Binary Diffusion Probabilistic Model

• URL: http://arxiv.org/abs/2501.13915v2
• Date: Tue, 30 Sep 2025 13:58:31 GMT
• Title: Binary Diffusion Probabilistic Model
• Authors: Vitaliy Kinakh, Slava Voloshynovskiy,
• Abstract summary: We propose a generative framework specifically designed for data representations in binary form.<n> Binary Diffusion Probabilistic Model encodes images into binary representations using multi bit-plane and learnable binary embeddings.<n>These binary representations offer fine-grained noise control, accelerate convergence, and reduce inference cost.
• Score: 6.329379610824222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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)
• One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models [65.96186414865747]
  Text-to-Image (T2I) diffusion models face a trade-off between inference speed and image quality.<n>We introduce the first Time-independent Unified TiUE for the student model UNet architecture.<n>Using a one-pass scheme, TiUE shares encoder features across multiple decoder time steps, enabling parallel sampling.
  arXiv  Detail & Related papers  (2025-05-28T04:23:22Z)
• Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms [31.42317398879432]
  Current inference approaches mainly fall into two categories: exact simulation and approximate methods such as $tau$-leaping.<n>In this work, we advance the latter category by tailoring the first extension of high-order numerical inference schemes to discrete diffusion models.<n>We rigorously analyze the proposed schemes and establish the second-order accuracy of the $theta$-trapezoidal method in KL divergence.
  arXiv  Detail & Related papers  (2025-02-01T00:25:21Z)
• Latent Schrodinger Bridge: Prompting Latent Diffusion for Fast Unpaired Image-to-Image Translation [58.19676004192321]
  Diffusion models (DMs), which enable both image generation from noise and inversion from data, have inspired powerful unpaired image-to-image (I2I) translation algorithms. We tackle this problem with Schrodinger Bridges (SBs), which are differential equations (SDEs) between distributions with minimal transport cost. Inspired by this observation, we propose Latent Schrodinger Bridges (LSBs) that approximate the SB ODE via pre-trained Stable Diffusion. We demonstrate that our algorithm successfully conduct competitive I2I translation in unsupervised setting with only a fraction of cost required by previous DM-
  arXiv  Detail & Related papers  (2024-11-22T11:24:14Z)
• Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior [0.18434042562191813]
  This paper presents a novel and highly computationally efficient image restoration method. It embeds a DDPM denoiser within an empirical Bayesian Langevin algorithm. It improves on state-of-the-art strategies both in image estimation accuracy and computing time.
  arXiv  Detail & Related papers  (2024-09-06T16:20:24Z)
• Binarized Diffusion Model for Image Super-Resolution [61.963833405167875]
  Binarization, an ultra-compression algorithm, offers the potential for effectively accelerating advanced diffusion models (DMs) Existing binarization methods result in significant performance degradation. We introduce a novel binarized diffusion model, BI-DiffSR, for image SR.
  arXiv  Detail & Related papers  (2024-06-09T10:30:25Z)
• SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder [13.453138169497903]
  SeNM-VAE is a semi-supervised noise modeling method that leverages both paired and unpaired datasets to generate realistic degraded data. We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks. Our approach excels in the quality of synthetic degraded images compared to other unpaired and paired noise modeling methods.
  arXiv  Detail & Related papers  (2024-03-26T09:03:40Z)
• Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction [31.503662384666274]
  In science and engineering, the goal is to infer an unknown image from a small number of measurements collected from a known forward model describing certain imaging modality. Motivated Score-based diffusion models, due to its empirical success, have emerged as an impressive candidate of an exemplary prior in image reconstruction.
  arXiv  Detail & Related papers  (2024-03-25T15:58:26Z)
• Generalized Consistency Trajectory Models for Image Manipulation [59.576781858809355]
  Diffusion models (DMs) excel in unconditional generation, as well as on applications such as image editing and restoration. This work aims to unlock the full potential of consistency trajectory models (CTMs) by proposing generalized CTMs (GCTMs) We discuss the design space of GCTMs and demonstrate their efficacy in various image manipulation tasks such as image-to-image translation, restoration, and editing.
