title: Accurate Compression of Text-to-Image Diffusion Models via Vector
    Quantization
summary: Text-to-image diffusion models have emerged as a powerful framework for
  high-quality image generation given textual prompts. Their success has driven
  the rapid development of production-grade diffusion models that consistently
  increase in size and already contain billions of parameters. As a result,
  state-of-the-art text-to-image models are becoming less accessible in practice,
  especially in resource-limited environments. Post-training quantization (PTQ)
  tackles this issue by compressing the pretrained model weights into lower-bit
  representations. Recent diffusion quantization techniques primarily rely on
  uniform scalar quantization, providing decent performance for the models
  compressed to 4 bits. This work demonstrates that more versatile vector
  quantization (VQ) may achieve higher compression rates for large-scale
  text-to-image diffusion models. Specifically, we tailor vector-based PTQ
  methods to recent billion-scale text-to-image models (SDXL and SDXL-Turbo), and
  show that the diffusion models of 2B+ parameters compressed to around 3 bits
  using VQ exhibit the similar image quality and textual alignment as previous
  4-bit compression techniques.
paper_authors._id: 66d7fbe9b61dd11022037abc
paper_authors.name: Vage Egiazarian
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paper_authors.fullname: Vage Egiazarian
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paper_authors.name: Denis Kuznedelev
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paper_authors.name: Anton Voronov
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paper_authors.fullname: Anton Voronov
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paper_authors.name: Ruslan Svirschevski
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paper_authors.fullname: ruslan.svirschevski
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paper_authors.name: Michael Goin
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paper_authors.fullname: Michael Goin
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paper_authors.user: mgoin
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paper_authors.name: Daniil Pavlov
paper_authors._id: 66d7fbe9b61dd11022037ac2
paper_authors.name: Dan Alistarh
paper_authors.status: admin_assigned
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paper_authors.fullname: Dan Alistarh
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paper_authors.user: dalistarh
paper_authors._id: 66d7fbe9b61dd11022037ac3
paper_authors.name: Dmitry Baranchuk
paper_authors.status: admin_assigned
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paper_authors._id: 62b6cc49752323892323bc04
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paper_authors.fullname: Dmitry Baranchuk
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paper_authors.user: dbaranchuk
paper_title: Accurate Compression of Text-to-Image Diffusion Models via Vector
    Quantization
paper_summary: Text-to-image diffusion models have emerged as a powerful framework for
  high-quality image generation given textual prompts. Their success has driven
  the rapid development of production-grade diffusion models that consistently
  increase in size and already contain billions of parameters. As a result,
  state-of-the-art text-to-image models are becoming less accessible in practice,
  especially in resource-limited environments. Post-training quantization (PTQ)
  tackles this issue by compressing the pretrained model weights into lower-bit
  representations. Recent diffusion quantization techniques primarily rely on
  uniform scalar quantization, providing decent performance for the models
  compressed to 4 bits. This work demonstrates that more versatile vector
  quantization (VQ) may achieve higher compression rates for large-scale
  text-to-image diffusion models. Specifically, we tailor vector-based PTQ
  methods to recent billion-scale text-to-image models (SDXL and SDXL-Turbo), and
  show that the diffusion models of 2B+ parameters compressed to around 3 bits
  using VQ exhibit the similar image quality and textual alignment as previous
  4-bit compression techniques.
paper_ai_keywords: diffusion models text-to-image models high-quality image generation production-grade diffusion models post-training quantization (PTQ) pretrained model weights uniform scalar quantization vector quantization (VQ) SDXL SDXL-Turbo parameter compression image quality textual alignment