Flux1-Schnell-Quantized

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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.

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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

1. 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
   

2. Download the GGUF model file from this repository.
3. 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

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For research inquiries and press, please reach out to [email protected]

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