Improving Diffusion Models for Virtual Try-on
Paper • 2403.05139 • Published • 6
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("yisol/IDM-VTON-DC", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Check out more codes on our github repository!
IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild
This is an official implementation of paper 'Improving Diffusion Models for Authentic Virtual Try-on in the Wild'
🤗 Try our huggingface Demo
TODO LIST
- demo model
- inference code
- training code
Acknowledgements
For the demo, GPUs are supported from zerogpu, and auto masking generation codes are based on OOTDiffusion and DCI-VTON.
Parts of the code are based on IP-Adapter.
Citation
@article{choi2024improving,
title={Improving Diffusion Models for Virtual Try-on},
author={Choi, Yisol and Kwak, Sangkyung and Lee, Kyungmin and Choi, Hyungwon and Shin, Jinwoo},
journal={arXiv preprint arXiv:2403.05139},
year={2024}
}
License
The codes and checkpoints in this repository are under the CC BY-NC-SA 4.0 license.
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