = string("c_out_11_to_fp16"), val = tensor([[[0x1.1dp-5]]])]; + tensor x_pred_11_cast_fp16 = transpose(perm = x_pred_11_perm_0, x = x_677_cast_fp16)[name = string("transpose_174")]; + tensor var_4624_cast_fp16 = mul(x = c_out_11_to_fp16, y = x_pred_11_cast_fp16)[name = string("op_4624_cast_fp16")]; + tensor x_mid_dn_5_cast_fp16 = add(x = var_4623_cast_fp16, y = var_4624_cast_fp16)[name = string("x_mid_dn_5_cast_fp16")]; + tensor var_4627_cast_fp16 = sub(x = x_noisy_11_cast_fp16, y = x_mid_dn_5_cast_fp16)[name = string("op_4627_cast_fp16")]; + tensor _inversed_d_mid_5_y_0_to_fp16 = const()[name = string("_inversed_d_mid_5_y_0_to_fp16"), val = tensor([0x1.c4cp+4])]; + tensor _inversed_d_mid_5_cast_fp16 = mul(x = var_4627_cast_fp16, y = _inversed_d_mid_5_y_0_to_fp16)[name = string("_inversed_d_mid_5_cast_fp16")]; + tensor var_4633_to_fp16 = const()[name = string("op_4633_to_fp16"), val = tensor([-0x1.1fp-4])]; + tensor var_4634_cast_fp16 = mul(x = _inversed_d_mid_5_cast_fp16, y = var_4633_to_fp16)[name = string("op_4634_cast_fp16")]; + tensor x_679_cast_fp16 = add(x = x_noisy_9_cast_fp16, y = var_4634_cast_fp16)[name = string("x_679_cast_fp16")]; + tensor var_4639_begin_0 = const()[name = string("op_4639_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor var_4639_end_0 = const()[name = string("op_4639_end_0"), val = tensor([3, 1, 1, 256])]; + tensor var_4639_end_mask_0 = const()[name = string("op_4639_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_4639_squeeze_mask_0 = const()[name = string("op_4639_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor var_4639_cast_fp16 = slice_by_index(begin = var_4639_begin_0, end = var_4639_end_0, end_mask = var_4639_end_mask_0, squeeze_mask = var_4639_squeeze_mask_0, x = noises_aux_to_fp16)[name = string("op_4639_cast_fp16")]; + fp16 var_4642_to_fp16 = const()[name = string("op_4642_to_fp16"), val = fp16(0x1.37p-8)]; + tensor var_4643_cast_fp16 = mul(x = var_4639_cast_fp16, y = var_4642_to_fp16)[name = string("op_4643_cast_fp16")]; + tensor x_noisy_13_cast_fp16 = add(x = x_679_cast_fp16, y = var_4643_cast_fp16)[name = string("x_noisy_13_cast_fp16")]; + int32 var_4667 = const()[name = string("op_4667"), val = int32(-1)]; + tensor c_in_13_to_fp16 = const()[name = string("c_in_13_to_fp16"), val = tensor([[[0x1.41p+2]]])]; + tensor x_689_cast_fp16 = mul(x = c_in_13_to_fp16, y = x_noisy_13_cast_fp16)[name = string("x_689_cast_fp16")]; + int32 x_685_axis_0 = const()[name = string("x_685_axis_0"), val = int32(0)]; + tensor var_5053_to_fp16 = const()[name = string("op_5053_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49357632)))]; + tensor x_685_cast_fp16 = stack(axis = x_685_axis_0, values = (var_5053_to_fp16, var_423_cast_fp16))[name = string("x_685_cast_fp16")]; + tensor var_5058 = const()[name = string("op_5058"), val = tensor([1, 2, 0])]; + tensor input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor([2])]; + bool input_391_keep_dims_0 = const()[name = string("input_391_keep_dims_0"), val = bool(false)]; + tensor x_687_cast_fp16 = transpose(perm = var_5058, x = x_685_cast_fp16)[name = string("transpose_173")]; + tensor input_391_cast_fp16 = reduce_sum(axes = input_391_axes_0, keep_dims = input_391_keep_dims_0, x = x_687_cast_fp16)[name = string("input_391_cast_fp16")]; + tensor linear_152_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_0_weight_to_fp16, x = input_391_cast_fp16)[name = string("linear_152_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = linear_152_cast_fp16)[name = string("input_395_cast_fp16")]; + tensor linear_153_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_2_weight_to_fp16, x = input_395_cast_fp16)[name = string("linear_153_cast_fp16")]; + string mapping_25_mode_0 = const()[name = string("mapping_25_mode_0"), val = string("EXACT")]; + tensor mapping_25_cast_fp16 = gelu(mode = mapping_25_mode_0, x = linear_153_cast_fp16)[name = string("mapping_25_cast_fp16")]; + tensor var_5068_reps_0 = const()[name = string("op_5068_reps_0"), val = tensor([1, 57, 1])]; + tensor var_5068_cast_fp16 = tile(reps = var_5068_reps_0, x = x_689_cast_fp16)[name = string("op_5068_cast_fp16")]; + bool x_691_interleave_0 = const()[name = string("x_691_interleave_0"), val = bool(false)]; + tensor x_691_cast_fp16 = concat(axis = var_4667, interleave = x_691_interleave_0, values = (var_5068_cast_fp16, embedding_to_fp16))[name = string("x_691_cast_fp16")]; + tensor var_5071_axes_0 = const()[name = string("op_5071_axes_0"), val = tensor([1])]; + tensor var_5071_cast_fp16 = expand_dims(axes = var_5071_axes_0, x = mapping_25_cast_fp16)[name = string("op_5071_cast_fp16")]; + tensor mapping_27_reps_0 = const()[name = string("mapping_27_reps_0"), val = tensor([1, 57, 1])]; + tensor mapping_27_cast_fp16 = tile(reps = mapping_27_reps_0, x = var_5071_cast_fp16)[name = string("mapping_27_cast_fp16")]; + tensor x_693_cast_fp16 = add(x = x_691_cast_fp16, y = mapping_27_cast_fp16)[name = string("x_693_cast_fp16")]; + tensor var_5083_split_sizes_0 = const()[name = string("op_5083_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_5083_axis_0 = const()[name = string("op_5083_axis_0"), val = int32(1)]; + tensor var_5083_cast_fp16_0, tensor var_5083_cast_fp16_1 = split(axis = var_5083_axis_0, split_sizes = var_5083_split_sizes_0, x = h_3_cast_fp16)[name = string("op_5083_cast_fp16")]; + tensor gamma_147_perm_0 = const()[name = string("gamma_147_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_147_perm_0 = const()[name = string("beta_147_perm_0"), val = tensor([0, -1, 1])]; + tensor x_697_axes_0 = const()[name = string("x_697_axes_0"), val = tensor([-1])]; + fp16 var_4663_to_fp16 = const()[name = string("op_4663_to_fp16"), val = fp16(0x1.5p-17)]; + tensor x_697_cast_fp16 = layer_norm(axes = x_697_axes_0, epsilon = var_4663_to_fp16, x = x_693_cast_fp16)[name = string("x_697_cast_fp16")]; + fp16 var_5089_promoted_to_fp16 = const()[name = string("op_5089_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_147_cast_fp16 = transpose(perm = gamma_147_perm_0, x = var_5083_cast_fp16_0)[name = string("transpose_172")]; + tensor var_5090_cast_fp16 = add(x = gamma_147_cast_fp16, y = var_5089_promoted_to_fp16)[name = string("op_5090_cast_fp16")]; + tensor var_5091_cast_fp16 = mul(x = var_5090_cast_fp16, y = x_697_cast_fp16)[name = string("op_5091_cast_fp16")]; + tensor beta_147_cast_fp16 = transpose(perm = beta_147_perm_0, x = var_5083_cast_fp16_1)[name = string("transpose_171")]; + tensor x_699_cast_fp16 = add(x = var_5091_cast_fp16, y = beta_147_cast_fp16)[name = string("x_699_cast_fp16")]; + tensor var_5102_split_sizes_0 = const()[name = string("op_5102_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_5102_axis_0 = const()[name = string("op_5102_axis_0"), val = int32(1)]; + tensor var_5102_cast_fp16_0, tensor var_5102_cast_fp16_1 = split(axis = var_5102_axis_0, split_sizes = var_5102_split_sizes_0, x = h_7_cast_fp16)[name = string("op_5102_cast_fp16")]; + tensor gamma_151_perm_0 = const()[name = string("gamma_151_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_151_perm_0 = const()[name = string("beta_151_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_5108_promoted_to_fp16 = const()[name = string("op_5108_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_151_cast_fp16 = transpose(perm = gamma_151_perm_0, x = var_5102_cast_fp16_0)[name = string("transpose_170")]; + tensor var_5109_cast_fp16 = add(x = gamma_151_cast_fp16, y = var_5108_promoted_to_fp16)[name = string("op_5109_cast_fp16")]; + tensor var_5110_cast_fp16 = mul(x = var_5109_cast_fp16, y = x_697_cast_fp16)[name = string("op_5110_cast_fp16")]; + tensor beta_151_cast_fp16 = transpose(perm = beta_151_perm_0, x = var_5102_cast_fp16_1)[name = string("transpose_169")]; + tensor x_705_cast_fp16 = add(x = var_5110_cast_fp16, y = beta_151_cast_fp16)[name = string("x_705_cast_fp16")]; + tensor linear_156_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_q_weight_to_fp16, x = x_699_cast_fp16)[name = string("linear_156_cast_fp16")]; + tensor linear_157_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_kv_weight_to_fp16, x = x_705_cast_fp16)[name = string("linear_157_cast_fp16")]; + tensor var_5116_split_sizes_0 = const()[name = string("op_5116_split_sizes_0"), val = tensor([512, 512])]; + int32 var_5116_axis_0 = const()[name = string("op_5116_axis_0"), val = int32(-1)]; + tensor var_5116_cast_fp16_0, tensor var_5116_cast_fp16_1 = split(axis = var_5116_axis_0, split_sizes = var_5116_split_sizes_0, x = linear_157_cast_fp16)[name = string("op_5116_cast_fp16")]; + tensor var_5124 = const()[name = string("op_5124"), val = tensor([1, 57, 8, 64])]; + tensor x_709_cast_fp16 = reshape(shape = var_5124, x = linear_156_cast_fp16)[name = string("x_709_cast_fp16")]; + tensor var_5134 = const()[name = string("op_5134"), val = tensor([1, 57, 8, 64])]; + tensor x_713_cast_fp16 = reshape(shape = var_5134, x = var_5116_cast_fp16_0)[name = string("x_713_cast_fp16")]; + tensor var_5144 = const()[name = string("op_5144"), val = tensor([1, 57, 8, 64])]; + tensor x_717_cast_fp16 = reshape(shape = var_5144, x = var_5116_cast_fp16_1)[name = string("x_717_cast_fp16")]; + tensor var_5146 = const()[name = string("op_5146"), val = tensor([0, 2, 1, 3])]; + bool sim_73_transpose_x_0 = const()[name = string("sim_73_transpose_x_0"), val = bool(false)]; + bool sim_73_transpose_y_0 = const()[name = string("sim_73_transpose_y_0"), val = bool(false)]; + tensor transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = x_713_cast_fp16)[name = string("transpose_166")]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = x_709_cast_fp16)[name = string("transpose_167")]; + tensor sim_73_cast_fp16 = matmul(transpose_x = sim_73_transpose_x_0, transpose_y = sim_73_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("sim_73_cast_fp16")]; + fp16 var_5150_to_fp16 = const()[name = string("op_5150_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_75_cast_fp16 = mul(x = sim_73_cast_fp16, y = var_5150_to_fp16)[name = string("sim_75_cast_fp16")]; + tensor attn_37_cast_fp16 = softmax(axis = var_4667, x = sim_75_cast_fp16)[name = string("attn_37_cast_fp16")]; + bool x_719_transpose_x_0 = const()[name = string("x_719_transpose_x_0"), val = bool(false)]; + bool x_719_transpose_y_0 = const()[name = string("x_719_transpose_y_0"), val = bool(false)]; + tensor v_37_cast_fp16 = transpose(perm = var_5146, x = x_717_cast_fp16)[name = string("transpose_168")]; + tensor x_719_cast_fp16 = matmul(transpose_x = x_719_transpose_x_0, transpose_y = x_719_transpose_y_0, x = attn_37_cast_fp16, y = v_37_cast_fp16)[name = string("x_719_cast_fp16")]; + tensor var_5172 = const()[name = string("op_5172"), val = tensor([0, 2, 1, 3])]; + tensor var_5174 = const()[name = string("op_5174"), val = tensor([1, 57, 512])]; + tensor x_721_cast_fp16 = transpose(perm = var_5172, x = x_719_cast_fp16)[name = string("transpose_165")]; + tensor input_407_cast_fp16 = reshape(shape = var_5174, x = x_721_cast_fp16)[name = string("input_407_cast_fp16")]; + tensor linear_158_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_weight_to_fp16, x = input_407_cast_fp16)[name = string("linear_158_cast_fp16")]; + tensor input_409_cast_fp16 = add(x = linear_158_cast_fp16, y = x_693_cast_fp16)[name = string("input_409_cast_fp16")]; + tensor linear_159_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_weight_to_fp16, x = input_409_cast_fp16)[name = string("linear_159_cast_fp16")]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("EXACT")]; + tensor input_413_cast_fp16 = gelu(mode = input_413_mode_0, x = linear_159_cast_fp16)[name = string("input_413_cast_fp16")]; + tensor linear_160_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_weight_to_fp16, x = input_413_cast_fp16)[name = string("linear_160_cast_fp16")]; + tensor x_723_cast_fp16 = add(x = linear_160_cast_fp16, y = input_409_cast_fp16)[name = string("x_723_cast_fp16")]; + tensor x_725_cast_fp16 = add(x = x_723_cast_fp16, y = mapping_27_cast_fp16)[name = string("x_725_cast_fp16")]; + tensor var_5190_split_sizes_0 = const()[name = string("op_5190_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_5190_axis_0 = const()[name = string("op_5190_axis_0"), val = int32(1)]; + tensor var_5190_cast_fp16_0, tensor var_5190_cast_fp16_1 = split(axis = var_5190_axis_0, split_sizes = var_5190_split_sizes_0, x = h_11_cast_fp16)[name = string("op_5190_cast_fp16")]; + tensor gamma_155_perm_0 = const()[name = string("gamma_155_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_155_perm_0 = const()[name = string("beta_155_perm_0"), val = tensor([0, -1, 1])]; + tensor x_729_axes_0 = const()[name = string("x_729_axes_0"), val = tensor([-1])]; + tensor x_729_cast_fp16 = layer_norm(axes = x_729_axes_0, epsilon = var_4663_to_fp16, x = x_725_cast_fp16)[name = string("x_729_cast_fp16")]; + fp16 var_5196_promoted_to_fp16 = const()[name = string("op_5196_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_155_cast_fp16 = transpose(perm = gamma_155_perm_0, x = var_5190_cast_fp16_0)[name = string("transpose_164")]; + tensor var_5197_cast_fp16 = add(x = gamma_155_cast_fp16, y = var_5196_promoted_to_fp16)[name = string("op_5197_cast_fp16")]; + tensor var_5198_cast_fp16 = mul(x = var_5197_cast_fp16, y = x_729_cast_fp16)[name = string("op_5198_cast_fp16")]; + tensor beta_155_cast_fp16 = transpose(perm = beta_155_perm_0, x = var_5190_cast_fp16_1)[name = string("transpose_163")]; + tensor x_731_cast_fp16 = add(x = var_5198_cast_fp16, y = beta_155_cast_fp16)[name = string("x_731_cast_fp16")]; + tensor var_5209_split_sizes_0 = const()[name = string("op_5209_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_5209_axis_0 = const()[name = string("op_5209_axis_0"), val = int32(1)]; + tensor var_5209_cast_fp16_0, tensor var_5209_cast_fp16_1 = split(axis = var_5209_axis_0, split_sizes = var_5209_split_sizes_0, x = h_15_cast_fp16)[name = string("op_5209_cast_fp16")]; + tensor gamma_159_perm_0 = const()[name = string("gamma_159_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_159_perm_0 = const()[name = string("beta_159_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_5215_promoted_to_fp16 = const()[name = string("op_5215_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_159_cast_fp16 = transpose(perm = gamma_159_perm_0, x = var_5209_cast_fp16_0)[name = string("transpose_162")]; + tensor var_5216_cast_fp16 = add(x = gamma_159_cast_fp16, y = var_5215_promoted_to_fp16)[name = string("op_5216_cast_fp16")]; + tensor var_5217_cast_fp16 = mul(x = var_5216_cast_fp16, y = x_729_cast_fp16)[name = string("op_5217_cast_fp16")]; + tensor beta_159_cast_fp16 = transpose(perm = beta_159_perm_0, x = var_5209_cast_fp16_1)[name = string("transpose_161")]; + tensor x_737_cast_fp16 = add(x = var_5217_cast_fp16, y = beta_159_cast_fp16)[name = string("x_737_cast_fp16")]; + tensor linear_163_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_q_weight_to_fp16, x = x_731_cast_fp16)[name = string("linear_163_cast_fp16")]; + tensor linear_164_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_kv_weight_to_fp16, x = x_737_cast_fp16)[name = string("linear_164_cast_fp16")]; + tensor var_5223_split_sizes_0 = const()[name = string("op_5223_split_sizes_0"), val = tensor([512, 512])]; + int32 var_5223_axis_0 = const()[name = string("op_5223_axis_0"), val = int32(-1)]; + tensor var_5223_cast_fp16_0, tensor var_5223_cast_fp16_1 = split(axis = var_5223_axis_0, split_sizes = var_5223_split_sizes_0, x = linear_164_cast_fp16)[name = string("op_5223_cast_fp16")]; + tensor var_5231 = const()[name = string("op_5231"), val = tensor([1, 57, 8, 64])]; + tensor x_741_cast_fp16 = reshape(shape = var_5231, x = linear_163_cast_fp16)[name = string("x_741_cast_fp16")]; + tensor var_5241 = const()[name = string("op_5241"), val = tensor([1, 57, 8, 64])]; + tensor x_745_cast_fp16 = reshape(shape = var_5241, x = var_5223_cast_fp16_0)[name = string("x_745_cast_fp16")]; + tensor var_5251 = const()[name = string("op_5251"), val = tensor([1, 57, 8, 64])]; + tensor x_749_cast_fp16 = reshape(shape = var_5251, x = var_5223_cast_fp16_1)[name = string("x_749_cast_fp16")]; + tensor var_5253 = const()[name = string("op_5253"), val = tensor([0, 2, 1, 3])]; + bool sim_77_transpose_x_0 = const()[name = string("sim_77_transpose_x_0"), val = bool(false)]; + bool sim_77_transpose_y_0 = const()[name = string("sim_77_transpose_y_0"), val = bool(false)]; + tensor transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = x_745_cast_fp16)[name = string("transpose_158")]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = x_741_cast_fp16)[name = string("transpose_159")]; + tensor sim_77_cast_fp16 = matmul(transpose_x = sim_77_transpose_x_0, transpose_y = sim_77_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("sim_77_cast_fp16")]; + fp16 var_5257_to_fp16 = const()[name = string("op_5257_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_79_cast_fp16 = mul(x = sim_77_cast_fp16, y = var_5257_to_fp16)[name = string("sim_79_cast_fp16")]; + tensor attn_39_cast_fp16 = softmax(axis = var_4667, x = sim_79_cast_fp16)[name = string("attn_39_cast_fp16")]; + bool x_751_transpose_x_0 = const()[name = string("x_751_transpose_x_0"), val = bool(false)]; + bool x_751_transpose_y_0 = const()[name = string("x_751_transpose_y_0"), val = bool(false)]; + tensor v_39_cast_fp16 = transpose(perm = var_5253, x = x_749_cast_fp16)[name = string("transpose_160")]; + tensor x_751_cast_fp16 = matmul(transpose_x = x_751_transpose_x_0, transpose_y = x_751_transpose_y_0, x = attn_39_cast_fp16, y = v_39_cast_fp16)[name = string("x_751_cast_fp16")]; + tensor var_5279 = const()[name = string("op_5279"), val = tensor([0, 2, 1, 3])]; + tensor var_5281 = const()[name = string("op_5281"), val = tensor([1, 57, 512])]; + tensor x_753_cast_fp16 = transpose(perm = var_5279, x = x_751_cast_fp16)[name = string("transpose_157")]; + tensor input_423_cast_fp16 = reshape(shape = var_5281, x = x_753_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor linear_165_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_weight_to_fp16, x = input_423_cast_fp16)[name = string("linear_165_cast_fp16")]; + tensor input_425_cast_fp16 = add(x = linear_165_cast_fp16, y = x_725_cast_fp16)[name = string("input_425_cast_fp16")]; + tensor linear_166_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_weight_to_fp16, x = input_425_cast_fp16)[name = string("linear_166_cast_fp16")]; + string input_429_mode_0 = const()[name = string("input_429_mode_0"), val = string("EXACT")]; + tensor input_429_cast_fp16 = gelu(mode = input_429_mode_0, x = linear_166_cast_fp16)[name = string("input_429_cast_fp16")]; + tensor linear_167_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_weight_to_fp16, x = input_429_cast_fp16)[name = string("linear_167_cast_fp16")]; + tensor x_755_cast_fp16 = add(x = linear_167_cast_fp16, y = input_425_cast_fp16)[name = string("x_755_cast_fp16")]; + tensor x_757_cast_fp16 = add(x = x_755_cast_fp16, y = mapping_27_cast_fp16)[name = string("x_757_cast_fp16")]; + tensor var_5297_split_sizes_0 = const()[name = string("op_5297_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_5297_axis_0 = const()[name = string("op_5297_axis_0"), val = int32(1)]; + tensor var_5297_cast_fp16_0, tensor var_5297_cast_fp16_1 = split(axis = var_5297_axis_0, split_sizes = var_5297_split_sizes_0, x = h_19_cast_fp16)[name = string("op_5297_cast_fp16")]; + tensor gamma_163_perm_0 = const()[name = string("gamma_163_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_163_perm_0 = const()[name = string("beta_163_perm_0"), val = tensor([0, -1, 1])]; + tensor x_761_axes_0 = const()[name = string("x_761_axes_0"), val = tensor([-1])]; + tensor x_761_cast_fp16 = layer_norm(axes = x_761_axes_0, epsilon = var_4663_to_fp16, x = x_757_cast_fp16)[name = string("x_761_cast_fp16")]; + fp16 var_5303_promoted_to_fp16 = const()[name = string("op_5303_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_163_cast_fp16 = transpose(perm = gamma_163_perm_0, x = var_5297_cast_fp16_0)[name = string("transpose_156")]; + tensor var_5304_cast_fp16 = add(x = gamma_163_cast_fp16, y = var_5303_promoted_to_fp16)[name = string("op_5304_cast_fp16")]; + tensor var_5305_cast_fp16 = mul(x = var_5304_cast_fp16, y = x_761_cast_fp16)[name = string("op_5305_cast_fp16")]; + tensor beta_163_cast_fp16 = transpose(perm = beta_163_perm_0, x = var_5297_cast_fp16_1)[name = string("transpose_155")]; + tensor x_763_cast_fp16 = add(x = var_5305_cast_fp16, y = beta_163_cast_fp16)[name = string("x_763_cast_fp16")]; + tensor var_5316_split_sizes_0 = const()[name = string("op_5316_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_5316_axis_0 = const()[name = string("op_5316_axis_0"), val = int32(1)]; + tensor var_5316_cast_fp16_0, tensor var_5316_cast_fp16_1 = split(axis = var_5316_axis_0, split_sizes = var_5316_split_sizes_0, x = h_23_cast_fp16)[name = string("op_5316_cast_fp16")]; + tensor gamma_167_perm_0 = const()[name = string("gamma_167_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_167_perm_0 = const()[name = string("beta_167_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_5322_promoted_to_fp16 = const()[name = string("op_5322_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_167_cast_fp16 = transpose(perm = gamma_167_perm_0, x = var_5316_cast_fp16_0)[name = string("transpose_154")]; + tensor var_5323_cast_fp16 = add(x = gamma_167_cast_fp16, y = var_5322_promoted_to_fp16)[name = string("op_5323_cast_fp16")]; + tensor var_5324_cast_fp16 = mul(x = var_5323_cast_fp16, y = x_761_cast_fp16)[name = string("op_5324_cast_fp16")]; + tensor beta_167_cast_fp16 = transpose(perm = beta_167_perm_0, x = var_5316_cast_fp16_1)[name = string("transpose_153")]; + tensor x_769_cast_fp16 = add(x = var_5324_cast_fp16, y = beta_167_cast_fp16)[name = string("x_769_cast_fp16")]; + tensor linear_170_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_q_weight_to_fp16, x = x_763_cast_fp16)[name = string("linear_170_cast_fp16")]; + tensor linear_171_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_kv_weight_to_fp16, x = x_769_cast_fp16)[name = string("linear_171_cast_fp16")]; + tensor var_5330_split_sizes_0 = const()[name = string("op_5330_split_sizes_0"), val = tensor([512, 512])]; + int32 var_5330_axis_0 = const()[name = string("op_5330_axis_0"), val = int32(-1)]; + tensor var_5330_cast_fp16_0, tensor var_5330_cast_fp16_1 = split(axis = var_5330_axis_0, split_sizes = var_5330_split_sizes_0, x = linear_171_cast_fp16)[name = string("op_5330_cast_fp16")]; + tensor var_5338 = const()[name = string("op_5338"), val = tensor([1, 57, 8, 64])]; + tensor x_773_cast_fp16 = reshape(shape = var_5338, x = linear_170_cast_fp16)[name = string("x_773_cast_fp16")]; + tensor var_5348 = const()[name = string("op_5348"), val = tensor([1, 57, 8, 64])]; + tensor x_777_cast_fp16 = reshape(shape = var_5348, x = var_5330_cast_fp16_0)[name = string("x_777_cast_fp16")]; + tensor var_5358 = const()[name = string("op_5358"), val = tensor([1, 57, 8, 64])]; + tensor x_781_cast_fp16 = reshape(shape = var_5358, x = var_5330_cast_fp16_1)[name = string("x_781_cast_fp16")]; + tensor var_5360 = const()[name = string("op_5360"), val = tensor([0, 2, 1, 3])]; + bool sim_81_transpose_x_0 = const()[name = string("sim_81_transpose_x_0"), val = bool(false)]; + bool sim_81_transpose_y_0 = const()[name = string("sim_81_transpose_y_0"), val = bool(false)]; + tensor transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = x_777_cast_fp16)[name = string("transpose_150")]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = x_773_cast_fp16)[name = string("transpose_151")]; + tensor sim_81_cast_fp16 = matmul(transpose_x = sim_81_transpose_x_0, transpose_y = sim_81_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("sim_81_cast_fp16")]; + fp16 var_5364_to_fp16 = const()[name = string("op_5364_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_83_cast_fp16 = mul(x = sim_81_cast_fp16, y = var_5364_to_fp16)[name = string("sim_83_cast_fp16")]; + tensor attn_41_cast_fp16 = softmax(axis = var_4667, x = sim_83_cast_fp16)[name = string("attn_41_cast_fp16")]; + bool x_783_transpose_x_0 = const()[name = string("x_783_transpose_x_0"), val = bool(false)]; + bool x_783_transpose_y_0 = const()[name = string("x_783_transpose_y_0"), val = bool(false)]; + tensor v_41_cast_fp16 = transpose(perm = var_5360, x = x_781_cast_fp16)[name = string("transpose_152")]; + tensor x_783_cast_fp16 = matmul(transpose_x = x_783_transpose_x_0, transpose_y = x_783_transpose_y_0, x = attn_41_cast_fp16, y = v_41_cast_fp16)[name = string("x_783_cast_fp16")]; + tensor var_5386 = const()[name = string("op_5386"), val = tensor([0, 2, 1, 3])]; + tensor var_5388 = const()[name = string("op_5388"), val = tensor([1, 57, 512])]; + tensor x_785_cast_fp16 = transpose(perm = var_5386, x = x_783_cast_fp16)[name = string("transpose_149")]; + tensor input_439_cast_fp16 = reshape(shape = var_5388, x = x_785_cast_fp16)[name = string("input_439_cast_fp16")]; + tensor linear_172_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_weight_to_fp16, x = input_439_cast_fp16)[name = string("linear_172_cast_fp16")]; + tensor input_441_cast_fp16 = add(x = linear_172_cast_fp16, y = x_757_cast_fp16)[name = string("input_441_cast_fp16")]; + tensor linear_173_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_weight_to_fp16, x = input_441_cast_fp16)[name = string("linear_173_cast_fp16")]; + string input_445_mode_0 = const()[name = string("input_445_mode_0"), val = string("EXACT")]; + tensor input_445_cast_fp16 = gelu(mode = input_445_mode_0, x = linear_173_cast_fp16)[name = string("input_445_cast_fp16")]; + tensor linear_174_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_weight_to_fp16, x = input_445_cast_fp16)[name = string("linear_174_cast_fp16")]; + tensor x_787_cast_fp16 = add(x = linear_174_cast_fp16, y = input_441_cast_fp16)[name = string("x_787_cast_fp16")]; + tensor var_5397_axes_0 = const()[name = string("op_5397_axes_0"), val = tensor([1])]; + bool var_5397_keep_dims_0 = const()[name = string("op_5397_keep_dims_0"), val = bool(false)]; + tensor var_5397_cast_fp16 = reduce_mean(axes = var_5397_axes_0, keep_dims = var_5397_keep_dims_0, x = x_787_cast_fp16)[name = string("op_5397_cast_fp16")]; + tensor x_789_axes_0 = const()[name = string("x_789_axes_0"), val = tensor([1])]; + tensor x_789_cast_fp16 = expand_dims(axes = x_789_axes_0, x = var_5397_cast_fp16)[name = string("x_789_cast_fp16")]; + tensor var_5399 = const()[name = string("op_5399"), val = tensor([0, 2, 1])]; + string x_791_pad_type_0 = const()[name = string("x_791_pad_type_0"), val = string("valid")]; + tensor x_791_strides_0 = const()[name = string("x_791_strides_0"), val = tensor([1])]; + tensor x_791_pad_0 = const()[name = string("x_791_pad_0"), val = tensor([0, 0])]; + tensor x_791_dilations_0 = const()[name = string("x_791_dilations_0"), val = tensor([1])]; + int32 x_791_groups_0 = const()[name = string("x_791_groups_0"), val = int32(1)]; + tensor input_447_cast_fp16 = transpose(perm = var_5399, x = x_789_cast_fp16)[name = string("transpose_148")]; + tensor x_791_cast_fp16 = conv(bias = unet_wrap_kdiffusion_net_to_out_1_bias_to_fp16, dilations = x_791_dilations_0, groups = x_791_groups_0, pad = x_791_pad_0, pad_type = x_791_pad_type_0, strides = x_791_strides_0, weight = unet_wrap_kdiffusion_net_to_out_1_weight_to_fp16, x = input_447_cast_fp16)[name = string("x_791_cast_fp16")]; + tensor x_pred_13_perm_0 = const()[name = string("x_pred_13_perm_0"), val = tensor([0, -1, -2])]; + tensor c_skip_13_to_fp16 = const()[name = string("c_skip_13_to_fp16"), val = tensor([[[0x1.fe8p-1]]])]; + tensor var_5407_cast_fp16 = mul(x = c_skip_13_to_fp16, y = x_noisy_13_cast_fp16)[name = string("op_5407_cast_fp16")]; + tensor c_out_13_to_fp16 = const()[name = string("c_out_13_to_fp16"), val = tensor([[[0x1.37cp-8]]])]; + tensor x_pred_13_cast_fp16 = transpose(perm = x_pred_13_perm_0, x = x_791_cast_fp16)[name = string("transpose_147")]; + tensor var_5408_cast_fp16 = mul(x = c_out_13_to_fp16, y = x_pred_13_cast_fp16)[name = string("op_5408_cast_fp16")]; + tensor x_dn_cast_fp16 = add(x = var_5407_cast_fp16, y = var_5408_cast_fp16)[name = string("x_dn_cast_fp16")]; + tensor var_5411_cast_fp16 = sub(x = x_noisy_13_cast_fp16, y = x_dn_cast_fp16)[name = string("op_5411_cast_fp16")]; + tensor _inversed_d_y_0_to_fp16 = const()[name = string("_inversed_d_y_0_to_fp16"), val = tensor([0x1.a44p+7])]; + tensor _inversed_d_cast_fp16 = mul(x = var_5411_cast_fp16, y = _inversed_d_y_0_to_fp16)[name = string("_inversed_d_cast_fp16")]; + tensor var_5414_to_fp16 = const()[name = string("op_5414_to_fp16"), val = tensor([-0x1.37cp-9])]; + tensor var_5415_cast_fp16 = mul(x = _inversed_d_cast_fp16, y = var_5414_to_fp16)[name = string("op_5415_cast_fp16")]; + tensor x_noisy_cast_fp16 = add(x = x_noisy_13_cast_fp16, y = var_5415_cast_fp16)[name = string("x_noisy_cast_fp16")]; + int32 var_5427 = const()[name = string("op_5427"), val = int32(-1)]; + tensor c_in_to_fp16 = const()[name = string("c_in_to_fp16"), val = tensor([[[0x1.414p+2]]])]; + tensor x_801_cast_fp16 = mul(x = c_in_to_fp16, y = x_noisy_cast_fp16)[name = string("x_801_cast_fp16")]; + int32 x_797_axis_0 = const()[name = string("x_797_axis_0"), val = int32(0)]; + tensor var_5813_to_fp16 = const()[name = string("op_5813_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49359744)))]; + tensor x_797_cast_fp16 = stack(axis = x_797_axis_0, values = (var_5813_to_fp16, var_423_cast_fp16))[name = string("x_797_cast_fp16")]; + tensor var_5818 = const()[name = string("op_5818"), val = tensor([1, 2, 0])]; + tensor input_455_axes_0 = const()[name = string("input_455_axes_0"), val = tensor([2])]; + bool input_455_keep_dims_0 = const()[name = string("input_455_keep_dims_0"), val = bool(false)]; + tensor x_799_cast_fp16 = transpose(perm = var_5818, x = x_797_cast_fp16)[name = string("transpose_146")]; + tensor input_455_cast_fp16 = reduce_sum(axes = input_455_axes_0, keep_dims = input_455_keep_dims_0, x = x_799_cast_fp16)[name = string("input_455_cast_fp16")]; + tensor linear_177_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_0_weight_to_fp16, x = input_455_cast_fp16)[name = string("linear_177_cast_fp16")]; + string input_459_mode_0 = const()[name = string("input_459_mode_0"), val = string("EXACT")]; + tensor input_459_cast_fp16 = gelu(mode = input_459_mode_0, x = linear_177_cast_fp16)[name = string("input_459_cast_fp16")]; + tensor linear_178_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_to_mapping_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_to_mapping_2_weight_to_fp16, x = input_459_cast_fp16)[name = string("linear_178_cast_fp16")]; + string mapping_29_mode_0 = const()[name = string("mapping_29_mode_0"), val = string("EXACT")]; + tensor mapping_29_cast_fp16 = gelu(mode = mapping_29_mode_0, x = linear_178_cast_fp16)[name = string("mapping_29_cast_fp16")]; + tensor var_5828_reps_0 = const()[name = string("op_5828_reps_0"), val = tensor([1, 57, 1])]; + tensor var_5828_cast_fp16 = tile(reps = var_5828_reps_0, x = x_801_cast_fp16)[name = string("op_5828_cast_fp16")]; + bool x_803_interleave_0 = const()[name = string("x_803_interleave_0"), val = bool(false)]; + tensor x_803_cast_fp16 = concat(axis = var_5427, interleave = x_803_interleave_0, values = (var_5828_cast_fp16, embedding_to_fp16))[name = string("x_803_cast_fp16")]; + tensor var_5831_axes_0 = const()[name = string("op_5831_axes_0"), val = tensor([1])]; + tensor var_5831_cast_fp16 = expand_dims(axes = var_5831_axes_0, x = mapping_29_cast_fp16)[name = string("op_5831_cast_fp16")]; + tensor mapping_reps_0 = const()[name = string("mapping_reps_0"), val = tensor([1, 57, 1])]; + tensor mapping_cast_fp16 = tile(reps = mapping_reps_0, x = var_5831_cast_fp16)[name = string("mapping_cast_fp16")]; + tensor x_805_cast_fp16 = add(x = x_803_cast_fp16, y = mapping_cast_fp16)[name = string("x_805_cast_fp16")]; + tensor var_5843_split_sizes_0 = const()[name = string("op_5843_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_5843_axis_0 = const()[name = string("op_5843_axis_0"), val = int32(1)]; + tensor var_5843_cast_fp16_0, tensor var_5843_cast_fp16_1 = split(axis = var_5843_axis_0, split_sizes = var_5843_split_sizes_0, x = h_3_cast_fp16)[name = string("op_5843_cast_fp16")]; + tensor gamma_171_perm_0 = const()[name = string("gamma_171_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_171_perm_0 = const()[name = string("beta_171_perm_0"), val = tensor([0, -1, 1])]; + tensor x_809_axes_0 = const()[name = string("x_809_axes_0"), val = tensor([-1])]; + fp16 var_5423_to_fp16 = const()[name = string("op_5423_to_fp16"), val = fp16(0x1.5p-17)]; + tensor x_809_cast_fp16 = layer_norm(axes = x_809_axes_0, epsilon = var_5423_to_fp16, x = x_805_cast_fp16)[name = string("x_809_cast_fp16")]; + fp16 var_5849_promoted_to_fp16 = const()[name = string("op_5849_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_171_cast_fp16 = transpose(perm = gamma_171_perm_0, x = var_5843_cast_fp16_0)[name = string("transpose_145")]; + tensor var_5850_cast_fp16 = add(x = gamma_171_cast_fp16, y = var_5849_promoted_to_fp16)[name = string("op_5850_cast_fp16")]; + tensor var_5851_cast_fp16 = mul(x = var_5850_cast_fp16, y = x_809_cast_fp16)[name = string("op_5851_cast_fp16")]; + tensor beta_171_cast_fp16 = transpose(perm = beta_171_perm_0, x = var_5843_cast_fp16_1)[name = string("transpose_144")]; + tensor x_811_cast_fp16 = add(x = var_5851_cast_fp16, y = beta_171_cast_fp16)[name = string("x_811_cast_fp16")]; + tensor var_5862_split_sizes_0 = const()[name = string("op_5862_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_5862_axis_0 = const()[name = string("op_5862_axis_0"), val = int32(1)]; + tensor var_5862_cast_fp16_0, tensor var_5862_cast_fp16_1 = split(axis = var_5862_axis_0, split_sizes = var_5862_split_sizes_0, x = h_7_cast_fp16)[name = string("op_5862_cast_fp16")]; + tensor gamma_175_perm_0 = const()[name = string("gamma_175_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_175_perm_0 = const()[name = string("beta_175_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_5868_promoted_to_fp16 = const()[name = string("op_5868_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_175_cast_fp16 = transpose(perm = gamma_175_perm_0, x = var_5862_cast_fp16_0)[name = string("transpose_143")]; + tensor var_5869_cast_fp16 = add(x = gamma_175_cast_fp16, y = var_5868_promoted_to_fp16)[name = string("op_5869_cast_fp16")]; + tensor var_5870_cast_fp16 = mul(x = var_5869_cast_fp16, y = x_809_cast_fp16)[name = string("op_5870_cast_fp16")]; + tensor beta_175_cast_fp16 = transpose(perm = beta_175_perm_0, x = var_5862_cast_fp16_1)[name = string("transpose_142")]; + tensor x_817_cast_fp16 = add(x = var_5870_cast_fp16, y = beta_175_cast_fp16)[name = string("x_817_cast_fp16")]; + tensor linear_181_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_q_weight_to_fp16, x = x_811_cast_fp16)[name = string("linear_181_cast_fp16")]; + tensor linear_182_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_to_kv_weight_to_fp16, x = x_817_cast_fp16)[name = string("linear_182_cast_fp16")]; + tensor var_5876_split_sizes_0 = const()[name = string("op_5876_split_sizes_0"), val = tensor([512, 512])]; + int32 var_5876_axis_0 = const()[name = string("op_5876_axis_0"), val = int32(-1)]; + tensor var_5876_cast_fp16_0, tensor var_5876_cast_fp16_1 = split(axis = var_5876_axis_0, split_sizes = var_5876_split_sizes_0, x = linear_182_cast_fp16)[name = string("op_5876_cast_fp16")]; + tensor var_5884 = const()[name = string("op_5884"), val = tensor([1, 57, 8, 64])]; + tensor x_821_cast_fp16 = reshape(shape = var_5884, x = linear_181_cast_fp16)[name = string("x_821_cast_fp16")]; + tensor var_5894 = const()[name = string("op_5894"), val = tensor([1, 57, 8, 64])]; + tensor x_825_cast_fp16 = reshape(shape = var_5894, x = var_5876_cast_fp16_0)[name = string("x_825_cast_fp16")]; + tensor var_5904 = const()[name = string("op_5904"), val = tensor([1, 57, 8, 64])]; + tensor x_829_cast_fp16 = reshape(shape = var_5904, x = var_5876_cast_fp16_1)[name = string("x_829_cast_fp16")]; + tensor var_5906 = const()[name = string("op_5906"), val = tensor([0, 2, 1, 3])]; + bool sim_85_transpose_x_0 = const()[name = string("sim_85_transpose_x_0"), val = bool(false)]; + bool sim_85_transpose_y_0 = const()[name = string("sim_85_transpose_y_0"), val = bool(false)]; + tensor transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = x_825_cast_fp16)[name = string("transpose_139")]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = x_821_cast_fp16)[name = string("transpose_140")]; + tensor sim_85_cast_fp16 = matmul(transpose_x = sim_85_transpose_x_0, transpose_y = sim_85_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("sim_85_cast_fp16")]; + fp16 var_5910_to_fp16 = const()[name = string("op_5910_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_87_cast_fp16 = mul(x = sim_85_cast_fp16, y = var_5910_to_fp16)[name = string("sim_87_cast_fp16")]; + tensor attn_43_cast_fp16 = softmax(axis = var_5427, x = sim_87_cast_fp16)[name = string("attn_43_cast_fp16")]; + bool x_831_transpose_x_0 = const()[name = string("x_831_transpose_x_0"), val = bool(false)]; + bool x_831_transpose_y_0 = const()[name = string("x_831_transpose_y_0"), val = bool(false)]; + tensor v_43_cast_fp16 = transpose(perm = var_5906, x = x_829_cast_fp16)[name = string("transpose_141")]; + tensor x_831_cast_fp16 = matmul(transpose_x = x_831_transpose_x_0, transpose_y = x_831_transpose_y_0, x = attn_43_cast_fp16, y = v_43_cast_fp16)[name = string("x_831_cast_fp16")]; + tensor var_5932 = const()[name = string("op_5932"), val = tensor([0, 2, 1, 3])]; + tensor var_5934 = const()[name = string("op_5934"), val = tensor([1, 57, 512])]; + tensor x_833_cast_fp16 = transpose(perm = var_5932, x = x_831_cast_fp16)[name = string("transpose_138")]; + tensor input_471_cast_fp16 = reshape(shape = var_5934, x = x_833_cast_fp16)[name = string("input_471_cast_fp16")]; + tensor linear_183_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_attention_attention_to_out_weight_to_fp16, x = input_471_cast_fp16)[name = string("linear_183_cast_fp16")]; + tensor input_473_cast_fp16 = add(x = linear_183_cast_fp16, y = x_805_cast_fp16)[name = string("input_473_cast_fp16")]; + tensor linear_184_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_0_weight_to_fp16, x = input_473_cast_fp16)[name = string("linear_184_cast_fp16")]; + string input_477_mode_0 = const()[name = string("input_477_mode_0"), val = string("EXACT")]; + tensor input_477_cast_fp16 = gelu(mode = input_477_mode_0, x = linear_184_cast_fp16)[name = string("input_477_cast_fp16")]; + tensor linear_185_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_0_feed_forward_2_weight_to_fp16, x = input_477_cast_fp16)[name = string("linear_185_cast_fp16")]; + tensor x_835_cast_fp16 = add(x = linear_185_cast_fp16, y = input_473_cast_fp16)[name = string("x_835_cast_fp16")]; + tensor x_837_cast_fp16 = add(x = x_835_cast_fp16, y = mapping_cast_fp16)[name = string("x_837_cast_fp16")]; + tensor var_5950_split_sizes_0 = const()[name = string("op_5950_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_5950_axis_0 = const()[name = string("op_5950_axis_0"), val = int32(1)]; + tensor var_5950_cast_fp16_0, tensor var_5950_cast_fp16_1 = split(axis = var_5950_axis_0, split_sizes = var_5950_split_sizes_0, x = h_11_cast_fp16)[name = string("op_5950_cast_fp16")]; + tensor gamma_179_perm_0 = const()[name = string("gamma_179_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_179_perm_0 = const()[name = string("beta_179_perm_0"), val = tensor([0, -1, 1])]; + tensor x_841_axes_0 = const()[name = string("x_841_axes_0"), val = tensor([-1])]; + tensor x_841_cast_fp16 = layer_norm(axes = x_841_axes_0, epsilon = var_5423_to_fp16, x = x_837_cast_fp16)[name = string("x_841_cast_fp16")]; + fp16 var_5956_promoted_to_fp16 = const()[name = string("op_5956_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_179_cast_fp16 = transpose(perm = gamma_179_perm_0, x = var_5950_cast_fp16_0)[name = string("transpose_137")]; + tensor var_5957_cast_fp16 = add(x = gamma_179_cast_fp16, y = var_5956_promoted_to_fp16)[name = string("op_5957_cast_fp16")]; + tensor var_5958_cast_fp16 = mul(x = var_5957_cast_fp16, y = x_841_cast_fp16)[name = string("op_5958_cast_fp16")]; + tensor beta_179_cast_fp16 = transpose(perm = beta_179_perm_0, x = var_5950_cast_fp16_1)[name = string("transpose_136")]; + tensor x_843_cast_fp16 = add(x = var_5958_cast_fp16, y = beta_179_cast_fp16)[name = string("x_843_cast_fp16")]; + tensor var_5969_split_sizes_0 = const()[name = string("op_5969_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_5969_axis_0 = const()[name = string("op_5969_axis_0"), val = int32(1)]; + tensor var_5969_cast_fp16_0, tensor var_5969_cast_fp16_1 = split(axis = var_5969_axis_0, split_sizes = var_5969_split_sizes_0, x = h_15_cast_fp16)[name = string("op_5969_cast_fp16")]; + tensor gamma_183_perm_0 = const()[name = string("gamma_183_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_183_perm_0 = const()[name = string("beta_183_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_5975_promoted_to_fp16 = const()[name = string("op_5975_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_183_cast_fp16 = transpose(perm = gamma_183_perm_0, x = var_5969_cast_fp16_0)[name = string("transpose_135")]; + tensor var_5976_cast_fp16 = add(x = gamma_183_cast_fp16, y = var_5975_promoted_to_fp16)[name = string("op_5976_cast_fp16")]; + tensor var_5977_cast_fp16 = mul(x = var_5976_cast_fp16, y = x_841_cast_fp16)[name = string("op_5977_cast_fp16")]; + tensor beta_183_cast_fp16 = transpose(perm = beta_183_perm_0, x = var_5969_cast_fp16_1)[name = string("transpose_134")]; + tensor x_849_cast_fp16 = add(x = var_5977_cast_fp16, y = beta_183_cast_fp16)[name = string("x_849_cast_fp16")]; + tensor linear_188_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_q_weight_to_fp16, x = x_843_cast_fp16)[name = string("linear_188_cast_fp16")]; + tensor linear_189_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_to_kv_weight_to_fp16, x = x_849_cast_fp16)[name = string("linear_189_cast_fp16")]; + tensor var_5983_split_sizes_0 = const()[name = string("op_5983_split_sizes_0"), val = tensor([512, 512])]; + int32 var_5983_axis_0 = const()[name = string("op_5983_axis_0"), val = int32(-1)]; + tensor var_5983_cast_fp16_0, tensor var_5983_cast_fp16_1 = split(axis = var_5983_axis_0, split_sizes = var_5983_split_sizes_0, x = linear_189_cast_fp16)[name = string("op_5983_cast_fp16")]; + tensor var_5991 = const()[name = string("op_5991"), val = tensor([1, 57, 8, 64])]; + tensor x_853_cast_fp16 = reshape(shape = var_5991, x = linear_188_cast_fp16)[name = string("x_853_cast_fp16")]; + tensor var_6001 = const()[name = string("op_6001"), val = tensor([1, 57, 8, 64])]; + tensor x_857_cast_fp16 = reshape(shape = var_6001, x = var_5983_cast_fp16_0)[name = string("x_857_cast_fp16")]; + tensor var_6011 = const()[name = string("op_6011"), val = tensor([1, 57, 8, 64])]; + tensor x_861_cast_fp16 = reshape(shape = var_6011, x = var_5983_cast_fp16_1)[name = string("x_861_cast_fp16")]; + tensor var_6013 = const()[name = string("op_6013"), val = tensor([0, 2, 1, 3])]; + bool sim_89_transpose_x_0 = const()[name = string("sim_89_transpose_x_0"), val = bool(false)]; + bool sim_89_transpose_y_0 = const()[name = string("sim_89_transpose_y_0"), val = bool(false)]; + tensor transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = x_857_cast_fp16)[name = string("transpose_131")]; + tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = x_853_cast_fp16)[name = string("transpose_132")]; + tensor sim_89_cast_fp16 = matmul(transpose_x = sim_89_transpose_x_0, transpose_y = sim_89_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("sim_89_cast_fp16")]; + fp16 var_6017_to_fp16 = const()[name = string("op_6017_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_91_cast_fp16 = mul(x = sim_89_cast_fp16, y = var_6017_to_fp16)[name = string("sim_91_cast_fp16")]; + tensor attn_45_cast_fp16 = softmax(axis = var_5427, x = sim_91_cast_fp16)[name = string("attn_45_cast_fp16")]; + bool x_863_transpose_x_0 = const()[name = string("x_863_transpose_x_0"), val = bool(false)]; + bool x_863_transpose_y_0 = const()[name = string("x_863_transpose_y_0"), val = bool(false)]; + tensor v_45_cast_fp16 = transpose(perm = var_6013, x = x_861_cast_fp16)[name = string("transpose_133")]; + tensor x_863_cast_fp16 = matmul(transpose_x = x_863_transpose_x_0, transpose_y = x_863_transpose_y_0, x = attn_45_cast_fp16, y = v_45_cast_fp16)[name = string("x_863_cast_fp16")]; + tensor var_6039 = const()[name = string("op_6039"), val = tensor([0, 2, 1, 3])]; + tensor var_6041 = const()[name = string("op_6041"), val = tensor([1, 57, 512])]; + tensor x_865_cast_fp16 = transpose(perm = var_6039, x = x_863_cast_fp16)[name = string("transpose_130")]; + tensor input_487_cast_fp16 = reshape(shape = var_6041, x = x_865_cast_fp16)[name = string("input_487_cast_fp16")]; + tensor linear_190_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_attention_attention_to_out_weight_to_fp16, x = input_487_cast_fp16)[name = string("linear_190_cast_fp16")]; + tensor input_489_cast_fp16 = add(x = linear_190_cast_fp16, y = x_837_cast_fp16)[name = string("input_489_cast_fp16")]; + tensor linear_191_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_0_weight_to_fp16, x = input_489_cast_fp16)[name = string("linear_191_cast_fp16")]; + string input_493_mode_0 = const()[name = string("input_493_mode_0"), val = string("EXACT")]; + tensor input_493_cast_fp16 = gelu(mode = input_493_mode_0, x = linear_191_cast_fp16)[name = string("input_493_cast_fp16")]; + tensor linear_192_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_1_feed_forward_2_weight_to_fp16, x = input_493_cast_fp16)[name = string("linear_192_cast_fp16")]; + tensor x_867_cast_fp16 = add(x = linear_192_cast_fp16, y = input_489_cast_fp16)[name = string("x_867_cast_fp16")]; + tensor x_869_cast_fp16 = add(x = x_867_cast_fp16, y = mapping_cast_fp16)[name = string("x_869_cast_fp16")]; + tensor var_6057_split_sizes_0 = const()[name = string("op_6057_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_6057_axis_0 = const()[name = string("op_6057_axis_0"), val = int32(1)]; + tensor var_6057_cast_fp16_0, tensor var_6057_cast_fp16_1 = split(axis = var_6057_axis_0, split_sizes = var_6057_split_sizes_0, x = h_19_cast_fp16)[name = string("op_6057_cast_fp16")]; + tensor gamma_187_perm_0 = const()[name = string("gamma_187_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_187_perm_0 = const()[name = string("beta_187_perm_0"), val = tensor([0, -1, 1])]; + tensor x_873_axes_0 = const()[name = string("x_873_axes_0"), val = tensor([-1])]; + tensor x_873_cast_fp16 = layer_norm(axes = x_873_axes_0, epsilon = var_5423_to_fp16, x = x_869_cast_fp16)[name = string("x_873_cast_fp16")]; + fp16 var_6063_promoted_to_fp16 = const()[name = string("op_6063_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_187_cast_fp16 = transpose(perm = gamma_187_perm_0, x = var_6057_cast_fp16_0)[name = string("transpose_129")]; + tensor var_6064_cast_fp16 = add(x = gamma_187_cast_fp16, y = var_6063_promoted_to_fp16)[name = string("op_6064_cast_fp16")]; + tensor var_6065_cast_fp16 = mul(x = var_6064_cast_fp16, y = x_873_cast_fp16)[name = string("op_6065_cast_fp16")]; + tensor beta_187_cast_fp16 = transpose(perm = beta_187_perm_0, x = var_6057_cast_fp16_1)[name = string("transpose_128")]; + tensor x_875_cast_fp16 = add(x = var_6065_cast_fp16, y = beta_187_cast_fp16)[name = string("x_875_cast_fp16")]; + tensor var_6076_split_sizes_0 = const()[name = string("op_6076_split_sizes_0"), val = tensor([1024, 1024])]; + int32 var_6076_axis_0 = const()[name = string("op_6076_axis_0"), val = int32(1)]; + tensor var_6076_cast_fp16_0, tensor var_6076_cast_fp16_1 = split(axis = var_6076_axis_0, split_sizes = var_6076_split_sizes_0, x = h_23_cast_fp16)[name = string("op_6076_cast_fp16")]; + tensor gamma_perm_0 = const()[name = string("gamma_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_perm_0 = const()[name = string("beta_perm_0"), val = tensor([0, -1, 1])]; + fp16 var_6082_promoted_to_fp16 = const()[name = string("op_6082_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor gamma_cast_fp16 = transpose(perm = gamma_perm_0, x = var_6076_cast_fp16_0)[name = string("transpose_127")]; + tensor var_6083_cast_fp16 = add(x = gamma_cast_fp16, y = var_6082_promoted_to_fp16)[name = string("op_6083_cast_fp16")]; + tensor var_6084_cast_fp16 = mul(x = var_6083_cast_fp16, y = x_873_cast_fp16)[name = string("op_6084_cast_fp16")]; + tensor beta_cast_fp16 = transpose(perm = beta_perm_0, x = var_6076_cast_fp16_1)[name = string("transpose_126")]; + tensor x_881_cast_fp16 = add(x = var_6084_cast_fp16, y = beta_cast_fp16)[name = string("x_881_cast_fp16")]; + tensor linear_195_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_q_weight_to_fp16, x = x_875_cast_fp16)[name = string("linear_195_cast_fp16")]; + tensor linear_196_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_to_kv_weight_to_fp16, x = x_881_cast_fp16)[name = string("linear_196_cast_fp16")]; + tensor var_6090_split_sizes_0 = const()[name = string("op_6090_split_sizes_0"), val = tensor([512, 512])]; + int32 var_6090_axis_0 = const()[name = string("op_6090_axis_0"), val = int32(-1)]; + tensor var_6090_cast_fp16_0, tensor var_6090_cast_fp16_1 = split(axis = var_6090_axis_0, split_sizes = var_6090_split_sizes_0, x = linear_196_cast_fp16)[name = string("op_6090_cast_fp16")]; + tensor var_6098 = const()[name = string("op_6098"), val = tensor([1, 57, 8, 64])]; + tensor x_885_cast_fp16 = reshape(shape = var_6098, x = linear_195_cast_fp16)[name = string("x_885_cast_fp16")]; + tensor var_6108 = const()[name = string("op_6108"), val = tensor([1, 57, 8, 64])]; + tensor x_889_cast_fp16 = reshape(shape = var_6108, x = var_6090_cast_fp16_0)[name = string("x_889_cast_fp16")]; + tensor var_6118 = const()[name = string("op_6118"), val = tensor([1, 57, 8, 64])]; + tensor x_893_cast_fp16 = reshape(shape = var_6118, x = var_6090_cast_fp16_1)[name = string("x_893_cast_fp16")]; + tensor var_6120 = const()[name = string("op_6120"), val = tensor([0, 2, 1, 3])]; + bool sim_93_transpose_x_0 = const()[name = string("sim_93_transpose_x_0"), val = bool(false)]; + bool sim_93_transpose_y_0 = const()[name = string("sim_93_transpose_y_0"), val = bool(false)]; + tensor transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = x_889_cast_fp16)[name = string("transpose_123")]; + tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = x_885_cast_fp16)[name = string("transpose_124")]; + tensor sim_93_cast_fp16 = matmul(transpose_x = sim_93_transpose_x_0, transpose_y = sim_93_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("sim_93_cast_fp16")]; + fp16 var_6124_to_fp16 = const()[name = string("op_6124_to_fp16"), val = fp16(0x1p-3)]; + tensor sim_cast_fp16 = mul(x = sim_93_cast_fp16, y = var_6124_to_fp16)[name = string("sim_cast_fp16")]; + tensor attn_cast_fp16 = softmax(axis = var_5427, x = sim_cast_fp16)[name = string("attn_cast_fp16")]; + bool x_895_transpose_x_0 = const()[name = string("x_895_transpose_x_0"), val = bool(false)]; + bool x_895_transpose_y_0 = const()[name = string("x_895_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = var_6120, x = x_893_cast_fp16)[name = string("transpose_125")]; + tensor x_895_cast_fp16 = matmul(transpose_x = x_895_transpose_x_0, transpose_y = x_895_transpose_y_0, x = attn_cast_fp16, y = v_cast_fp16)[name = string("x_895_cast_fp16")]; + tensor var_6146 = const()[name = string("op_6146"), val = tensor([0, 2, 1, 3])]; + tensor var_6148 = const()[name = string("op_6148"), val = tensor([1, 57, 512])]; + tensor x_897_cast_fp16 = transpose(perm = var_6146, x = x_895_cast_fp16)[name = string("transpose_122")]; + tensor input_503_cast_fp16 = reshape(shape = var_6148, x = x_897_cast_fp16)[name = string("input_503_cast_fp16")]; + tensor linear_197_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_attention_attention_to_out_weight_to_fp16, x = input_503_cast_fp16)[name = string("linear_197_cast_fp16")]; + tensor input_505_cast_fp16 = add(x = linear_197_cast_fp16, y = x_869_cast_fp16)[name = string("input_505_cast_fp16")]; + tensor linear_198_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_0_weight_to_fp16, x = input_505_cast_fp16)[name = string("linear_198_cast_fp16")]; + string input_509_mode_0 = const()[name = string("input_509_mode_0"), val = string("EXACT")]; + tensor input_509_cast_fp16 = gelu(mode = input_509_mode_0, x = linear_198_cast_fp16)[name = string("input_509_cast_fp16")]; + tensor linear_199_cast_fp16 = linear(bias = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_bias_to_fp16, weight = unet_wrap_kdiffusion_net_blocks_2_feed_forward_2_weight_to_fp16, x = input_509_cast_fp16)[name = string("linear_199_cast_fp16")]; + tensor x_899_cast_fp16 = add(x = linear_199_cast_fp16, y = input_505_cast_fp16)[name = string("x_899_cast_fp16")]; + tensor var_6157_axes_0 = const()[name = string("op_6157_axes_0"), val = tensor([1])]; + bool var_6157_keep_dims_0 = const()[name = string("op_6157_keep_dims_0"), val = bool(false)]; + tensor var_6157_cast_fp16 = reduce_mean(axes = var_6157_axes_0, keep_dims = var_6157_keep_dims_0, x = x_899_cast_fp16)[name = string("op_6157_cast_fp16")]; + tensor x_901_axes_0 = const()[name = string("x_901_axes_0"), val = tensor([1])]; + tensor x_901_cast_fp16 = expand_dims(axes = x_901_axes_0, x = var_6157_cast_fp16)[name = string("x_901_cast_fp16")]; + tensor var_6159 = const()[name = string("op_6159"), val = tensor([0, 2, 1])]; + string x_903_pad_type_0 = const()[name = string("x_903_pad_type_0"), val = string("valid")]; + tensor x_903_strides_0 = const()[name = string("x_903_strides_0"), val = tensor([1])]; + tensor x_903_pad_0 = const()[name = string("x_903_pad_0"), val = tensor([0, 0])]; + tensor x_903_dilations_0 = const()[name = string("x_903_dilations_0"), val = tensor([1])]; + int32 x_903_groups_0 = const()[name = string("x_903_groups_0"), val = int32(1)]; + tensor input_cast_fp16 = transpose(perm = var_6159, x = x_901_cast_fp16)[name = string("transpose_121")]; + tensor x_903_cast_fp16 = conv(bias = unet_wrap_kdiffusion_net_to_out_1_bias_to_fp16, dilations = x_903_dilations_0, groups = x_903_groups_0, pad = x_903_pad_0, pad_type = x_903_pad_type_0, strides = x_903_strides_0, weight = unet_wrap_kdiffusion_net_to_out_1_weight_to_fp16, x = input_cast_fp16)[name = string("x_903_cast_fp16")]; + tensor x_pred_perm_0 = const()[name = string("x_pred_perm_0"), val = tensor([0, -1, -2])]; + tensor c_skip_to_fp16 = const()[name = string("c_skip_to_fp16"), val = tensor([[[0x1.fecp-1]]])]; + tensor var_6167_cast_fp16 = mul(x = c_skip_to_fp16, y = x_noisy_cast_fp16)[name = string("op_6167_cast_fp16")]; + tensor c_out_to_fp16 = const()[name = string("c_out_to_fp16"), val = tensor([[[0x1.38p-9]]])]; + tensor x_pred_cast_fp16 = transpose(perm = x_pred_perm_0, x = x_903_cast_fp16)[name = string("transpose_120")]; + tensor var_6168_cast_fp16 = mul(x = c_out_to_fp16, y = x_pred_cast_fp16)[name = string("op_6168_cast_fp16")]; + tensor x_mid_dn_cast_fp16 = add(x = var_6167_cast_fp16, y = var_6168_cast_fp16)[name = string("x_mid_dn_cast_fp16")]; + tensor var_6171_cast_fp16 = sub(x = x_noisy_cast_fp16, y = x_mid_dn_cast_fp16)[name = string("op_6171_cast_fp16")]; + tensor _inversed_d_mid_y_0_to_fp16 = const()[name = string("_inversed_d_mid_y_0_to_fp16"), val = tensor([0x1.a44p+8])]; + tensor _inversed_d_mid_cast_fp16 = mul(x = var_6171_cast_fp16, y = _inversed_d_mid_y_0_to_fp16)[name = string("_inversed_d_mid_cast_fp16")]; + tensor var_6177_to_fp16 = const()[name = string("op_6177_to_fp16"), val = tensor([-0x1.37cp-8])]; + tensor var_6178_cast_fp16 = mul(x = _inversed_d_mid_cast_fp16, y = var_6177_to_fp16)[name = string("op_6178_cast_fp16")]; + tensor x_cast_fp16 = add(x = x_noisy_13_cast_fp16, y = var_6178_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_6183_begin_0 = const()[name = string("op_6183_begin_0"), val = tensor([3, 0, 0, 0])]; + tensor var_6183_end_0 = const()[name = string("op_6183_end_0"), val = tensor([4, 1, 1, 256])]; + tensor var_6183_end_mask_0 = const()[name = string("op_6183_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_6183_squeeze_mask_0 = const()[name = string("op_6183_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor var_6183_cast_fp16 = slice_by_index(begin = var_6183_begin_0, end = var_6183_end_0, end_mask = var_6183_end_mask_0, squeeze_mask = var_6183_squeeze_mask_0, x = noises_aux_to_fp16)[name = string("op_6183_cast_fp16")]; + fp16 var_6186_to_fp16 = const()[name = string("op_6186_to_fp16"), val = fp16(0x1.a34p-14)]; + tensor var_6187_cast_fp16 = mul(x = var_6183_cast_fp16, y = var_6186_to_fp16)[name = string("op_6187_cast_fp16")]; + tensor var_6189 = add(x = x_cast_fp16, y = var_6187_cast_fp16)[name = string("op_6189_cast_fp16")]; + } -> (var_6189); +} \ No newline at end of file diff --git a/iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/weights/weight.bin b/iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..5d524e318268dea3c586fd0de5ce641710361300 --- /dev/null +++ b/iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f823b5c638d2eb2fd91bf8e4efe4a90b2e1d3d9e2f5ab40e7e93cb03cd212aca +size 49361856 diff --git a/iteration_3/compiled/fused_f0n_har_source.mlmodelc/analytics/coremldata.bin 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b/iteration_3/compiled/fused_f0n_har_source.