FireRed-Image-Edit-1.0-Fast is a high-performance, AI-driven image editing application that utilizes advanced diffusers and the QIE+ Pipeline for precise, prompt-based image modifications.
85
stars
46
commits
Python
primary language
Aug 8, 2026
updated
FireRed-Image-Edit-1.0-Fast is an experimental, high-performance image editing and style-transfer platform built on top of the FireRedTeam/FireRed-Image-Edit-1.1 pipeline. The application integrates an optimized transformer architecture (prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V19) alongside Flash Attention 3 (QwenDoubleStreamAttnProcessorFA3) to execute complex single- and multi-image edit instructions in a rapid 4-step sampling window.
To ensure responsible usage, the pipeline incorporates an integrated NCII (Non-Consensual Intimate Imagery) safety guard model (hfmlsoc/ncii-light-guard-v01). The application is served via a single-page web app built with a FastAPI backend server (gradio.Server) and a dark red-themed frontend interface featuring a dual-view canvas, A/B comparison slider, history filmstrip, and interactive prompt suggestions.
hfmlsoc/ncii-light-guard-v01) to detect and block non-consensual intimate imagery requests before execution.QwenDoubleStreamAttnProcessorFA3 processor layer to accelerate cross-attention inference phases while reducing active GPU memory consumption.├── examples/
│ ├── 1.jpg
│ ├── 10.jpg
│ ├── 11.png
│ ├── 2.jpg
│ ├── 3.jpeg
│ ├── 4.jpg
│ ├── 5.jpg
│ ├── 6.jpg
│ ├── 7.webp
│ ├── 8.jpg
│ └── 9.png
├── qwenimage/
│ ├── __init__.py
│ ├── pipeline_qwenimage_edit_plus.py
│ ├── qwen_fa3_processor.py
│ └── transformer_qwenimage.py
├── app.py
├── index.html
├── LICENSE.txt
├── pre-requirements.txt
├── pyproject.toml
├── README.md
├── requirements.txt
└── uv.lock
To set up the FireRed-Image-Edit-1.0-Fast environment locally, configure your system according to the specifications below. A modern CUDA-enabled GPU is required.
torch==2.11.0 or above is required for best compatibility.--extra-index-url [https://download.pytorch.org/whl/cu130](https://download.pytorch.org/whl/cu130)), matching the environment used on the live Hugging Face demo.uv (Recommended)uv is an ultra-fast Python package and project manager written in Rust. It ensures rapid virtual environment setup and exact dependency synchronization based on the uv.lock file.
Step 1 — Install uv
curl -LsSf https://astral.sh/uv/install.sh | shpowershell -c "irm https://astral.sh/uv/install.ps1 | iex"Step 2 — Clone the repository
git clone https://github.com/PRITHIVSAKTHIUR/FireRed-Image-Edit-1.0-Fast.git
cd FireRed-Image-Edit-1.0-Fast
Step 3 — Initialize the project and install dependencies
uv sync
Step 4 — Run the script
uv run app.py
1. Update Package Manager Upgrade your local package manager:
pip install pip>=26.1.2
2. Install Core Dependencies
Install the primary deep learning stack, transformer libraries, and core computing utilities listed in requirements.txt:
pip install -r requirements.txt
requirements.txt)--extra-index-url https://download.pytorch.org/whl/cu130
torch==2.11.0
torchvision==0.26.0
transformers==5.14.1
accelerate==1.14.0
diffusers==0.39.0
peft==0.19.1
gradio==6.22.0
av==17.1.0
spaces==0.51.1
huggingface-hub==1.24.0
kernels==0.16.0
Once the web server initializes, open your browser to the local address output in your terminal (typically http://127.0.0.1:7860/).
46 commits
Python
60.1%
HTML
39.9%
FireRed-Image-Edit-1.0-Fast is a high-performance, AI-driven image editing application that utilizes advanced diffusers and the QIE+ Pipeline for precise, prompt-based image modifications.
85
stars
46
commits
Python
primary language
Aug 8, 2026
updated
FireRed-Image-Edit-1.0-Fast is an experimental, high-performance image editing and style-transfer platform built on top of the FireRedTeam/FireRed-Image-Edit-1.1 pipeline. The application integrates an optimized transformer architecture (prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V19) alongside Flash Attention 3 (QwenDoubleStreamAttnProcessorFA3) to execute complex single- and multi-image edit instructions in a rapid 4-step sampling window.
To ensure responsible usage, the pipeline incorporates an integrated NCII (Non-Consensual Intimate Imagery) safety guard model (hfmlsoc/ncii-light-guard-v01). The application is served via a single-page web app built with a FastAPI backend server (gradio.Server) and a dark red-themed frontend interface featuring a dual-view canvas, A/B comparison slider, history filmstrip, and interactive prompt suggestions.
hfmlsoc/ncii-light-guard-v01) to detect and block non-consensual intimate imagery requests before execution.QwenDoubleStreamAttnProcessorFA3 processor layer to accelerate cross-attention inference phases while reducing active GPU memory consumption.├── examples/
│ ├── 1.jpg
│ ├── 10.jpg
│ ├── 11.png
│ ├── 2.jpg
│ ├── 3.jpeg
│ ├── 4.jpg
│ ├── 5.jpg
│ ├── 6.jpg
│ ├── 7.webp
│ ├── 8.jpg
│ └── 9.png
├── qwenimage/
│ ├── __init__.py
│ ├── pipeline_qwenimage_edit_plus.py
│ ├── qwen_fa3_processor.py
│ └── transformer_qwenimage.py
├── app.py
├── index.html
├── LICENSE.txt
├── pre-requirements.txt
├── pyproject.toml
├── README.md
├── requirements.txt
└── uv.lock
To set up the FireRed-Image-Edit-1.0-Fast environment locally, configure your system according to the specifications below. A modern CUDA-enabled GPU is required.
torch==2.11.0 or above is required for best compatibility.--extra-index-url [https://download.pytorch.org/whl/cu130](https://download.pytorch.org/whl/cu130)), matching the environment used on the live Hugging Face demo.uv (Recommended)uv is an ultra-fast Python package and project manager written in Rust. It ensures rapid virtual environment setup and exact dependency synchronization based on the uv.lock file.
Step 1 — Install uv
curl -LsSf https://astral.sh/uv/install.sh | shpowershell -c "irm https://astral.sh/uv/install.ps1 | iex"Step 2 — Clone the repository
git clone https://github.com/PRITHIVSAKTHIUR/FireRed-Image-Edit-1.0-Fast.git
cd FireRed-Image-Edit-1.0-Fast
Step 3 — Initialize the project and install dependencies
uv sync
Step 4 — Run the script
uv run app.py
1. Update Package Manager Upgrade your local package manager:
pip install pip>=26.1.2
2. Install Core Dependencies
Install the primary deep learning stack, transformer libraries, and core computing utilities listed in requirements.txt:
pip install -r requirements.txt
requirements.txt)--extra-index-url https://download.pytorch.org/whl/cu130
torch==2.11.0
torchvision==0.26.0
transformers==5.14.1
accelerate==1.14.0
diffusers==0.39.0
peft==0.19.1
gradio==6.22.0
av==17.1.0
spaces==0.51.1
huggingface-hub==1.24.0
kernels==0.16.0
Once the web server initializes, open your browser to the local address output in your terminal (typically http://127.0.0.1:7860/).
46 commits
Python
60.1%
HTML
39.9%