Binary Diffusion Probabilistic Model (2501.13915v1)

Abstract: We introduce the Binary Diffusion Probabilistic Model (BDPM), a novel generative model optimized for binary data representations. While denoising diffusion probabilistic models (DDPMs) have demonstrated notable success in tasks like image synthesis and restoration, traditional DDPMs rely on continuous data representations and mean squared error (MSE) loss for training, applying Gaussian noise models that may not be optimal for discrete or binary data structures. BDPM addresses this by decomposing images into bitplanes and employing XOR-based noise transformations, with a denoising model trained using binary cross-entropy loss. This approach enables precise noise control and computationally efficient inference, significantly lowering computational costs and improving model convergence. When evaluated on image restoration tasks such as image super-resolution, inpainting, and blind image restoration, BDPM outperforms state-of-the-art methods on the FFHQ, CelebA, and CelebA-HQ datasets. Notably, BDPM requires fewer inference steps than traditional DDPM models to reach optimal results, showcasing enhanced inference efficiency.

• The paper introduces a novel approach that replaces Gaussian diffusion with binary bit-plane formulations to handle discrete data more effectively.
• It leverages XOR-based noise transformations and binary cross-entropy loss to enhance image restoration performance while reducing computational steps.
• The BDPM outperforms traditional models on key metrics across various datasets, offering improved efficiency and practical benefits for AI applications.

Insights into the Binary Diffusion Probabilistic Model

The paper introduces the Binary Diffusion Probabilistic Model (BDPM), a novel advancement in generative modeling optimized for binary data representations. While traditional denoising diffusion probabilistic models (DDPMs) have achieved significant success in areas such as image synthesis, their reliance on Gaussian diffusion processes and continuous data representations limits their effectiveness for tasks involving inherently discrete or binary data. BDPM innovatively addresses this issue by introducing a binary bit-plane approach coupled with XOR-based noise transformations and training via binary cross-entropy loss. This model significantly enhances inference efficiency and requires fewer computational resources compared to its Gaussian counterparts.

Core Contributions

The primary contribution of BDPM lies in its shift from Gaussian to binary formulations within diffusion models. The proposed model adeptly handles binary data by decomposing images into bit-planes, allowing precise noise control through XOR transformations. This approach not only aligns with the discrete nature of binary data but also ensures computational efficiency by reducing inference steps necessary to reach optimal results. As a result, BDPM exhibits superior performance in various image restoration tasks, such as super-resolution, inpainting, and blind image restoration, across datasets including FFHQ, CelebA, and CelebA-HQ.

Numerical Results and Efficiency

BDPM's performance is compelling across several benchmarks. When evaluated on image restoration tasks, BDPM outperforms state-of-the-art methods, offering improved results on key metrics such as FID, LPIPS, PSNR, and SSIM. Notably, BDPM achieves these results with a reduced number of inference steps—30 for super-resolution and 100 for inpainting—compared to existing models, showcasing enhanced inference efficiency. This efficiency is particularly significant given that BDPM comprises only 35.8 million parameters, yet it outperforms larger models that employ traditional Gaussian diffusion processes.

Theoretical and Practical Implications

Theoretically, BDPM expands the field of diffusion models by effectively bridging the gap between continuous representation frameworks and the discrete nature of real-world data, such as digital images. This alignment provides a promising foundation for further exploration and development in binary diffusion processes, particularly in generative tasks that benefit from discrete data fidelity and computational efficiency. Practically, BDPM's reduced computational requirements and enhanced performance open pathways for integrating advanced generative models into resource-constrained environments, facilitating broader adoption in diverse applications ranging from real-time image processing to scalable AI systems.

Future Prospects in AI Development

The success of BDPM in demonstrating efficient and accurate binary data handling suggests broader applications and adaptations within the field of AI. Future developments could explore extending BDPM to other types of discrete datasets beyond image data, potentially revolutionizing approaches in tabular data generation, text-based modeling, and categorical data synthesis. Additionally, the methodological innovations introduced with BDPM may serve as the basis for novel hybrid models that further integrate binary and multi-modal data in a unified framework, advancing the capabilities of AI in processing complex, high-dimensional data structures.

In summary, the Binary Diffusion Probabilistic Model represents a substantive advancement in the domain of generative models for binary data. By addressing the limitations of existing DDPMs and introducing a framework tailored for discrete data, BDPM marks a significant step toward more efficient and accurate generative modeling, with promising implications for both theoretical development and practical application in artificial intelligence.