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..21a5465c1b48e3880275455056d184a15c8605c0 --- /dev/null +++ b/iteration_3/compiled/fused_f0n_har_source.mlmodelc/metadata.json @@ -0,0 +1,113 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Float32", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32)", + "shortDescription" : "", + "shape" : "[]", + "name" : "f0", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32)", + "shortDescription" : "", + "shape" : "[]", + "name" : "var_496", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32)", + "shortDescription" : "", + "shape" : "[]", + "name" : "var_537", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 9, + "mlProgramOperationTypeHistogram" : { + "Ios18.expandDims" : 5, + "Ios18.mul" : 24, + "UpsampleNearestNeighbor" : 3, + "Ios18.sin" : 1, + "Split" : 12, + "Ios18.greater" : 1, + "Ios18.add" : 30, + "Ios18.reshape" : 12, + "Ios18.instanceNorm" : 11, + "Ios16.upsampleBilinear" : 1, + "Ios18.leakyRelu" : 12, + "Ios18.linear" : 13, + "Ios18.conv" : 16, + "Ios18.lstm" : 1, + "Ios18.transpose" : 7, + "Ios18.cast" : 1, + "Ios18.tanh" : 1, + "Ios18.convTranspose" : 2, + "Ios18.sliceByIndex" : 2, + "Ios16.cumsum" : 1, + "Ios18.squeeze" : 6 + }, + "computePrecision" : "Mixed (Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "15.0", + "tvOS" : "18.0", + "visionOS" : "2.0", + "watchOS" : "11.0", + "iOS" : "18.0", + "macCatalyst" : "18.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-05-08", + "com.github.apple.coremltools.source" : "torch==2.11.0", + "com.github.apple.coremltools.version" : "9.0", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "inputSchema" : [ + { + "dataType" : "Float32", + "hasShapeFlexibility" : "1", + "isOptional" : "0", + "shapeFlexibility" : "1 × 640 × 1...2048", + "shapeRange" : "[[1, 1], [640, 640], [1, 2048]]", + "formattedType" : "MultiArray (Float32 1 × 640 × 147)", + "type" : "MultiArray", + "shape" : "[1, 640, 147]", + "name" : "en", + "shortDescription" : "" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 128)", + "shortDescription" : "", + "shape" : "[1, 128]", + "name" : "s", + "type" : "MultiArray" + } + ], + "generatedClassName" : "fused_f0n_har_source", + "method" : "predict" + } +] \ No newline at end of file diff --git a/iteration_3/compiled/fused_f0n_har_source.mlmodelc/model.mil b/iteration_3/compiled/fused_f0n_har_source.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..54f44459e4bda0cef0af90d735a3b33a10c09942 --- /dev/null +++ b/iteration_3/compiled/fused_f0n_har_source.mlmodelc/model.mil @@ -0,0 +1,431 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor en, tensor s) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"en", [1, 640, 147]}}), ("RangeDims", {{"en", [[1, 1], [640, 640], [1, 2048]]}})))] { + tensor f0n_wrap_predictor_F0_0_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_F0_0_norm1_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor f0n_wrap_predictor_F0_0_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_F0_0_norm1_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4224)))]; + tensor f0n_wrap_predictor_F0_0_conv1_bias = const()[name = string("f0n_wrap_predictor_F0_0_conv1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(528576)))]; + tensor f0n_wrap_predictor_F0_0_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_F0_0_norm2_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530688)))]; + tensor f0n_wrap_predictor_F0_0_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_F0_0_norm2_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534848)))]; + tensor f0n_wrap_predictor_F0_0_conv2_bias = const()[name = string("f0n_wrap_predictor_F0_0_conv2_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1059200)))]; + tensor f0n_wrap_predictor_F0_1_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_F0_1_norm1_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1061312)))]; + tensor f0n_wrap_predictor_F0_1_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_F0_1_norm1_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065472)))]; + tensor f0n_wrap_predictor_F0_1_pool_bias = const()[name = string("f0n_wrap_predictor_F0_1_pool_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1589824)))]; + tensor f0n_wrap_predictor_F0_1_conv1_bias = const()[name = string("f0n_wrap_predictor_F0_1_conv1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1591936)))]; + tensor f0n_wrap_predictor_F0_1_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_F0_1_norm2_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1593024)))]; + tensor f0n_wrap_predictor_F0_1_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_F0_1_norm2_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1595136)))]; + tensor f0n_wrap_predictor_F0_1_conv2_bias = const()[name = string("f0n_wrap_predictor_F0_1_conv2_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1857344)))]; + tensor f0n_wrap_predictor_F0_2_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_F0_2_norm1_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1858432)))]; + tensor f0n_wrap_predictor_F0_2_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_F0_2_norm1_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1860544)))]; + tensor f0n_wrap_predictor_F0_2_conv1_bias = const()[name = string("f0n_wrap_predictor_F0_2_conv1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2122752)))]; + tensor f0n_wrap_predictor_F0_2_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_F0_2_norm2_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2123840)))]; + tensor f0n_wrap_predictor_F0_2_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_F0_2_norm2_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2125952)))]; + tensor f0n_wrap_predictor_F0_2_conv2_bias = const()[name = string("f0n_wrap_predictor_F0_2_conv2_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2388160)))]; + tensor f0n_wrap_predictor_F0_proj_bias = const()[name = string("f0n_wrap_predictor_F0_proj_bias"), val = tensor([0x1.ad0bfcp-4])]; + tensor f0n_wrap_predictor_F0_proj_weight = const()[name = string("f0n_wrap_predictor_F0_proj_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2389248)))]; + tensor f0n_wrap_predictor_N_0_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_N_0_norm1_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2390336)))]; + tensor f0n_wrap_predictor_N_0_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_N_0_norm1_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2394496)))]; + tensor f0n_wrap_predictor_N_0_conv1_bias = const()[name = string("f0n_wrap_predictor_N_0_conv1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2918848)))]; + tensor f0n_wrap_predictor_N_0_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_N_0_norm2_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2920960)))]; + tensor f0n_wrap_predictor_N_0_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_N_0_norm2_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2925120)))]; + tensor f0n_wrap_predictor_N_0_conv2_bias = const()[name = string("f0n_wrap_predictor_N_0_conv2_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3449472)))]; + tensor f0n_wrap_predictor_N_1_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_N_1_norm1_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3451584)))]; + tensor f0n_wrap_predictor_N_1_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_N_1_norm1_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455744)))]; + tensor f0n_wrap_predictor_N_1_pool_bias = const()[name = string("f0n_wrap_predictor_N_1_pool_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3980096)))]; + tensor f0n_wrap_predictor_N_1_conv1_bias = const()[name = string("f0n_wrap_predictor_N_1_conv1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3982208)))]; + tensor f0n_wrap_predictor_N_1_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_N_1_norm2_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3983296)))]; + tensor f0n_wrap_predictor_N_1_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_N_1_norm2_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3985408)))]; + tensor f0n_wrap_predictor_N_1_conv2_bias = const()[name = string("f0n_wrap_predictor_N_1_conv2_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4247616)))]; + tensor f0n_wrap_predictor_N_2_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_N_2_norm1_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4248704)))]; + tensor f0n_wrap_predictor_N_2_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_N_2_norm1_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4250816)))]; + tensor f0n_wrap_predictor_N_2_conv1_bias = const()[name = string("f0n_wrap_predictor_N_2_conv1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4513024)))]; + tensor f0n_wrap_predictor_N_2_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_N_2_norm2_fc_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4514112)))]; + tensor f0n_wrap_predictor_N_2_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_N_2_norm2_fc_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4516224)))]; + tensor f0n_wrap_predictor_N_2_conv2_bias = const()[name = string("f0n_wrap_predictor_N_2_conv2_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4778432)))]; + tensor f0n_wrap_predictor_N_proj_bias = const()[name = string("f0n_wrap_predictor_N_proj_bias"), val = tensor([0x1.6de2cap-4])]; + tensor f0n_wrap_predictor_N_proj_weight = const()[name = string("f0n_wrap_predictor_N_proj_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4779520)))]; + tensor har_wrap_harmonics = const()[name = string("har_wrap_harmonics"), val = tensor([[[0x1p+0, 0x1p+1, 0x1.8p+1, 0x1p+2, 0x1.4p+2, 0x1.8p+2, 0x1.cp+2, 0x1p+3, 0x1.2p+3]]])]; + tensor har_wrap_l_linear_bias = const()[name = string("har_wrap_l_linear_bias"), val = tensor([0x1.23e7f2p-6])]; + tensor har_wrap_l_linear_weight = const()[name = string("har_wrap_l_linear_weight"), val = tensor([[-0x1.2aaee4p-12, 0x1.3872d6p-4, -0x1.e6ccbep-7, 0x1.e1debcp-8, -0x1.6ca714p-10, 0x1.8ba75p-10, 0x1.b33eecp-9, -0x1.cd2accp-8, 0x1.07f694p-7]])]; + fp32 var_8 = const()[name = string("op_8"), val = fp32(0x1.4f8b58p-17)]; + fp32 var_9 = const()[name = string("op_9"), val = fp32(0x1.99999ap-3)]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([-1, 0, -2])]; + tensor add_0 = const()[name = string("add_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4780608)))]; + tensor add_1 = const()[name = string("add_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4784768)))]; + tensor concat_4 = const()[name = string("concat_4"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4788928)))]; + tensor concat_5 = const()[name = string("concat_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7410432)))]; + tensor concat_6 = const()[name = string("concat_6"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8459072)))]; + tensor concat_7 = const()[name = string("concat_7"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11080576)))]; + tensor x_batch_first_lstm_h0_reshaped = const()[name = string("x_batch_first_lstm_h0_reshaped"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12129216)))]; + string x_batch_first_direction_0 = const()[name = string("x_batch_first_direction_0"), val = string("bidirectional")]; + bool x_batch_first_output_sequence_0 = const()[name = string("x_batch_first_output_sequence_0"), val = bool(true)]; + string x_batch_first_recurrent_activation_0 = const()[name = string("x_batch_first_recurrent_activation_0"), val = string("sigmoid")]; + string x_batch_first_cell_activation_0 = const()[name = string("x_batch_first_cell_activation_0"), val = string("tanh")]; + string x_batch_first_activation_0 = const()[name = string("x_batch_first_activation_0"), val = string("tanh")]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = en)[name = string("transpose_8")]; + tensor x_batch_first_0, tensor x_batch_first_1, tensor x_batch_first_2 = lstm(activation = x_batch_first_activation_0, bias = add_0, bias_back = add_1, cell_activation = x_batch_first_cell_activation_0, direction = x_batch_first_direction_0, initial_c = x_batch_first_lstm_h0_reshaped, initial_h = x_batch_first_lstm_h0_reshaped, output_sequence = x_batch_first_output_sequence_0, recurrent_activation = x_batch_first_recurrent_activation_0, weight_hh = concat_5, weight_hh_back = concat_7, weight_ih = concat_4, weight_ih_back = concat_6, x = transpose_1)[name = string("x_batch_first")]; + tensor x_perm_0 = const()[name = string("x_perm_0"), val = tensor([1, 0, 2])]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([0, -1, -2])]; + tensor h_1 = linear(bias = f0n_wrap_predictor_F0_0_norm1_fc_bias, weight = f0n_wrap_predictor_F0_0_norm1_fc_weight, x = s)[name = string("linear_0")]; + tensor var_84 = const()[name = string("op_84"), val = tensor([1, 1024, 1])]; + tensor h_3 = reshape(shape = var_84, x = h_1)[name = string("h_3")]; + tensor var_86_split_sizes_0 = const()[name = string("op_86_split_sizes_0"), val = tensor([512, 512])]; + int32 var_86_axis_0 = const()[name = string("op_86_axis_0"), val = int32(1)]; + tensor var_86_0, tensor var_86_1 = split(axis = var_86_axis_0, split_sizes = var_86_split_sizes_0, x = h_3)[name = string("op_86")]; + fp32 var_88_promoted = const()[name = string("op_88_promoted"), val = fp32(0x1p+0)]; + tensor var_89 = add(x = var_86_0, y = var_88_promoted)[name = string("op_89")]; + tensor x = transpose(perm = x_perm_0, x = x_batch_first_0)[name = string("transpose_7")]; + tensor input_3 = transpose(perm = input_3_perm_0, x = x)[name = string("transpose_6")]; + tensor var_90 = instance_norm(epsilon = var_8, x = input_3)[name = string("op_90")]; + tensor var_91 = mul(x = var_89, y = var_90)[name = string("op_91")]; + tensor input_5 = add(x = var_91, y = var_86_1)[name = string("input_5")]; + tensor input_7 = leaky_relu(alpha = var_9, x = input_5)[name = string("input_7")]; + tensor weight_1 = const()[name = string("weight_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12131328)))]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([1, 1])]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor input_9 = conv(bias = f0n_wrap_predictor_F0_0_conv1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = weight_1, x = input_7)[name = string("input_9")]; + tensor h_5 = linear(bias = f0n_wrap_predictor_F0_0_norm2_fc_bias, weight = f0n_wrap_predictor_F0_0_norm2_fc_weight, x = s)[name = string("linear_1")]; + tensor var_107 = const()[name = string("op_107"), val = tensor([1, 1024, 1])]; + tensor h_7 = reshape(shape = var_107, x = h_5)[name = string("h_7")]; + tensor var_109_split_sizes_0 = const()[name = string("op_109_split_sizes_0"), val = tensor([512, 512])]; + int32 var_109_axis_0 = const()[name = string("op_109_axis_0"), val = int32(1)]; + tensor var_109_0, tensor var_109_1 = split(axis = var_109_axis_0, split_sizes = var_109_split_sizes_0, x = h_7)[name = string("op_109")]; + fp32 var_111_promoted = const()[name = string("op_111_promoted"), val = fp32(0x1p+0)]; + tensor var_112 = add(x = var_109_0, y = var_111_promoted)[name = string("op_112")]; + tensor var_113 = instance_norm(epsilon = var_8, x = input_9)[name = string("op_113")]; + tensor var_114 = mul(x = var_112, y = var_113)[name = string("op_114")]; + tensor input_11 = add(x = var_114, y = var_109_1)[name = string("input_11")]; + tensor input_13 = leaky_relu(alpha = var_9, x = input_11)[name = string("input_13")]; + tensor weight_3 = const()[name = string("weight_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15277120)))]; + string out_1_pad_type_0 = const()[name = string("out_1_pad_type_0"), val = string("custom")]; + tensor out_1_pad_0 = const()[name = string("out_1_pad_0"), val = tensor([1, 1])]; + tensor out_1_strides_0 = const()[name = string("out_1_strides_0"), val = tensor([1])]; + tensor out_1_dilations_0 = const()[name = string("out_1_dilations_0"), val = tensor([1])]; + int32 out_1_groups_0 = const()[name = string("out_1_groups_0"), val = int32(1)]; + tensor out_1 = conv(bias = f0n_wrap_predictor_F0_0_conv2_bias, dilations = out_1_dilations_0, groups = out_1_groups_0, pad = out_1_pad_0, pad_type = out_1_pad_type_0, strides = out_1_strides_0, weight = weight_3, x = input_13)[name = string("out_1")]; + tensor var_124 = add(x = out_1, y = input_3)[name = string("op_124")]; + fp32 _inversed_input_15_y_0 = const()[name = string("_inversed_input_15_y_0"), val = fp32(0x1.6a09e6p-1)]; + tensor _inversed_input_15 = mul(x = var_124, y = _inversed_input_15_y_0)[name = string("_inversed_input_15")]; + tensor h_9 = linear(bias = f0n_wrap_predictor_F0_1_norm1_fc_bias, weight = f0n_wrap_predictor_F0_1_norm1_fc_weight, x = s)[name = string("linear_2")]; + tensor var_154 = const()[name = string("op_154"), val = tensor([1, 1024, 1])]; + tensor h_11 = reshape(shape = var_154, x = h_9)[name = string("h_11")]; + tensor var_156_split_sizes_0 = const()[name = string("op_156_split_sizes_0"), val = tensor([512, 512])]; + int32 var_156_axis_0 = const()[name = string("op_156_axis_0"), val = int32(1)]; + tensor var_156_0, tensor var_156_1 = split(axis = var_156_axis_0, split_sizes = var_156_split_sizes_0, x = h_11)[name = string("op_156")]; + fp32 var_158_promoted = const()[name = string("op_158_promoted"), val = fp32(0x1p+0)]; + tensor var_159 = add(x = var_156_0, y = var_158_promoted)[name = string("op_159")]; + tensor var_160 = instance_norm(epsilon = var_8, x = _inversed_input_15)[name = string("op_160")]; + tensor var_161 = mul(x = var_159, y = var_160)[name = string("op_161")]; + tensor input_17 = add(x = var_161, y = var_156_1)[name = string("input_17")]; + tensor input_19 = leaky_relu(alpha = var_9, x = input_17)[name = string("input_19")]; + tensor var_164 = const()[name = string("op_164"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18422912)))]; + string conv_transpose_0_pad_type_0 = const()[name = string("conv_transpose_0_pad_type_0"), val = string("custom")]; + tensor conv_transpose_0_pad_0 = const()[name = string("conv_transpose_0_pad_0"), val = tensor([0, 0])]; + tensor conv_transpose_0_strides_0 = const()[name = string("conv_transpose_0_strides_0"), val = tensor([2])]; + int32 conv_transpose_0_groups_0 = const()[name = string("conv_transpose_0_groups_0"), val = int32(512)]; + tensor conv_transpose_0_dilations_0 = const()[name = string("conv_transpose_0_dilations_0"), val = tensor([1])]; + tensor conv_transpose_0 = conv_transpose(bias = f0n_wrap_predictor_F0_1_pool_bias, dilations = conv_transpose_0_dilations_0, groups = conv_transpose_0_groups_0, pad = conv_transpose_0_pad_0, pad_type = conv_transpose_0_pad_type_0, strides = conv_transpose_0_strides_0, weight = var_164, x = input_19)[name = string("conv_transpose_0")]; + tensor input_21_begin_0 = const()[name = string("input_21_begin_0"), val = tensor([0, 0, 1])]; + tensor input_21_end_0 = const()[name = string("input_21_end_0"), val = tensor([0, 0, 0])]; + tensor input_21_begin_mask_0 = const()[name = string("input_21_begin_mask_0"), val = tensor([true, true, false])]; + tensor input_21_end_mask_0 = const()[name = string("input_21_end_mask_0"), val = tensor([true, true, true])]; + tensor input_21 = slice_by_index(begin = input_21_begin_0, begin_mask = input_21_begin_mask_0, end = input_21_end_0, end_mask = input_21_end_mask_0, x = conv_transpose_0)[name = string("input_21")]; + tensor weight_5 = const()[name = string("weight_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18429120)))]; + string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("custom")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([1, 1])]; + tensor input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor([1])]; + tensor input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor([1])]; + int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)]; + tensor input_23 = conv(bias = f0n_wrap_predictor_F0_1_conv1_bias, dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = weight_5, x = input_21)[name = string("input_23")]; + tensor h_13 = linear(bias = f0n_wrap_predictor_F0_1_norm2_fc_bias, weight = f0n_wrap_predictor_F0_1_norm2_fc_weight, x = s)[name = string("linear_3")]; + tensor var_184 = const()[name = string("op_184"), val = tensor([1, 512, 1])]; + tensor h_15 = reshape(shape = var_184, x = h_13)[name = string("h_15")]; + tensor var_186_split_sizes_0 = const()[name = string("op_186_split_sizes_0"), val = tensor([256, 256])]; + int32 var_186_axis_0 = const()[name = string("op_186_axis_0"), val = int32(1)]; + tensor var_186_0, tensor var_186_1 = split(axis = var_186_axis_0, split_sizes = var_186_split_sizes_0, x = h_15)[name = string("op_186")]; + fp32 var_188_promoted = const()[name = string("op_188_promoted"), val = fp32(0x1p+0)]; + tensor var_189 = add(x = var_186_0, y = var_188_promoted)[name = string("op_189")]; + tensor var_190 = instance_norm(epsilon = var_8, x = input_23)[name = string("op_190")]; + tensor var_191 = mul(x = var_189, y = var_190)[name = string("op_191")]; + tensor input_25 = add(x = var_191, y = var_186_1)[name = string("input_25")]; + tensor input_27 = leaky_relu(alpha = var_9, x = input_25)[name = string("input_27")]; + tensor weight_7 = const()[name = string("weight_7"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20002048)))]; + string out_3_pad_type_0 = const()[name = string("out_3_pad_type_0"), val = string("custom")]; + tensor out_3_pad_0 = const()[name = string("out_3_pad_0"), val = tensor([1, 1])]; + tensor out_3_strides_0 = const()[name = string("out_3_strides_0"), val = tensor([1])]; + tensor out_3_dilations_0 = const()[name = string("out_3_dilations_0"), val = tensor([1])]; + int32 out_3_groups_0 = const()[name = string("out_3_groups_0"), val = int32(1)]; + tensor out_3 = conv(bias = f0n_wrap_predictor_F0_1_conv2_bias, dilations = out_3_dilations_0, groups = out_3_groups_0, pad = out_3_pad_0, pad_type = out_3_pad_type_0, strides = out_3_strides_0, weight = weight_7, x = input_27)[name = string("out_3")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = _inversed_input_15)[name = string("expand_dims_0")]; + int32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = int32(2)]; + int32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = int32(1)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor input_29_axes_0 = const()[name = string("input_29_axes_0"), val = tensor([3])]; + tensor input_29 = squeeze(axes = input_29_axes_0, x = upsample_nearest_neighbor_0)[name = string("input_29")]; + tensor weight_9 = const()[name = string("weight_9"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20788544)))]; + string var_208_pad_type_0 = const()[name = string("op_208_pad_type_0"), val = string("valid")]; + tensor var_208_strides_0 = const()[name = string("op_208_strides_0"), val = tensor([1])]; + tensor var_208_pad_0 = const()[name = string("op_208_pad_0"), val = tensor([0, 0])]; + tensor var_208_dilations_0 = const()[name = string("op_208_dilations_0"), val = tensor([1])]; + int32 var_208_groups_0 = const()[name = string("op_208_groups_0"), val = int32(1)]; + tensor var_208 = conv(dilations = var_208_dilations_0, groups = var_208_groups_0, pad = var_208_pad_0, pad_type = var_208_pad_type_0, strides = var_208_strides_0, weight = weight_9, x = input_29)[name = string("op_208")]; + tensor var_209 = add(x = out_3, y = var_208)[name = string("op_209")]; + fp32 _inversed_input_31_y_0 = const()[name = string("_inversed_input_31_y_0"), val = fp32(0x1.6a09e6p-1)]; + tensor _inversed_input_31 = mul(x = var_209, y = _inversed_input_31_y_0)[name = string("_inversed_input_31")]; + tensor h_17 = linear(bias = f0n_wrap_predictor_F0_2_norm1_fc_bias, weight = f0n_wrap_predictor_F0_2_norm1_fc_weight, x = s)[name = string("linear_4")]; + tensor var_230 = const()[name = string("op_230"), val = tensor([1, 512, 1])]; + tensor h_19 = reshape(shape = var_230, x = h_17)[name = string("h_19")]; + tensor var_232_split_sizes_0 = const()[name = string("op_232_split_sizes_0"), val = tensor([256, 256])]; + int32 var_232_axis_0 = const()[name = string("op_232_axis_0"), val = int32(1)]; + tensor var_232_0, tensor