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QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning

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arxiv 2402.03666 v6 pith:ORP25M3Y submitted 2024-02-06 cs.CV

Haoxuan Wang , Yuzhang Shang , Zhihang Yuan , Junyi Wu , Junchi Yan , Yan Yan This is my paper · ORCID
classification cs.CV
keywords quantizationfinetuningacrossbit-widthdiffusiondistributionslayerslow-bit
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The practical deployment of diffusion models is still hindered by the high memory and computational overhead. Although quantization paves a way for model compression and acceleration, existing methods face challenges in achieving low-bit quantization efficiently. In this paper, we identify imbalanced activation distributions as a primary source of quantization difficulty, and propose to adjust these distributions through weight finetuning to be more quantization-friendly. We provide both theoretical and empirical evidence supporting finetuning as a practical and reliable solution. Building on this approach, we further distinguish two critical types of quantized layers: those responsible for retaining essential temporal information and those particularly sensitive to bit-width reduction. By selectively finetuning these layers under both local and global supervision, we mitigate performance degradation while enhancing quantization efficiency. Our method demonstrates its efficacy across three high-resolution image generation tasks, obtaining state-of-the-art performance across multiple bit-width settings.

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Injecting spatial importance maps (e.g., CFG delta) into the OBS Hessian improves subject preservation in pruned diffusion models at high sparsity, but gains over the baseline are small and without error bars.

  2. Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling

    cs.CV 2025-09 conditional novelty 6.0 of 10

    Q-Sched's quantization-aware scheduler with a reference-free JAQ loss lets 2-8 step quantized diffusion models reach lower FID than full-precision baselines.

  3. MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MPQ-DMv2 adds binary residual quantization, temporal relation distillation, and SVD-initialized LoRA to mixed-precision quantization, improving low-bit diffusion model generation quality.

  4. Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Q-VDiT quantizes video diffusion transformers to 3-4 bit weights by adding a learned rank-1 error correction (TQE) and a temporal distribution distillation loss (TMD), nearly doubling VBench scene consistency at W3A6 ...

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    A mix of signed and unsigned 4-bit floating-point formats, timestep-aware LoRA experts, and a denoising-weighted loss keeps diffusion-model image quality close to full precision.

  6. DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models

    cs.CV 2025-01 conditional novelty 6.0 of 10

    DGQ quantizes text-to-image diffusion models to 4-8 bits without fine-tuning by preserving activation outliers and applying prompt-specific log quantization to cross-attention scores.

  7. MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models

    cs.CV 2024-12 conditional novelty 6.0 of 10

    MPQ-DM combines kurtosis-based intra-layer mixed-precision weight quantization with time-smoothed relation distillation to keep diffusion models accurate at 2 to 4 bit widths.

  8. PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution

    cs.CV 2024-11 conditional novelty 6.0 of 10

    PassionSR quantizes one-step diffusion super-resolution models to 6-8 bits via learnable quantizer boundaries, equivalent transformations, and a two-stage calibration, while keeping quality close to full precision.

  9. Importance-Based Token Merging for Efficient Image and Video Generation

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    A token-merging method that anchors computation on high-CFG-importance tokens improves generation quality at fixed inference speedups.

  10. Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts

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    Across four NAS/DNN-predictor regression benchmarks, GEN (deep graph convolution) achieves the best average rank over 11 GNN message-passing layers, though attention GATv2 wins on the largest graphs.

  11. Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion

    cs.CV 2024-12 conditional novelty 4.0 of 10

    A serial-to-parallel distillation pipeline with per-timestep activation quantization makes W4A8 quantized Stable Diffusion models generate images much closer to the full-precision model than prior PTQ methods.

  12. Diffusion Model Quantization: A Review

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    A structured review and benchmark of methods for quantizing diffusion models, with a taxonomy of post-training and quantization-aware approaches and an analysis of quantization artifacts.

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