FlowEdit

Project | Arxiv | Proceedings | Demo | ComfyUI | Data

[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 <br>

   • 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. <br> 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<br> See FLUX_exp.yaml for FLUX usage example and SD3_exp.yaml for Stable Diffusion 3 usage example. <br> 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

• FLUX
• HunyuanLoom

Implemented by logtd

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

Community 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.

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}
}