var_232_1 = split(axis = var_232_axis_0, split_sizes = var_232_split_sizes_0, x = h_19)[name = string("op_232")]; + fp32 var_234_promoted = const()[name = string("op_234_promoted"), val = fp32(0x1p+0)]; + tensor var_235 = add(x = var_232_0, y = var_234_promoted)[name = string("op_235")]; + tensor var_236 = instance_norm(epsilon = var_8, x = _inversed_input_31)[name = string("op_236")]; + tensor var_237 = mul(x = var_235, y = var_236)[name = string("op_237")]; + tensor input_33 = add(x = var_237, y = var_232_1)[name = string("input_33")]; + tensor input_35 = leaky_relu(alpha = var_9, x = input_33)[name = string("input_35")]; + tensor weight_11 = const()[name = string("weight_11"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21312896)))]; + string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")]; + tensor input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor([1, 1])]; + tensor input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor([1])]; + tensor input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor([1])]; + int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)]; + tensor input_37 = conv(bias = f0n_wrap_predictor_F0_2_conv1_bias, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = weight_11, x = input_35)[name = string("input_37")]; + tensor h_21 = linear(bias = f0n_wrap_predictor_F0_2_norm2_fc_bias, weight = f0n_wrap_predictor_F0_2_norm2_fc_weight, x = s)[name = string("linear_5")]; + tensor var_253 = const()[name = string("op_253"), val = tensor([1, 512, 1])]; + tensor h_23 = reshape(shape = var_253, x = h_21)[name = string("h_23")]; + tensor var_255_split_sizes_0 = const()[name = string("op_255_split_sizes_0"), val = tensor([256, 256])]; + int32 var_255_axis_0 = const()[name = string("op_255_axis_0"), val = int32(1)]; + tensor var_255_0, tensor var_255_1 = split(axis = var_255_axis_0, split_sizes = var_255_split_sizes_0, x = h_23)[name = string("op_255")]; + fp32 var_257_promoted = const()[name = string("op_257_promoted"), val = fp32(0x1p+0)]; + tensor var_258 = add(x = var_255_0, y = var_257_promoted)[name = string("op_258")]; + tensor var_259 = instance_norm(epsilon = var_8, x = input_37)[name = string("op_259")]; + tensor var_260 = mul(x = var_258, y = var_259)[name = string("op_260")]; + tensor input_39 = add(x = var_260, y = var_255_1)[name = string("input_39")]; + tensor input_41 = leaky_relu(alpha = var_9, x = input_39)[name = string("input_41")]; + tensor weight_13 = const()[name = string("weight_13"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22099392)))]; + string out_5_pad_type_0 = const()[name = string("out_5_pad_type_0"), val = string("custom")]; + tensor out_5_pad_0 = const()[name = string("out_5_pad_0"), val = tensor([1, 1])]; + tensor out_5_strides_0 = const()[name = string("out_5_strides_0"), val = tensor([1])]; + tensor out_5_dilations_0 = const()[name = string("out_5_dilations_0"), val = tensor([1])]; + int32 out_5_groups_0 = const()[name = string("out_5_groups_0"), val = int32(1)]; + tensor out_5 = conv(bias = f0n_wrap_predictor_F0_2_conv2_bias, dilations = out_5_dilations_0, groups = out_5_groups_0, pad = out_5_pad_0, pad_type = out_5_pad_type_0, strides = out_5_strides_0, weight = weight_13, x = input_41)[name = string("out_5")]; + tensor var_270 = add(x = out_5, y = _inversed_input_31)[name = string("op_270")]; + fp32 _inversed_input_43_y_0 = const()[name = string("_inversed_input_43_y_0"), val = fp32(0x1.6a09e6p-1)]; + tensor _inversed_input_43 = mul(x = var_270, y = _inversed_input_43_y_0)[name = string("_inversed_input_43")]; + string F0_1_pad_type_0 = const()[name = string("F0_1_pad_type_0"), val = string("valid")]; + tensor F0_1_strides_0 = const()[name = string("F0_1_strides_0"), val = tensor([1])]; + tensor F0_1_pad_0 = const()[name = string("F0_1_pad_0"), val = tensor([0, 0])]; + tensor F0_1_dilations_0 = const()[name = string("F0_1_dilations_0"), val = tensor([1])]; + int32 F0_1_groups_0 = const()[name = string("F0_1_groups_0"), val = int32(1)]; + tensor F0_1 = conv(bias = f0n_wrap_predictor_F0_proj_bias, dilations = F0_1_dilations_0, groups = F0_1_groups_0, pad = F0_1_pad_0, pad_type = F0_1_pad_type_0, strides = F0_1_strides_0, weight = f0n_wrap_predictor_F0_proj_weight, x = _inversed_input_43)[name = string("F0_1")]; + tensor h_25 = linear(bias = f0n_wrap_predictor_N_0_norm1_fc_bias, weight = f0n_wrap_predictor_N_0_norm1_fc_weight, x = s)[name = string("linear_6")]; + tensor var_299 = const()[name = string("op_299"), val = tensor([1, 1024, 1])]; + tensor h_27 = reshape(shape = var_299, x = h_25)[name = string("h_27")]; + tensor var_301_split_sizes_0 = const()[name = string("op_301_split_sizes_0"), val = tensor([512, 512])]; + int32 var_301_axis_0 = const()[name = string("op_301_axis_0"), val = int32(1)]; + tensor var_301_0, tensor var_301_1 = split(axis = var_301_axis_0, split_sizes = var_301_split_sizes_0, x = h_27)[name = string("op_301")]; + fp32 var_303_promoted = const()[name = string("op_303_promoted"), val = fp32(0x1p+0)]; + tensor var_304 = add(x = var_301_0, y = var_303_promoted)[name = string("op_304")]; + tensor var_306 = mul(x = var_304, y = var_90)[name = string("op_306")]; + tensor input_47 = add(x = var_306, y = var_301_1)[name = string("input_47")]; + tensor input_49 = leaky_relu(alpha = var_9, x = input_47)[name = string("input_49")]; + tensor weight_17 = const()[name = string("weight_17"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22885888)))]; + string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")]; + tensor input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor([1, 1])]; + tensor input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor([1])]; + tensor input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor([1])]; + int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)]; + tensor input_51 = conv(bias = f0n_wrap_predictor_N_0_conv1_bias, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = weight_17, x = input_49)[name = string("input_51")]; + tensor h_29 = linear(bias = f0n_wrap_predictor_N_0_norm2_fc_bias, weight = f0n_wrap_predictor_N_0_norm2_fc_weight, x = s)[name = string("linear_7")]; + tensor var_322 = const()[name = string("op_322"), val = tensor([1, 1024, 1])]; + tensor h_31 = reshape(shape = var_322, x = h_29)[name = string("h_31")]; + tensor var_324_split_sizes_0 = const()[name = string("op_324_split_sizes_0"), val = tensor([512, 512])]; + int32 var_324_axis_0 = const()[name = string("op_324_axis_0"), val = int32(1)]; + tensor var_324_0, tensor var_324_1 = split(axis = var_324_axis_0, split_sizes = var_324_split_sizes_0, x = h_31)[name = string("op_324")]; + fp32 var_326_promoted = const()[name = string("op_326_promoted"), val = fp32(0x1p+0)]; + tensor var_327 = add(x = var_324_0, y = var_326_promoted)[name = string("op_327")]; + tensor var_328 = instance_norm(epsilon = var_8, x = input_51)[name = string("op_328")]; + tensor var_329 = mul(x = var_327, y = var_328)[name = string("op_329")]; + tensor input_53 = add(x = var_329, y = var_324_1)[name = string("input_53")]; + tensor input_55 = leaky_relu(alpha = var_9, x = input_53)[name = string("input_55")]; + tensor weight_19 = const()[name = string("weight_19"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26031680)))]; + string out_7_pad_type_0 = const()[name = string("out_7_pad_type_0"), val = string("custom")]; + tensor out_7_pad_0 = const()[name = string("out_7_pad_0"), val = tensor([1, 1])]; + tensor out_7_strides_0 = const()[name = string("out_7_strides_0"), val = tensor([1])]; + tensor out_7_dilations_0 = const()[name = string("out_7_dilations_0"), val = tensor([1])]; + int32 out_7_groups_0 = const()[name = string("out_7_groups_0"), val = int32(1)]; + tensor out_7 = conv(bias = f0n_wrap_predictor_N_0_conv2_bias, dilations = out_7_dilations_0, groups = out_7_groups_0, pad = out_7_pad_0, pad_type = out_7_pad_type_0, strides = out_7_strides_0, weight = weight_19, x = input_55)[name = string("out_7")]; + tensor var_339 = add(x = out_7, y = input_3)[name = string("op_339")]; + fp32 _inversed_input_57_y_0 = const()[name = string("_inversed_input_57_y_0"), val = fp32(0x1.6a09e6p-1)]; + tensor _inversed_input_57 = mul(x = var_339, y = _inversed_input_57_y_0)[name = string("_inversed_input_57")]; + tensor h_33 = linear(bias = f0n_wrap_predictor_N_1_norm1_fc_bias, weight = f0n_wrap_predictor_N_1_norm1_fc_weight, x = s)[name = string("linear_8")]; + tensor var_369 = const()[name = string("op_369"), val = tensor([1, 1024, 1])]; + tensor h_35 = reshape(shape = var_369, x = h_33)[name = string("h_35")]; + tensor var_371_split_sizes_0 = const()[name = string("op_371_split_sizes_0"), val = tensor([512, 512])]; + int32 var_371_axis_0 = const()[name = string("op_371_axis_0"), val = int32(1)]; + tensor var_371_0, tensor var_371_1 = split(axis = var_371_axis_0, split_sizes = var_371_split_sizes_0, x = h_35)[name = string("op_371")]; + fp32 var_373_promoted = const()[name = string("op_373_promoted"), val = fp32(0x1p+0)]; + tensor var_374 = add(x = var_371_0, y = var_373_promoted)[name = string("op_374")]; + tensor var_375 = instance_norm(epsilon = var_8, x = _inversed_input_57)[name = string("op_375")]; + tensor var_376 = mul(x = var_374, y = var_375)[name = string("op_376")]; + tensor input_59 = add(x = var_376, y = var_371_1)[name = string("input_59")]; + tensor input_61 = leaky_relu(alpha = var_9, x = input_59)[name = string("input_61")]; + tensor var_379 = const()[name = string("op_379"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29177472)))]; + string conv_transpose_1_pad_type_0 = const()[name = string("conv_transpose_1_pad_type_0"), val = string("custom")]; + tensor conv_transpose_1_pad_0 = const()[name = string("conv_transpose_1_pad_0"), val = tensor([0, 0])]; + tensor conv_transpose_1_strides_0 = const()[name = string("conv_transpose_1_strides_0"), val = tensor([2])]; + int32 conv_transpose_1_groups_0 = const()[name = string("conv_transpose_1_groups_0"), val = int32(512)]; + tensor conv_transpose_1_dilations_0 = const()[name = string("conv_transpose_1_dilations_0"), val = tensor([1])]; + tensor conv_transpose_1 = conv_transpose(bias = f0n_wrap_predictor_N_1_pool_bias, dilations = conv_transpose_1_dilations_0, groups = conv_transpose_1_groups_0, pad = conv_transpose_1_pad_0, pad_type = conv_transpose_1_pad_type_0, strides = conv_transpose_1_strides_0, weight = var_379, x = input_61)[name = string("conv_transpose_1")]; + tensor input_63_begin_0 = const()[name = string("input_63_begin_0"), val = tensor([0, 0, 1])]; + tensor input_63_end_0 = const()[name = string("input_63_end_0"), val = tensor([0, 0, 0])]; + tensor input_63_begin_mask_0 = const()[name = string("input_63_begin_mask_0"), val = tensor([true, true, false])]; + tensor input_63_end_mask_0 = const()[name = string("input_63_end_mask_0"), val = tensor([true, true, true])]; + tensor input_63 = slice_by_index(begin = input_63_begin_0, begin_mask = input_63_begin_mask_0, end = input_63_end_0, end_mask = input_63_end_mask_0, x = conv_transpose_1)[name = string("input_63")]; + tensor weight_21 = const()[name = string("weight_21"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29183680)))]; + string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")]; + tensor input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor([1, 1])]; + tensor input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor([1])]; + tensor input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor([1])]; + int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)]; + tensor input_65 = conv(bias = f0n_wrap_predictor_N_1_conv1_bias, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = weight_21, x = input_63)[name = string("input_65")]; + tensor h_37 = linear(bias = f0n_wrap_predictor_N_1_norm2_fc_bias, weight = f0n_wrap_predictor_N_1_norm2_fc_weight, x = s)[name = string("linear_9")]; + tensor var_399 = const()[name = string("op_399"), val = tensor([1, 512, 1])]; + tensor h_39 = reshape(shape = var_399, x = h_37)[name = string("h_39")]; + tensor var_401_split_sizes_0 = const()[name = string("op_401_split_sizes_0"), val = tensor([256, 256])]; + int32 var_401_axis_0 = const()[name = string("op_401_axis_0"), val = int32(1)]; + tensor var_401_0, tensor var_401_1 = split(axis = var_401_axis_0, split_sizes = var_401_split_sizes_0, x = h_39)[name = string("op_401")]; + fp32 var_403_promoted = const()[name = string("op_403_promoted"), val = fp32(0x1p+0)]; + tensor var_404 = add(x = var_401_0, y = var_403_promoted)[name = string("op_404")]; + tensor var_405 = instance_norm(epsilon = var_8, x = input_65)[name = string("op_405")]; + tensor var_406 = mul(x = var_404, y = var_405)[name = string("op_406")]; + tensor input_67 = add(x = var_406, y = var_401_1)[name = string("input_67")]; + tensor input_69 = leaky_relu(alpha = var_9, x = input_67)[name = string("input_69")]; + tensor weight_23 = const()[name = string("weight_23"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30756608)))]; + string out_9_pad_type_0 = const()[name = string("out_9_pad_type_0"), val = string("custom")]; + tensor out_9_pad_0 = const()[name = string("out_9_pad_0"), val = tensor([1, 1])]; + tensor out_9_strides_0 = const()[name = string("out_9_strides_0"), val = tensor([1])]; + tensor out_9_dilations_0 = const()[name = string("out_9_dilations_0"), val = tensor([1])]; + int32 out_9_groups_0 = const()[name = string("out_9_groups_0"), val = int32(1)]; + tensor out_9 = conv(bias = f0n_wrap_predictor_N_1_conv2_bias, dilations = out_9_dilations_0, groups = out_9_groups_0, pad = out_9_pad_0, pad_type = out_9_pad_type_0, strides = out_9_strides_0, weight = weight_23, x = input_69)[name = string("out_9")]; + tensor expand_dims_1_axes_0 = const()[name = string("expand_dims_1_axes_0"), val = tensor([3])]; + tensor expand_dims_1 = expand_dims(axes = expand_dims_1_axes_0, x = _inversed_input_57)[name = string("expand_dims_1")]; + int32 upsample_nearest_neighbor_1_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_1_scale_factor_height_0"), val = int32(2)]; + int32 upsample_nearest_neighbor_1_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_1_scale_factor_width_0"), val = int32(1)]; + tensor upsample_nearest_neighbor_1 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_1_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_1_scale_factor_width_0, x = expand_dims_1)[name = string("upsample_nearest_neighbor_1")]; + tensor input_71_axes_0 = const()[name = string("input_71_axes_0"), val = tensor([3])]; + tensor input_71 = squeeze(axes = input_71_axes_0, x = upsample_nearest_neighbor_1)[name = string("input_71")]; + tensor weight_25 = const()[name = string("weight_25"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31543104)))]; + string var_423_pad_type_0 = const()[name = string("op_423_pad_type_0"), val = string("valid")]; + tensor var_423_strides_0 = const()[name = string("op_423_strides_0"), val = tensor([1])]; + tensor var_423_pad_0 = const()[name = string("op_423_pad_0"), val = tensor([0, 0])]; + tensor var_423_dilations_0 = const()[name = string("op_423_dilations_0"), val = tensor([1])]; + int32 var_423_groups_0 = const()[name = string("op_423_groups_0"), val = int32(1)]; + tensor var_423 = conv(dilations = var_423_dilations_0, groups = var_423_groups_0, pad = var_423_pad_0, pad_type = var_423_pad_type_0, strides = var_423_strides_0, weight = weight_25, x = input_71)[name = string("op_423")]; + tensor var_424 = add(x = out_9, y = var_423)[name = string("op_424")]; + fp32 _inversed_input_73_y_0 = const()[name = string("_inversed_input_73_y_0"), val = fp32(0x1.6a09e6p-1)]; + tensor _inversed_input_73 = mul(x = var_424, y = _inversed_input_73_y_0)[name = string("_inversed_input_73")]; + tensor h_41 = linear(bias = f0n_wrap_predictor_N_2_norm1_fc_bias, weight = f0n_wrap_predictor_N_2_norm1_fc_weight, x = s)[name = string("linear_10")]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 512, 1])]; + tensor h_43 = reshape(shape = var_445, x = h_41)[name = string("h_43")]; + tensor var_447_split_sizes_0 = const()[name = string("op_447_split_sizes_0"), val = tensor([256, 256])]; + int32 var_447_axis_0 = const()[name = string("op_447_axis_0"), val = int32(1)]; + tensor var_447_0, tensor var_447_1 = split(axis = var_447_axis_0, split_sizes = var_447_split_sizes_0, x = h_43)[name = string("op_447")]; + fp32 var_449_promoted = const()[name = string("op_449_promoted"), val = fp32(0x1p+0)]; + tensor var_450 = add(x = var_447_0, y = var_449_promoted)[name = string("op_450")]; + tensor var_451 = instance_norm(epsilon = var_8, x = _inversed_input_73)[name = string("op_451")]; + tensor var_452 = mul(x = var_450, y = var_451)[name = string("op_452")]; + tensor input_75 = add(x = var_452, y = var_447_1)[name = string("input_75")]; + tensor input_77 = leaky_relu(alpha = var_9, x = input_75)[name = string("input_77")]; + tensor weight_27 = const()[name = string("weight_27"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32067456)))]; + string input_79_pad_type_0 = const()[name = string("input_79_pad_type_0"), val = string("custom")]; + tensor input_79_pad_0 = const()[name = string("input_79_pad_0"), val = tensor([1, 1])]; + tensor input_79_strides_0 = const()[name = string("input_79_strides_0"), val = tensor([1])]; + tensor input_79_dilations_0 = const()[name = string("input_79_dilations_0"), val = tensor([1])]; + int32 input_79_groups_0 = const()[name = string("input_79_groups_0"), val = int32(1)]; + tensor input_79 = conv(bias = f0n_wrap_predictor_N_2_conv1_bias, dilations = input_79_dilations_0, groups = input_79_groups_0, pad = input_79_pad_0, pad_type = input_79_pad_type_0, strides = input_79_strides_0, weight = weight_27, x = input_77)[name = string("input_79")]; + tensor h_45 = linear(bias = f0n_wrap_predictor_N_2_norm2_fc_bias, weight = f0n_wrap_predictor_N_2_norm2_fc_weight, x = s)[name = string("linear_11")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([1, 512, 1])]; + tensor h = reshape(shape = var_468, x = h_45)[name = string("h")]; + tensor var_470_split_sizes_0 = const()[name = string("op_470_split_sizes_0"), val = tensor([256, 256])]; + int32 var_470_axis_0 = const()[name = string("op_470_axis_0"), val = int32(1)]; + tensor var_470_0, tensor var_470_1 = split(axis = var_470_axis_0, split_sizes = var_470_split_sizes_0, x = h)[name = string("op_470")]; + fp32 var_472_promoted = const()[name = string("op_472_promoted"), val = fp32(0x1p+0)]; + tensor var_473 = add(x = var_470_0, y = var_472_promoted)[name = string("op_473")]; + tensor var_474 = instance_norm(epsilon = var_8, x = input_79)[name = string("op_474")]; + tensor var_475 = mul(x = var_473, y = var_474)[name = string("op_475")]; + tensor input_81 = add(x = var_475, y = var_470_1)[name = string("input_81")]; + tensor input_83 = leaky_relu(alpha = var_9, x = input_81)[name = string("input_83")]; + tensor weight_29 = const()[name = string("weight_29"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32853952)))]; + string out_pad_type_0 = const()[name = string("out_pad_type_0"), val = string("custom")]; + tensor out_pad_0 = const()[name = string("out_pad_0"), val = tensor([1, 1])]; + tensor out_strides_0 = const()[name = string("out_strides_0"), val = tensor([1])]; + tensor out_dilations_0 = const()[name = string("out_dilations_0"), val = tensor([1])]; + int32 out_groups_0 = const()[name = string("out_groups_0"), val = int32(1)]; + tensor out = conv(bias = f0n_wrap_predictor_N_2_conv2_bias, dilations = out_dilations_0, groups = out_groups_0, pad = out_pad_0, pad_type = out_pad_type_0, strides = out_strides_0, weight = weight_29, x = input_83)[name = string("out")]; + tensor var_485 = add(x = out, y = _inversed_input_73)[name = string("op_485")]; + fp32 _inversed_input_85_y_0 = const()[name = string("_inversed_input_85_y_0"), val = fp32(0x1.6a09e6p-1)]; + tensor _inversed_input_85 = mul(x = var_485, y = _inversed_input_85_y_0)[name = string("_inversed_input_85")]; + string N_1_pad_type_0 = const()[name = string("N_1_pad_type_0"), val = string("valid")]; + tensor N_1_strides_0 = const()[name = string("N_1_strides_0"), val = tensor([1])]; + tensor N_1_pad_0 = const()[name = string("N_1_pad_0"), val = tensor([0, 0])]; + tensor N_1_dilations_0 = const()[name = string("N_1_dilations_0"), val = tensor([1])]; + int32 N_1_groups_0 = const()[name = string("N_1_groups_0"), val = int32(1)]; + tensor N_1 = conv(bias = f0n_wrap_predictor_N_proj_bias, dilations = N_1_dilations_0, groups = N_1_groups_0, pad = N_1_pad_0, pad_type = N_1_pad_type_0, strides = N_1_strides_0, weight = f0n_wrap_predictor_N_proj_weight, x = _inversed_input_85)[name = string("N_1")]; + tensor f0_axes_0 = const()[name = string("f0_axes_0"), val = tensor([1])]; + tensor f0 = squeeze(axes = f0_axes_0, x = F0_1)[name = string("f0")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496 = squeeze(axes = var_496_axes_0, x = N_1)[name = string("op_496")]; + fp32 var_501 = const()[name = string("op_501"), val = fp32(0x1.4p+3)]; + int32 var_506 = const()[name = string("op_506"), val = int32(1)]; + tensor f0_lo_axes_0 = const()[name = string("f0_lo_axes_0"), val = tensor([-1])]; + tensor f0_lo = expand_dims(axes = f0_lo_axes_0, x = f0)[name = string("f0_lo")]; + tensor fn_lo = mul(x = f0_lo, y = har_wrap_harmonics)[name = string("fn_lo")]; + fp32 _inversed_rad_lo_y_0 = const()[name = string("_inversed_rad_lo_y_0"), val = fp32(0x1.5d867cp-15)]; + tensor _inversed_rad_lo = mul(x = fn_lo, y = _inversed_rad_lo_y_0)[name = string("_inversed_rad_lo")]; + bool var_514_exclusive_0 = const()[name = string("op_514_exclusive_0"), val = bool(false)]; + bool var_514_reverse_0 = const()[name = string("op_514_reverse_0"), val = bool(false)]; + tensor var_514 = cumsum(axis = var_506, exclusive = var_514_exclusive_0, reverse = var_514_reverse_0, x = _inversed_rad_lo)[name = string("op_514")]; + fp32 var_515 = const()[name = string("op_515"), val = fp32(0x1.921fb6p+2)]; + tensor phase_lo = mul(x = var_514, y = var_515)[name = string("phase_lo")]; + fp32 var_517_promoted = const()[name = string("op_517_promoted"), val = fp32(0x1.2cp+8)]; + tensor var_518 = mul(x = phase_lo, y = var_517_promoted)[name = string("op_518")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor expand_dims_2_axes_0 = const()[name = string("expand_dims_2_axes_0"), val = tensor([3])]; + tensor input_87 = transpose(perm = input_87_perm_0, x = var_518)[name = string("transpose_5")]; + tensor expand_dims_2 = expand_dims(axes = expand_dims_2_axes_0, x = input_87)[name = string("expand_dims_2")]; + int32 upsample_bilinear_0_scale_factor_height_0 = const()[name = string("upsample_bilinear_0_scale_factor_height_0"), val = int32(300)]; + bool upsample_bilinear_0_align_corners_0 = const()[name = string("upsample_bilinear_0_align_corners_0"), val = bool(false)]; + int32 upsample_bilinear_0_scale_factor_width_0 = const()[name = string("upsample_bilinear_0_scale_factor_width_0"), val = int32(1)]; + tensor upsample_bilinear_0 = upsample_bilinear(align_corners = upsample_bilinear_0_align_corners_0, scale_factor_height = upsample_bilinear_0_scale_factor_height_0, scale_factor_width = upsample_bilinear_0_scale_factor_width_0, x = expand_dims_2)[name = string("upsample_bilinear_0")]; + tensor var_521_axes_0 = const()[name = string("op_521_axes_0"), val = tensor([3])]; + tensor var_521 = squeeze(axes = var_521_axes_0, x = upsample_bilinear_0)[name = string("op_521")]; + tensor var_523 = sin(x = var_521)[name = string("op_523")]; + fp32 var_524 = const()[name = string("op_524"), val = fp32(0x1.99999ap-4)]; + tensor sines = mul(x = var_523, y = var_524)[name = string("sines")]; + tensor var_526 = greater(x = fn_lo, y = var_501)[name = string("op_526")]; + string uv_lo_dtype_0 = const()[name = string("uv_lo_dtype_0"), val = string("fp32")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + tensor expand_dims_3_axes_0 = const()[name = string("expand_dims_3_axes_0"), val = tensor([3])]; + tensor uv_lo = cast(dtype = uv_lo_dtype_0, x = var_526)[name = string("cast_26")]; + tensor input_89 = transpose(perm = input_89_perm_0, x = uv_lo)[name = string("transpose_4")]; + tensor expand_dims_3 = expand_dims(axes = expand_dims_3_axes_0, x = input_89)[name = string("expand_dims_3")]; + int32 upsample_nearest_neighbor_2_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_2_scale_factor_height_0"), val = int32(300)]; + int32 upsample_nearest_neighbor_2_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_2_scale_factor_width_0"), val = int32(1)]; + tensor upsample_nearest_neighbor_2 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_2_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_2_scale_factor_width_0, x = expand_dims_3)[name = string("upsample_nearest_neighbor_2")]; + tensor var_530_axes_0 = const()[name = string("op_530_axes_0"), val = tensor([3])]; + tensor var_530 = squeeze(axes = var_530_axes_0, x = upsample_nearest_neighbor_2)[name = string("op_530")]; + tensor input_91 = mul(x = sines, y = var_530)[name = string("input_91")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([0, 2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = input_91)[name = string("transpose_3")]; + tensor input = linear(bias = har_wrap_l_linear_bias, weight = har_wrap_l_linear_weight, x = transpose_0)[name = string("linear_12")]; + tensor sine_merge = tanh(x = input)[name = string("sine_merge")]; + tensor var_537_perm_0 = const()[name = string("op_537_perm_0"), val = tensor([0, 2, 1])]; + tensor var_537 = transpose(perm = var_537_perm_0, x = sine_merge)[name = string("transpose_2")]; + } -> (f0, var_496, var_537); +} \ No newline at end of file diff --git a/iteration_3/compiled/fused_f0n_har_source.mlmodelc/weights/weight.bin b/iteration_3/compiled/fused_f0n_har_source.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..d933cbff16915732e7d34641b1ae6b18e3070cde --- /dev/null +++ b/iteration_3/compiled/fused_f0n_har_source.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce01b0523d8e925108b5f9ba113d5ff1721bb3e5e25f3eb6d8a2dfaa56876c59 +size 33640448 diff --git a/iteration_3/compiled/ref_encoder_fp16.mlmodelc/analytics/coremldata.bin b/iteration_3/compiled/ref_encoder_fp16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..8fbf6abdbf7c5f3a899652a170ebfb46869c9240 --- /dev/null +++ b/iteration_3/compiled/ref_encoder_fp16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfe5cf2e8252bfc82f19da3cd60f265c31abde5d69c982233a46244585677ad1 +size 243 diff --git a/iteration_3/compiled/ref_encoder_fp16.mlmodelc/coremldata.bin b/iteration_3/compiled/ref_encoder_fp16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..79c65713e62be06a918c0976cff02d653e617570 --- /dev/null +++ b/iteration_3/compiled/ref_encoder_fp16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b9d2f56f7354ae06e94e716169c3d8e1ff4c35b5dca1de8a548bbcbd6feb876b +size 372 diff --git a/iteration_3/compiled/ref_encoder_fp16.mlmodelc/metadata.json b/iteration_3/compiled/ref_encoder_fp16.