Efficient Quantization Strategies for Latent Diffusion Models
Authors: name: Yuewei Yang; name: Xiaoliang Dai; name: Jialiang Wang; name: Peizhao Zhang; name: Hongbo Zhang
Published: 2023-12-09 01:47:16

Abstract: 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.