Model reference · open weights
FireRed-Image-Edit-1.0 is an open-weight image model from FireRedTeam. FireRed-Image-Edit-1.0 (BF16) weighs 57.7 GB; the smallest configuration that runs it is H100 80 GB.
FireRed-Image-Edit-1.0 is a 20.4B parameter image-to-image model developed by FireRedTeam for general-purpose image editing. It supports English and Chinese and is designed for tasks such as photo restoration, multi-image editing, and maintaining text style fidelity. The model is released under the Apache 2.0 license.
Summary of the FireRedTeam/FireRed-Image-Edit-1.0 model card, 2026-10-01
What it is
| Released by | FireRedTeam |
|---|---|
| Type | Image models |
| Task | Image edit |
| Parameters (lead) | 20.4B |
| Runs with | diffusers |
| Released | 2026-02-12 |
| Popularity | 27k downloads / month |
| Weights | 57.7 GB (FireRed-Image-Edit-1.0 (BF16), file size) |
| Licence | Open weights |
What it runs on
Weights 57.7 GB (file size) · its biggest part 40.9 GB · working memory for one 1024×1024 image about 5.0 GB · overhead about 537 MB.
| Card | One 1024×1024 image | Counted memory |
|---|---|---|
| RTX 3060 12 GB … L40S 48 GB | does not fit | |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
Estimates, not measurements: the weights are the build's file size; one 1024×1024 image needs about 5 GB of working memory (larger images more); "encoders offloaded" means only the biggest part is on the card at once — diffusers' model offload, or ComfyUI unloading the text encoder. diffusers can also place a pipeline's parts on separate cards (device_map) — not estimated here. Counted memory is 92 % of what CUDA reports for the card.
From the model card
🤗 HuggingFace | 🖥️ 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.
Some real outputs produced by FireRed-Image-Edit across genearl editing.
pip install git+https://github.com/huggingface/diffusers
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.
🔹 Proprietary Models
🔹 Open-source Models
The code and the weights of FireRed-Image-Edit are licensed under Apache 2.0.
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.
We would like to thank the developers of the amazing open-source projects, including Qwen-Image, Diffusers and HuggingFace
Quoted from the model card on Hugging Face — the full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. Runs as it is with diffusers — on the machine, in Python.
# on your rented machine: pip install diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
pipe = DiffusionPipeline.from_pretrained("FireRedTeam/FireRed-Image-Edit-1.0", torch_dtype=torch.bfloat16).to("cuda")
start = load_image("/workspace/in.png")
image = pipe(prompt="the same scene at golden hour", image=start).images[0]
image.save("/workspace/out.png")