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2229c5ae05bde97142c915a3f4a189053af73cd4 --- /dev/null +++ b/iteration_3/compiled/ref_encoder_fp16.mlmodelc/metadata.json @@ -0,0 +1,72 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Float16", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 256)", + "shortDescription" : "", + "shape" : "[1, 256]", + "name" : "var_794", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 9, + "mlProgramOperationTypeHistogram" : { + "Ios18.linear" : 2, + "Ios18.conv" : 34, + "Ios18.leakyRelu" : 20, + "Ios18.expandDims" : 4, + "Ios18.concat" : 5, + "Ios18.add" : 8, + "Ios16.reduceMean" : 2, + "Ios18.avgPool" : 8, + "Ios18.sliceByIndex" : 4, + "Ios18.cast" : 1, + "Ios18.reshape" : 2, + "Ios18.mul" : 8 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "15.0", + "tvOS" : "18.0", + "visionOS" : "2.0", + "watchOS" : "11.0", + "iOS" : "18.0", + "macCatalyst" : "18.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-05-08", + "com.github.apple.coremltools.source" : "torch==2.11.0", + "com.github.apple.coremltools.version" : "9.0", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1 × 80 × 231)", + "shortDescription" : "", + "shape" : "[1, 1, 80, 231]", + "name" : "mel", + "type" : "MultiArray" + } + ], + "generatedClassName" : "ref_encoder_fp16", + "method" : "predict" + } +] \ No newline at end of file diff --git a/iteration_3/compiled/ref_encoder_fp16.mlmodelc/model.mil b/iteration_3/compiled/ref_encoder_fp16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..6ae39372f3a5dccad4428aadaf961042b08f3ca3 --- /dev/null +++ b/iteration_3/compiled/ref_encoder_fp16.mlmodelc/model.mil @@ -0,0 +1,433 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor mel) { + int32 var_5 = const()[name = string("op_5"), val = int32(-1)]; + fp32 var_10 = const()[name = string("op_10"), val = fp32(0x1.99999ap-3)]; + string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("custom")]; + tensor input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor([1, 1])]; + tensor input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor([1, 1])]; + int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)]; + string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")]; + tensor weight_3_to_fp16 = const()[name = string("weight_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor style_encoder_shared_0_bias_to_fp16 = const()[name = string("style_encoder_shared_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1280)))]; + tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_121")]; + tensor input_1_cast_fp16 = conv(bias = style_encoder_shared_0_bias_to_fp16, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = weight_3_to_fp16, x = mel_to_fp16)[name = string("input_1_cast_fp16")]; + string x_1_pad_type_0 = const()[name = string("x_1_pad_type_0"), val = string("valid")]; + tensor x_1_strides_0 = const()[name = string("x_1_strides_0"), val = tensor([1, 1])]; + tensor x_1_pad_0 = const()[name = string("x_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor x_1_dilations_0 = const()[name = string("x_1_dilations_0"), val = tensor([1, 1])]; + int32 x_1_groups_0 = const()[name = string("x_1_groups_0"), val = int32(1)]; + tensor weight_7_to_fp16 = const()[name = string("weight_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1472)))]; + tensor x_1_cast_fp16 = conv(dilations = x_1_dilations_0, groups = x_1_groups_0, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = x_1_strides_0, weight = weight_7_to_fp16, x = input_1_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_81_begin_0 = const()[name = string("op_81_begin_0"), val = tensor([0, 0, 0, -1])]; + tensor var_81_end_0 = const()[name = string("op_81_end_0"), val = tensor([1, 128, 80, 231])]; + tensor var_81_end_mask_0 = const()[name = string("op_81_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_81_squeeze_mask_0 = const()[name = string("op_81_squeeze_mask_0"), val = tensor([false, false, false, true])]; + tensor var_81_cast_fp16 = slice_by_index(begin = var_81_begin_0, end = var_81_end_0, end_mask = var_81_end_mask_0, squeeze_mask = var_81_squeeze_mask_0, x = x_1_cast_fp16)[name = string("op_81_cast_fp16")]; + tensor var_82_axes_0 = const()[name = string("op_82_axes_0"), val = tensor([-1])]; + tensor var_82_cast_fp16 = expand_dims(axes = var_82_axes_0, x = var_81_cast_fp16)[name = string("op_82_cast_fp16")]; + bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)]; + tensor x_3_cast_fp16 = concat(axis = var_5, interleave = x_3_interleave_0, values = (x_1_cast_fp16, var_82_cast_fp16))[name = string("x_3_cast_fp16")]; + tensor var_85 = const()[name = string("op_85"), val = tensor([2, 2])]; + tensor var_86 = const()[name = string("op_86"), val = tensor([2, 2])]; + string var_88_pad_type_0 = const()[name = string("op_88_pad_type_0"), val = string("custom")]; + tensor var_88_pad_0 = const()[name = string("op_88_pad_0"), val = tensor([0, 0, 0, 0])]; + bool var_88_exclude_padding_from_average_0 = const()[name = string("op_88_exclude_padding_from_average_0"), val = bool(false)]; + bool var_88_ceil_mode_0 = const()[name = string("op_88_ceil_mode_0"), val = bool(false)]; + tensor var_88_cast_fp16 = avg_pool(ceil_mode = var_88_ceil_mode_0, exclude_padding_from_average = var_88_exclude_padding_from_average_0, kernel_sizes = var_85, pad = var_88_pad_0, pad_type = var_88_pad_type_0, strides = var_86, x = x_3_cast_fp16)[name = string("op_88_cast_fp16")]; + tensor input_3_cast_fp16 = leaky_relu(alpha = var_10, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("custom")]; + tensor input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor([1, 1])]; + tensor input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor([1, 1])]; + int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)]; + tensor weight_11_to_fp16 = const()[name = string("weight_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17920)))]; + tensor style_encoder_shared_1_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91712)))]; + tensor input_5_cast_fp16 = conv(bias = style_encoder_shared_1_conv1_bias_to_fp16, dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = weight_11_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")]; + tensor input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor([2, 2])]; + int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(64)]; + tensor input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor([1, 1])]; + tensor weight_15_to_fp16 = const()[name = string("weight_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91904)))]; + tensor style_encoder_shared_1_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_1_downsample_res_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93120)))]; + tensor input_7_cast_fp16 = conv(bias = style_encoder_shared_1_downsample_res_conv_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = weight_15_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor input_9_cast_fp16 = leaky_relu(alpha = var_10, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + string var_133_pad_type_0 = const()[name = string("op_133_pad_type_0"), val = string("custom")]; + tensor var_133_pad_0 = const()[name = string("op_133_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor var_133_strides_0 = const()[name = string("op_133_strides_0"), val = tensor([1, 1])]; + tensor var_133_dilations_0 = const()[name = string("op_133_dilations_0"), val = tensor([1, 1])]; + int32 var_133_groups_0 = const()[name = string("op_133_groups_0"), val = int32(1)]; + tensor weight_19_to_fp16 = const()[name = string("weight_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93312)))]; + tensor style_encoder_shared_1_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240832)))]; + tensor var_133_cast_fp16 = conv(bias = style_encoder_shared_1_conv2_bias_to_fp16, dilations = var_133_dilations_0, groups = var_133_groups_0, pad = var_133_pad_0, pad_type = var_133_pad_type_0, strides = var_133_strides_0, weight = weight_19_to_fp16, x = input_9_cast_fp16)[name = string("op_133_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_88_cast_fp16, y = var_133_cast_fp16)[name = string("x_5_cast_fp16")]; + fp16 _inversed_input_11_y_0_to_fp16 = const()[name = string("_inversed_input_11_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_input_11_cast_fp16 = mul(x = x_5_cast_fp16, y = _inversed_input_11_y_0_to_fp16)[name = string("_inversed_input_11_cast_fp16")]; + string x_7_pad_type_0 = const()[name = string("x_7_pad_type_0"), val = string("valid")]; + tensor x_7_strides_0 = const()[name = string("x_7_strides_0"), val = tensor([1, 1])]; + tensor x_7_pad_0 = const()[name = string("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor x_7_dilations_0 = const()[name = string("x_7_dilations_0"), val = tensor([1, 1])]; + int32 x_7_groups_0 = const()[name = string("x_7_groups_0"), val = int32(1)]; + tensor weight_23_to_fp16 = const()[name = string("weight_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241152)))]; + tensor x_7_cast_fp16 = conv(dilations = x_7_dilations_0, groups = x_7_groups_0, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = x_7_strides_0, weight = weight_23_to_fp16, x = _inversed_input_11_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_169 = const()[name = string("op_169"), val = tensor([2, 2])]; + tensor var_170 = const()[name = string("op_170"), val = tensor([2, 2])]; + string var_172_pad_type_0 = const()[name = string("op_172_pad_type_0"), val = string("custom")]; + tensor var_172_pad_0 = const()[name = string("op_172_pad_0"), val = tensor([0, 0, 0, 0])]; + bool var_172_exclude_padding_from_average_0 = const()[name = string("op_172_exclude_padding_from_average_0"), val = bool(false)]; + bool var_172_ceil_mode_0 = const()[name = string("op_172_ceil_mode_0"), val = bool(false)]; + tensor var_172_cast_fp16 = avg_pool(ceil_mode = var_172_ceil_mode_0, exclude_padding_from_average = var_172_exclude_padding_from_average_0, kernel_sizes = var_169, pad = var_172_pad_0, pad_type = var_172_pad_type_0, strides = var_170, x = x_7_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor input_13_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([1, 1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor weight_27_to_fp16 = const()[name = string("weight_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306752)))]; + tensor style_encoder_shared_2_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601728)))]; + tensor input_15_cast_fp16 = conv(bias = style_encoder_shared_2_conv1_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = weight_27_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("custom")]; + tensor input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor([2, 2])]; + int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(128)]; + tensor input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor([1, 1])]; + tensor weight_31_to_fp16 = const()[name = string("weight_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(602048)))]; + tensor style_encoder_shared_2_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_2_downsample_res_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604416)))]; + tensor input_17_cast_fp16 = conv(bias = style_encoder_shared_2_downsample_res_conv_bias_to_fp16, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = weight_31_to_fp16, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor input_19_cast_fp16 = leaky_relu(alpha = var_10, x = input_17_cast_fp16)[name = string("input_19_cast_fp16")]; + string var_217_pad_type_0 = const()[name = string("op_217_pad_type_0"), val = string("custom")]; + tensor var_217_pad_0 = const()[name = string("op_217_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor var_217_strides_0 = const()[name = string("op_217_strides_0"), val = tensor([1, 1])]; + tensor var_217_dilations_0 = const()[name = string("op_217_dilations_0"), val = tensor([1, 1])]; + int32 var_217_groups_0 = const()[name = string("op_217_groups_0"), val = int32(1)]; + tensor weight_35_to_fp16 = const()[name = string("weight_35_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604736)))]; + tensor style_encoder_shared_2_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1194624)))]; + tensor var_217_cast_fp16 = conv(bias = style_encoder_shared_2_conv2_bias_to_fp16, dilations = var_217_dilations_0, groups = var_217_groups_0, pad = var_217_pad_0, pad_type = var_217_pad_type_0, strides = var_217_strides_0, weight = weight_35_to_fp16, x = input_19_cast_fp16)[name = string("op_217_cast_fp16")]; + tensor x_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_217_cast_fp16)[name = string("x_9_cast_fp16")]; + fp16 _inversed_input_21_y_0_to_fp16 = const()[name = string("_inversed_input_21_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_input_21_cast_fp16 = mul(x = x_9_cast_fp16, y = _inversed_input_21_y_0_to_fp16)[name = string("_inversed_input_21_cast_fp16")]; + string x_11_pad_type_0 = const()[name = string("x_11_pad_type_0"), val = string("valid")]; + tensor x_11_strides_0 = const()[name = string("x_11_strides_0"), val = tensor([1, 1])]; + tensor x_11_pad_0 = const()[name = string("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor x_11_dilations_0 = const()[name = string("x_11_dilations_0"), val = tensor([1, 1])]; + int32 x_11_groups_0 = const()[name = string("x_11_groups_0"), val = int32(1)]; + tensor weight_39_to_fp16 = const()[name = string("weight_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1195200)))]; + tensor x_11_cast_fp16 = conv(dilations = x_11_dilations_0, groups = x_11_groups_0, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = x_11_strides_0, weight = weight_39_to_fp16, x = _inversed_input_21_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_253 = const()[name = string("op_253"), val = tensor([2, 2])]; + tensor var_254 = const()[name = string("op_254"), val = tensor([2, 2])]; + string var_256_pad_type_0 = const()[name = string("op_256_pad_type_0"), val = string("custom")]; + tensor var_256_pad_0 = const()[name = string("op_256_pad_0"), val = tensor([0, 0, 0, 0])]; + bool var_256_exclude_padding_from_average_0 = const()[name = string("op_256_exclude_padding_from_average_0"), val = bool(false)]; + bool var_256_ceil_mode_0 = const()[name = string("op_256_ceil_mode_0"), val = bool(false)]; + tensor var_256_cast_fp16 = avg_pool(ceil_mode = var_256_ceil_mode_0, exclude_padding_from_average = var_256_exclude_padding_from_average_0, kernel_sizes = var_253, pad = var_256_pad_0, pad_type = var_256_pad_type_0, strides = var_254, x = x_11_cast_fp16)[name = string("op_256_cast_fp16")]; + tensor input_23_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")]; + tensor input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor([1, 1])]; + tensor input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor([1, 1])]; + int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)]; + tensor weight_43_to_fp16 = const()[name = string("weight_43_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1457408)))]; + tensor style_encoder_shared_3_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_3_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2637120)))]; + tensor input_25_cast_fp16 = conv(bias = style_encoder_shared_3_conv1_bias_to_fp16, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = weight_43_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")]; + tensor input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor([2, 2])]; + int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(256)]; + tensor input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor([1, 1])]; + tensor weight_47_to_fp16 = const()[name = string("weight_47_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2637696)))]; + tensor style_encoder_shared_3_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_3_downsample_res_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2642368)))]; + tensor input_27_cast_fp16 = conv(bias = style_encoder_shared_3_downsample_res_conv_bias_to_fp16, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = weight_47_to_fp16, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = leaky_relu(alpha = var_10, x = input_27_cast_fp16)[name = string("input_29_cast_fp16")]; + string var_301_pad_type_0 = const()[name = string("op_301_pad_type_0"), val = string("custom")]; + tensor var_301_pad_0 = const()[name = string("op_301_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor var_301_strides_0 = const()[name = string("op_301_strides_0"), val = tensor([1, 1])]; + tensor var_301_dilations_0 = const()[name = string("op_301_dilations_0"), val = tensor([1, 1])]; + int32 var_301_groups_0 = const()[name = string("op_301_groups_0"), val = int32(1)]; + tensor weight_51_to_fp16 = const()[name = string("weight_51_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2642944)))]; + tensor style_encoder_shared_3_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_3_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5002304)))]; + tensor var_301_cast_fp16 = conv(bias = style_encoder_shared_3_conv2_bias_to_fp16, dilations = var_301_dilations_0, groups = var_301_groups_0, pad = var_301_pad_0, pad_type = var_301_pad_type_0, strides = var_301_strides_0, weight = weight_51_to_fp16, x = input_29_cast_fp16)[name = string("op_301_cast_fp16")]; + tensor x_13_cast_fp16 = add(x = var_256_cast_fp16, y = var_301_cast_fp16)[name = string("x_13_cast_fp16")]; + fp16 _inversed_x_15_y_0_to_fp16 = const()[name = string("_inversed_x_15_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = _inversed_x_15_y_0_to_fp16)[name = string("_inversed_x_15_cast_fp16")]; + tensor var_320_begin_0 = const()[name = string("op_320_begin_0"), val = tensor([0, 0, 0, -1])]; + tensor var_320_end_0 = const()[name = string("op_320_end_0"), val = tensor([1, 512, 10, 29])]; + tensor var_320_end_mask_0 = const()[name = string("op_320_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_320_squeeze_mask_0 = const()[name = string("op_320_squeeze_mask_0"), val = tensor([false, false, false, true])]; + tensor var_320_cast_fp16 = slice_by_index(begin = var_320_begin_0, end = var_320_end_0, end_mask = var_320_end_mask_0, squeeze_mask = var_320_squeeze_mask_0, x = _inversed_x_15_cast_fp16)[name = string("op_320_cast_fp16")]; + tensor var_321_axes_0 = const()[name = string("op_321_axes_0"), val = tensor([-1])]; + tensor var_321_cast_fp16 = expand_dims(axes = var_321_axes_0, x = var_320_cast_fp16)[name = string("op_321_cast_fp16")]; + bool x_17_interleave_0 = const()[name = string("x_17_interleave_0"), val = bool(false)]; + tensor x_17_cast_fp16 = concat(axis = var_5, interleave = x_17_interleave_0, values = (_inversed_x_15_cast_fp16, var_321_cast_fp16))[name = string("x_17_cast_fp16")]; + tensor var_324 = const()[name = string("op_324"), val = tensor([2, 2])]; + tensor var_325 = const()[name = string("op_325"), val = tensor([2, 2])]; + string var_327_pad_type_0 = const()[name = string("op_327_pad_type_0"), val = string("custom")]; + tensor var_327_pad_0 = const()[name = string("op_327_pad_0"), val = tensor([0, 0, 0, 0])]; + bool var_327_exclude_padding_from_average_0 = const()[name = string("op_327_exclude_padding_from_average_0"), val = bool(false)]; + bool var_327_ceil_mode_0 = const()[name = string("op_327_ceil_mode_0"), val = bool(false)]; + tensor var_327_cast_fp16 = avg_pool(ceil_mode = var_327_ceil_mode_0, exclude_padding_from_average = var_327_exclude_padding_from_average_0, kernel_sizes = var_324, pad = var_327_pad_0, pad_type = var_327_pad_type_0, strides = var_325, x = x_17_cast_fp16)[name = string("op_327_cast_fp16")]; + tensor input_31_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_x_15_cast_fp16)[name = string("input_31_cast_fp16")]; + string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")]; + tensor input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor([1, 1])]; + tensor input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor([1, 1])]; + int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)]; + tensor weight_55_to_fp16 = const()[name = string("weight_55_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5003392)))]; + tensor style_encoder_shared_4_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_4_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9722048)))]; + tensor input_33_cast_fp16 = conv(bias = style_encoder_shared_4_conv1_bias_to_fp16, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = weight_55_to_fp16, x = input_31_cast_fp16)[name = string("input_33_cast_fp16")]; + string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("custom")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor([2, 2])]; + int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(512)]; + tensor input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor([1, 1])]; + tensor weight_59_to_fp16 = const()[name = string("weight_59_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9723136)))]; + tensor style_encoder_shared_4_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_4_downsample_res_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9732416)))]; + tensor input_35_cast_fp16 = conv(bias = style_encoder_shared_4_downsample_res_conv_bias_to_fp16, dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = weight_59_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor input_37_cast_fp16 = leaky_relu(alpha = var_10, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")]; + string var_372_pad_type_0 = const()[name = string("op_372_pad_type_0"), val = string("custom")]; + tensor var_372_pad_0 = const()[name = string("op_372_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor var_372_strides_0 = const()[name = string("op_372_strides_0"), val = tensor([1, 1])]; + tensor var_372_dilations_0 = const()[name = string("op_372_dilations_0"), val = tensor([1, 1])]; + int32 var_372_groups_0 = const()[name = string("op_372_groups_0"), val = int32(1)]; + tensor weight_63_to_fp16 = const()[name = string("weight_63_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9733504)))]; + tensor style_encoder_shared_4_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_4_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14452160)))]; + tensor var_372_cast_fp16 = conv(bias = style_encoder_shared_4_conv2_bias_to_fp16, dilations = var_372_dilations_0, groups = var_372_groups_0, pad = var_372_pad_0, pad_type = var_372_pad_type_0, strides = var_372_strides_0, weight = weight_63_to_fp16, x = input_37_cast_fp16)[name = string("op_372_cast_fp16")]; + tensor x_19_cast_fp16 = add(x = var_327_cast_fp16, y = var_372_cast_fp16)[name = string("x_19_cast_fp16")]; + fp16 _inversed_input_39_y_0_to_fp16 = const()[name = string("_inversed_input_39_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_input_39_cast_fp16 = mul(x = x_19_cast_fp16, y = _inversed_input_39_y_0_to_fp16)[name = string("_inversed_input_39_cast_fp16")]; + tensor input_41_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")]; + tensor input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor([1, 1])]; + tensor input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor([1, 1])]; + int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)]; + tensor weight_67_to_fp16 = const()[name = string("weight_67_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14453248)))]; + tensor style_encoder_shared_6_bias_to_fp16 = const()[name = string("style_encoder_shared_6_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27560512)))]; + tensor input_43_cast_fp16 = conv(bias = style_encoder_shared_6_bias_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = weight_67_to_fp16, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor([-2, -1])]; + bool input_45_keep_dims_0 = const()[name = string("input_45_keep_dims_0"), val = bool(true)]; + tensor input_45_cast_fp16 = reduce_mean(axes = input_45_axes_0, keep_dims = input_45_keep_dims_0, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor h_1_cast_fp16 = leaky_relu(alpha = var_10, x = input_45_cast_fp16)[name = string("h_1_cast_fp16")]; + tensor var_393 = const()[name = string("op_393"), val = tensor([1, -1])]; + tensor input_47_cast_fp16 = reshape(shape = var_393, x = h_1_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor style_encoder_unshared_weight_to_fp16 = const()[name = string("style_encoder_unshared_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27561600)))]; + tensor style_encoder_unshared_bias_to_fp16 = const()[name = string("style_encoder_unshared_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27692736)))]; + tensor linear_0_cast_fp16 = linear(bias = style_encoder_unshared_bias_to_fp16, weight = style_encoder_unshared_weight_to_fp16, x = input_47_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_399 = const()[name = string("op_399"), val = int32(-1)]; + fp32 var_404 = const()[name = string("op_404"), val = fp32(0x1.99999ap-3)]; + string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")]; + tensor input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor([1, 1])]; + tensor input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor([1, 1])]; + int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)]; + tensor weight_71_to_fp16 = const()[name = string("weight_71_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27693056)))]; + tensor predictor_encoder_shared_0_bias_to_fp16 = const()[name = string("predictor_encoder_shared_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27694272)))]; + tensor input_49_cast_fp16 = conv(bias = predictor_encoder_shared_0_bias_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = weight_71_to_fp16, x = mel_to_fp16)[name = string("input_49_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1, 1])]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1, 1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor weight_75_to_fp16 = const()[name = string("weight_75_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27694464)))]; + tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = weight_75_to_fp16, x = input_49_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_475_begin_0 = const()[name = string("op_475_begin_0"), val = tensor([0, 0, 0, -1])]; + tensor var_475_end_0 = const()[name = string("op_475_end_0"), val = tensor([1, 128, 80, 231])]; + tensor var_475_end_mask_0 = const()[name = string("op_475_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_475_squeeze_mask_0 = const()[name = string("op_475_squeeze_mask_0"), val = tensor([false, false, false, true])]; + tensor var_475_cast_fp16 = slice_by_index(begin = var_475_begin_0, end = var_475_end_0, end_mask = var_475_end_mask_0, squeeze_mask = var_475_squeeze_mask_0, x = x_21_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + tensor var_476_cast_fp16 = expand_dims(axes = var_476_axes_0, x = var_475_cast_fp16)[name = string("op_476_cast_fp16")]; + bool x_23_interleave_0 = const()[name = string("x_23_interleave_0"), val = bool(false)]; + tensor x_23_cast_fp16 = concat(axis = var_399, interleave = x_23_interleave_0, values = (x_21_cast_fp16, var_476_cast_fp16))[name = string("x_23_cast_fp16")]; + tensor var_479 = const()[name = string("op_479"), val = tensor([2, 2])]; + tensor var_480 = const()[name = string("op_480"), val = tensor([2, 2])]; + string var_482_pad_type_0 = const()[name = string("op_482_pad_type_0"), val = string("custom")]; + tensor var_482_pad_0 = const()[name = string("op_482_pad_0"), val = tensor([0, 0, 0, 0])]; + bool var_482_exclude_padding_from_average_0 = const()[name = string("op_482_exclude_padding_from_average_0"), val = bool(false)]; + bool var_482_ceil_mode_0 = const()[name = string("op_482_ceil_mode_0"), val = bool(false)]; + tensor var_482_cast_fp16 = avg_pool(ceil_mode = var_482_ceil_mode_0, exclude_padding_from_average = var_482_exclude_padding_from_average_0, kernel_sizes = var_479, pad = var_482_pad_0, pad_type = var_482_pad_type_0, strides = var_480, x = x_23_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor input_51_cast_fp16 = leaky_relu(alpha = var_404, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")]; + string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("custom")]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor([1, 1])]; + tensor input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor([1, 1])]; + int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)]; + tensor weight_79_to_fp16 = const()[name = string("weight_79_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27710912)))]; + tensor predictor_encoder_shared_1_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27784704)))]; + tensor input_53_cast_fp16 = conv(bias = predictor_encoder_shared_1_conv1_bias_to_fp16, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = weight_79_to_fp16, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")]; + tensor input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor([2, 2])]; + int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(64)]; + tensor input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor([1, 1])]; + tensor weight_83_to_fp16 = const()[name = string("weight_83_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27784896)))]; + tensor predictor_encoder_shared_1_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_1_downsample_res_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27786112)))]; + tensor input_55_cast_fp16 = conv(bias = predictor_encoder_shared_1_downsample_res_conv_bias_to_fp16, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = weight_83_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = leaky_relu(alpha = var_404, x = input_55_cast_fp16)[name = string("input_57_cast_fp16")]; + string var_527_pad_type_0 = const()[name = string("op_527_pad_type_0"), val = string("custom")]; + tensor var_527_pad_0 = const()[name = string("op_527_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor var_527_strides_0 = const()[name = string("op_527_strides_0"), val = tensor([1, 1])]; + tensor var_527_dilations_0 = const()[name = string("op_527_dilations_0"), val = tensor([1, 1])]; + int32 var_527_groups_0 = const()[name = string("op_527_groups_0"), val = int32(1)]; + tensor weight_87_to_fp16 = const()[name = string("weight_87_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27786304)))]; + tensor predictor_encoder_shared_1_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27933824)))]; + tensor var_527_cast_fp16 = conv(bias = predictor_encoder_shared_1_conv2_bias_to_fp16, dilations = var_527_dilations_0, groups = var_527_groups_0, pad = var_527_pad_0, pad_type = var_527_pad_type_0, strides = var_527_strides_0, weight = weight_87_to_fp16, x = input_57_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor x_25_cast_fp16 = add(x = var_482_cast_fp16, y = var_527_cast_fp16)[name = string("x_25_cast_fp16")]; + fp16 _inversed_input_59_y_0_to_fp16 = const()[name = string("_inversed_input_59_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_input_59_cast_fp16 = mul(x = x_25_cast_fp16, y = _inversed_input_59_y_0_to_fp16)[name = string("_inversed_input_59_cast_fp16")]; + string x_27_pad_type_0 = const()[name = string("x_27_pad_type_0"), val = string("valid")]; + tensor x_27_strides_0 = const()[name = string("x_27_strides_0"), val = tensor([1, 1])]; + tensor x_27_pad_0 = const()[name = string("x_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor x_27_dilations_0 = const()[name = string("x_27_dilations_0"), val = tensor([1, 1])]; + int32 x_27_groups_0 = const()[name = string("x_27_groups_0"), val = int32(1)]; + tensor weight_91_to_fp16 = const()[name = string("weight_91_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27934144)))]; + tensor x_27_cast_fp16 = conv(dilations = x_27_dilations_0, groups = x_27_groups_0, pad = x_27_pad_0, pad_type = x_27_pad_type_0, strides = x_27_strides_0, weight = weight_91_to_fp16, x = _inversed_input_59_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_563 = const()[name = string("op_563"), val = tensor([2, 2])]; + tensor var_564 = const()[name = string("op_564"), val = tensor([2, 2])]; + string var_566_pad_type_0 = const()[name = string("op_566_pad_type_0"), val = string("custom")]; + tensor var_566_pad_0 = const()[name = string("op_566_pad_0"), val = tensor([0, 0, 0, 0])]; + bool var_566_exclude_padding_from_average_0 = const()[name = string("op_566_exclude_padding_from_average_0"), val = bool(false)]; + bool var_566_ceil_mode_0 = const()[name = string("op_566_ceil_mode_0"), val = bool(false)]; + tensor var_566_cast_fp16 = avg_pool(ceil_mode = var_566_ceil_mode_0, exclude_padding_from_average = var_566_exclude_padding_from_average_0, kernel_sizes = var_563, pad = var_566_pad_0, pad_type = var_566_pad_type_0, strides = var_564, x = x_27_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor input_61_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")]; + tensor input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor([1, 1])]; + tensor input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor([1, 1])]; + int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)]; + tensor weight_95_to_fp16 = const()[name = string("weight_95_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27999744)))]; + tensor predictor_encoder_shared_2_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28294720)))]; + tensor input_63_cast_fp16 = conv(bias = predictor_encoder_shared_2_conv1_bias_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = weight_95_to_fp16, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")]; + string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")]; + tensor input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor([2, 2])]; + int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(128)]; + tensor input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor([1, 1])]; + tensor weight_99_to_fp16 = const()[name = string("weight_99_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28295040)))]; + tensor predictor_encoder_shared_2_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_2_downsample_res_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28297408)))]; + tensor input_65_cast_fp16 = conv(bias = predictor_encoder_shared_2_downsample_res_conv_bias_to_fp16, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = weight_99_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor input_67_cast_fp16 = leaky_relu(alpha = var_404, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")]; + string var_611_pad_type_0 = const()[name = string("op_611_pad_type_0"), val = string("custom")]; + tensor var_611_pad_0 = const()[name = string("op_611_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor var_611_strides_0 = const()[name = string("op_611_strides_0"), val = tensor([1, 1])]; + tensor var_611_dilations_0 = const()[name = string("op_611_dilations_0"), val = tensor([1, 1])]; + int32 var_611_groups_0 = const()[name = string("op_611_groups_0"), val = int32(1)]; + tensor weight_103_to_fp16 = const()[name = string("weight_103_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28297728)))]; + tensor predictor_encoder_shared_2_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28887616)))]; + tensor var_611_cast_fp16 = conv(bias = predictor_encoder_shared_2_conv2_bias_to_fp16, dilations = var_611_dilations_0, groups = var_611_groups_0, pad = var_611_pad_0, pad_type = var_611_pad_type_0, strides = var_611_strides_0, weight = weight_103_to_fp16, x = input_67_cast_fp16)[name = string("op_611_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_566_cast_fp16, y = var_611_cast_fp16)[name = string("x_29_cast_fp16")]; + fp16 _inversed_input_69_y_0_to_fp16 = const()[name = string("_inversed_input_69_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_input_69_cast_fp16 = mul(x = x_29_cast_fp16, y = _inversed_input_69_y_0_to_fp16)[name = string("_inversed_input_69_cast_fp16")]; + string x_31_pad_type_0 = const()[name = string("x_31_pad_type_0"), val = string("valid")]; + tensor x_31_strides_0 = const()[name = string("x_31_strides_0"), val = tensor([1, 1])]; + tensor x_31_pad_0 = const()[name = string("x_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor x_31_dilations_0 = const()[name = string("x_31_dilations_0"), val = tensor([1, 1])]; + int32 x_31_groups_0 = const()[name = string("x_31_groups_0"), val = int32(1)]; + tensor weight_107_to_fp16 = const()[name = string("weight_107_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28888192)))]; + tensor x_31_cast_fp16 = conv(dilations = x_31_dilations_0, groups = x_31_groups_0, pad = x_31_pad_0, pad_type = x_31_pad_type_0, strides = x_31_strides_0, weight = weight_107_to_fp16, x = _inversed_input_69_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_647 = const()[name = string("op_647"), val = tensor([2, 2])]; + tensor var_648 = const()[name = string("op_648"), val = tensor([2, 2])]; + string var_650_pad_type_0 = const()[name = string("op_650_pad_type_0"), val = string("custom")]; + tensor var_650_pad_0 = const()[name = string("op_650_pad_0"), val = tensor([0, 0, 0, 0])]; + bool var_650_exclude_padding_from_average_0 = const()[name = string("op_650_exclude_padding_from_average_0"), val = bool(false)]; + bool var_650_ceil_mode_0 = const()[name = string("op_650_ceil_mode_0"), val = bool(false)]; + tensor var_650_cast_fp16 = avg_pool(ceil_mode = var_650_ceil_mode_0, exclude_padding_from_average = var_650_exclude_padding_from_average_0, kernel_sizes = var_647, pad = var_650_pad_0, pad_type = var_650_pad_type_0, strides = var_648, x = x_31_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor input_71_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_input_69_cast_fp16)[name = string("input_71_cast_fp16")]; + string input_73_pad_type_0 = const()[name = string("input_73_pad_type_0"), val = string("custom")]; + tensor input_73_pad_0 = const()[name = string("input_73_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_73_strides_0 = const()[name = string("input_73_strides_0"), val = tensor([1, 1])]; + tensor input_73_dilations_0 = const()[name = string("input_73_dilations_0"), val = tensor([1, 1])]; + int32 input_73_groups_0 = const()[name = string("input_73_groups_0"), val = int32(1)]; + tensor weight_111_to_fp16 = const()[name = string("weight_111_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29150400)))]; + tensor predictor_encoder_shared_3_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_3_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30330112)))]; + tensor input_73_cast_fp16 = conv(bias = predictor_encoder_shared_3_conv1_bias_to_fp16, dilations = input_73_dilations_0, groups = input_73_groups_0, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = input_73_strides_0, weight = weight_111_to_fp16, x = input_71_cast_fp16)[name = string("input_73_cast_fp16")]; + string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")]; + tensor input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor([2, 2])]; + int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(256)]; + tensor input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor([1, 1])]; + tensor weight_115_to_fp16 = const()[name = string("weight_115_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30330688)))]; + tensor predictor_encoder_shared_3_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_3_downsample_res_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30335360)))]; + tensor input_75_cast_fp16 = conv(bias = predictor_encoder_shared_3_downsample_res_conv_bias_to_fp16, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = weight_115_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor input_77_cast_fp16 = leaky_relu(alpha = var_404, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")]; + string var_695_pad_type_0 = const()[name = string("op_695_pad_type_0"), val = string("custom")]; + tensor var_695_pad_0 = const()[name = string("op_695_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor var_695_strides_0 = const()[name = string("op_695_strides_0"), val = tensor([1, 1])]; + tensor var_695_dilations_0 = const()[name = string("op_695_dilations_0"), val = tensor([1, 1])]; + int32 var_695_groups_0 = const()[name = string("op_695_groups_0"), val = int32(1)]; + tensor weight_119_to_fp16 = const()[name = string("weight_119_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30335936)))]; + tensor predictor_encoder_shared_3_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_3_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32695296)))]; + tensor var_695_cast_fp16 = conv(bias = predictor_encoder_shared_3_conv2_bias_to_fp16, dilations = var_695_dilations_0, groups = var_695_groups_0, pad = var_695_pad_0, pad_type = var_695_pad_type_0, strides = var_695_strides_0, weight = weight_119_to_fp16, x = input_77_cast_fp16)[name = string("op_695_cast_fp16")]; + tensor x_33_cast_fp16 = add(x = var_650_cast_fp16, y = var_695_cast_fp16)[name = string("x_33_cast_fp16")]; + fp16 _inversed_x_35_y_0_to_fp16 = const()[name = string("_inversed_x_35_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_x_35_cast_fp16 = mul(x = x_33_cast_fp16, y = _inversed_x_35_y_0_to_fp16)[name = string("_inversed_x_35_cast_fp16")]; + tensor var_714_begin_0 = const()[name = string("op_714_begin_0"), val = tensor([0, 0, 0, -1])]; + tensor var_714_end_0 = const()[name = string("op_714_end_0"), val = tensor([1, 512, 10, 29])]; + tensor var_714_end_mask_0 = const()[name = string("op_714_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_714_squeeze_mask_0 = const()[name = string("op_714_squeeze_mask_0"), val = tensor([false, false, false, true])]; + tensor var_714_cast_fp16 = slice_by_index(begin = var_714_begin_0, end = var_714_end_0, end_mask = var_714_end_mask_0, squeeze_mask = var_714_squeeze_mask_0, x = _inversed_x_35_cast_fp16)[name = string("op_714_cast_fp16")]; + tensor var_715_axes_0 = const()[name = string("op_715_axes_0"), val = tensor([-1])]; + tensor var_715_cast_fp16 = expand_dims(axes = var_715_axes_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + bool x_37_interleave_0 = const()[name = string("x_37_interleave_0"), val = bool(false)]; + tensor x_37_cast_fp16 = concat(axis = var_399, interleave = x_37_interleave_0, values = (_inversed_x_35_cast_fp16, var_715_cast_fp16))[name = string("x_37_cast_fp16")]; + tensor var_718 = const()[name = string("op_718"), val = tensor([2, 2])]; + tensor var_719 = const()[name = string("op_719"), val = tensor([2, 2])]; + string var_721_pad_type_0 = const()[name = string("op_721_pad_type_0"), val = string("custom")]; + tensor var_721_pad_0 = const()[name = string("op_721_pad_0"), val = tensor([0, 0, 0, 0])]; + bool var_721_exclude_padding_from_average_0 = const()[name = string("op_721_exclude_padding_from_average_0"), val = bool(false)]; + bool var_721_ceil_mode_0 = const()[name = string("op_721_ceil_mode_0"), val = bool(false)]; + tensor var_721_cast_fp16 = avg_pool(ceil_mode = var_721_ceil_mode_0, exclude_padding_from_average = var_721_exclude_padding_from_average_0, kernel_sizes = var_718, pad = var_721_pad_0, pad_type = var_721_pad_type_0, strides = var_719, x = x_37_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_79_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_x_35_cast_fp16)[name = string("input_79_cast_fp16")]; + string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("custom")]; + tensor input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor([1, 1])]; + tensor input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor([1, 1])]; + int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(1)]; + tensor weight_123_to_fp16 = const()[name = string("weight_123_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32696384)))]; + tensor predictor_encoder_shared_4_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_4_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37415040)))]; + tensor input_81_cast_fp16 = conv(bias = predictor_encoder_shared_4_conv1_bias_to_fp16, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = weight_123_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")]; + tensor input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor([2, 2])]; + int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(512)]; + tensor input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor([1, 1])]; + tensor weight_127_to_fp16 = const()[name = string("weight_127_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37416128)))]; + tensor predictor_encoder_shared_4_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_4_downsample_res_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37425408)))]; + tensor input_83_cast_fp16 = conv(bias = predictor_encoder_shared_4_downsample_res_conv_bias_to_fp16, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = weight_127_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = leaky_relu(alpha = var_404, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string var_766_pad_type_0 = const()[name = string("op_766_pad_type_0"), val = string("custom")]; + tensor var_766_pad_0 = const()[name = string("op_766_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor var_766_strides_0 = const()[name = string("op_766_strides_0"), val = tensor([1, 1])]; + tensor var_766_dilations_0 = const()[name = string("op_766_dilations_0"), val = tensor([1, 1])]; + int32 var_766_groups_0 = const()[name = string("op_766_groups_0"), val = int32(1)]; + tensor weight_131_to_fp16 = const()[name = string("weight_131_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37426496)))]; + tensor predictor_encoder_shared_4_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_4_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42145152)))]; + tensor var_766_cast_fp16 = conv(bias = predictor_encoder_shared_4_conv2_bias_to_fp16, dilations = var_766_dilations_0, groups = var_766_groups_0, pad = var_766_pad_0, pad_type = var_766_pad_type_0, strides = var_766_strides_0, weight = weight_131_to_fp16, x = input_85_cast_fp16)[name = string("op_766_cast_fp16")]; + tensor x_cast_fp16 = add(x = var_721_cast_fp16, y = var_766_cast_fp16)[name = string("x_cast_fp16")]; + fp16 _inversed_input_87_y_0_to_fp16 = const()[name = string("_inversed_input_87_y_0_to_fp16"), val = fp16(0x1.6ap-1)]; + tensor _inversed_input_87_cast_fp16 = mul(x = x_cast_fp16, y = _inversed_input_87_y_0_to_fp16)[name = string("_inversed_input_87_cast_fp16")]; + tensor input_89_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_input_87_cast_fp16)[name = string("input_89_cast_fp16")]; + string input_91_pad_type_0 = const()[name = string("input_91_pad_type_0"), val = string("valid")]; + tensor input_91_strides_0 = const()[name = string("input_91_strides_0"), val = tensor([1, 1])]; + tensor input_91_pad_0 = const()[name = string("input_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_91_dilations_0 = const()[name = string("input_91_dilations_0"), val = tensor([1, 1])]; + int32 input_91_groups_0 = const()[name = string("input_91_groups_0"), val = int32(1)]; + tensor weight_135_to_fp16 = const()[name = string("weight_135_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42146240)))]; + tensor predictor_encoder_shared_6_bias_to_fp16 = const()[name = string("predictor_encoder_shared_6_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55253504)))]; + tensor input_91_cast_fp16 = conv(bias = predictor_encoder_shared_6_bias_to_fp16, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = weight_135_to_fp16, x = input_89_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor input_93_axes_0 = const()[name = string("input_93_axes_0"), val = tensor([-2, -1])]; + bool input_93_keep_dims_0 = const()[name = string("input_93_keep_dims_0"), val = bool(true)]; + tensor input_93_cast_fp16 = reduce_mean(axes = input_93_axes_0, keep_dims = input_93_keep_dims_0, x = input_91_cast_fp16)[name = string("input_93_cast_fp16")]; + tensor h_cast_fp16 = leaky_relu(alpha = var_404, x = input_93_cast_fp16)[name = string("h_cast_fp16")]; + tensor var_787 = const()[name = string("op_787"), val = tensor([1, -1])]; + tensor input_cast_fp16 = reshape(shape = var_787, x = h_cast_fp16)[name = string("input_cast_fp16")]; + tensor predictor_encoder_unshared_weight_to_fp16 = const()[name = string("predictor_encoder_unshared_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55254592)))]; + tensor predictor_encoder_unshared_bias_to_fp16 = const()[name = string("predictor_encoder_unshared_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55385728)))]; + tensor linear_1_cast_fp16 = linear(bias = predictor_encoder_unshared_bias_to_fp16, weight = predictor_encoder_unshared_weight_to_fp16, x = input_cast_fp16)[name = string("linear_1_cast_fp16")]; + int32 var_793 = const()[name = string("op_793"), val = int32(1)]; + bool var_794_interleave_0 = const()[name = string("op_794_interleave_0"), val = bool(false)]; + tensor var_794 = concat(axis = var_793, interleave = var_794_interleave_0, values = (linear_0_cast_fp16, linear_1_cast_fp16))[name = string("op_794_cast_fp16")]; + } -> (var_794); +} \ No newline at end of file diff --git a/iteration_3/compiled/ref_encoder_fp16.mlmodelc/weights/weight.bin 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a/iteration_3/compiled/text_encoder_fp16.mlmodelc/coremldata.bin b/iteration_3/compiled/text_encoder_fp16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..31aa71160389dc0ad8067b40705154a29d9f25d5 --- /dev/null +++ b/iteration_3/compiled/text_encoder_fp16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9aabdea8db6713d2e02aa4d97a57e9fbfad5c44de155f57dd2c0870c523e41fa +size 452 diff --git a/iteration_3/compiled/text_encoder_fp16.mlmodelc/metadata.json b/iteration_3/compiled/text_encoder_fp16.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b2ab601fcaf6546094365ed5f44a22604dae8ef5 --- /dev/null +++ b/iteration_3/compiled/text_encoder_fp16.mlmodelc/metadata.json @@ -0,0 +1,98 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Float16", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16)", + "shortDescription" : "", + "shape" : "[]", + "name" : "var_159", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 9, + "mlProgramOperationTypeHistogram" : { + "Ios18.conv" : 3, + "Ios18.sub" : 1, + "Select" : 1, + "Ios18.leakyRelu" : 3, + "Ios18.expandDims" : 1, + "Ios18.gather" : 1, + "Ios18.lstm" : 1, + "Ios18.add" : 1, + "Ios18.layerNorm" : 3, + "Ios18.transpose" : 9, + "Ios18.cast" : 4, + "Ios18.greaterEqual" : 1, + "Identity" : 1, + "Ios18.mul" : 5 + }, + "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "15.0", + "tvOS" : "18.0", + "visionOS" : "2.0", + "watchOS" : "11.0", + "iOS" : "18.0", + "macCatalyst" : "18.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.conversion_date" : "2026-05-08", + "com.github.apple.coremltools.source" : "torch==2.11.0", + "com.github.apple.coremltools.version" : "9.0", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "inputSchema" : [ + { + "dataType" : "Int32", + "hasShapeFlexibility" : "1", + "isOptional" : "0", + "shapeFlexibility" : "1 × 1...512", + "shapeRange" : "[[1, 1], [1, 512]]", + "formattedType" : "MultiArray (Int32 1 × 57)", + "type" : "MultiArray", + "shape" : "[1, 57]", + "name" : "tokens", + "shortDescription" : "" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Int32", + "formattedType" : "MultiArray (Int32 1)", + "shortDescription" : "", + "shape" : "[1]", + "name" : "input_lengths", + "type" : "MultiArray" + }, + { + "dataType" : "Float32", + "hasShapeFlexibility" : "1", + "isOptional" : "0", + "shapeFlexibility" : "1 × 1...512", + "shapeRange" : "[[1, 1], [1, 512]]", + "formattedType" : "MultiArray (Float32 1 × 57)", + "type" : "MultiArray", + "shape" : "[1, 57]", + "name" : "text_mask", + "shortDescription" : "" + } + ], + "generatedClassName" : "text_encoder_fp16", + "method" : "predict" + } +] \ No newline at end of file diff --git a/iteration_3/compiled/text_encoder_fp16.mlmodelc/model.mil b/iteration_3/compiled/text_encoder_fp16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..d6933992e42d1956d18e4a56c775270b04e754fe --- /dev/null +++ b/iteration_3/compiled/text_encoder_fp16.mlmodelc/model.mil @@ -0,0 +1,110 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor input_lengths, tensor text_mask, tensor tokens) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"text_mask", [1, 57]}, {"tokens", [1, 57]}}), ("RangeDims", {{"text_mask", [[1, 1], [1, 512]]}, {"tokens", [[1, 1], [1, 512]]}})))] { + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor text_encoder_embedding_weight_to_fp16 = const()[name = string("text_encoder_embedding_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string tokens_to_int16_dtype_0 = const()[name = string("tokens_to_int16_dtype_0"), val = string("int16")]; + string cast_2_dtype_0 = const()[name = string("cast_2_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor tokens_to_int16 = cast(dtype = tokens_to_int16_dtype_0, x = tokens)[name = string("cast_6")]; + tensor cast_2 = cast(dtype = cast_2_dtype_0, x = tokens_to_int16)[name = string("cast_5")]; + tensor greater_equal_0 = greater_equal(x = cast_2, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(178)]; + tensor add_2 = add(x = cast_2, y = slice_by_index_0)[name = string("add_2")]; + tensor select_0 = select(a = cast_2, b = add_2, cond = greater_equal_0)[name = string("select_0")]; + int32 x_1_cast_fp16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_axis_0"), val = int32(0)]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_4")]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_to_int16, validate_indices = x_1_validate_indices_0, x = text_encoder_embedding_weight_to_fp16)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor x_3_perm_0 = const()[name = string("x_3_perm_0"), val = tensor([0, 2, 1])]; + tensor var_30_axes_0 = const()[name = string("op_30_axes_0"), val = tensor([1])]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")]; + tensor var_30_cast_fp16 = expand_dims(axes = var_30_axes_0, x = text_mask_to_fp16)[name = string("op_30_cast_fp16")]; + fp16 var_31_to_fp16 = const()[name = string("op_31_to_fp16"), val = fp16(0x1p+0)]; + tensor keep_cast_fp16 = sub(x = var_31_to_fp16, y = var_30_cast_fp16)[name = string("keep_cast_fp16")]; + tensor x_3_cast_fp16 = transpose(perm = x_3_perm_0, x = x_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_10")]; + tensor input_1_cast_fp16 = mul(x = x_3_cast_fp16, y = keep_cast_fp16)[name = string("input_1_cast_fp16")]; + fp32 var_35 = const()[name = string("op_35"), val = fp32(0x1.99999ap-3)]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor weight_3_to_fp16 = const()[name = string("weight_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182400)))]; + tensor text_encoder_cnn_0_0_bias_to_fp16 = const()[name = string("text_encoder_cnn_0_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2803904)))]; + tensor x_5_cast_fp16 = conv(bias = text_encoder_cnn_0_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = weight_3_to_fp16, x = input_1_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([0, -1, 1])]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor text_encoder_cnn_0_1_gamma_to_fp16 = const()[name = string("text_encoder_cnn_0_1_gamma_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2804992)))]; + tensor text_encoder_cnn_0_1_beta_to_fp16 = const()[name = string("text_encoder_cnn_0_1_beta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2806080)))]; + fp16 var_38_to_fp16 = const()[name = string("op_38_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = x_5_cast_fp16)[name = string("transpose_9")]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = text_encoder_cnn_0_1_beta_to_fp16, epsilon = var_38_to_fp16, gamma = text_encoder_cnn_0_1_gamma_to_fp16, x = input_3_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_5_perm_0 = const()[name = string("input_5_perm_0"), val = tensor([0, -1, 1])]; + tensor input_5_cast_fp16 = transpose(perm = input_5_perm_0, x = x_7_cast_fp16)[name = string("transpose_8")]; + tensor x_9_cast_fp16 = leaky_relu(alpha = var_35, x = input_5_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_9_cast_fp16, y = keep_cast_fp16)[name = string("input_7_cast_fp16")]; + fp32 var_65 = const()[name = string("op_65"), val = fp32(0x1.99999ap-3)]; + string x_11_pad_type_0 = const()[name = string("x_11_pad_type_0"), val = string("custom")]; + tensor x_11_pad_0 = const()[name = string("x_11_pad_0"), val = tensor([2, 2])]; + tensor x_11_strides_0 = const()[name = string("x_11_strides_0"), val = tensor([1])]; + tensor x_11_dilations_0 = const()[name = string("x_11_dilations_0"), val = tensor([1])]; + int32 x_11_groups_0 = const()[name = string("x_11_groups_0"), val = int32(1)]; + tensor weight_7_to_fp16 = const()[name = string("weight_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2807168)))]; + tensor text_encoder_cnn_1_0_bias_to_fp16 = const()[name = string("text_encoder_cnn_1_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5428672)))]; + tensor x_11_cast_fp16 = conv(bias = text_encoder_cnn_1_0_bias_to_fp16, dilations = x_11_dilations_0, groups = x_11_groups_0, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = x_11_strides_0, weight = weight_7_to_fp16, x = input_7_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, -1, 1])]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor text_encoder_cnn_1_1_gamma_to_fp16 = const()[name = string("text_encoder_cnn_1_1_gamma_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5429760)))]; + tensor text_encoder_cnn_1_1_beta_to_fp16 = const()[name = string("text_encoder_cnn_1_1_beta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430848)))]; + fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_11_cast_fp16)[name = string("transpose_7")]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = text_encoder_cnn_1_1_beta_to_fp16, epsilon = var_68_to_fp16, gamma = text_encoder_cnn_1_1_gamma_to_fp16, x = input_9_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_11_perm_0 = const()[name = string("input_11_perm_0"), val = tensor([0, -1, 1])]; + tensor input_11_cast_fp16 = transpose(perm = input_11_perm_0, x = x_13_cast_fp16)[name = string("transpose_6")]; + tensor x_15_cast_fp16 = leaky_relu(alpha = var_65, x = input_11_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor input_13_cast_fp16 = mul(x = x_15_cast_fp16, y = keep_cast_fp16)[name = string("input_13_cast_fp16")]; + fp32 var_95 = const()[name = string("op_95"), val = fp32(0x1.99999ap-3)]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor weight_11_to_fp16 = const()[name = string("weight_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431936)))]; + tensor text_encoder_cnn_2_0_bias_to_fp16 = const()[name = string("text_encoder_cnn_2_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053440)))]; + tensor x_17_cast_fp16 = conv(bias = text_encoder_cnn_2_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = weight_11_to_fp16, x = input_13_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, -1, 1])]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor text_encoder_cnn_2_1_gamma_to_fp16 = const()[name = string("text_encoder_cnn_2_1_gamma_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054528)))]; + tensor text_encoder_cnn_2_1_beta_to_fp16 = const()[name = string("text_encoder_cnn_2_1_beta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8055616)))]; + fp16 var_98_to_fp16 = const()[name = string("op_98_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = x_17_cast_fp16)[name = string("transpose_5")]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, beta = text_encoder_cnn_2_1_beta_to_fp16, epsilon = var_98_to_fp16, gamma = text_encoder_cnn_2_1_gamma_to_fp16, x = input_15_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, -1, 1])]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_19_cast_fp16)[name = string("transpose_4")]; + tensor x_21_cast_fp16 = leaky_relu(alpha = var_95, x = input_17_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = keep_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([2, 0, 1])]; + string x_25_batch_first_direction_0 = const()[name = string("x_25_batch_first_direction_0"), val = string("bidirectional")]; + bool x_25_batch_first_output_sequence_0 = const()[name = string("x_25_batch_first_output_sequence_0"), val = bool(true)]; + string x_25_batch_first_recurrent_activation_0 = const()[name = string("x_25_batch_first_recurrent_activation_0"), val = string("sigmoid")]; + string x_25_batch_first_cell_activation_0 = const()[name = string("x_25_batch_first_cell_activation_0"), val = string("tanh")]; + string x_25_batch_first_activation_0 = const()[name = string("x_25_batch_first_activation_0"), val = string("tanh")]; + tensor x_25_batch_first_lstm_h0_reshaped_to_fp16 = const()[name = string("x_25_batch_first_lstm_h0_reshaped_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8056704)))]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8057792)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9106432)))]; + tensor add_0_to_fp16 = const()[name = string("add_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9630784)))]; + tensor concat_6_to_fp16 = const()[name = string("concat_6_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9632896)))]; + tensor concat_7_to_fp16 = const()[name = string("concat_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10681536)))]; + tensor add_1_to_fp16 = const()[name = string("add_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11205888)))]; + tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = x_23_cast_fp16)[name = string("transpose_3")]; + tensor x_25_batch_first_cast_fp16_0, tensor x_25_batch_first_cast_fp16_1, tensor x_25_batch_first_cast_fp16_2 = lstm(activation = x_25_batch_first_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, cell_activation = x_25_batch_first_cell_activation_0, direction = x_25_batch_first_direction_0, initial_c = x_25_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_25_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_25_batch_first_output_sequence_0, recurrent_activation = x_25_batch_first_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_hh_back = concat_7_to_fp16, weight_ih = concat_4_to_fp16, weight_ih_back = concat_6_to_fp16, x = transpose_0_cast_fp16)[name = string("x_25_batch_first_cast_fp16")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 2, 0])]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = x_25_batch_first_cast_fp16_0)[name = string("transpose_2")]; + tensor var_159 = mul(x = transpose_1_cast_fp16, y = keep_cast_fp16)[name = string("op_159_cast_fp16")]; + tensor input_lengths_tmp = identity(x = input_lengths)[name = string("input_lengths_tmp")]; + } -> (var_159); +} \ No newline at end of file diff --git a/iteration_3/compiled/text_encoder_fp16.mlmodelc/weights/weight.bin b/iteration_3/compiled/text_encoder_fp16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..f76c020d2909d0cc3cab131c76389ffc691aa3c1 --- /dev/null +++ b/iteration_3/compiled/text_encoder_fp16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d7f6e5869bb9d523956183e0facdff160c301d28113290efa329ae7bf72d3ce +size 11208000 diff --git a/iteration_3/packages/.DS_Store b/iteration_3/packages/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..9a9c3c5175eefc17adffba9c51a3e971025bd103 Binary files /dev/null and b/iteration_3/packages/.DS_Store differ diff --git a/iteration_3/swift/.DS_Store b/iteration_3/swift/.DS_Store new file mode 100644 index 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"iter3-bench-arm64-apple-macosx-release.exe": [""] + "iter3-tts-arm64-apple-macosx-release.exe": [""] + "main": ["","","",""] + "test": ["","","",""] +default: "main" +nodes: + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/": + is-directory-structure: true + content-exclusion-patterns: [".git",".build"] + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3TTS/": + is-directory-structure: true + content-exclusion-patterns: [".git",".build"] +commands: + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/sources": + tool: write-auxiliary-file + inputs: ["","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/main.swift"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/sources"] + description: "Write auxiliary file /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/sources" + + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/sources": + tool: write-auxiliary-file + inputs: ["","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3TTS/main.swift"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/sources"] + description: "Write auxiliary file /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/sources" + + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench.product/Objects.LinkFileList": + tool: write-auxiliary-file + inputs: ["","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/main.swift.o"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench.product/Objects.LinkFileList"] + description: "Write auxiliary file /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench.product/Objects.LinkFileList" + + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts.product/Objects.LinkFileList": + tool: write-auxiliary-file + inputs: ["","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/main.swift.o"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts.product/Objects.LinkFileList"] + description: "Write auxiliary file /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts.product/Objects.LinkFileList" + + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/swift-version--6988338F2F200930.txt": + tool: write-auxiliary-file + inputs: ["","/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/swiftc"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/swift-version--6988338F2F200930.txt"] + always-out-of-date: "true" + description: "Write auxiliary file /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/swift-version--6988338F2F200930.txt" + + "": + tool: phony + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3Bench.swiftmodule"] + outputs: [""] + + "": + tool: phony + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3TTS.swiftmodule"] + outputs: [""] + + "": + tool: phony + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench"] + outputs: [""] + + "": + tool: phony + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts"] + outputs: [""] + + "C.Iter3Bench-arm64-apple-macosx-release.module": + tool: shell + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/main.swift","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/swift-version--6988338F2F200930.txt","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/sources"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3Bench.swiftmodule"] + description: "Compiling Swift Module 'Iter3Bench' (1 sources)" + args: 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+ + "C.Iter3TTS-arm64-apple-macosx-release.module": + tool: shell + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3TTS/main.swift","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/swift-version--6988338F2F200930.txt","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/sources"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3TTS.swiftmodule"] + description: "Compiling Swift Module 'Iter3TTS' (1 sources)" + args: ["/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/swiftc","-module-name","Iter3TTS","-emit-dependencies","-emit-module","-emit-module-path","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3TTS.swiftmodule","-output-file-map","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/output-file-map.json","-whole-module-optimization","-num-threads","8","-c","@/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/sources","-I","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules","-target","arm64-apple-macosx14.0","-whole-module-optimization","-num-threads","8","-serialize-diagnostics","-O","-j8","-DSWIFT_PACKAGE","-DSWIFT_MODULE_RESOURCE_BUNDLE_UNAVAILABLE","-module-cache-path","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache","-parseable-output","-Xfrontend","-entry-point-function-name","-Xfrontend","Iter3TTS_main","-swift-version","5","-plugin-path","/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/host/plugins/testing","-sdk","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk","-F","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/Library/Frameworks","-I","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/usr/lib","-L","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/usr/lib","-g","-Xcc","-isysroot","-Xcc","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk","-Xcc","-F","-Xcc","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/Library/Frameworks","-Xcc","-fPIC","-Xcc","-g","-package-name","swift"] + + "C.iter3-bench-arm64-apple-macosx-release.exe": + tool: shell + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench.product/Objects.LinkFileList"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench"] + description: "Linking ./.build/arm64-apple-macosx/release/iter3-bench" + args: 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+ + "C.iter3-tts-arm64-apple-macosx-release.exe": + tool: shell + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts.product/Objects.LinkFileList"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts"] + description: "Linking ./.build/arm64-apple-macosx/release/iter3-tts" + args: 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+ + "PackageStructure": + tool: package-structure-tool + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3TTS/","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Package.swift","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Package.resolved"] + outputs: [""] + description: "Planning build" + allow-missing-inputs: true + diff --git a/iteration_3/swift/.build/release.yaml b/iteration_3/swift/.build/release.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fa170d7f1aec351f126c17867cd80e71079472dd --- /dev/null +++ b/iteration_3/swift/.build/release.yaml @@ -0,0 +1,107 @@ +client: + name: basic + file-system: device-agnostic +tools: {} +targets: + "Iter3Bench-arm64-apple-macosx-release.module": [""] + "Iter3TTS-arm64-apple-macosx-release.module": [""] + "PackageStructure": [""] + "iter3-bench-arm64-apple-macosx-release.exe": [""] + "iter3-tts-arm64-apple-macosx-release.exe": [""] + "main": ["","","",""] + "test": ["","","",""] +default: "main" +nodes: + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/": + is-directory-structure: true + content-exclusion-patterns: [".git",".build"] + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3TTS/": + is-directory-structure: true + content-exclusion-patterns: [".git",".build"] +commands: + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/sources": + tool: write-auxiliary-file + inputs: ["","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/main.swift"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/sources"] + description: "Write auxiliary file /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/sources" + + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/sources": + tool: write-auxiliary-file + inputs: ["","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3TTS/main.swift"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/sources"] + description: "Write auxiliary file /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/sources" + + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench.product/Objects.LinkFileList": + tool: write-auxiliary-file + inputs: ["","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/main.swift.o"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench.product/Objects.LinkFileList"] + description: "Write auxiliary file /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench.product/Objects.LinkFileList" + + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts.product/Objects.LinkFileList": + tool: write-auxiliary-file + inputs: ["","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/main.swift.o"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts.product/Objects.LinkFileList"] + description: "Write auxiliary file /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts.product/Objects.LinkFileList" + + "/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/swift-version--6988338F2F200930.txt": + tool: write-auxiliary-file + inputs: ["","/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/swiftc"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/swift-version--6988338F2F200930.txt"] + always-out-of-date: "true" + description: "Write auxiliary file /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/swift-version--6988338F2F200930.txt" + + "": + tool: phony + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3Bench.swiftmodule"] + outputs: [""] + + "": + tool: phony + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3TTS.swiftmodule"] + outputs: [""] + + "": + tool: phony + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench"] + outputs: [""] + + "": + tool: phony + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts"] + outputs: [""] + + "C.Iter3Bench-arm64-apple-macosx-release.module": + tool: shell + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/main.swift","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/swift-version--6988338F2F200930.txt","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/sources"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3Bench.swiftmodule"] + description: "Compiling Swift Module 'Iter3Bench' (1 sources)" + args: ["/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/swiftc","-module-name","Iter3Bench","-emit-dependencies","-emit-module","-emit-module-path","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3Bench.swiftmodule","-output-file-map","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/output-file-map.json","-whole-module-optimization","-num-threads","8","-c","@/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/sources","-I","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules","-target","arm64-apple-macosx14.0","-whole-module-optimization","-num-threads","8","-serialize-diagnostics","-O","-j8","-DSWIFT_PACKAGE","-DSWIFT_MODULE_RESOURCE_BUNDLE_UNAVAILABLE","-module-cache-path","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache","-parseable-output","-Xfrontend","-entry-point-function-name","-Xfrontend","Iter3Bench_main","-swift-version","5","-plugin-path","/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/host/plugins/testing","-sdk","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk","-F","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/Library/Frameworks","-I","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/usr/lib","-L","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/usr/lib","-g","-Xcc","-isysroot","-Xcc","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk","-Xcc","-F","-Xcc","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/Library/Frameworks","-Xcc","-fPIC","-Xcc","-g","-package-name","swift"] + + "C.Iter3TTS-arm64-apple-macosx-release.module": + tool: shell + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3TTS/main.swift","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/swift-version--6988338F2F200930.txt","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/sources"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3TTS.swiftmodule"] + description: "Compiling Swift Module 'Iter3TTS' (1 sources)" + args: ["/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/swiftc","-module-name","Iter3TTS","-emit-dependencies","-emit-module","-emit-module-path","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3TTS.swiftmodule","-output-file-map","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/output-file-map.json","-whole-module-optimization","-num-threads","8","-c","@/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/sources","-I","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules","-target","arm64-apple-macosx14.0","-whole-module-optimization","-num-threads","8","-serialize-diagnostics","-O","-j8","-DSWIFT_PACKAGE","-DSWIFT_MODULE_RESOURCE_BUNDLE_UNAVAILABLE","-module-cache-path","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache","-parseable-output","-Xfrontend","-entry-point-function-name","-Xfrontend","Iter3TTS_main","-swift-version","5","-plugin-path","/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/host/plugins/testing","-sdk","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk","-F","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/Library/Frameworks","-I","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/usr/lib","-L","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/usr/lib","-g","-Xcc","-isysroot","-Xcc","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk","-Xcc","-F","-Xcc","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/Library/Frameworks","-Xcc","-fPIC","-Xcc","-g","-package-name","swift"] + + "C.iter3-bench-arm64-apple-macosx-release.exe": + tool: shell + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench.product/Objects.LinkFileList"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench"] + description: "Linking ./.build/arm64-apple-macosx/release/iter3-bench" + args: ["/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/swiftc","-L","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release","-o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench","-module-name","iter3_bench","-Xlinker","-no_warn_duplicate_libraries","-emit-executable","-Xlinker","-dead_strip","-Xlinker","-alias","-Xlinker","_Iter3Bench_main","-Xlinker","_main","-Xlinker","-rpath","-Xlinker","@loader_path","@/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench.product/Objects.LinkFileList","-Xlinker","-rpath","-Xlinker","/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift-6.2/macosx","-target","arm64-apple-macosx14.0","-plugin-path","/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/host/plugins/testing","-sdk","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk","-F","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/Library/Frameworks","-I","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/usr/lib","-L","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/usr/lib","-g"] + + "C.iter3-tts-arm64-apple-macosx-release.exe": + tool: shell + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/main.swift.o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts.product/Objects.LinkFileList"] + outputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts"] + description: "Linking ./.build/arm64-apple-macosx/release/iter3-tts" + args: ["/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/swiftc","-L","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release","-o","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts","-module-name","iter3_tts","-Xlinker","-no_warn_duplicate_libraries","-emit-executable","-Xlinker","-dead_strip","-Xlinker","-alias","-Xlinker","_Iter3TTS_main","-Xlinker","_main","-Xlinker","-rpath","-Xlinker","@loader_path","@/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts.product/Objects.LinkFileList","-Xlinker","-rpath","-Xlinker","/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift-6.2/macosx","-target","arm64-apple-macosx14.0","-plugin-path","/Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/host/plugins/testing","-sdk","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk","-F","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/Library/Frameworks","-I","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/usr/lib","-L","/Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/usr/lib","-g"] + + "PackageStructure": + tool: package-structure-tool + inputs: ["/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3TTS/","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Package.swift","/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Package.resolved"] + outputs: [""] + description: "Planning build" + allow-missing-inputs: true + diff --git a/iteration_3/swift/.build/workspace-state.json b/iteration_3/swift/.build/workspace-state.json new file mode 100644 index 0000000000000000000000000000000000000000..7c0cb06983a96afc60d69f6a48955b7669c8cba3 --- /dev/null +++ b/iteration_3/swift/.build/workspace-state.json @@ -0,0 +1,14 @@ +{ + "object" : { + "artifacts" : [ + + ], + "dependencies" : [ + + ], + "prebuilts" : [ + + ] + }, + "version" : 7 +} \ No newline at end of file diff --git a/iteration_3/swift/Package.swift b/iteration_3/swift/Package.swift new file mode 100644 index 0000000000000000000000000000000000000000..70e6cbe03f0b30c7e94ff910534511091fd2a11b --- /dev/null +++ b/iteration_3/swift/Package.swift @@ -0,0 +1,15 @@ +// swift-tools-version:5.10 +import PackageDescription + +let package = Package( + name: "Iter3Bench", + platforms: [.macOS(.v14)], + products: [ + .executable(name: "iter3-bench", targets: ["Iter3Bench"]), + .executable(name: "iter3-tts", targets: ["Iter3TTS"]), + ], + targets: [ + .executableTarget(name: "Iter3Bench", path: "Sources/Iter3Bench"), + .executableTarget(name: "Iter3TTS", path: "Sources/Iter3TTS"), + ] +) diff --git a/iteration_3/swift/README.md b/iteration_3/swift/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5541c54158140ddbcbecb58218dfebc6aca77367 --- /dev/null +++ b/iteration_3/swift/README.md @@ -0,0 +1,94 @@ +# iter3-bench (Swift) + +Self-contained Swift CLI that loads each iteration_3 `.mlmodelc` with +the placement recommended by `_STAGE_COMPUTE` in +`coreml/inference.py`, runs four warm predictions per stage on +synthesised inputs (shape resolved from each model's own description), +and reports load + warm latency. + +## Build & run + +```bash +# 1. Compile mlpackages (one-time) +DST=../compiled +SRC=../packages +mkdir -p "$DST" +for pkg in "$SRC"/*.mlpackage; do + xcrun coremlcompiler compile "$pkg" "$DST" +done + +# 2. Build & run the Swift