yisol / IDM-VTON
[ECCV2024] IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild
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Crawler viewIDM-VTON: Improving Diffusion Models for Authentic Virtual Try-on in the Wild This is the official implementation of the paper "Improving Diffusion Models for Authentic Virtual Try-on in the Wild". Star ⭐ us if you like it! --- Requirements Data preparation VITON-HD You can download VITON-HD dataset from VITON-HD. After download VITON-HD dataset, move vitonhd_test_tagged.json into the test folder, and move vitonhd_train_tagged.json into the train folder. Structure of the Dataset directory should be as follows. DressCode You can download DressCode dataset from DressCode. We provide pre-computed densepose images and captions for garments here. We used detectron2 for obtaining densepose images, refer here for more details. After download the DressCode dataset, place image-densepose directories and caption text files as follows. Training Preparation Download pre-trained ip-adapter for sdxl(IP-Adapter/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin) and image encoder(IP-Adapter/models/image_encoder) here. Move ip-adapter to ckpt/ip_adapter, and image encoder to ckpt/image_encoder. Start training using python file with arguments, or, you can simply run with the script file. Inference VITON-HD Inference using python file with arguments, or, you can simply run with the script file. DressCode For DressCode dataset, put the category you want to generate images via category argument, or, you can simply run with the script file. Start a local gradio demo Download checkpoints for human parsing here. Place the checkpoints under the ckpt folder. Run the following command: Acknowledgements Thanks ZeroGPU for providing free GPU. Thanks IP-Adapter for base codes. Thanks OOTDiffusion and DCI-VTON for masking generation. Thanks SCHP for human segmentation. Thanks Densepose for human densepose. Star History Citation License The codes and checkpoints in this repository are under the CC BY-NC-SA 4.0 license.