title: Efficient Quantization Strategies for Latent Diffusion Models
summary: Latent Diffusion Models (LDMs) capture the dynamic evolution of latent
  variables over time, blending patterns and multimodality in a generative
  system. Despite the proficiency of LDM in various applications, such as
  text-to-image generation, facilitated by robust text encoders and a variational
  autoencoder, the critical need to deploy large generative models on edge
  devices compels a search for more compact yet effective alternatives. Post
  Training Quantization (PTQ), a method to compress the operational size of deep
  learning models, encounters challenges when applied to LDM due to temporal and
  structural complexities. This study proposes a quantization strategy that
  efficiently quantize LDMs, leveraging Signal-to-Quantization-Noise Ratio (SQNR)
  as a pivotal metric for evaluation. By treating the quantization discrepancy as
  relative noise and identifying sensitive part(s) of a model, we propose an
  efficient quantization approach encompassing both global and local strategies.
  The global quantization process mitigates relative quantization noise by
  initiating higher-precision quantization on sensitive blocks, while local
  treatments address specific challenges in quantization-sensitive and
  time-sensitive modules. The outcomes of our experiments reveal that the
  implementation of both global and local treatments yields a highly efficient
  and effective Post Training Quantization (PTQ) of LDMs.
paper_authors._id: 6577f2903ceeb2f078f909a0
paper_authors.name: Yuewei Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T08:36:27.713Z
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paper_authors.avatarUrl: /avatars/7cc04dfc63c1c3f2012c5cb1334d064a.svg
paper_authors.fullname: Yuewei Yang
paper_authors.type: user
paper_authors.user: yy353
paper_authors._id: 6577f2903ceeb2f078f909a1
paper_authors.name: Xiaoliang Dai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T08:36:33.704Z
paper_authors._id: 6549417b3ce45eb764faf993
paper_authors.avatarUrl: /avatars/d310f475d0697f5f13b3d4141ea0ccaf.svg
paper_authors.fullname: Xiaoliang Dai
paper_authors.type: user
paper_authors.user: daixl1992
paper_authors._id: 6577f2903ceeb2f078f909a2
paper_authors.name: Jialiang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T08:36:39.400Z
paper_authors._id: 65300c8a5da070af779cf047
paper_authors.avatarUrl: /avatars/8e24a2a1d7a8ced05a4c8e25b3d1be5a.svg
paper_authors.fullname: Jialiang Wang
paper_authors.type: user
paper_authors.user: jwhuggingf
paper_authors._id: 6577f2903ceeb2f078f909a3
paper_authors.name: Peizhao Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T08:36:46.079Z
paper_authors._id: 6433a635a5aed21dd110381e
paper_authors.avatarUrl: /avatars/b1fa4fe1c8bd7989875233820fa425af.svg
paper_authors.fullname: Peizhao Zhang
paper_authors.type: user
paper_authors.user: stzpz
paper_authors._id: 6577f2903ceeb2f078f909a4
paper_authors.name: Hongbo Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T08:37:10.085Z
paper_authors._id: 6182ac4df82814e44d033d51
paper_authors.avatarUrl: /avatars/ff56e4a0654388be9b4c052237d8e3cb.svg
paper_authors.fullname: Hongbo Zhang
paper_authors.type: user
paper_authors.user: StevenZHB
paper_title: Efficient Quantization Strategies for Latent Diffusion Models
paper_summary: Latent Diffusion Models (LDMs) capture the dynamic evolution of latent
  variables over time, blending patterns and multimodality in a generative
  system. Despite the proficiency of LDM in various applications, such as
  text-to-image generation, facilitated by robust text encoders and a variational
  autoencoder, the critical need to deploy large generative models on edge
  devices compels a search for more compact yet effective alternatives. Post
  Training Quantization (PTQ), a method to compress the operational size of deep
  learning models, encounters challenges when applied to LDM due to temporal and
  structural complexities. This study proposes a quantization strategy that
  efficiently quantize LDMs, leveraging Signal-to-Quantization-Noise Ratio (SQNR)
  as a pivotal metric for evaluation. By treating the quantization discrepancy as
  relative noise and identifying sensitive part(s) of a model, we propose an
  efficient quantization approach encompassing both global and local strategies.
  The global quantization process mitigates relative quantization noise by
  initiating higher-precision quantization on sensitive blocks, while local
  treatments address specific challenges in quantization-sensitive and
  time-sensitive modules. The outcomes of our experiments reveal that the
  implementation of both global and local treatments yields a highly efficient
  and effective Post Training Quantization (PTQ) of LDMs.
paper_ai_keywords: Latent Diffusion models (LDMs) text-to-image generation variational autoencoder Post Training Quantization (PTQ) Signal-to-Quantization-Noise Ratio (SQNR) quantization discrepancy quantization-sensitive time-sensitive modules global and local strategies higher-precision Quantization