Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning (2208.04202v2)

Abstract: We present Bit Diffusion: a simple and generic approach for generating discrete data with continuous state and continuous time diffusion models. The main idea behind our approach is to first represent the discrete data as binary bits, and then train a continuous diffusion model to model these bits as real numbers which we call analog bits. To generate samples, the model first generates the analog bits, which are then thresholded to obtain the bits that represent the discrete variables. We further propose two simple techniques, namely Self-Conditioning and Asymmetric Time Intervals, which lead to a significant improvement in sample quality. Despite its simplicity, the proposed approach can achieve strong performance in both discrete image generation and image captioning tasks. For discrete image generation, we significantly improve previous state-of-the-art on both CIFAR-10 (which has 3K discrete 8-bit tokens) and ImageNet-64x64 (which has 12K discrete 8-bit tokens), outperforming the best autoregressive model in both sample quality (measured by FID) and efficiency. For image captioning on MS-COCO dataset, our approach achieves competitive results compared to autoregressive models.

Analyzing the Use of Diffusion Models for Discrete Data Generation

The paper "Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning" introduces a novel approach using diffusion models for generating discrete data. Traditional autoregressive models, commonly used for discrete data generation, suffer from computational inefficiencies due to their sequential nature. This limitation becomes evident as data dimensionality increases. The present paper addresses such challenges by employing diffusion models, typically applied to continuous data, in discrete contexts through a concept termed "analog bits."

Core Methodology

The methodology revolves around representing discrete data as binary bits and modeling these as real-valued analog bits. This transformation allows conventional continuous state diffusion models to operate seamlessly within the discrete field. The discrete data, once encoded into analog bits, are processed through the diffusion model. The model then generates samples in the form of analog bits, which undergo thresholding to revert to discrete representations.

Two auxiliary techniques are proposed to enhance sample quality:

1. Self-Conditioning: The model introspects by utilizing previously generated samples as conditioning inputs in subsequent sampling iterations. This self-reference improves output fidelity without incurring significant computational overhead.
2. Asymmetric Time Intervals: By adjusting time parameterization via temporal asymmetries during sampling, the model maintains robust performance even with fewer sampling steps, offering an important trade-off between efficiency and quality.

Experimental Analysis

Experiments adhere to tasks in discrete image generation on Cifar-10 and ImageNet 64, along with image captioning tasks on the MS-COCO dataset. Significant improvements in sample quality over traditional autoregressive models are reported, particularly in the image generation tasks. For example, in categorical Cifar-10, the Bit Diffusion model achieves a Fréchet Inception Distance (FID) of 6.93, markedly outperforming the previous state-of-the-art autoregressive model's 12.75.

Theoretical and Practical Implications

The implications of this work extend to several dimensions in AI and generative modeling. The paper effectively demonstrates that diffusion models can be adapted for discrete data, challenging the predominance of autoregressive techniques. The introduction of analog bits as a mediating representation between discrete and continuous domains opens new pathways for diffusing models to be applied to a broader array of data types without necessitating substantial reconfiguration.

Furthermore, the methodology shows potential for applications demanding high-quality discrete data generation while maintaining relevance in contexts where computational resources are constrained. The self-conditioning mechanism suggests a novel strategy to refine generation processes, which could influence the design of future generative models across various domains, including natural language processing and image synthesis.

Future Directions

The proposed framework naturally leads to several future research trajectories. For instance, exploring alternative binary encoding mechanisms and dynamic conditioning techniques could augment model performance further. Additionally, extending this approach to more complex, structured discrete data and integrating it with hybrid modeling techniques may yield comprehensive and efficient generative frameworks in the future.

Conclusion

In summary, this work makes a noteworthy contribution to the field of discrete data generation by harnessing the capabilities of diffusion models, traditionally confined to continuous domains. By introducing analog bits and additional conditioning techniques, it not only heightens our understanding of diffusion model application but also sets a precedent for their implementation across a wider spectrum of generative tasks. As the field advances, the principles and methodologies set forth in this paper potentially position diffusion models as a formidable force in discrete generative modeling.