mlsanityData to 7 Oct 2026

Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features

arXiv 2410.03558 ↗First posted 4 Oct 2024cs.CV cs.AIcited by 23

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at https://github.com/Darkbblue/generic-diffusion-feature.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 50 of its 67 references that are in the map. First posted on arXiv 4 Oct 2024.

Builds on

50 of 67 references.

Closest ancestors · 12

Same direction, newest first

  1. 2023Harnessing Diffusion Models for Visual Perception with Meta Prompts22 Dec
  2. 2023SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching26 Oct
  3. 2023Text-image Alignment for Diffusion-based Perception29 Sep
  4. 2023Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks13 Aug
  5. 2023DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models11 Aug
  6. 2023Emergent Correspondence from Image Diffusion6 Jun
  7. 2023A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence24 May
  8. 2023Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence23 May
  9. 2023DDP: Diffusion Model for Dense Visual Prediction30 Mar
  10. 2023Your Diffusion Model is Secretly a Zero-Shot Classifier28 Mar
  11. 2023Unleashing Text-to-Image Diffusion Models for Visual Perception3 Mar
  12. 2022Dual Diffusion Implicit Bridges for Image-to-Image Translation16 Mar

Other references · 38

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  7. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  8. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  9. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  10. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  11. 2022Scalable Diffusion Models with Transformerscited by 5,931
  12. 2022Flow Matching for Generative Modelingcited by 5,426
26 more
  1. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  2. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  3. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  4. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  5. 2020Transformer Feed-Forward Layers Are Key-Value Memoriescited by 1,307
  6. 2024VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Modelscited by 568
  7. 2021Label-Efficient Semantic Segmentation with Diffusion Modelscited by 495
  8. 2023Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Modelscited by 371
  9. 2017Road Extraction by Deep Residual U-Netcited by 333
  10. 2021Deep ViT Features as Dense Visual Descriptorscited by 327
  11. 2023Repurposing Diffusion-Based Image Generators for Monocular Depth Estimationcited by 323
  12. 2022MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretrainingcited by 187
  13. 2019SPair-71k: A Large-scale Benchmark for Semantic Correspondencecited by 140
  14. 2022Open-Vocabulary Universal Image Segmentation with MaskCLIPcited by 131
  15. 2023Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusioncited by 131
  16. 2023Diffusion Model as Representation Learnercited by 95
  17. 2023Diffusion Models Beat GANs on Image Classificationcited by 57
  18. 2023Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Featurescited by 55
  19. 2020Learning to Compose Hypercolumns for Visual Correspondencecited by 53
  20. 2023Do text-free diffusion models learn discriminative visual representations?cited by 33
  21. 2023Prompting Diffusion Representations for Cross-Domain Semantic Segmentationcited by 30
  22. 2023Analyzing Vision Transformers for Image Classification in Class Embedding Spacecited by 26
  23. 2023A Unified Perspective for Loss-Oriented Imbalanced Learning via Localizationcited by 24
  24. 2024Your Diffusion Model is Secretly a Certifiably Robust Classifiercited by 24
  25. 2023From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Modelscited by 16
  26. 2022Optimizing Partial Area Under the Top-k Curve: Theory and Practicecited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 15; 2026: 8.

23 papers.

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  1. 2025Emergent Temporal Correspondences from Video Diffusion Transformerscited by 30
  2. 2025Seg4Diff: Unveiling Open-Vocabulary Segmentation in Text-to-Image Diffusion Transformerscited by 24
  3. 2025Aligning Text to Image in Diffusion Models is Easier Than You Thinkcited by 21
  4. 2025Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Modelscited by 6
  5. 2025StyleKeeper: Prevent Content Leakage using Negative Visual Query Guidancecited by 6
  6. 2025MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schr"odinger Bridgescited by 5
  7. 2025Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentationcited by 4
  8. 2025Appreciate the View: A Task-Aware Evaluation Framework for Novel View Synthesiscited by 4
  9. 2025Towards Multimodal Understanding via Stable Diffusion as a Task-Aware Feature Extractorcited by 3
  10. 2025Probing and Leveraging Video Diffusion Transformer Features for Robust Point Trackingcited by 3
  11. 2026GenMask: Adapting DiT for Segmentation via Direct Mask Generationcited by 3
  12. 2026Making Training-Free Diffusion Segmentors Scale with the Generative Powercited by 2
11 more
  1. 2025Leveraging Prior Knowledge of Diffusion Model for Person Searchcited by 1
  2. 2025BachVid: Training-Free Video Generation with Consistent Background and Charactercited by 1
  3. 2026Self-transcendence: Is External Feature Guidance Indispensable for Accelerating Diffusion Transformer Training?cited by 1
  4. 2026Guiding a Diffusion Model by Swapping Its Tokenscited by 1
  5. 2025Canonical Latent Representations in Conditional Diffusion Modelscited by 0
  6. 2025Depth-Sequence Transformer (DST) for Segment-Specific ICA Calcification Mapping on Non-Contrast CTcited by 0
  7. 2025Exploring Conditions for Diffusion models in Robotic Controlcited by 0
  8. 2026General and Efficient Steering of Diffusion Modelscited by 0
  9. 2026BiGain: Unified Token Compression for Joint Generation and Classificationcited by 0
  10. 2026Noise2Map: End-to-End Diffusion Model for Semantic Segmentation and Change Detectioncited by 0
  11. 2026Diffuse2Seg: Diffusion Models Can Segment Anything Without Supervisioncited by 0

Similar, not cited

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  1. 2025Diffusion Classifiers Understand Compositionality, but Conditions Apply
  2. 2026How Do Diffusion Classifiers Decide? A Bias-Centric Evaluation
  3. 2023Discffusion: Discriminative Diffusion Models as Few-shot Vision and Language Learners
  4. 2023Mitigating Exposure Bias in Discriminator Guided Diffusion Models
  5. 2026A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning
  6. 2026A Decomposable Probe for Few-Step Diffusion Models: Prompt, Latent, and Score Selectivity across Backbone Families and Distillation Paradigms
  7. 2024Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models
  8. 2025Unleashing Diffusion Transformers for Visual Correspondence by Modulating Massive Activations