---
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 CATVTON-Flux

CATVTON-Flux is an advanced virtual try-on solution that combines CATVTON (Contrastive Appearance and Topology Virtual Try-On) with Flux fill inpainting model for realistic and accurate clothing transfer.

## Update:
Latest Achievement (2024/11/24):
CatVton-Flux-Alpha achieved SOTA performance with FID: 5.593255043029785 on VITON-HD dataset. Test configuration: scale 30, step 30. My VITON-HD test inferencing results available [here](https://drive.google.com/file/d/1T2W5R1xH_uszGVD8p6UUAtWyx43rxGmI/view?usp=sharing)

## 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-on 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/catvton-flux-alpha", 
    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

#### Metrics

FID: 5.593255043029785 (SOTA)

### Results

[More Information Needed]

#### 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}
}
```