mlsanityData to 14 Oct 2026

Binary Diffusion Probabilistic Model

arXiv 2501.13915 ↗First posted 23 Jan 2025cs.CVcited by 1

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

We propose the Binary Diffusion Probabilistic Model (BDPM), a generative framework specifically designed for data representations in binary form. Conventional denoising diffusion probabilistic models (DDPMs) assume continuous inputs, use mean squared error objectives and Gaussian perturbations, i.e., assumptions that are not suited to discrete and binary representations. BDPM instead encodes images into binary representations using multi bit-plane and learnable binary embeddings, perturbs them via XOR-based noise, and trains a model by optimizing a binary cross-entropy loss. These binary representations offer fine-grained noise control, accelerate convergence, and reduce inference cost. On image-to-image translation tasks, such as super-resolution, inpainting, and blind restoration, BDPM based on a small denoiser and multi bit-plane representation outperforms state-of-the-art methods on FFHQ, CelebA, and CelebA-HQ using a few sampling steps. In class-conditional generation on ImageNet-1k, BDPM based on learnable binary embeddings achieves competitive results among models with both low parameter counts and a few sampling steps.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 58 of its 64 references that are in the map. First posted on arXiv 23 Jan 2025.

Builds on

58 of 64 references.

Closest ancestors · 8

Same direction, newest first

  1. 2024MaskBit: Embedding-free Image Generation via Bit Tokens24 Sep
  2. 2023Binary Latent Diffusion10 Apr
  3. 2022Scalable Diffusion Models with Transformers19 Dec
  4. 2022Classifier-Free Diffusion Guidance26 Jul
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  6. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models6 Aug
  7. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  8. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 50

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  6. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  7. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  8. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  9. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  10. 2016Improved Techniques for Training GANscited by 2,970
  11. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  12. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 2,022
38 more
  1. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,778
  2. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
  3. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,416
  4. 2021Palette: Image-to-Image Diffusion Modelscited by 1,300
  5. 2022MaskGIT: Masked Generative Image Transformercited by 1,099
  6. 2022Denoising Diffusion Restoration Modelscited by 963
  7. 2024Autoregressive Model Beats Diffusion: Llama for Scalable Image Generationcited by 777
  8. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 738
  9. 2023Language Model Beats Diffusion -- Tokenizer is Key to Visual Generationcited by 640
  10. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  11. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelcited by 583
  12. 2022StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasetscited by 492
  13. 2023Exploiting Diffusion Prior for Real-World Image Super-Resolutioncited by 489
  14. 2018Assessing Generative Models via Precision and Recallcited by 406
  15. 2023ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shiftingcited by 365
  16. 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
  17. 2021Towards Real-World Blind Face Restoration with Generative Facial Priorcited by 349
  18. 2024An Image is Worth 32 Tokens for Reconstruction and Generationcited by 302
  19. 2023DiffBIR: Towards Blind Image Restoration with Generative Diffusion Priorcited by 257
  20. 2022Towards Robust Blind Face Restoration with Codebook Lookup Transformercited by 231
  21. 2021Large Scale Image Completion via Co-Modulated Generative Adversarial Networkscited by 220
  22. 2022MAT: Mask-Aware Transformer for Large Hole Image Inpaintingcited by 218
  23. 2019EdgeConnect: Generative Image Inpainting with Adversarial Edge Learningcited by 163
  24. 2024Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generationcited by 155
  25. 2021High-Fidelity Pluralistic Image Completion with Transformerscited by 134
  26. 2016Discrete Variational Autoencoderscited by 125
  27. 2022VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decodercited by 116
  28. 2022DifFace: Blind Face Restoration with Diffused Error Contractioncited by 97
  29. 2021Aggregated Contextual Transformations for High-Resolution Image Inpaintingcited by 89
  30. 2023DR2: Diffusion-based Robust Degradation Remover for Blind Face Restorationcited by 79
  31. 2020HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishmentcited by 70
  32. 2021Bridging Global Context Interactions for High-Fidelity Image Completioncited by 40
  33. 2021Image Inpainting by End-to-End Cascaded Refinement with Mask Awarenesscited by 35
  34. 2024Scalable Autoregressive Image Generation with Mambacited by 26
  35. 2023Towards Real-World Blind Face Restoration with Generative Diffusion Priorcited by 22
  36. 2024Effective Diffusion Transformer Architecture for Image Super-Resolutioncited by 21
  37. 2022Image Completion with Heterogeneously Filtered Spectral Hintscited by 17
  38. 2024BFRFormer: Transformer-based generator for Real-World Blind Face Restorationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Bernoulli Flow Models: Self-Consistent Generative Modeling for Binary Datacited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Structured Denoising Diffusion Models in Discrete State-Spaces
  2. 2025Compressed Image Generation with Denoising Diffusion Codebook Models
  3. 2024Denoising Diffusion Probabilistic Models in Six Simple Steps
  4. 2022Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generation
  5. 2023CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
  6. 2023Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations
  7. 2023PartDiff: Image Super-resolution with Partial Diffusion Models
  8. 2022Representation Learning with Diffusion Models