Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance

• URL: http://arxiv.org/abs/2403.17377v1
• Date: Tue, 26 Mar 2024 04:49:11 GMT
• Title: Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
• Authors: Donghoon Ahn, Hyoungwon Cho, Jaewon Min, Wooseok Jang, Jungwoo Kim, SeonHwa Kim, Hyun Hee Park, Kyong Hwan Jin, Seungryong Kim,
• Abstract summary: Perturbed-Attention Guidance (PAG) improves diffusion sample quality across both unconditional and conditional settings. In both ADM and Stable Diffusion, PAG surprisingly improves sample quality in conditional and even unconditional scenarios.
• Score: 28.354284737867136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies have demonstrated that diffusion models are capable of generating high-quality samples, but their quality heavily depends on sampling guidance techniques, such as classifier guidance (CG) and classifier-free guidance (CFG). These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restoration. In this paper, we propose a novel sampling guidance, called Perturbed-Attention Guidance (PAG), which improves diffusion sample quality across both unconditional and conditional settings, achieving this without requiring additional training or the integration of external modules. PAG is designed to progressively enhance the structure of samples throughout the denoising process. It involves generating intermediate samples with degraded structure by substituting selected self-attention maps in diffusion U-Net with an identity matrix, by considering the self-attention mechanisms' ability to capture structural information, and guiding the denoising process away from these degraded samples. In both ADM and Stable Diffusion, PAG surprisingly improves sample quality in conditional and even unconditional scenarios. Moreover, PAG significantly improves the baseline performance in various downstream tasks where existing guidances such as CG or CFG cannot be fully utilized, including ControlNet with empty prompts and image restoration such as inpainting and deblurring.

