Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion

• URL: http://arxiv.org/abs/2412.06661v2
• Date: Wed, 07 May 2025 16:57:47 GMT
• Title: Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion
• Authors: Shuaiting Li, Juncan Deng, Zeyu Wang, Kedong Xu, Rongtao Deng, Hong Gu, Haibin Shen, Kejie Huang,
• Abstract summary: We propose an efficient quantization framework for Stable Diffusion models (SDM)<n>Our framework simultaneously maintains training-inference consistency and ensures optimization stability.<n>Our method demonstrates superior performance over state-of-the-art approaches with shorter training times.
• Score: 9.402892455344677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image generation via Stable Diffusion models (SDM) have demonstrated remarkable capabilities. However, their computational intensity, particularly in the iterative denoising process, hinders real-time deployment in latency-sensitive applications. While Recent studies have explored post-training quantization (PTQ) and quantization-aware training (QAT) methods to compress Diffusion models, existing methods often overlook the consistency between results generated by quantized models and those from floating-point models. This consistency is paramount for professional applications where both efficiency and output reliability are essential. To ensure that quantized SDM generates high-quality and consistent images, we propose an efficient quantization framework for SDM. Our framework introduces a Serial-to-Parallel pipeline that simultaneously maintains training-inference consistency and ensures optimization stability. Building upon this foundation, we further develop several techniques including multi-timestep activation quantization, time information precalculation, inter-layer distillation, and selective freezing, to achieve high-fidelity generation in comparison to floating-point models while maintaining quantization efficiency. Through comprehensive evaluation across multiple Stable Diffusion variants (v1-4, v2-1, XL 1.0, and v3), our method demonstrates superior performance over state-of-the-art approaches with shorter training times. Under W4A8 quantization settings, we achieve significant improvements in both distribution similarity and visual fidelity, while preserving a high image quality.

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