  Diffusion models have revolutionized image synthesis, setting new benchmarks
in quality and creativity. However, their widespread adoption is hindered by
the intensive computation required during the iterative denoising process.
Post-training quantization (PTQ) presents a solution to accelerate sampling,
aibeit at the expense of sample quality, extremely in low-bit settings.
Addressing this, our study introduces a unified Quantization Noise Correction
Scheme (QNCD), aimed at minishing quantization noise throughout the sampling
process. We identify two primary quantization challenges: intra and inter
quantization noise. Intra quantization noise, mainly exacerbated by embeddings
in the resblock module, extends activation quantization ranges, increasing
disturbances in each single denosing step. Besides, inter quantization noise
stems from cumulative quantization deviations across the entire denoising
process, altering data distributions step-by-step. QNCD combats these through
embedding-derived feature smoothing for eliminating intra quantization noise
and an effective runtime noise estimatiation module for dynamicly filtering
inter quantization noise. Extensive experiments demonstrate that our method
outperforms previous quantization methods for diffusion models, achieving
lossless results in W4A8 and W8A8 quantization settings on ImageNet (LDM-4).
Code is available at: https://github.com/huanpengchu/QNCD
