---
title: FastFit-MR-1024
canonical_url: "https://www.modelscope.cn/models/iSolver/FastFit-MR-1024"
md_url: "https://www.modelscope.cn/models/iSolver/FastFit-MR-1024.md"
repository: iSolver/FastFit-MR-1024
chinese_name: "FastFit虚拟试衣模型"
last_updated: 2026-06-28
license: other
pipeline_tag: image-to-image
tasks:
  - image-to-image
base_model:
  - runwayml/stable-diffusion-inpainting
base_model_relation: finetune
parameters: 815.6M
tensor_type:
  - BF16
library_name:
  - safetensors
  - diffusers
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 6
stars: 1
tags:
  - virtual-try-on
  - diffusion
  - stable-diffusion
  - multi-reference
  - computer-vision
  - comfyui
  - gradio
  - aigc
---

# FastFit-MR-1024

> FastFit-MR-1024 - iSolver 在 ModelScope 开源的模型。FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models

iSolver/FastFit-MR-1024 是 ModelScope 魔搭社区上的 815.6M 参数image-to-image模型，采用 other 许可，基于 runwayml/stable-diffusion-inpainting 构建。

- **Repository**: iSolver/FastFit-MR-1024
- **License**: other
- **Tasks**: image-to-image
- **Parameters**: 815.6M
- **Base model**: runwayml/stable-diffusion-inpainting
- **Tags**: virtual-try-on, diffusion, stable-diffusion, multi-reference, computer-vision, comfyui, gradio, aigc
- **Downloads**: 6
- **Stars**: 1
- **Last updated**: 2026-06-28

Source: https://www.modelscope.cn/models/iSolver/FastFit-MR-1024

---

<h1 align="center"> FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models </h1>

<p align="center" style="font-size: 18px;">Supported by <a href="https://lavieai.com/">LavieAI</a></p>            


 <div align="center">
  <a href="https://huggingface.co/papers/2508.20586" style="margin: 0 2px; text-decoration: none;">
    <img src='https://img.shields.io/badge/Paper-2508.20586-blue?style=flat&logo=HuggingFace&logoColor=blue' alt='HF Paper'>
  </a>
  <a href="https://arxiv.org/abs/2508.20586" style="margin: 0 2px; text-decoration: none;">
    <img src='https://img.shields.io/badge/arXiv-2508.20586-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'>
  </a>
  <a href='https://huggingface.co/zhengchong/FastFit-MR-1024' style="margin: 0 2px; text-decoration: none;">
    <img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'>
  </a>
  <a href="https://github.com/Zheng-Chong/FastFit" style="margin: 0 2px; text-decoration: none;">
    <img src='https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub' alt='GitHub'>
  </a>
  <a href="https://fastfit.lavieai.com" style="margin: 0 2px; text-decoration: none;">
    <img src='https://img.shields.io/badge/Demo-Gradio-gold?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
  </a>
  <a href="https://github.com/Zheng-Chong/FastFit/tree/main" style="margin: 0 2px; text-decoration: none;">
    <img src='https://img.shields.io/badge/License-NonCommercial-lightgreen?style=flat&logo=Lisence' alt='License'>
  </a>
</div>

<br>

FastFit is a diffusion-based framework optimized for **high-speed**, **multi-reference virtual try-on**. It enables **simultaneous try-on of multiple fashion items**—such as **tops, bottoms, dresses, shoes, and bags**—on a single person. The framework leverages **reference KV caching** during inference to **significantly accelerate generation**.
 
## Updates 
- **`2025/08/29`**: 📄 We release the [arXiv paper](https://arxiv.org/abs/2508.20586) of FastFit!
- **`2025/08/06`**: ⚙️ 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`**: 🚀 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`**: 🎉 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`**: 👕 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

<div align="center">
  <img src="assets/img/dataset.png" alt="DressCode-MR Dataset" width="800">
</div>

[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

<div align="center">
  <img src="assets/img/comfyui.png" alt="ComfyUI Workflow" width="800">
</div>


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 <dataset_name> \
    --data_dir </path/to/your/dataset> \
    --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 </path/to/ground_truth_folder> \
    --pred_folder </path/to/prediction_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/).