mlsanityData to 14 Oct 2026

SegQuant: A Semantics-Aware and Generalizable Quantization Framework for Diffusion Models

arXiv 2507.14811 ↗First posted 20 Jul 2025cs.CV cs.AIcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Diffusion models have demonstrated exceptional generative capabilities but are computationally intensive, posing significant challenges for deployment in resource-constrained or latency-sensitive environments. Quantization offers an effective means to reduce model size and computational cost, with post-training quantization (PTQ) being particularly appealing due to its compatibility with pre-trained models without requiring retraining or training data. However, existing PTQ methods for diffusion models often rely on architecture-specific heuristics that limit their generalizability and hinder integration with industrial deployment pipelines. To address these limitations, we propose SegQuant, a unified quantization framework that adaptively combines complementary techniques to enhance cross-model versatility. SegQuant consists of a segment-aware, graph-based quantization strategy (SegLinear) that captures structural semantics and spatial heterogeneity, along with a dual-scale quantization scheme (DualScale) that preserves polarity-asymmetric activations, which is crucial for maintaining visual fidelity in generated outputs. SegQuant is broadly applicable beyond Transformer-based diffusion models, achieving strong performance while ensuring seamless compatibility with mainstream deployment tools.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 38 of its 53 references that are in the map. First posted on arXiv 20 Jul 2025.

Builds on

38 of 53 references.

Closest ancestors · 16

Same direction, newest first

  1. 2025Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers28 May
  2. 2024SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models7 Nov
  3. 2024Timestep-Aware Correction for Quantized Diffusion Models4 Jul
  4. 2024ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation4 Jun
  5. 2024PTQ4DiT: Post-training Quantization for Diffusion Transformers25 May
  6. 2024QNCD: Quantization Noise Correction for Diffusion Models28 Mar
  7. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesis5 Mar
  8. 2024EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models9 Jan
  9. 2023TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models27 Nov
  10. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis4 Jul
  11. 2023PTQD: Accurate Post-Training Quantization for Diffusion Models18 May
  12. 2023Q-Diffusion: Quantizing Diffusion Models8 Feb
4 more
  1. 2022Scalable Diffusion Models with Transformers19 Dec
  2. 2022Post-training Quantization on Diffusion Models28 Nov
  3. 2020Denoising Diffusion Implicit Models6 Oct
  4. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  5. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  6. 2022Flow Matching for Generative Modelingcited by 5,702
  7. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  8. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,424
  9. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,333
  10. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  11. 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,386
  12. 2023ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generationcited by 1,045
10 more
  1. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 991
  2. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
  3. 2020Up or Down? Adaptive Rounding for Post-Training Quantizationcited by 621
  4. 2021BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstructioncited by 479
  5. 2024Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generationcited by 256
  6. 2021PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantizationcited by 179
  7. 2023A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captionscited by 108
  8. 2024AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizercited by 29
  9. 2024ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformerscited by 7
  10. 2025AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantizationcited by 3

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  1. 2025Diffusion Model Quantization: A Review
  2. 2024Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
  3. 2023Efficient Quantization Strategies for Latent Diffusion Models
  4. 2024PQD: Post-training Quantization for Efficient Diffusion Models
  5. 2024DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation
  6. 2024Qua2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models
  7. 2023EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models
  8. 2023Towards Accurate Post-training Quantization for Diffusion Models