Which Layer Causes Distribution Deviation? Entropy-Guided Adaptive Pruning for Diffusion and Flow Models

• URL: http://arxiv.org/abs/2511.21122v1
• Date: Wed, 26 Nov 2025 07:20:48 GMT
• Title: Which Layer Causes Distribution Deviation? Entropy-Guided Adaptive Pruning for Diffusion and Flow Models
• Authors: Changlin Li, Jiawei Zhang, Zeyi Shi, Zongxin Yang, Zhihui Li, Xiaojun Chang,
• Abstract summary: EntPruner is an entropy-guided automatic progressive pruning framework for diffusion and flow models.<n>Experiments on DiT and SiT models demonstrate the effectiveness of EntPruner, achieving up to 2.22$times$ inference speedup.
• Score: 77.55829017952728
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large-scale vision generative models, including diffusion and flow models, have demonstrated remarkable performance in visual generation tasks. However, transferring these pre-trained models to downstream tasks often results in significant parameter redundancy. In this paper, we propose EntPruner, an entropy-guided automatic progressive pruning framework for diffusion and flow models. First, we introduce entropy-guided pruning, a block-level importance assessment strategy specifically designed for generative models. Unlike discriminative models, generative models require preserving the diversity and condition-fidelity of the output distribution. As the importance of each module can vary significantly across downstream tasks, EntPruner prioritizes pruning of less important blocks using data-dependent Conditional Entropy Deviation (CED) as a guiding metric. CED quantifies how much the distribution diverges from the learned conditional data distribution after removing a block. Second, we propose a zero-shot adaptive pruning framework to automatically determine when and how much to prune during training. This dynamic strategy avoids the pitfalls of one-shot pruning, mitigating mode collapse, and preserving model performance. Extensive experiments on DiT and SiT models demonstrate the effectiveness of EntPruner, achieving up to 2.22$\times$ inference speedup while maintaining competitive generation quality on ImageNet and three downstream datasets.

Related papers

• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning [14.707830064594056]
  Diffusion autoencoders (DAs) use an input-dependent latent variable to capture representations alongside the diffusion process.<n>Better generative modelling is the primary goal of another class of diffusion models -- those that learn their forward (noising) process.
  arXiv  Detail & Related papers  (2025-05-30T18:14:09Z)
• Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing [25.50590395490414]
  We propose an inference-time scaling approach for pretrained flow models.<n>We show that SDE-based generation, particularly variance-preserving (VP) interpolant-based generation, improves particle sampling methods for inference-time scaling in flow models.
  arXiv  Detail & Related papers  (2025-03-25T06:30:45Z)
• Model Integrity when Unlearning with T2I Diffusion Models [11.321968363411145]
  We propose approximate Machine Unlearning algorithms to reduce the generation of specific types of images, characterized by samples from a forget distribution'' We then propose unlearning algorithms that demonstrate superior effectiveness in preserving model integrity compared to existing baselines.
  arXiv  Detail & Related papers  (2024-11-04T13:15:28Z)
• Guided Flows for Generative Modeling and Decision Making [55.42634941614435]
  We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text synthesis-to-speech. Notably, we are first to apply flow models for plan generation in the offline reinforcement learning setting ax speedup in compared to diffusion models.
  arXiv  Detail & Related papers  (2023-11-22T15:07:59Z)
• Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution [67.9215891673174]
  We propose score entropy as a novel loss that naturally extends score matching to discrete spaces. We test our Score Entropy Discrete Diffusion models on standard language modeling tasks.
  arXiv  Detail & Related papers  (2023-10-25T17:59:12Z)
• Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption [73.98706049140098]
  We propose a novel phasic content fusing few-shot diffusion model with directional distribution consistency loss. Specifically, we design a phasic training strategy with phasic content fusion to help our model learn content and style information when t is large. Finally, we propose a cross-domain structure guidance strategy that enhances structure consistency during domain adaptation.
  arXiv  Detail & Related papers  (2023-09-07T14:14:11Z)
• Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging [24.64264715041198]
  We introduce Sparse Model Soups (SMS), a novel method for merging sparse models by initiating each prune-retrain cycle with the averaged model from the previous phase. SMS preserves sparsity, exploits sparse network benefits, is modular and fully parallelizable, and substantially improves IMP's performance.
  arXiv  Detail & Related papers  (2023-06-29T08:49:41Z)
• Structural Pruning for Diffusion Models [65.02607075556742]
  We present Diff-Pruning, an efficient compression method tailored for learning lightweight diffusion models from pre-existing ones. Our empirical assessment, undertaken across several datasets highlights two primary benefits of our proposed method.
  arXiv  Detail & Related papers  (2023-05-18T12:38:21Z)
• The Score-Difference Flow for Implicit Generative Modeling [1.1929584800629673]
  Implicit generative modeling aims to produce samples of synthetic data matching a target data distribution.<n>Recent work has approached the IGM problem from the perspective of pushing synthetic source data toward the target distribution.<n>We present the score difference between arbitrary target and source distributions as a flow that optimally reduces the Kullback-Leibler divergence between them.
  arXiv  Detail & Related papers  (2023-04-25T15:21:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.