bench +swift build -c release +.build/release/iter3-bench --compiled ../compiled +``` + +## Sample output (M-series Mac) + +``` + [text_encoder | CPU_ONLY ] load=33ms warm: min=1.1 avg=1.2 max=1.5 ms + [bert | ALL ] load=607ms warm: min=6.6 avg=8.8 max=12.7 ms + [ref_encoder | CPU_AND_GPU] load=236ms warm: min=11.1 avg=12.1 max=14.0 ms + [fused_diffusion_sampler | ALL ] load=1394ms warm: min=14.2 avg=16.9 max=23.9 ms + [duration_predictor | CPU_ONLY ] load=123ms warm: min=2.4 avg=2.5 max=2.7 ms + [fused_f0n_har_source | CPU_ONLY ] load=189ms warm: min=10.7 avg=10.9 max=11.2 ms + [decoder_pre | CPU_AND_NE ] load=1461ms warm: min=3.8 avg=3.9 max=4.0 ms + [decoder_upsample | CPU_ONLY ] load=1022ms warm: min=278.0 avg=304.2 max=375.8 ms +``` + +Pipeline-stage sum ≈ 360 ms (synthetic inputs). + +## Scope + +`iter3-bench` is a **scaffolding** sanity check — it proves all 8 +mlmodelc stages load and predict in Swift with the documented +placement, and gives a baseline for per-stage cost without leaving +Swift. It does **not** produce audio (synthetic random inputs). + +## `iter3-tts` (side-loaded audio) + +A second target wires the same 8 stages into a real-audio path by +side-loading the Python eager glue (phonemizer, ref-mel extraction, +alignment matmul + asr-shift, s/ref split) as on-disk fixtures. + +```bash +# 1. Dump every stage's input + output as .npy fixtures +cd .. # styletts2 root +uv run python iteration_3/swift/dump_intermediates.py \ + --text "StyleTTS 2 is a text to speech model." \ + --reference reference_audio/696_92939_000016_000006.wav + +# 2. Run the Swift consumer +cd iteration_3/swift +swift build -c release +.build/release/iter3-tts \ + --compiled ../compiled \ + --fixtures fixtures \ + --output fixtures_swift.wav +``` + +Sample output (warm): + +``` + [text_encoder | CPU_ONLY ] load=37ms predict=3.0ms + [bert | ALL ] load=160ms predict=137.5ms + [ref_encoder | CPU_AND_GPU] load=39ms predict=66.8ms + [fused_diffusion_sampler | ALL ] load=71ms predict=149.8ms + [duration_predictor | CPU_ONLY ] load=16ms predict=3.9ms + [fused_f0n_har_source | CPU_ONLY ] load=20ms predict=14.7ms + [decoder_pre | CPU_AND_NE ] load=41ms predict=6.1ms + [decoder_upsample | CPU_ONLY ] load=70ms predict=289.4ms +Pipeline total: 1148ms +Wrote fixtures_swift.wav (3.67s @ 24000 Hz) +``` + +Parity vs `fixtures_python.wav`: cosine similarity 1.000000, +max|Δ| ≈ 3×10⁻⁵ (within int16 PCM quantization). + +To extend to a fully-Swift end-to-end pipeline, port the eager glue +to Swift: + +* phoneme tokenisation (espeak + TextCleaner) +* reference-audio mel extraction (for `ref_encoder.mel`) +* alignment matmul `d_en @ pred_aln_trg` and asr-shift to build the + predictor input `en` for `fused_f0n_har_source` +* `s`/`ref` split off `fused_diffusion_sampler.var_6189` diff --git a/iteration_3/swift/Sources/.DS_Store b/iteration_3/swift/Sources/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..554a68f2f83ff5f5c2d5ab06157d3ea16059ff07 Binary files /dev/null and b/iteration_3/swift/Sources/.DS_Store differ diff --git a/iteration_3/swift/Sources/Iter3Bench/main.swift b/iteration_3/swift/Sources/Iter3Bench/main.swift new file mode 100644 index 0000000000000000000000000000000000000000..9656f8916f94bacd90f8f8db001f61a3546800ed --- /dev/null +++ b/iteration_3/swift/Sources/Iter3Bench/main.swift @@ -0,0 +1,312 @@ +// Iter3Bench: load each iteration_3 .mlmodelc with the placement +// recommended by `_STAGE_COMPUTE` in coreml/inference.py, run a couple +// of warm predictions on synthesised inputs (shape resolved from the +// model description itself), and report per-stage timing. +// +// Build & run: +// cd iteration_3/swift +// swift build -c release +// .build/release/iter3-bench --compiled ../compiled +// +// To produce real audio, swap the synthetic inputs for tensors captured +// from `coreml.inference` (or write the eager-glue stages — alignment +// matmul, asr-shift, s/ref split — in Swift). This binary is intended +// as a scaffolding sanity check that all 8 stages load and predict in +// the placement matrix the Python pipeline recommends. + +import CoreML +import Foundation + +// MARK: - Stage manifest + +struct StageSpec { + let name: String + let modelFile: String + let computeUnits: MLComputeUnits +} + +let MANIFEST: [StageSpec] = [ + StageSpec(name: "text_encoder", + modelFile: "text_encoder_fp16.mlmodelc", + computeUnits: .cpuOnly), + StageSpec(name: "bert", + modelFile: "bert_fp16.mlmodelc", + computeUnits: .all), + StageSpec(name: "ref_encoder", + modelFile: "ref_encoder_fp16.mlmodelc", + computeUnits: .cpuAndGPU), + StageSpec(name: "fused_diffusion_sampler", + modelFile: "fused_diffusion_sampler_fp16.mlmodelc", + computeUnits: .all), + StageSpec(name: "duration_predictor", + modelFile: "duration_predictor_fp16.mlmodelc", + computeUnits: .cpuOnly), + StageSpec(name: "fused_f0n_har_source", + modelFile: "fused_f0n_har_source.mlmodelc", + computeUnits: .cpuOnly), + StageSpec(name: "decoder_pre", + modelFile: "decoder_pre_fp16.mlmodelc", + computeUnits: .cpuAndNeuralEngine), + StageSpec(name: "decoder_upsample", + modelFile: "decoder_upsample_fp16.mlmodelc", + computeUnits: .cpuOnly), +] + +// MARK: - Helpers + +enum BenchError: Error { + case unresolvedShape(String) + case unsupportedFeatureType(String) + case missingInput(String) +} + +extension MLComputeUnits { + var label: String { + switch self { + case .cpuOnly: return "CPU_ONLY" + case .cpuAndGPU: return "CPU_AND_GPU" + case .cpuAndNeuralEngine: return "CPU_AND_NE" + case .all: return "ALL" + @unknown default: return "?" + } + } +} + +/// Resolve a possibly-flexible NSArray shape to a concrete +/// `[Int]`. RangeDim placeholders sometimes show up as `0` on the +/// description; we substitute a representative default so the Swift +/// caller can build a tensor of that size. +func concreteShape( + inputName: String, + constraint: MLMultiArrayConstraint +) throws -> [Int] { + let raw = constraint.shape.map { $0.intValue } + var resolved: [Int] = [] + for (axis, dim) in raw.enumerated() { + if dim > 0 { + resolved.append(dim) + } else { + // Try the enumerated/range constraint to get a concrete pick. + let shapeConstraint = constraint.shapeConstraint + switch shapeConstraint.type { + case .enumerated: + guard let candidate = shapeConstraint.enumeratedShapes.first else { + throw BenchError.unresolvedShape( + "\(inputName): empty enumerated shape on axis \(axis)") + } + resolved.append(candidate[axis].intValue) + case .range: + let ranges = shapeConstraint.sizeRangeForDimension + let r = ranges[axis].rangeValue + let lower = r.location + let upper = r.location + r.length + // Pick a representative size (147 frames is the sample + // used in the Python conversion script). + let repValue = max(lower, min(upper, 147)) + resolved.append(repValue) + case .unspecified: + throw BenchError.unresolvedShape( + "\(inputName): unspecified shape on axis \(axis)") + @unknown default: + throw BenchError.unresolvedShape( + "\(inputName): unknown shape constraint") + } + } + } + return resolved +} + +/// Build a synthetic MLMultiArray matching a constraint, filled with +/// modest pseudo-random values. Integer-typed inputs are filled with +/// 0.. MLMultiArray { + let shape = try concreteShape(inputName: inputName, constraint: constraint) + let nsShape = shape.map { NSNumber(value: $0) } + let array = try MLMultiArray(shape: nsShape, dataType: constraint.dataType) + let count = array.count + switch constraint.dataType { + case .float32: + let ptr = array.dataPointer.bindMemory(to: Float.self, capacity: count) + for i in 0.. UInt16 { + let bits = value.bitPattern + let sign = UInt16((bits >> 31) & 0x1) << 15 + let exp = Int((bits >> 23) & 0xff) - 127 + 15 + let frac = bits & 0x7fffff + if exp <= 0 { + return sign + } + if exp >= 0x1f { + return sign | (0x1f << 10) + } + return sign | (UInt16(exp) << 10) | UInt16(frac >> 13) +} + +func buildInputs(model: MLModel) throws -> MLDictionaryFeatureProvider { + var dict: [String: MLFeatureValue] = [:] + for (name, desc) in model.modelDescription.inputDescriptionsByName { + guard let constraint = desc.multiArrayConstraint else { + // String / image / sequence inputs: not used by StyleTTS2 stages. + throw BenchError.unsupportedFeatureType("\(name) is non-multiArray") + } + let array = try makeArray(inputName: name, constraint: constraint) + dict[name] = MLFeatureValue(multiArray: array) + } + return try MLDictionaryFeatureProvider(dictionary: dict) +} + +// MARK: - Runner + +func benchOne( + spec: StageSpec, + compiledRoot: URL, + iterations: Int = 4 +) -> Bool { + let modelURL = compiledRoot.appendingPathComponent(spec.modelFile) + guard FileManager.default.fileExists(atPath: modelURL.path) else { + print(" [\(spec.name)] missing: \(modelURL.path)") + return false + } + let cfg = MLModelConfiguration() + cfg.computeUnits = spec.computeUnits + + let loadStart = DispatchTime.now() + let model: MLModel + do { + model = try MLModel(contentsOf: modelURL, configuration: cfg) + } catch { + print(" [\(spec.name)] load failed: \(error)") + return false + } + let loadMs = + Double(DispatchTime.now().uptimeNanoseconds - loadStart.uptimeNanoseconds) / 1e6 + + fputs(" [\(spec.name)] loaded; building inputs…\n", stderr) + let inputs: MLDictionaryFeatureProvider + do { + inputs = try buildInputs(model: model) + } catch { + print(" [\(spec.name)] input synth failed: \(error)") + return false + } + fputs(" [\(spec.name)] inputs built; warmup predict…\n", stderr) + + // Warmup + do { + _ = try model.prediction(from: inputs) + } catch { + print(" [\(spec.name)] warmup predict failed: \(error)") + return false + } + fputs(" [\(spec.name)] warmup done\n", stderr) + + var times: [Double] = [] + times.reserveCapacity(iterations) + for i in 0.. String { + s.count >= w ? s : s + String(repeating: " ", count: w - s.count) + } + func num(_ d: Double, _ frac: Int = 1) -> String { + String(format: "%.\(frac)f", d) + } + let line = + " [\(pad(spec.name, 25)) | \(pad(spec.computeUnits.label, 11))] " + + "load=\(num(loadMs, 0))ms warm: min=\(num(mn)) avg=\(num(av)) max=\(num(mx)) ms" + print(line) + print(" inputs: \(model.modelDescription.inputDescriptionsByName.keys.sorted().joined(separator: ", "))") + print(" outputs: \(outputs)") + return true +} + +// MARK: - Entry + +func parseCompiledRoot() -> URL { + let args = CommandLine.arguments + if let i = args.firstIndex(of: "--compiled"), i + 1 < args.count { + return URL(fileURLWithPath: args[i + 1]) + } + // Default: ../compiled relative to swift/ folder + let exe = URL(fileURLWithPath: args[0]) + let candidate = exe + .deletingLastPathComponent() // .build/release + .deletingLastPathComponent() // .build + .deletingLastPathComponent() // swift + .appendingPathComponent("compiled") + return candidate +} + +let compiledRoot = parseCompiledRoot() +print("iter3-bench") +print(" compiled root: \(compiledRoot.path)") +print("") + +var ok = 0 +var fail = 0 +for spec in MANIFEST { + fputs(">>> running \(spec.name)\n", stderr) + if benchOne(spec: spec, compiledRoot: compiledRoot) { + ok += 1 + } else { + fail += 1 + } + fputs("<<< done \(spec.name)\n", stderr) +} + +print("") +print("\(ok)/\(MANIFEST.count) stages OK; \(fail) failed") +exit(fail == 0 ? 0 : 1) diff --git a/iteration_3/swift/Sources/Iter3TTS/main.swift b/iteration_3/swift/Sources/Iter3TTS/main.swift new file mode 100644 index 0000000000000000000000000000000000000000..36f8f1571b19641c869a0f80013b5258402d7a27 --- /dev/null +++ b/iteration_3/swift/Sources/Iter3TTS/main.swift @@ -0,0 +1,505 @@ +// Iter3TTS: side-loaded CoreML pipeline. +// +// Reads .npy fixtures dumped by `dump_intermediates.py`, runs each +// iteration_3 .mlmodelc stage's predict in Swift with the documented +// placement, and writes a 24 kHz mono WAV from decoder_upsample's +// output. Inter-stage glue (alignment matmul, asr-shift, s/ref split) +// is *not* re-implemented here — the dumper precomputes each stage's +// inputs in Python. +// +// Build & run: +// cd iteration_3/swift +// swift build -c release +// .build/release/iter3-tts \ +// --compiled ../compiled \ +// --fixtures fixtures \ +// --output fixtures_swift.wav + +import CoreML +import Foundation + +// MARK: - Stage placement (mirrors Iter3Bench) + +struct StageSpec { + let name: String + let modelFile: String + let computeUnits: MLComputeUnits +} + +let MANIFEST: [StageSpec] = [ + StageSpec(name: "text_encoder", + modelFile: "text_encoder_fp16.mlmodelc", + computeUnits: .cpuOnly), + StageSpec(name: "bert", + modelFile: "bert_fp16.mlmodelc", + computeUnits: .all), + StageSpec(name: "ref_encoder", + modelFile: "ref_encoder_fp16.mlmodelc", + computeUnits: .cpuAndGPU), + StageSpec(name: "fused_diffusion_sampler", + modelFile: "fused_diffusion_sampler_fp16.mlmodelc", + computeUnits: .all), + StageSpec(name: "duration_predictor", + modelFile: "duration_predictor_fp16.mlmodelc", + computeUnits: .cpuOnly), + StageSpec(name: "fused_f0n_har_source", + modelFile: "fused_f0n_har_source.mlmodelc", + computeUnits: .cpuOnly), + StageSpec(name: "decoder_pre", + modelFile: "decoder_pre_fp16.mlmodelc", + computeUnits: .cpuAndNeuralEngine), + StageSpec(name: "decoder_upsample", + modelFile: "decoder_upsample_fp16.mlmodelc", + computeUnits: .cpuOnly), +] + +let SAMPLE_RATE = 24_000 + +// MARK: - Errors + +enum TTSError: Error, CustomStringConvertible { + case missing(String) + case unsupportedDtype(String) + case manifestShape(String) + case predict(String) + case io(String) + case parse(String) + + var description: String { + switch self { + case .missing(let s): return "missing: \(s)" + case .unsupportedDtype(let s): return "unsupported dtype: \(s)" + case .manifestShape(let s): return "manifest/shape mismatch: \(s)" + case .predict(let s): return "predict: \(s)" + case .io(let s): return "io: \(s)" + case .parse(let s): return "parse: \(s)" + } + } +} + +extension MLComputeUnits { + var label: String { + switch self { + case .cpuOnly: return "CPU_ONLY" + case .cpuAndGPU: return "CPU_AND_GPU" + case .cpuAndNeuralEngine: return "CPU_AND_NE" + case .all: return "ALL" + @unknown default: return "?" + } + } +} + +// MARK: - .npy reader (v1.0/v2.0/v3.0, C-contiguous, ' NpyArray { + let blob = try Data(contentsOf: url, options: .alwaysMapped) + if blob.count < 10 { throw TTSError.parse("\(url.path): too small") } + + // Magic + let magic: [UInt8] = [0x93, 0x4E, 0x55, 0x4D, 0x50, 0x59] + for i in 0..<6 where blob[i] != magic[i] { + throw TTSError.parse("\(url.path): bad magic") + } + let major = blob[6] + let _ = blob[7] + var headerLen: Int + var headerStart: Int + switch major { + case 1: + let lo = Int(blob[8]) + let hi = Int(blob[9]) + headerLen = lo | (hi << 8) + headerStart = 10 + case 2, 3: + let b8 = Int(blob[8]) + let b9 = Int(blob[9]) + let b10 = Int(blob[10]) + let b11 = Int(blob[11]) + headerLen = b8 | (b9 << 8) | (b10 << 16) | (b11 << 24) + headerStart = 12 + default: + throw TTSError.parse("\(url.path): unsupported npy version \(major)") + } + let headerEnd = headerStart + headerLen + guard headerEnd <= blob.count else { + throw TTSError.parse("\(url.path): truncated header") + } + let headerData = blob[headerStart.. String { + guard let r = header.range(of: "'\(key)'") else { + throw TTSError.parse("\(url.path): missing key '\(key)'") + } + let after = header[r.upperBound...] + guard let colon = after.firstIndex(of: ":") else { + throw TTSError.parse("\(url.path): malformed '\(key)'") + } + let rest = after[after.index(after: colon)...].drop(while: { $0 == " " }) + // Value is up to next comma at depth 0 (parens count). + var depth = 0 + var end = rest.startIndex + for idx in rest.indices { + let c = rest[idx] + if c == "(" || c == "[" { depth += 1 } + else if c == ")" || c == "]" { depth -= 1 } + else if c == "," && depth == 0 { end = idx; break } + end = rest.index(after: idx) + } + return String(rest[rest.startIndex.. MLMultiArray { + let nsShape = npy.shape.map { NSNumber(value: $0) } + let strides = computeStrides(npy.shape).map { NSNumber(value: $0) } + return try MLMultiArray( + dataPointer: npy.dataPointer, + shape: nsShape, + dataType: npy.mlDataType, + strides: strides, + deallocator: nil) +} + +func computeStrides(_ shape: [Int]) -> [Int] { + var strides = Array(repeating: 1, count: shape.count) + if shape.count <= 1 { return strides } + for i in (0..<(shape.count - 1)).reversed() { + strides[i] = strides[i + 1] * shape[i + 1] + } + return strides +} + +// MARK: - Manifest + +struct StageCall { + struct Field { + let name: String + let shape: [Int] + let dtype: String + } + let dir: String + let inputs: [Field] + let outputs: [Field] +} + +struct ManifestData { + let stageOrder: [String] + let calls: [String: StageCall] +} + +func loadManifest(_ url: URL) throws -> ManifestData { + let data = try Data(contentsOf: url) + guard let any = try? JSONSerialization.jsonObject(with: data) else { + throw TTSError.parse("manifest.json: not JSON") + } + guard let root = any as? [String: Any], + let order = root["stage_order"] as? [String], + let stages = root["stages"] as? [String: Any] + else { + throw TTSError.parse("manifest.json: missing stage_order/stages") + } + + var out: [String: StageCall] = [:] + for s in order { + guard let stageDict = stages[s] as? [String: Any], + let calls = stageDict["calls"] as? [[String: Any]], + let call = calls.first + else { + throw TTSError.parse("manifest.json: missing stage \(s)") + } + let dir = (call["dir"] as? String) ?? s + func parseFields(_ key: String) throws -> [StageCall.Field] { + guard let arr = call[key] as? [[String: Any]] else { + throw TTSError.parse("manifest.json: \(s).\(key) malformed") + } + return try arr.map { d in + guard let n = d["name"] as? String, + let sh = d["shape"] as? [Int], + let dt = d["dtype"] as? String + else { + throw TTSError.parse("manifest.json: bad field in \(s).\(key)") + } + return StageCall.Field(name: n, shape: sh, dtype: dt) + } + } + out[s] = StageCall( + dir: dir, + inputs: try parseFields("inputs"), + outputs: try parseFields("outputs")) + } + return ManifestData(stageOrder: order, calls: out) +} + +// MARK: - WAV writer (mono float32 → int16 little-endian PCM) + +func writeWavMonoF32(samples: [Float], sampleRate: Int, to url: URL) throws { + let n = samples.count + let byteRate = sampleRate * 2 + let dataSize = n * 2 + let chunkSize = 36 + dataSize + + var data = Data() + data.reserveCapacity(44 + dataSize) + + func appendString(_ s: String) { + data.append(s.data(using: .ascii)!) + } + func appendU32LE(_ v: UInt32) { + var x = v.littleEndian + withUnsafeBytes(of: &x) { data.append(contentsOf: $0) } + } + func appendU16LE(_ v: UInt16) { + var x = v.littleEndian + withUnsafeBytes(of: &x) { data.append(contentsOf: $0) } + } + + appendString("RIFF") + appendU32LE(UInt32(chunkSize)) + appendString("WAVE") + appendString("fmt ") + appendU32LE(16) // PCM fmt-chunk size + appendU16LE(1) // PCM format + appendU16LE(1) // mono + appendU32LE(UInt32(sampleRate)) + appendU32LE(UInt32(byteRate)) + appendU16LE(2) // block align + appendU16LE(16) // bits per sample + appendString("data") + appendU32LE(UInt32(dataSize)) + + // Float32 [-1, 1] → Int16 + var pcm = [Int16](repeating: 0, count: n) + for i in 0.. Args { + let argv = CommandLine.arguments + func read(_ flag: String, _ defaultURL: URL) -> URL { + if let i = argv.firstIndex(of: flag), i + 1 < argv.count { + return URL(fileURLWithPath: argv[i + 1]) + } + return defaultURL + } + let cwd = URL(fileURLWithPath: FileManager.default.currentDirectoryPath) + return Args( + compiledRoot: read("--compiled", cwd.appendingPathComponent("../compiled")), + fixtures: read("--fixtures", cwd.appendingPathComponent("fixtures")), + output: read("--output", cwd.appendingPathComponent("fixtures_swift.wav"))) +} + +func runStage( + spec: StageSpec, + call: StageCall, + fixtures: URL, + compiledRoot: URL +) throws -> [String: MLMultiArray] { + let modelURL = compiledRoot.appendingPathComponent(spec.modelFile) + guard FileManager.default.fileExists(atPath: modelURL.path) else { + throw TTSError.missing(modelURL.path) + } + let cfg = MLModelConfiguration() + cfg.computeUnits = spec.computeUnits + + let loadStart = DispatchTime.now() + let model = try MLModel(contentsOf: modelURL, configuration: cfg) + let loadMs = + Double(DispatchTime.now().uptimeNanoseconds - loadStart.uptimeNanoseconds) / 1e6 + + // Build inputs from .npy fixtures. + let stageDir = fixtures.appendingPathComponent(call.dir) + var feed: [String: MLFeatureValue] = [:] + var heldArrays: [NpyArray] = [] + for f in call.inputs { + let url = stageDir.appendingPathComponent("in_\(f.name).npy") + let npy = try loadNpy(at: url) + if npy.shape != f.shape { + throw TTSError.manifestShape( + "\(spec.name).\(f.name): manifest \(f.shape) vs npy \(npy.shape)") + } + let arr = try makeMultiArray(npy) + feed[f.name] = MLFeatureValue(multiArray: arr) + heldArrays.append(npy) // keep buffer alive + } + let provider = try MLDictionaryFeatureProvider(dictionary: feed) + + let predStart = DispatchTime.now() + let result: MLFeatureProvider + do { + result = try model.prediction(from: provider) + } catch { + throw TTSError.predict("\(spec.name): \(error)") + } + let predMs = + Double(DispatchTime.now().uptimeNanoseconds - predStart.uptimeNanoseconds) / 1e6 + + var outputs: [String: MLMultiArray] = [:] + for f in call.outputs { + guard let v = result.featureValue(for: f.name)?.multiArrayValue else { + throw TTSError.predict("\(spec.name): missing output \(f.name)") + } + outputs[f.name] = v + } + + func pad(_ s: String, _ w: Int) -> String { + s.count >= w ? s : s + String(repeating: " ", count: w - s.count) + } + print( + " [\(pad(spec.name, 25)) | \(pad(spec.computeUnits.label, 11))] " + + "load=\(Int(loadMs))ms predict=\(String(format: "%.1f", predMs))ms") + + _ = heldArrays // explicit: buffers outlive predict() + return outputs +} + +// MARK: - Entry + +let args = parseArgs() +print("iter3-tts") +print(" compiled root: \(args.compiledRoot.path)") +print(" fixtures: \(args.fixtures.path)") +print(" output: \(args.output.path)") +print("") + +let manifestURL = args.fixtures.appendingPathComponent("manifest.json") +let manifest = try loadManifest(manifestURL) +let stageByName = Dictionary(uniqueKeysWithValues: MANIFEST.map { ($0.name, $0) }) + +var lastOutputs: [String: MLMultiArray] = [:] +let totalStart = DispatchTime.now() +for stageName in manifest.stageOrder { + guard let spec = stageByName[stageName] else { + throw TTSError.parse("no MANIFEST entry for \(stageName)") + } + guard let call = manifest.calls[stageName] else { + throw TTSError.parse("no manifest call for \(stageName)") + } + lastOutputs = try runStage( + spec: spec, + call: call, + fixtures: args.fixtures, + compiledRoot: args.compiledRoot) +} +let totalMs = + Double(DispatchTime.now().uptimeNanoseconds - totalStart.uptimeNanoseconds) / 1e6 +print("\nPipeline total: \(Int(totalMs))ms") + +// Final stage's first (and only) output is the audio: shape (1, 1, T). +guard let lastSpec = manifest.stageOrder.last, + let call = manifest.calls[lastSpec], + let firstOutName = call.outputs.first?.name, + let audioArr = lastOutputs[firstOutName] +else { + throw TTSError.parse("no audio output from final stage") +} +let audioCount = audioArr.count +var samples = [Float](repeating: 0, count: audioCount) +switch audioArr.dataType { +case .float32: + let p = audioArr.dataPointer.bindMemory(to: Float.self, capacity: audioCount) + for i in 0../{in_*.npy, out_*.npy}` +plus a `manifest.json` describing shapes, dtypes, and the stage order. +Also writes `iteration_3/swift/fixtures_python.wav` for parity check. +""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import numpy as np + + +HERE = Path(__file__).resolve().parent # iteration_3/swift +STYLETTS_ROOT = HERE.parent.parent # models/tts/styletts2 +FIXTURES = HERE / "fixtures" +FIXTURES.mkdir(parents=True, exist_ok=True) + +# Make `coreml.inference` importable. +if str(STYLETTS_ROOT) not in sys.path: + sys.path.insert(0, str(STYLETTS_ROOT)) + +import coreml.inference as inf # noqa: E402 + +orig_load = inf._load_stage +orig_predict = inf._predict + +# stage_name → {"compute": str, "precision": str, "inputs": [...], "outputs": [...]} +manifest: dict[str, dict] = {} +# stage_name → call index (some stages run >1 time, e.g. diffusion_unet +# in the unfused path; under iteration_3 every stage runs once) +call_counts: dict[str, int] = {} + + +def _np_dtype_str(arr: np.ndarray) -> str: + return str(arr.dtype) + + +def patched_load(stage, *, precision=None, compute_units=None): + m = orig_load(stage, precision=precision, compute_units=compute_units) + setattr(m, "_dump_stage", stage) + setattr(m, "_dump_precision", precision or inf._STAGE_PRECISION[stage]) + setattr(m, "_dump_compute", str(compute_units or inf._STAGE_COMPUTE[stage]).split(".")[-1]) + return m + + +def patched_predict(model, feed): + stage = getattr(model, "_dump_stage", None) + if stage is None: + return orig_predict(model, feed) + + idx = call_counts.get(stage, 0) + call_counts[stage] = idx + 1 + suffix = "" if idx == 0 else f"_call{idx}" + + out_dir = FIXTURES / f"{stage}{suffix}" + out_dir.mkdir(parents=True, exist_ok=True) + + inputs_meta = [] + for k, v in feed.items(): + arr = np.ascontiguousarray(np.asarray(v)) + np.save(out_dir / f"in_{k}.npy", arr) + inputs_meta.append({"name": k, "shape": list(arr.shape), "dtype": _np_dtype_str(arr)}) + + outs = orig_predict(model, feed) + out_names = inf._spec_outputs_in_order(model) + outputs_meta = [] + for n, arr in zip(out_names, outs): + arr = np.ascontiguousarray(np.asarray(arr)) + np.save(out_dir / f"out_{n}.npy", arr) + outputs_meta.append({"name": n, "shape": list(arr.shape), "dtype": _np_dtype_str(arr)}) + + manifest.setdefault(stage, { + "compute": getattr(model, "_dump_compute", "?"), + "precision": getattr(model, "_dump_precision", "?"), + "calls": [], + }) + manifest[stage]["calls"].append({ + "index": idx, + "dir": f"{stage}{suffix}", + "inputs": inputs_meta, + "outputs": outputs_meta, + }) + return outs + + +inf._load_stage = patched_load +inf._predict = patched_predict + + +def main() -> int: + # Default --output to a sibling WAV next to fixtures/. + new_argv = ["dump_intermediates.py"] + user_args = sys.argv[1:] + if "--output" not in user_args: + new_argv += ["--output", str(HERE / "fixtures_python.wav")] + new_argv += user_args + sys.argv = new_argv + + ret = inf.main() + + # Stage order is the order in which stages were first loaded (which + # matches inference.py's load order: text_encoder, bert, + # ref_encoder, fused_diffusion_sampler, duration_predictor, + # fused_f0n_har_source, decoder_pre, decoder_upsample). + ordered = list(manifest.keys()) + out = { + "version": 1, + "sample_rate": 24000, + "stage_order": ordered, + "stages": manifest, + } + (FIXTURES / "manifest.json").write_text(json.dumps(out, indent=2)) + print(f"\nDumped {len(manifest)} stages to {FIXTURES}") + print(f"Stage order: {ordered}") + return ret + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/iteration_3/swift/fixtures/.DS_Store b/iteration_3/swift/fixtures/.DS_Store new 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