  arXiv  Detail & Related papers  (2024-03-19T07:24:54Z)
• Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection [1.5550533143704954]
  We present a novel and fast unsupervised anomaly detection approach based on latent Bernoulli diffusion models. We achieve state-of-the-art performance compared to other diffusion-based unsupervised anomaly detection algorithms.
  arXiv  Detail & Related papers  (2024-03-18T11:15:03Z)
• Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations [5.517338199249029]
  SR-DDPM is a class of generative models that produce high-quality images by reversing a noisy diffusion process. By exploiting the similarity between diverse data distributions, our method can scale to multiple tasks without compromising the image quality. We evaluate our method on standard image datasets and show that it outperforms both unconditional and conditional DDPM in terms of FID and SSIM metrics.
  arXiv  Detail & Related papers  (2023-11-27T22:30:26Z)
• Denoising Diffusion Bridge Models [54.87947768074036]
  Diffusion models are powerful generative models that map noise to data using processes. For many applications such as image editing, the model input comes from a distribution that is not random noise. In our work, we propose Denoising Diffusion Bridge Models (DDBMs)
  arXiv  Detail & Related papers  (2023-09-29T03:24:24Z)
• SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation [96.11061713135385]
  This work presents a new score-decomposed diffusion model to explicitly optimize the tangled distributions during image generation. We equalize the refinement parts of the score function and energy guidance, which permits multi-objective optimization on the manifold. SDDM outperforms existing SBDM-based methods with much fewer diffusion steps on several I2I benchmarks.
  arXiv  Detail & Related papers  (2023-08-04T06:21:57Z)
• ExposureDiffusion: Learning to Expose for Low-light Image Enhancement [87.08496758469835]
  This work addresses the issue by seamlessly integrating a diffusion model with a physics-based exposure model. Our method obtains significantly improved performance and reduced inference time compared with vanilla diffusion models. The proposed framework can work with both real-paired datasets, SOTA noise models, and different backbone networks.
  arXiv  Detail & Related papers  (2023-07-15T04:48:35Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Binary Latent Diffusion [36.70550531181131]
  We show that a binary latent space can be explored for compact yet expressive image representations. We present both conditional and unconditional image generation experiments with multiple datasets. The proposed framework can be seamlessly scaled to $1024 times 1024$ high-resolution image generation without resorting to latent hierarchy or multi-stage refinements.
  arXiv  Detail & Related papers  (2023-04-10T19:03:28Z)
• Multiscale Structure Guided Diffusion for Image Deblurring [24.09642909404091]
  Diffusion Probabilistic Models (DPMs) have been employed for image deblurring. We introduce a simple yet effective multiscale structure guidance as an implicit bias. We demonstrate more robust deblurring results with fewer artifacts on unseen data.
  arXiv  Detail & Related papers  (2022-12-04T10:40:35Z)
• Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning [90.02873747873444]
  Bit Diffusion is a generic approach for generating discrete data with continuous diffusion models. The proposed approach can achieve strong performance in both discrete image generation and image captioning tasks. For image captioning on MS-COCO dataset, our approach achieves competitive results compared to autoregressive models.
  arXiv  Detail & Related papers  (2022-08-08T15:08:40Z)
• Score-based diffusion models for accelerated MRI [35.3148116010546]
  We introduce a way to sample data from a conditional distribution given the measurements, such that the model can be readily used for solving inverse problems in imaging. Our model requires magnitude images only for training, and yet is able to reconstruct complex-valued data, and even extends to parallel imaging.
  arXiv  Detail & Related papers  (2021-10-08T08:42:03Z)
• Score-Based Generative Modeling through Stochastic Differential Equations [114.39209003111723]
  We present a differential equation that transforms a complex data distribution to a known prior distribution by injecting noise. A corresponding reverse-time SDE transforms the prior distribution back into the data distribution by slowly removing the noise. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks. We demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.
  arXiv  Detail & Related papers  (2020-11-26T19:39:10Z)

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.