--- base_model: runwayml/stable-diffusion-inpainting datasets: - zhengchong/DressCode-MR - zhengchong/DressCode-Test - zhengchong/VITON-HD language: - en license: other metrics: - fid - ssim - lpips pipeline_tag: image-to-image library_name: diffusers tags: - virtual-try-on - diffusion - stable-diffusion - multi-reference - computer-vision - comfyui - gradio - aigc thumbnail: https://raw.githubusercontent.com/Zheng-Chong/FastFit/main/assets/img/dataset.png ---

FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models

Supported by LavieAI and LoomlyAI

HF Paper arxiv huggingface GitHub Demo License

FastFit is a diffusion-based framework optimized for **high-speed**, **multi-reference virtual try-on**. It enables **simultaneous try-on of multiple fashion items**β€”such as **tops, bottoms, dresses, shoes, and bags**β€”on a single person. The framework leverages **reference KV caching** during inference to **significantly accelerate generation**. ## Updates - **`2025/08/29`**: πŸ“„ We release the [arXiv paper](https://arxiv.org/abs/2508.20586) of FastFit! - **`2025/08/06`**: βš™οΈ We release [the code for inference and evaluation](https://github.com/Zheng-Chong/FastFit/tree/main?tab=readme-ov-file#inference--evaluation-on-datasets) on the [DressCode-MR](https://huggingface.co/datasets/zhengchong/DressCode-MR), [DressCode](https://huggingface.co/datasets/zhengchong/DressCode-Test), and [VITON-HD](https://huggingface.co/datasets/zhengchong/VITON-HD) test datasets. - **`2025/08/05`**: 🧩 We release the [ComfyUI workflow](https://github.com/Zheng-Chong/FastFit/releases/tag/comfyui) for FastFit! - **`2025/08/04`**: πŸš€ Our [gradio demo](https://fastfit.lavieai.com) is online with Chinese & English support! The code of the demo is also released in [app.py](app.py). - **`2025/07/03`**: πŸŽ‰ We release the weights of [FastFit-MR](https://huggingface.co/zhengchong/FastFit-MR-1024) and [FastFit-SR](https://huggingface.co/zhengchong/FastFit-SR-1024) model on Hugging Face! - **`2025/06/24`**: πŸ‘• We release [DressCode-MR](https://huggingface.co/datasets/zhengchong/DressCode-MR) dataset with **28K+ Multi-reference virtual try-on Samples** on Hugging Face! ## DressCode-MR Dataset
DressCode-MR Dataset
[DressCode-MR](https://huggingface.co/datasets/zhengchong/DressCode-MR) is constructed based on the [DressCode](https://github.com/aimagelab/dress-code) dataset with **28K+ Multi-reference virtual try-on Samples**. - **Multi-reference Samples**: Each sample comprises a person's image paired with a set of compatible clothing and accessory items: tops, bottoms, dresses, shoes, and bags. - **Large Scale**: Contains a total of 28,179 high-quality multi-reference samples with 25,779 for training and 2,400 for testing. DressCode-MR is released under the exact same license as the original DressCode dataset. Therefore, before requesting access to DressCode-MR dataset, you must complete the following steps: 1. Apply and be granted a license to use the [DressCode](https://github.com/aimagelab/dress-code) dataset. 2. Use your educational/academic email address (e.g., one ending in .edu, .ac, etc.) to request access to [DressCode-MR](https://huggingface.co/datasets/zhengchong/DressCode-MR) on Hugging Face. (Any requests from non-academic email addresses will be rejected.) ## Installation ```powershell conda create -n fastfit python=3.10 conda activate fastfit pip install -r requirements.txt pip install easy-dwpose --no-dependencies # to resolve the version conflict # if error occurs for av, try: conda install -c conda-forge av ``` ## ComfyUI Workflow
ComfyUI Workflow
1. Clone the FastFit repository into your `ComfyUI/custom_nodes/` directory. ```powershell cd Your_ComfyUI_Dir/custom_nodes git clone https://github.com/Zheng-Chong/FastFit.git ``` 2. Install the required dependencies. ```powershell cd FastFit pip install -r requirements.txt pip install easy-dwpose --no-dependencies # to resolve the version conflict # if error occurs for av, try: conda install -c conda-forge av ``` 3. Install [rgthree-comfy](https://github.com/rgthree/rgthree-comfy) for image comparer. ```powershell cd Your_ComfyUI_Dir/custom_nodes git clone https://github.com/rgthree/rgthree-comfy.git cd rgthree-comfy pip install -r requirements.txt ``` 4. Restart ComfyUI. 5. Drag and drop the [fastfit_workflow.json](https://github.com/Zheng-Chong/FastFit/blob/main/assets/fastfit_workflow.json) file onto the ComfyUI web interface. ## Gradio Demo The model weights will be automatically downloaded from Hugging Face when you run the demo. ```bash python app.py ``` ## Inference & Evaluation on Datasets To perform inference on the [DressCode-MR](https://huggingface.co/datasets/zhengchong/DressCode-MR), [DressCode](https://huggingface.co/datasets/zhengchong/DressCode-Test), or [VITON-HD](https://huggingface.co/datasets/zhengchong/VITON-HD) test datasets, use the `infer_datasets.py` script, for example: ```bash python infer_datasets.py \ --dataset \ --data_dir \ --batch_size 4 \ --num_inference_steps 50 \ --guidance_scale 2.5 \ --mixed_precision bf16 \ --paired ``` - `--dataset`: Specify the target dataset. Choose from `dresscode-mr`, `dresscode`, or `viton-hd`. - `--data_dir`: The root directory path for the specified dataset. - `--paired`: Include this flag to run inference in the paired setting. Omit this flag for the unpaired setting. By default, inference results will be saved to the `results/` directory at the project root. --- After inference, use the `eval.py` script to ecalculate the evaluation metrics: ```bash python eval.py \ --gt_folder \ --pred_folder \ --paired \ --batch_size 16 \ --num_workers 4 ``` - `--gt_folder`: The directory path containing the ground truth images. - `--pred_folder`: The directory path containing the generated (predicted) images from the inference step. - `--paired`: Include this flag to evaluate results from the paired setting. Omit this flag for the unpaired setting. ## Citation ```bibtex @misc{chong2025fastfitacceleratingmultireferencevirtual, title={FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models}, author={Zheng Chong and Yanwei Lei and Shiyue Zhang and Zhuandi He and Zhen Wang and Xujie Zhang and Xiao Dong and Yiling Wu and Dongmei Jiang and Xiaodan Liang}, year={2025}, eprint={2508.20586}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2508.20586}, } ``` ## Acknowledgement Our code is modified based on [Diffusers](https://github.com/huggingface/diffusers). We adopt [Stable Diffusion v1.5 inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting) as the base model. We use a modified [AutoMasker](https://github.com/Zheng-Chong/CatVTON/blob/main/model/cloth_masker.py) to automatically generate masks in our [Gradio](https://github.com/gradio-app/gradio) App and [ComfyUI](https://github.com/comfyanonymous/ComfyUI) workflow. Thanks to all the contributors! ## License All weights, parameters, and code related to FastFit are governed by the [FastFit Non-Commercial License](https://github.com/Zheng-Chong/FastFit/tree/main). For commercial collaboration, please contact [LavieAI](https://lavieai.com/) or [LoomlyAI](https://www.loomlyai.com/en).