CAT-Tryoff-Flux is an advanced tryoff model. It used the same method of (CATVTON-FLUX)[ This model can extract and reconstruct the front view of clothing items from images of people wearing them.
Source du modèle
Description de la source
CAT-Tryoff-Flux is an advanced tryoff model. It used the same method of (CATVTON-FLUX)[https://huggingface.co/xiaozaa/catvton-flux-alpha]. This model can extract and reconstruct the front view of clothing items from images of people wearing them.
Sources
1 sourceVérifié 15 oct.
Artefacts du modèle
3 artefactsdiffusion_pytorch_model-00001-of-00003.safetensors
safetensors · 9,30 GB · SHA-256 5d72bc5d93b9…035d · Hugging Face
Téléchargerdiffusion_pytorch_model-00002-of-00003.safetensors
safetensors · 9,30 GB · SHA-256 9653e049deda…dfbf · Hugging Face
TéléchargerExtraits de sources
2 extraitsThe model is designed for virtual try-off applications, allowing users to visualize how different garments would look on a person. It can be used directly through command-line interface with the following parameters:
Input person image Person mask Garment image Random seed (optional)
transformer = FluxTransformer2DModel.from_pretrained(
"xiaozaa/cat-tryoff-flux",
torch_dtype=torch.bfloat16
)
pipe = FluxFillPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
transformer=transformer,
torch_dtype=torch.bfloat16
).to("cuda")
VITON-HD dataset
Finetuning Flux1-dev-fill
BibTeX:
@misc{chong2024catvtonconcatenationneedvirtual,
title={CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models},
author={Zheng Chong and Xiao Dong and Haoxiang Li and Shiyue Zhang and Wenqing Zhang and Xujie Zhang and Hanqing Zhao and Xiaodan Liang},
year={2024},
eprint={2407.15886},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2407.15886},
}
@article{lhhuang2024iclora,
title={In-Context LoRA for Diffusion Transformers},
author={Huang, Lianghua and Wang, Wei and Wu, Zhi-Fan and Shi, Yupeng and Dou, Huanzhang and Liang, Chen and Feng, Yutong and Liu, Yu and Zhou, Jingren},
journal={arXiv preprint arxiv:2410.23775},
year={2024}
}
diffusion_pytorch_model-00003-of-00003.safetensors
safetensors · 3,57 GB · SHA-256 32ab2b95c896…bd54 · Hugging Face
Télécharger--- library_name: diffusers license: cc-by-nc-2.0 base_model: - black-forest-labs/FLUX.1-Fill-dev pipeline_tag: image-to-image tags: - tryon - vto --- # Model Card for CAT-Tryoff-Flux CAT-Tryoff-Flux is an advanced tryoff model. It used the same method of (CATVTON-FLUX)[https://huggingface.co/xiaozaa/catvton-flux-alpha]. This model can extract and reconstruct the front view of clothing items from images of people wearing them. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [X/Twitter:Black Magic An](https://x.com/MrsZaaa) ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [github](https://github.com/nftblackmagic/catvton-flux) ## Uses The model is designed for virtual try-off applications, allowing users to visualize how different garments would look on a person. It can be used directly through command-line interface with the following parameters: Input person image Person mask Garment image Random seed (optional) ## How to Get Started with the Model ``` transformer = FluxTransformer2DModel.from_pretrained( "xiaozaa/cat-tryoff-flux", torch_dtype=torch.bfloat16 ) pipe = FluxFillPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16 ).to("cuda") ``` ## Training Details ### Training Data VITON-HD dataset ### Training Procedure Finetuning Flux1-dev-fill ## Evaluation #### Summary **BibTeX:** ``` @misc{chong2024catvtonconcatenationneedvirtual, title={CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models}, author={Zheng Chong and Xiao Dong and Haoxiang Li and Shiyue Zhang and Wenqing Zhang and Xujie Zhang and Hanqing Zhao and Xiaodan Liang}, year={2024}, eprint={2407.15886}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.15886}, } @article{lhhuang2024iclora, title={In-Context LoRA for Diffusion Transformers}, author={Huang, Lianghua and Wang, Wei and Wu, Zhi-Fan and Shi, Yupeng and Dou, Huanzhang and Liang, Chen and Feng, Yutong and Liu, Yu and Zhou, Jingren}, journal={arXiv preprint arxiv:2410.23775}, year={2024} } ```
Source context: 181 downloads · 17 likes · Pipeline image-to-image · Library diffusers · Repo xiaozaa/cat-tryoff-flux