Efficient Quantization Strategies for Latent Diffusion Models

• URL: http://arxiv.org/abs/2312.05431v1
• Date: Sat, 9 Dec 2023 01:47:16 GMT
• Title: Efficient Quantization Strategies for Latent Diffusion Models
• Authors: Yuewei Yang, Xiaoliang Dai, Jialiang Wang, Peizhao Zhang, Hongbo Zhang
• Abstract summary: Latent Diffusion Models (LDMs) capture the dynamic evolution of latent variables over time. Post Training Quantization (PTQ) is a method to compress the operational size of deep learning models. This study proposes a quantization strategy that efficiently quantize LDMs.
• Score: 20.942161659019554
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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