Instructions to use LanzK/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LanzK/test with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LanzK/test", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 2fd93a713925af1f559b542ba0596ad74ce265206dc77f00f198f0b3c8b9b74e
- Size of remote file:
- 492 MB
- SHA256:
- 679e202be6332d4a7c1838ba7acdb61d9dcb1135682287003c3728930c9850a6
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