  High computational overhead is a troublesome problem for diffusion models.
Recent studies have leveraged post-training quantization (PTQ) to compress
diffusion models. However, most of them only focus on unconditional models,
leaving the quantization of widely-used pretrained text-to-image models, e.g.,
Stable Diffusion, largely unexplored. In this paper, we propose a novel
post-training quantization method PCR (Progressive Calibration and Relaxing)
for text-to-image diffusion models, which consists of a progressive calibration
strategy that considers the accumulated quantization error across timesteps,
and an activation relaxing strategy that improves the performance with
negligible cost. Additionally, we demonstrate the previous metrics for
text-to-image diffusion model quantization are not accurate due to the
distribution gap. To tackle the problem, we propose a novel QDiffBench
benchmark, which utilizes data in the same domain for more accurate evaluation.
Besides, QDiffBench also considers the generalization performance of the
quantized model outside the calibration dataset. Extensive experiments on
Stable Diffusion and Stable Diffusion XL demonstrate the superiority of our
method and benchmark. Moreover, we are the first to achieve quantization for
Stable Diffusion XL while maintaining the performance.
