Optimizing Quantized Diffusion Models via Distillation with Cross-Timestep Error Correction

Abstract

Diffusion models (DMs) have attracted attention in generative modeling due to their ability to produce high-quality, diverse outputs by progressively adding noise to data and then denoising it. However, DMs are computationally intensive due to their iterative nature, requiring numerous forward passes and high-precision operations, making them less efficient for resource-constrained environments. Recent efforts to reduce these computational demands using quantization show promise by converting high-precision parameters to lower precision, but they face challenges unique to DMs, particularly in addressing cross-timestep error propagation in the iterative process. In this paper, we analyze cross-timestep error propagation in quantized DMs, revealing that previous methods focusing only on reducing noise estimation discrepancies are insufficient. Instead, we introduce Cross-Timestep Error Correction (CTEC), where the quantized model not only approximates the full-precision model but also corrects errors from the previous timestep. A distillation method is applied to learn this correction process effectively. We conduct extensive experiments on unconditional image generation with LSUN-Churches and LSUN-Bedrooms, as well as conditional image generation with ImageNet. Our findings demonstrate the effectiveness of our method in significantly reducing accumulated quantization errors across timesteps within the quantized diffusion process. This enhancement enables the generation of high-quality images, even when constrained by reduced bitwidths.