--- title: Leffa canonical_url: "https://www.modelscope.cn/studios/Mooccube/Leffa" md_url: "https://www.modelscope.cn/studios/Mooccube/Leffa.md" repository: Mooccube/Leffa chinese_name: "Leffa：高保真模特虚拟试穿与人物姿势调整，Meta开源的可控人物图像生成模型" last_updated: 2025-03-04 sdk_type: gradio sdk_version: 5.4.0 downloads: 0 stars: 99 --- # Leffa > Leffa - Mooccube 在 ModelScope 创建的在线 Demo。Leffa 是一个用于生成可控人物图像的统一框架，能够精确操控人物的外观（如虚拟试穿）和姿势（如姿势转移）。该框架通过在注意力层中引导目标查询关注正确的参考键，显著减少了细粒度细节的失真，同时保持了高图像质量。Leffa 的损失函数是模型无关的，可以用于提高其他扩散模型的性能。该项目由 franciszzj 开发，并在 Hugging Face 平台上发布。作为出色的换衣模型，Leffa… Mooccube/Leffa 是 ModelScope 魔搭社区上的在线可交互 Demo（创空间），基于 gradio 5.4.0 构建，中文名为「Leffa：高保真模特虚拟试穿与人物姿势调整，Meta开源的可控人物图像生成模型」。 - **Repository**: Mooccube/Leffa - **SDK**: gradio - **SDK version**: 5.4.0 - **Downloads**: 0 - **Stars**: 99 - **Last updated**: 2025-03-04 Source: https://www.modelscope.cn/studios/Mooccube/Leffa --- # *Leffa*: Learning Flow Fields in Attention for Controllable Person Image Generation [📚 Paper](https://arxiv.org/abs/2412.08486) - [🤖 Code](https://github.com/franciszzj/Leffa) - [🔥 Demo](https://huggingface.co/spaces/franciszzj/Leffa) - [🤗 Model](https://huggingface.co/franciszzj/Leffa) Star ⭐ us if you like it! ## News - 09/Jan/2025. Inference defaults to float16, generating an image in 6 seconds (on A100). - 02/Jan/2025. Update the mask generator to improve results. Add ref unet acceleration, boosting prediction speed by 30%. Include more controls in Advanced Options to enhance user experience. Enable intermediate result output for easier development. Enjoy using it! - 18/Dec/2024. Thanks to @[StartHua](https://github.com/StartHua) for integrating Leffa into ComfyUI! Here is the [repo](https://github.com/StartHua/Comfyui_leffa)! - 16/Dec/2024. The virtual try-on [model](https://huggingface.co/franciszzj/Leffa/blob/main/virtual_tryon_dc.pth) trained on DressCode is released. - 12/Dec/2024. The HuggingFace [demo](https://huggingface.co/spaces/franciszzj/Leffa) and [models](https://huggingface.co/franciszzj/Leffa) (virtual try-on model trained on VITON-HD and pose transfer model trained on DeepFashion) are released. - 11/Dec/2024. The [arXiv](https://arxiv.org/abs/2412.08486) version of the paper is released. *[Leffa](https://en.wiktionary.org/wiki/leffa)* is a unified framework for controllable person image generation that enables precise manipulation of both appearance (i.e., virtual try-on) and pose (i.e., pose transfer).
## Abstract Controllable person image generation aims to generate a person image conditioned on reference images, allowing precise control over the person’s appearance or pose. However, prior methods often distort fine-grained textural details from the reference image, despite achieving high overall image quality. We attribute these distortions to inadequate attention to corresponding regions in the reference image. To address this, we thereby propose **le**arning **f**low **f**ields in **a**ttention (***Leffa***), which explicitly guides the target query to attend to the correct reference key in the attention layer during training. Specifically, it is realized via a regularization loss on top of the attention map within a diffusion-based baseline. Our extensive experiments show that *Leffa* achieves state-of-the-art performance in controlling appearance (virtual try-on) and pose (pose transfer), significantly reducing fine-grained detail distortion while maintaining high image quality. Additionally, we show that our loss is model-agnostic and can be used to improve the performance of other diffusion models. ## Method An overview of our *Leffa* training pipeline for controllable person image generation. The left is our diffusion-based baseline; the right is our *Leffa* loss. Note that Isrc and Itgt are the same image during training.
## Visualization Qualitative visual results comparison with other methods. The input person image for the pose transfer is generated using our method in the virtual try-on. The visualization results demonstrate that our method not only generates high-quality images but also greatly reduces the distortion of fine-grained details.
## Installation Create a conda environment and install requirements: ```shell conda create -n leffa python==3.10 conda activate leffa cd Leffa pip install -r requirements.txt ``` ## Gradio App Run locally: ```shell python app.py ``` ## Evaluation We use this [code](https://github.com/franciszzj/VtonEval) for metric evaluation. ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=franciszzj/Leffa&type=Date)](https://star-history.com/#franciszzj/Leffa&Date) ## Acknowledgement Our code is based on [Diffusers](https://github.com/huggingface/diffusers) and [Transformers](https://github.com/huggingface/transformers). We use [SCHP](https://github.com/GoGoDuck912/Self-Correction-Human-Parsing/tree/master) and [DensePose](https://github.com/facebookresearch/DensePose) to generate masks and densepose in our [Demo](https://huggingface.co/spaces/franciszzj/Leffa). We also referred to the code of [IDM-VTON](https://github.com/yisol/IDM-VTON) and [CatVTON](https://github.com/Zheng-Chong/CatVTON). ## Citation If you find our work helpful or inspiring, please feel free to cite it. ``` @article{zhou2024learning, title={Learning Flow Fields in Attention for Controllable Person Image Generation}, author={Zhou, Zijian and Liu, Shikun and Han, Xiao and Liu, Haozhe and Ng, Kam Woh and Xie, Tian and Cong, Yuren and Li, Hang and Xu, Mengmeng and Pérez-Rúa, Juan-Manuel and Patel, Aditya and Xiang, Tao and Shi, Miaojing and He, Sen}, journal={arXiv preprint arXiv:2412.08486}, year={2024}, } ```