English

1.58-bit FLUX

Computer Vision and Pattern Recognition 2024-12-30 v1 Artificial Intelligence Machine Learning

Abstract

We present 1.58-bit FLUX, the first successful approach to quantizing the state-of-the-art text-to-image generation model, FLUX.1-dev, using 1.58-bit weights (i.e., values in {-1, 0, +1}) while maintaining comparable performance for generating 1024 x 1024 images. Notably, our quantization method operates without access to image data, relying solely on self-supervision from the FLUX.1-dev model. Additionally, we develop a custom kernel optimized for 1.58-bit operations, achieving a 7.7x reduction in model storage, a 5.1x reduction in inference memory, and improved inference latency. Extensive evaluations on the GenEval and T2I Compbench benchmarks demonstrate the effectiveness of 1.58-bit FLUX in maintaining generation quality while significantly enhancing computational efficiency.

Cite

@article{arxiv.2412.18653,
  title  = {1.58-bit FLUX},
  author = {Chenglin Yang and Celong Liu and Xueqing Deng and Dongwon Kim and Xing Mei and Xiaohui Shen and Liang-Chieh Chen},
  journal= {arXiv preprint arXiv:2412.18653},
  year   = {2024}
}
R2 v1 2026-06-28T20:48:23.549Z