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SwinliQ-AIs/
FLUX.1-schnell-4bit

Name: FLUX.1-schnell-4bit
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FLUX.1-schnell-mflux-v0.6.2-4bit

A 4-bit quantized version of the FLUX.1-schnell text-to-image model from Black Forest Labs, implemented using the mflux (version 0.6.2) quantization approach.

Overview

This repository contains a 4-bit quantized version of the FLUX.1-schnell model, which significantly reduces the memory footprint while maintaining most of the generation quality. The quantization was performed using the mflux methodology (v0.6.2).

Original Model

FLUX.1-schnell is a lightweight text-to-image diffusion model developed by Black Forest Labs. It's designed to be faster and more efficient than many larger models while still producing high-quality images.

Benefits of 4-bit Quantization

Reduced Memory Usage: ~85% reduction in memory requirements compared to the original model
Faster Loading Times: Smaller model size means quicker initialization
Lower Storage Requirements: Significantly smaller disk footprint
Accessibility: Can run on consumer hardware with limited VRAM

Model Structure

This repository contains the following components:

text_encoder/: CLIP text encoder (4-bit quantized)
text_encoder_2/: Secondary text encoder (4-bit quantized)
tokenizer/: CLIP tokenizer configuration and vocabulary
tokenizer_2/: Secondary tokenizer configuration
transformer/: Main diffusion model components (4-bit quantized)
vae/: Variational autoencoder for image encoding/decoding (4-bit quantized)

Usage

Requirements

Python
PyTorch
Transformers
Diffusers
mflux library (for 4-bit model support)

Installation

bash

pip install torch diffusers transformers accelerate
uv tool install mflux # check mflux README for more details

Example Usage

bash

# export path for mflux
% mflux-generate \        
    --path "dhairyashil/FLUX.1-schnell-mflux-v0.6.2-4bit" \        
    --model schnell \                                                                           
    --steps 2 \                                                                                 
    --seed 2 \       
    --height 1920 \
    --width 1024 \
    --prompt "hot chocolate dish"

Comparison Output

The images generated from above prompt for different models are shown at the top.

fp16 and 8-bit results are visibly look almost the same but 4-bit result looks a little deviated.

8-bit model is available for comparison.

Performance Comparison

Model Version	Memory Usage	Inference Speed	Quality
Original FP16	~57 GB	Base	Base
4-bit Quantized	~9 GB	Slightly slower	Slightly reduced

Limitations

Minor quality degradation compared to the original model
Slightly slower inference speed
May exhibit occasional artifacts not present in the original model

Acknowledgements

Black Forest Labs for creating the original FLUX.1-schnell model
Filip Strand for developing the mflux quantization methodology
The Hugging Face team for their Diffusers and Transformers libraries

License

This model inherits the license of the original FLUX.1-schnell model. Please refer to the original model repository for licensing information.

SwinliQ-AIs/FLUX.1-schnell-4bit