mlsanityData to 14 Oct 2026

ReaDiT Guidance: Control for Image and Video Generation using Diffusion Transformer Features

arXiv 2609.04649 ↗First posted 4 Sep 2026cs.CVcited by 0

On the map: Diffusion models → Video generation

Abstract (arXiv)

We present DiT Readout (ReaDiT) Guidance, a lightweight framework for controlling generation with Diffusion Transformer (DiT) models via their internal feature representations. ReaDiT Guidance uses features from a single DiT block to steer the generative process according to spatial targets - like depth, pose, or edge maps - provided at test time. Furthermore, since modern text-to-video models are largely built on DiT backbones, ReaDiT Guidance naturally extends to video generation, enabling camera and motion control. Experimental results demonstrate that our approach achieves competitive or improved results compared to existing feature-based and off-the-shelf adapter-based approaches while requiring fewer parameters.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 38 of its 42 references that are in the map. First posted on arXiv 4 Sep 2026.

Builds on

38 of 42 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025Time-to-Move: Training-Free Motion Controlled Video Generation via Dual-Clock Denoising9 Nov
  2. 2025Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Models8 Jun
  3. 2024Video Motion Transfer with Diffusion Transformers10 Dec
  4. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer12 Aug
  5. 2024Tora: Trajectory-oriented Diffusion Transformer for Video Generation31 Jul
  6. 2024Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model15 Apr
  7. 2023MotionDirector: Motion Customization of Text-to-Video Diffusion Models12 Oct

Other references · 31

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  5. 2022Scalable Diffusion Models with Transformerscited by 6,054
  6. 2022Flow Matching for Generative Modelingcited by 5,702
  7. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  8. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
  9. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
  10. 2013Auto-Encoding Variational Bayescited by 4,053
  11. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,224
  12. 2021Vision Transformers for Dense Predictioncited by 1,803
19 more
  1. 2024Depth Anything V2cited by 1,422
  2. 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,351
  3. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,064
  4. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 906
  5. 2024Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Thinkcited by 703
  6. 2015Holistically-Nested Edge Detectioncited by 592
  7. 2023Emergent Correspondence from Image Diffusioncited by 446
  8. 2024OminiControl: Minimal and Universal Control for Diffusion Transformercited by 340
  9. 2024Motion Prompting: Controlling Video Generation with Motion Trajectoriescited by 178
  10. 2024ControlNet++: Improving Conditional Controls with Efficient Consistency Feedbackcited by 154
  11. 2025EasyControl: Adding Efficient and Flexible Control for Diffusion Transformercited by 119
  12. 2024Image Conductor: Precision Control for Interactive Video Synthesiscited by 63
  13. 2025OminiControl2: Efficient Conditioning for Diffusion Transformerscited by 57
  14. 2023Readout Guidance: Learning Control from Diffusion Featurescited by 52
  15. 2024CleanDIFT: Diffusion Features without Noisecited by 44
  16. 2025Unleashing Diffusion Transformers for Visual Correspondence by Modulating Massive Activationscited by 23
  17. 2024CtrLoRA: An Extensible and Efficient Framework for Controllable Image Generationcited by 21
  18. 2024UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generationcited by 9
  19. 2025SDPose: Exploiting Diffusion Priors for Out-of-Domain and Robust Pose Estimationcited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Boosting Camera Motion Control for Video Diffusion Transformers
  2. 2024VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control
  3. 2025DyDiT++: Diffusion Transformers with Timestep and Spatial Dynamics for Efficient Visual Generation
  4. 2025DiTraj: training-free trajectory control for video diffusion transformer
  5. 2024DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation
  6. 2025Enhancing Image Generation Fidelity via Progressive Prompts
  7. 2025LMP: Leveraging Motion Prior in Zero-Shot Video Generation with Diffusion Transformer
  8. 2026PE-Field 4D: Video Generation Models as Canvas