1.58-bit FLUX
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913625587843072 |
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| author | Yang, Chenglin Liu, Celong Deng, Xueqing Kim, Dongwon Mei, Xing Shen, Xiaohui Chen, Liang-Chieh |
| author_facet | Yang, Chenglin Liu, Celong Deng, Xueqing Kim, Dongwon Mei, Xing Shen, Xiaohui Chen, Liang-Chieh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18653 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 1.58-bit FLUX Yang, Chenglin Liu, Celong Deng, Xueqing Kim, Dongwon Mei, Xing Shen, Xiaohui Chen, Liang-Chieh Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | 1.58-bit FLUX |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.18653 |