Model reference · open weights
FireRed-Image-Edit-1.1 is an open-weight image model from FireRedTeam. FireRed-Image-Edit-1.1 (BF16) weighs 57.7 GB; the smallest configuration that runs it is H100 80 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the FireRedTeam/FireRed-Image-Edit-1.1 model card, 2026-10-01
What it is
| Released by | FireRedTeam |
|---|---|
| Released | 2026-03-02 |
| Parameters | 20.4B |
| VRAM | 57.7 GB for the weights |
What it runs on
| Card | One image | 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 |
From the model card
🤗 HuggingFace | 🤖 ModelScope | 🖥️ Demo | 📄 Technical Report
We introduce FireRed-Image-Edit-1.1, an upgrade to our general-purpose image editing foundation model. Building upon the capabilities presented in the FireRed-Image-Edit-1.0 Technical Report, version 1.1 significantly enhances identity consistency, multi-image conditioning, and domain-specialized editing performance, bringing the model closer to real-world creative production needs.
Some real outputs produced by FireRed-Image-Edit across general editing.
FireRed-Image-Edit establishes a new state-of-the-art among open-source models on Imgedit, Gedit, and RedEdit, while surpassing our closed-source competitors in specific dimensions—a distinction further corroborated by human evaluations highlighting its superior prompt following and visual consistency.
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-1.1", 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")