title: 1.58-bit FLUX
summary: 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.
paper_authors._id: 6772d6ff33efe31653f02b0b
paper_authors.name: Chenglin Yang
paper_authors._id: 6772d6ff33efe31653f02b0c
paper_authors.name: Celong Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:46:38.723Z
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paper_authors.avatarUrl: /avatars/cb6ef2155d88b1e64c4ff36bd3a8f255.svg
paper_authors.fullname: Celong Liu
paper_authors.type: user
paper_authors.user: goddice
paper_authors._id: 6772d6ff33efe31653f02b0d
paper_authors.name: Xueqing Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:46:44.300Z
paper_authors._id: 65ca9b1743207e438a95e90c
paper_authors.avatarUrl: /avatars/8f7bde1c44d8e665a29ee08ce7fedfa4.svg
paper_authors.fullname: Xueqing Deng
paper_authors.type: user
paper_authors.user: xdeng77
paper_authors._id: 6772d6ff33efe31653f02b0e
paper_authors.name: Dongwon Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-16T08:31:52.344Z
paper_authors._id: 64dc5208c38427829de81b16
paper_authors.avatarUrl: /avatars/43a08e46a7a78f1e3d1f6645a9b1d26b.svg
paper_authors.fullname: Dongwon
paper_authors.type: user
paper_authors.user: kdwon
paper_authors._id: 6772d6ff33efe31653f02b0f
paper_authors.name: Xing Mei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:47:17.287Z
paper_authors._id: 642e748858ba61373f2ea217
paper_authors.avatarUrl: /avatars/e9f002cd662f07bd213c31a45176d5a5.svg
paper_authors.fullname: Xing Mei
paper_authors.type: user
paper_authors.user: xm2023
paper_authors._id: 6772d6ff33efe31653f02b10
paper_authors.name: Xiaohui Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:47:23.529Z
paper_authors._id: 6430aa1b32a732121cd81f98
paper_authors.avatarUrl: /avatars/5419f8d6d4d36fa5ac83e30667b9fd99.svg
paper_authors.fullname: Xiaohui Shen
paper_authors.type: user
paper_authors.user: XiaohuiShen
paper_authors._id: 6772d6ff33efe31653f02b11
paper_authors.name: Liang-Chieh Chen
paper_title: 1.58-bit FLUX
paper_summary: 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.
paper_ai_keywords: FLUX FLUX.1-dev quantizing text-to-image generation state-of-the-art constrained model 1.58-bit weights denoising autoencoders self-supervision custom kernel model storage inference memory inference latency GenEval T2I Compbench finding classification verbal generation quality computational efficiency