Papers › QNCD: Quantization Noise Correction for Diffusion Models

QNCD: Quantization Noise Correction for Diffusion Models

28 Mar 2024arXiv:2403.19140archive 2025-07-28

Huanpeng Chu, Wei Wu, Chengjie Zang, Kun Yuan

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

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interpolate_fn huanpengchu/qncd/ddim/dpm_solver_pytorch.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 6a35b62fbc80f70a · report
round_ste huanpengchu/qncd/quant/quant_layer.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 675da8641c602b03 · report
Normalize huanpengchu/qncd/ddim/models/diffusion.py official repository ran · our draft was wrong MIT (permissive) · c3a6b977022957cb · report
exists huanpengchu/qncd/ldm/modules/attention.py official repository ran · violated contract MIT (permissive) · aa5486a3650902d8 · report
expand_dims huanpengchu/qncd/ddim/dpm_solver_pytorch.py official repository ran · our draft was wrong MIT (permissive) · e6110366588c5c65 · report
get_timestep_embedding huanpengchu/qncd/ddim/models/diffusion.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · cb49209c125de1b4 · report
lp_loss huanpengchu/qncd/quant/quant_layer.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 08c6ea7576220a13 · report
model_wrapper huanpengchu/qncd/ddim/dpm_solver_pytorch.py official repository ran MIT (permissive) · 0e7df13e9be17236 · report
noise_estimation_loss huanpengchu/qncd/ddim/functions/losses.py official repository ran MIT (permissive) · 36a45cceb1209d2f · report
nonlinearity huanpengchu/qncd/ddim/models/diffusion.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 3137073275f8c21a · report
uniq huanpengchu/qncd/ldm/modules/attention.py official repository ran · our draft was wrong MIT (permissive) · 9a299fe5ae09e407 · report
cross_attn_forward huanpengchu/qncd/ldm/modules/attention.py official repository unverified MIT (permissive) · 51cfe301474f7004 · report

Tasks

DenoisingImage GenerationQuantization

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Diffusion

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