This study introduces an innovative quantization strategy for Latent Diffusion Models (LDMs) on resource-constrained devices. It combines global and local quantization approaches, effectively addressing challenges in post-training quantization. The strategy aims to enhance image quality in text-to-image generation tasks and emphasizes the need for more efficient quantization methods for LDMs in edge device deployment.
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Enhancing Latent Diffusion Models Using Quantization Strategy
Research Overview
In the era of edge computing, deploying sophisticated models like Latent Diffusion Models (LDMs) on resource-constrained devices poses a unique set of challenges. Researchers from Meta GenAI have introduced a groundbreaking quantization strategy to address the challenge of deploying LDMs on edge devices by proposing an efficient quantization strategy utilizing Signal-to-Quantization Noise Ratio (SQNR) as a key evaluation metric. The strategy combines global and local quantization approaches to alleviate relative quantization noise and achieve highly effective Post-Training Quantization (PTQ). Performance evaluation in text-to-image generation tasks demonstrates the effectiveness of the strategy, validated by FID and SQNR metrics.
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