From the Frontier Research Team at Takara.ai we present Flux.1 Q_4_k, a quantized GGUF model optimized for stable-diffusion.cpp, enabling efficient image generation on lower-end hardware. This model was used to create the Kurai Toori Dark Streets dataset.
Features
- Optimized for lower-end hardware through 4-bit quantization
- High-quality image generation despite compression
- Efficient performance with minimal quality degradation
- Wide-ranging capabilities beyond dark street scenes
Usage
- Clone and set up stable-diffusion.cpp:
git clone https://github.com/leejet/stable-diffusion.cpp.git cd stable-diffusion.cpp # Follow setup instructions in the stable-diffusion.cpp README
- Download the GGUF model file from this repository.
- Run the model using stable-diffusion.cpp, pointing to the downloaded file:
./sd -m path/to/flux.1-q_4_k.gguf -p "your prompt here"
Performance Benefits
- Reduced memory usage compared to full-precision models
- Faster inference times on consumer hardware
- Runs on less powerful hardware without significant quality loss
- Ideal for experimentation and rapid prototyping
Technical Details
This model is a 4-bit quantized version of the FLUX.1-schnell base model from Black Forest Labs. The quantization process preserves the creative capabilities of the original model while dramatically reducing its memory footprint and computational requirements.
Example Use Cases
- Generating urban nightscapes and cityscapes
- Creating artistic interpretations for creative projects
- Rapid prototyping of visual concepts
- Accessible AI image generation on consumer hardware
For research inquiries and press, please reach out to research@takara.ai
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Base model
black-forest-labs/FLUX.1-schnell