title: BitsFusion: 1.99 bits Weight Quantization of Diffusion Model
summary: Diffusion-based image generation models have achieved great success in recent
  years by showing the capability of synthesizing high-quality content. However,
  these models contain a huge number of parameters, resulting in a significantly
  large model size. Saving and transferring them is a major bottleneck for
  various applications, especially those running on resource-constrained devices.
  In this work, we develop a novel weight quantization method that quantizes the
  UNet from Stable Diffusion v1.5 to 1.99 bits, achieving a model with 7.9X
  smaller size while exhibiting even better generation quality than the original
  one. Our approach includes several novel techniques, such as assigning optimal
  bits to each layer, initializing the quantized model for better performance,
  and improving the training strategy to dramatically reduce quantization error.
  Furthermore, we extensively evaluate our quantized model across various
  benchmark datasets and through human evaluation to demonstrate its superior
  generation quality.
paper_authors._id: 666273193388180cc4f982ba
paper_authors.name: Yang Sui
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-09T20:05:14.872Z
paper_authors._id: 63787b13500186f250ba377c
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paper_authors.fullname: yangsui
paper_authors.type: user
paper_authors.user: yangsui
paper_authors._id: 666273193388180cc4f982bb
paper_authors.name: Yanyu Li
paper_authors._id: 666273193388180cc4f982bc
paper_authors.name: Anil Kag
paper_authors._id: 666273193388180cc4f982bd
paper_authors.name: Yerlan Idelbayev
paper_authors.status: admin_assigned
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paper_authors._id: 637d7f4a95b847187a701b72
paper_authors.avatarUrl: /avatars/214c08ae6941896d35518217f51a924c.svg
paper_authors.fullname: Yerlan Idelbayev
paper_authors.type: user
paper_authors.user: yerla
paper_authors._id: 666273193388180cc4f982be
paper_authors.name: Junli Cao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-07T12:29:17.461Z
paper_authors._id: 63f54aa73aa49d8cb97b84bc
paper_authors.avatarUrl: /avatars/c73c5870039611ab9162daad46a1ba20.svg
paper_authors.fullname: junli cao
paper_authors.type: user
paper_authors.user: jlcao2
paper_authors._id: 666273193388180cc4f982bf
paper_authors.name: Ju Hu
paper_authors._id: 666273193388180cc4f982c0
paper_authors.name: Dhritiman Sagar
paper_authors._id: 666273193388180cc4f982c1
paper_authors.name: Bo Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-09T20:05:10.134Z
paper_authors._id: 66641bd44ca290b4057e849e
paper_authors.avatarUrl: /avatars/84bf8f4deddd357f37535925d4ed993c.svg
paper_authors.fullname: Bo Yuan
paper_authors.type: user
paper_authors.user: yuanrutgers
paper_authors._id: 666273193388180cc4f982c2
paper_authors.name: Sergey Tulyakov
paper_authors._id: 666273193388180cc4f982c3
paper_authors.name: Jian Ren
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-07T09:19:52.592Z
paper_authors._id: 61f19829233c91cbd2f79e70
paper_authors.avatarUrl: /avatars/a0735a94542b4f7cda5aed8bc4be0538.svg
paper_authors.fullname: Jian Ren
paper_authors.type: user
paper_authors.user: alanspike
paper_title: BitsFusion: 1.99 bits Weight Quantization of Diffusion Model
paper_summary: Diffusion-based image generation models have achieved great success in recent
  years by showing the capability of synthesizing high-quality content. However,
  these models contain a huge number of parameters, resulting in a significantly
  large model size. Saving and transferring them is a major bottleneck for
  various applications, especially those running on resource-constrained devices.
  In this work, we develop a novel weight quantization method that quantizes the
  UNet from Stable Diffusion v1.5 to 1.99 bits, achieving a model with 7.9X
  smaller size while exhibiting even better generation quality than the original
  one. Our approach includes several novel techniques, such as assigning optimal
  bits to each layer, initializing the quantized model for better performance,
  and improving the training strategy to dramatically reduce quantization error.
  Furthermore, we extensively evaluate our quantized model across various
  benchmark datasets and through human evaluation to demonstrate its superior
  generation quality.
paper_ai_keywords: diffusion-based image generation models UNet Stable Diffusion weight quantization method layer quantized model quantization error