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
- Xet hash:
- 35975e7735178a0d3787a9d73acf74e18aaa9d4564e72c734e90b04610dfbb27
- Size of remote file:
- 10.3 GB
- SHA256:
- 357650fbfb3c7b4d94c1f5fd7664da819ad1ff5a839430484b4ec422d03f710a
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