mlsanityData to 14 Oct 2026

ReDDiT: Rehashing Noise for Discrete Visual Generation

arXiv 2505.19656 ↗First posted 26 May 2025cs.CVcited by 3

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

In the visual generative area, discrete diffusion models are gaining traction for their efficiency and compatibility. However, pioneered attempts still fall behind their continuous counterparts, which we attribute to noise (absorbing state) design and sampling heuristics. In this study, we propose a rehashing noise approach for discrete diffusion transformer (termed ReDDiT), with the aim to extend absorbing states and improve expressive capacity of discrete diffusion models. ReDDiT enriches the potential paths that latent variables traverse during training with randomized multi-index corruption. The derived rehash sampler, which reverses the randomized absorbing paths, guarantees high diversity and low discrepancy of the generation process. These reformulations lead to more consistent and competitive generation quality, mitigating the need for heavily tuned randomness. Experiments show that ReDDiT significantly outperforms the baseline model (reducing gFID from 6.18 to 1.61) and is on par with the continuous counterparts.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 48 of its 52 references that are in the map. First posted on arXiv 26 May 2025.

Builds on

48 of 52 references.

Closest ancestors · 15

Same direction, newest first

  1. 2024RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction24 Dec
  2. 2024Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think9 Oct
  3. 2023Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces18 May
  4. 2023MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer25 Mar
  5. 2022Scalable Diffusion Models with Transformers19 Dec
  6. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models2 Nov
  7. 2022Classifier-Free Diffusion Guidance26 Jul
  8. 2022A Continuous Time Framework for Discrete Denoising Models30 May
  9. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  10. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesis29 Nov
  11. 2021Structured Denoising Diffusion Models in Discrete State-Spaces7 Jul
  12. 2021Variational Diffusion Models1 Jul
3 more
  1. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  2. 2020Denoising Diffusion Implicit Models6 Oct
  3. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 33

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  4. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  5. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  6. 2016Improved Techniques for Training GANscited by 2,970
  7. 2019Root Mean Square Layer Normalizationcited by 1,480
  8. 2022MaskGIT: Masked Generative Image Transformercited by 1,099
  9. 2024Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Predictioncited by 1,023
  10. 2025Large Language Diffusion Modelscited by 912
  11. 2024Simple and Effective Masked Diffusion Language Modelscited by 848
  12. 2024Autoregressive Model Beats Diffusion: Llama for Scalable Image Generationcited by 777
21 more
  1. 2021Vector-quantized Image Modeling with Improved VQGANcited by 658
  2. 2024Autoregressive Image Generation without Vector Quantizationcited by 651
  3. 2023Muse: Text-To-Image Generation via Masked Generative Transformerscited by 648
  4. 2023Language Model Beats Diffusion -- Tokenizer is Key to Visual Generationcited by 640
  5. 2024Simplified and Generalized Masked Diffusion for Discrete Datacited by 626
  6. 2024Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Datacited by 381
  7. 2024Discrete Flow Matchingcited by 317
  8. 2024An Image is Worth 32 Tokens for Reconstruction and Generationcited by 302
  9. 2024Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understandingcited by 298
  10. 2025MMaDA: Multimodal Large Diffusion Language Modelscited by 250
  11. 2024Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Samplingcited by 208
  12. 2025dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Cachingcited by 169
  13. 2024Unlocking Guidance for Discrete State-Space Diffusion and Flow Modelscited by 129
  14. 2024RandAR: Decoder-only Autoregressive Visual Generation in Random Orderscited by 83
  15. 2022Improved Masked Image Generation with Token-Criticcited by 64
  16. 2023Towards Accurate Image Coding: Improved Autoregressive Image Generation with Dynamic Vector Quantizationcited by 60
  17. 2024Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspectivecited by 57
  18. 2024Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesiscited by 56
  19. 2025Unified Multimodal Discrete Diffusioncited by 49
  20. 2024Scalable Image Tokenization with Index Backpropagation Quantizationcited by 44
  21. 2024[MASK] is All You Needcited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Consolidating Reinforcement Learning for Multimodal Discrete Diffusion Modelscited by 12
  2. 2025Test-Time Anchoring for Discrete Diffusion Posterior Samplingcited by 5
  3. 2026dRAE: Representation Autoencoder with Hyper-Spherical Codescited by 0

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  4. 2024E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models
  5. 2026AsyncPatch Diffusion: spatially-flexible image generation
  6. 2025Adaptive Destruction Processes for Diffusion Samplers
  7. 2026Spectral Progressive Diffusion for Efficient Image and Video Generation
  8. 2024Think While You Generate: Discrete Diffusion with Planned Denoising