  Diffusion models have marked a significant breakthrough in the synthesis of
semantically coherent images. However, their extensive noise estimation
networks and the iterative generation process limit their wider application,
particularly on resource-constrained platforms like mobile devices. Existing
post-training quantization (PTQ) methods have managed to compress diffusion
models to low precision. Nevertheless, due to the iterative nature of diffusion
models, quantization errors tend to accumulate throughout the generation
process. This accumulation of error becomes particularly problematic in
low-precision scenarios, leading to significant distortions in the generated
images. We attribute this accumulation issue to two main causes: error
propagation and exposure bias. To address these problems, we propose a
timestep-aware correction method for quantized diffusion model, which
dynamically corrects the quantization error. By leveraging the proposed method
in low-precision diffusion models, substantial enhancement of output quality
could be achieved with only negligible computation overhead. Extensive
experiments underscore our method's effectiveness and generalizability. By
employing the proposed correction strategy, we achieve state-of-the-art (SOTA)
results on low-precision models.
