🤗 HuggingFace | 🤖 ModelScope | 🖥️ Demo | 📄 Technical Report

FireRed-Image-Edit is a general-purpose image editing model that delivers high-fidelity and consistent editing across a wide range of scenarios.

• Strong Editing Performance: FireRed-Image-Edit delivers leading open-source results with accurate instruction following, high image quality, and consistent visual coherence.
• Native Editing Capability: Built directly from text-to-image foundation model and endowed with editing capabilities.
• Text Style Preservation: Maintains text styles with high fidelity, achieving performance comparable to closed-source solutions.
• Photo Restoration: High-quality old photo restoration and enhancement.
• Multi-Image Editing: Flexible editing of multiple images such as virtual try-on.

• 2026.02.14: We released FireRed-Image-Edit-1.0 model weights. Check more details on Huggingface and ModelScope.
• 2026.02.10: We released the Technical Report of FireRed-Image-Edit-1.0.

FireRed-Image-Edit establishes a new state-of-the-art among open-source models on Imgedit, Gedit, and RedEdit, while surpassing or closed-source competitors in specific dimensions—a distinction further corroborated by human evaluations highlighting its superior prompt following and visual consistency.

Some real outputs produced by FireRed-Image-Edit across genearl editing.

• Creative scenarios

• Text scenarios:

• Tryon scenarios:

1. Install the latest version of diffusers

pip install git+https://github.com/huggingface/diffusers

2. Use the following code snippets to generate or edit images.

python inference.py \
    --input_image ./examples/edit_example.png \
    --prompt "在书本封面Python的下方，添加一行英文文字2nd Edition" \
    --output_image output_edit.png \
    --seed 43

To better validate the capabilities of our model, we propose a benchmark called REDEdit-Bench. Our main goal is to build more diverse scenarios and editing instructions that better align with human language, enabling a more comprehensive evaluation of current editing models. We collected over 3,000 images from the internet, and after careful expert-designed selection, we constructed 1,673 bilingual (Chinese–English) editing pairs across 15 categories.

We provide the inference and evaluation code for REDEdit-Bench. Please refer to the redbench_infer.py and redbench_eval.py scripts in the src/tools directory for more details.

The REDEdit-Bench dataset will be available soon.

The code and the weights of FireRed-Image-Edit are licensed under Apache 2.0.

• Release FireRed-Image-Edit-1.0 model.
• Release REDEdit-Bench, a comprehensive benchmark for image editing evaluation.
• Release FireRed-Image-Edit-1.0-Distilled model, a distilled version of FireRed-Image-Edit-1.0 for few-step generation.
• Release FireRed-Image model, a text-to-image generative model.

We kindly encourage citation of our work if you find it useful.

@article{firered2026rededit,
      title={FireRed-Image-Edit: A General-Purpose Image Editing Model}, 
      author={Super Intelligence Team},
      year={2026},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/xxxx.xxxxx}, 
}

FireRed-Image-Edit has not been specifically designed or comprehensively evaluated for every possible downstream application. Users should be aware of the potential risks and ethical considerations when using this project, and should use it responsibly and in compliance with all applicable laws and regulations.

• Prohibited Use: This project must not be used to generate content that is illegal, defamatory, pornographic, harmful, or that violates the privacy, rights, or interests of individuals or organizations.
• User Responsibility: Users are solely responsible for any content generated using this project. The authors and contributors assume no responsibility or liability for any misuse of the codebase or for any consequences resulting from its use.

We would like to thank the developers of the amazing open-source projects, including Qwen-Image, Diffusers and HuggingFace