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arxiv: 2412.18653 · v1 · pith:XHRWC26Inew · submitted 2024-12-24 · 💻 cs.CV · cs.AI· cs.LG

1.58-bit FLUX

Chenglin Yang , Celong Liu , Xueqing Deng , Dongwon Kim , Xing Mei , Xiaohui Shen , Liang-Chieh Chen This is my paper
classification 💻 cs.CV cs.AIcs.LG
keywords fluxmodelgenerationinferencemaintainingreductionwhileaccess
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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.

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