Related papers

• Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance [52.705112811734566]
  A novel diffusion-based framework is introduced for solving inverse problems using a piecewise guidance scheme.<n>The proposed method is problem-agnostic and readily adaptable to a variety of inverse problems.<n>The framework achieves a reduction in inference time of (25%) for inpainting with both random and center masks, and (23%) and (24%) for (4times) and (8times) super-resolution tasks.
  arXiv  Detail & Related papers  (2025-07-22T19:35:14Z)
• Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering [18.543769006014383]
  Diffusion models often exhibit inconsistent sample quality due to variations inherent in their sampling trajectories.<n>We introduce CFG-Rejection, an efficient, plug-and-play strategy that filters low-quality samples at an early stage of the denoising process.<n>We validate the effectiveness of CFG-Rejection in image generation through extensive experiments.
  arXiv  Detail & Related papers  (2025-05-29T11:08:24Z)
• Multimodal LLM-Guided Semantic Correction in Text-to-Image Diffusion [52.315729095824906]
  MLLM Semantic-Corrected Ping-Pong-Ahead Diffusion (PPAD) is a novel framework that introduces a Multimodal Large Language Model (MLLM) as a semantic observer during inference.<n>It performs real-time analysis on intermediate generations, identifies latent semantic inconsistencies, and translates feedback into controllable signals that actively guide the remaining denoising steps.<n>Extensive experiments demonstrate PPAD's significant improvements.
  arXiv  Detail & Related papers  (2025-05-26T14:42:35Z)
• Self-Guidance: Boosting Flow and Diffusion Generation on Their Own [32.91402070439289]
  We propose Self-Guidance, which improves the image quality by suppressing the generation of low-quality samples. With open-sourced diffusion models such as Stable Diffusion 3.5 and FLUX, Self-Guidance surpasses existing algorithms on multiple metrics. We find that SG has a surprisingly positive effect on the generation of physiologically correct human body structures.
  arXiv  Detail & Related papers  (2024-12-08T06:32:27Z)
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think [72.48325960659822]
  One main bottleneck in training large-scale diffusion models for generation lies in effectively learning these representations. We study this by introducing a straightforward regularization called REPresentation Alignment (REPA), which aligns the projections of noisy input hidden states in denoising networks with clean image representations obtained from external, pretrained visual encoders. The results are striking: our simple strategy yields significant improvements in both training efficiency and generation quality when applied to popular diffusion and flow-based transformers, such as DiTs and SiTs.
  arXiv  Detail & Related papers  (2024-10-09T14:34:53Z)
• DiffATR: Diffusion-based Generative Modeling for Audio-Text Retrieval [49.076590578101985]
  We present a diffusion-based ATR framework (DiffATR) that generates joint distribution from noise. Experiments on the AudioCaps and Clotho datasets with superior performances, verify the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-09-16T06:33:26Z)
• Compress Guidance in Conditional Diffusion Sampling [16.671575782090045]
  This work identifies and quantifies the problem, demonstrating that reducing or excluding guidance at numerous timesteps can mitigate this issue. We observe a significant improvement in image quality and diversity while also reducing the required guidance timesteps by nearly 40%.
  arXiv  Detail & Related papers  (2024-08-20T21:02:54Z)
• Diffusion Model Driven Test-Time Image Adaptation for Robust Skin Lesion Classification [24.08402880603475]
  We propose a test-time image adaptation method to enhance the accuracy of the model on test data. We modify the target test images by projecting them back to the source domain using a diffusion model. Our method makes the robustness more robust across various corruptions, architectures, and data regimes.
  arXiv  Detail & Related papers  (2024-05-18T13:28:51Z)
• Few-shot Online Anomaly Detection and Segmentation [29.693357653538474]
  This paper focuses on addressing the challenging yet practical few-shot online anomaly detection and segmentation (FOADS) task. Under the FOADS framework, models are trained on a few-shot normal dataset, followed by inspection and improvement of their capabilities by leveraging unlabeled streaming data containing both normal and abnormal samples simultaneously. In order to achieve improved performance with limited training samples, we employ multi-scale feature embedding extracted from a CNN pre-trained on ImageNet to obtain a robust representation.
  arXiv  Detail & Related papers  (2024-03-27T02:24:00Z)
• Mitigating Exposure Bias in Discriminator Guided Diffusion Models [4.5349436061325425]
  We propose SEDM-G++, which incorporates a modified sampling approach, combining Discriminator Guidance and Epsilon Scaling. Our proposed approach outperforms the current state-of-the-art, by achieving an FID score of 1.73 on the unconditional CIFAR-10 dataset.
  arXiv  Detail & Related papers  (2023-11-18T20:49:50Z)
• Bridging the Gap: Addressing Discrepancies in Diffusion Model Training for Classifier-Free Guidance [1.6804613362826175]
  Diffusion models have emerged as a pivotal advancement in generative models. In this paper we aim to underscore a discrepancy between conventional training methods and the desired conditional sampling behavior. We introduce an updated loss function that better aligns training objectives with sampling behaviors.
  arXiv  Detail & Related papers  (2023-11-02T02:03:12Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Test-time Adaptation with Slot-Centric Models [63.981055778098444]
  Slot-TTA is a semi-supervised scene decomposition model that at test time is adapted per scene through gradient descent on reconstruction or cross-view synthesis objectives. We show substantial out-of-distribution performance improvements against state-of-the-art supervised feed-forward detectors, and alternative test-time adaptation methods.
  arXiv  Detail & Related papers  (2022-03-21T17:59:50Z)
• Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding [76.89426311082927]
  Existing models are trained on clean data, which causes a textitgap between clean data training and real-world inference. We propose a method from the perspective of domain adaptation, by which both high- and low-quality samples are embedding into similar vector space. Experiments on the widely-used dataset, Snips, and large scale in-house dataset (10 million training examples) demonstrate that this method not only outperforms the baseline models on real-world (noisy) corpus but also enhances the robustness, that is, it produces high-quality results under a noisy environment.
  arXiv  Detail & Related papers  (2021-04-13T17:54:33Z)
• Salvage Reusable Samples from Noisy Data for Robust Learning [70.48919625304]
  We propose a reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
  arXiv  Detail & Related papers  (2020-08-06T02:07:21Z)

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.