FlowEdit

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基于预训练流模型实现免反演文本编辑,无需先反演再重建,直接按文字指令修改图像,简化流程并提升编辑效果。

Official implementation of the paper: "FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models"

暂无版本1,024star54fork同步于 2026-11-07 07:30 UTC

怎么上手

由 README 提炼,执行前请确认
pip install torch diffusers transformers accelerate sentencepiece protobuf
python run_script.py --exp_yaml FLUX_exp.yaml
硬件要求按 README 说明

README 未说明显存要求,仅注明测试环境为 CUDA 12.4;需 GPU 运行 SD3/Flux 等大模型。

需要 CUDA
下载:源码 zip·GitHub Releases(安装包)本站不镜像安装包

项目说明

由 README 自动翻译提炼

基于预训练流模型的免反演文本编辑官方实现,改图不用先反演

基于预训练流模型实现免反演文本编辑,无需先反演再重建,直接按文字指令修改图像,简化流程并提升编辑效果。

核心能力

  • 无需反演即可用源/目标提示词直接编辑图像
  • 支持 Stable Diffusion 3 与 Flux 两类流模型
  • 通过 edits.yaml 批量配置输入图、源提示词与目标提示词
  • 用实验 yaml 调节 n_max、n_min 等超参数
  • 提供 ComfyUI 的 FLUX 与 HunyuanLoom 实现链接

容易踩的坑

新版 diffusers 可能有兼容问题,建议装 diffusers==0.30.1;需自行准备 SD3/Flux 预训练权重;运行前要按 edits.yaml 格式准备输入图与提示词配置。

内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README

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    README 原文

    未翻译

    Zero-Shot Image Editing Python PyTorch

    FlowEdit

    Project | Arxiv | Proceedings | Demo | ComfyUI | Data

    Recorded Talk

    [ICCV 2025 Best Student Paper] Official Pytorch implementation of the paper: "FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models"

    Installation

    1. Clone the repository

    2. Install the required dependencies using pip install torch diffusers transformers accelerate sentencepiece protobuf

      • New version of diffusers may have compatibility issues, try install diffusers==0.30.1
      • Tested with CUDA version 12.4 and diffusers 0.30.0

    Running examples

    Run editing with Stable Diffusion 3: python run_script.py --exp_yaml SD3_exp.yaml

    Run editing with Flux: python run_script.py --exp_yaml FLUX_exp.yaml

    Usage - your own examples

    • Upload images to example_images folder.

    • Create an edits file that specifies: (a) a path to the input image, (b) a source prompt, (c) target prompts, and (d) target codes. The target codes summarize the changes between the source and target prompts and will appear in the output filename. See edits.yaml for example.

    • Create an experiment file containing the hyperparamaters needed for running FlowEdit, such as n_max, n_min. This file also includes the path to the edits.yaml file See FLUX_exp.yaml for FLUX usage example and SD3_exp.yaml for Stable Diffusion 3 usage example. For a detailed discussion on the impact of different hyperparameters and the values we used, please refer to our paper.

    Run python run_script.py --exp_yaml <path to your experiment yaml>

    ComfyUI implementation for different models

    Implemented by logtd

    LTX-Video ComfyUI implementation can be found in LTX-Video official repository.

    Community and Follow-Up Work

    • Training-Free-WAN-Editing🤗, combines WAN2.1 with FlowEdit to extend training-free to video editing. If you are interested in video editing, please feel free to take a look. Implemented by Kyujinpy.

    • DNAEdit refines the Gaussian noise in the noise domain, improving image and video editing (NeurIPS 2025 Spotlight). Project | Code | Arxiv | Proceedings

    • FlowAlign add optimal control-based trajectory control to the inversion free process (ICLR 2026). Code | Arxiv | Proceedings

    • DynaEdit extened FlowEdit for dynmaic video editing (EECV 2026). Project | Arxiv

    License

    This project is licensed under the MIT License.

    Citation

    If you use this code for your research, please cite our paper:

    @inproceedings{kulikov2025flowedit,
      title={Flowedit: Inversion-free text-based editing using pre-trained flow models},
      author={Kulikov, Vladimir and Kleiner, Matan and Huberman-Spiegelglas, Inbar and Michaeli, Tomer},
      booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
      pages={19721--19730},
      year={2025}
    }