Image-to-Image
Diffusers
ONNX
Safetensors
StableDiffusionXLInpaintPipeline
stable-diffusion-xl
inpainting
virtual try-on
Instructions to use yisol/IDM-VTON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yisol/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("yisol/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
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# IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild
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This is an official implementation of paper 'Improving Diffusion Models for Authentic Virtual Try-on in the Wild'
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# Check out more codes on our [github repository](https://github.com/yisol/IDM-VTON)!
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# IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild
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This is an official implementation of paper 'Improving Diffusion Models for Authentic Virtual Try-on in the Wild'
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