Image-to-Image
Diffusers
ONNX
Safetensors
StableDiffusionXLInpaintPipeline
stable-diffusion-xl
inpainting
virtual try-on
Instructions to use ModelsLab/IDM-VTON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ModelsLab/IDM-VTON with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ModelsLab/IDM-VTON", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 750 Bytes
23b8855 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | {
"_class_name": "StableDiffusionXLInpaintPipeline",
"_diffusers_version": "0.25.0",
"_name_or_path": "stabilityai/stable-diffusion-xl-base-1.0",
"force_zeros_for_empty_prompt": true,
"requires_aesthetics_score": false,
"scheduler": [
"diffusers",
"DDPMScheduler"
],
"text_encoder": [
"transformers",
"CLIPTextModel"
],
"feature_extractor": [
"transformers",
"CLIPImageProcessor"
],
"text_encoder_2": [
"transformers",
"CLIPTextModelWithProjection"
],
"tokenizer": [
"transformers",
"CLIPTokenizer"
],
"tokenizer_2": [
"transformers",
"CLIPTokenizer"
],
"unet": [
"diffusers",
"UNet2DConditionModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
} |