Simple Guidance Mechanisms for Discrete Diffusion Models (2412.10193v3)

Abstract: Diffusion models for continuous data gained widespread adoption owing to their high quality generation and control mechanisms. However, controllable diffusion on discrete data faces challenges given that continuous guidance methods do not directly apply to discrete diffusion. Here, we provide a straightforward derivation of classifier-free and classifier-based guidance for discrete diffusion, as well as a new class of diffusion models that leverage uniform noise and that are more guidable because they can continuously edit their outputs. We improve the quality of these models with a novel continuous-time variational lower bound that yields state-of-the-art performance, especially in settings involving guidance or fast generation. Empirically, we demonstrate that our guidance mechanisms combined with uniform noise diffusion improve controllable generation relative to autoregressive and diffusion baselines on several discrete data domains, including genomic sequences, small molecule design, and discretized image generation.

• The paper introduces discrete classifier-based (D-CBG) and classifier-free guidance (D-CFG) to enable controllable generation on non-differentiable discrete data.
• It adapts continuous diffusion methods to discrete domains using a continuous-time variational approach for refined training objectives.
• Experimental results show enhanced performance in genomic sequencing, molecular optimization, and image quality under fast inference conditions.

Insightful Overview of the Paper on Simple Guidance Mechanisms for Discrete Diffusion Models

The paper discusses advancements in diffusion models specifically targeting discrete data domains. It begins by outlining the prominent use of diffusion models for continuous data, highlighting their flexibility and control facilitated through classifier-based and classifier-free guidance mechanisms. However, the applicability of such diffusion models to discrete data, like genomic sequences, molecule design, and text generation, has been challenging due to the non-differentiable nature of discrete data, which limits the use of traditional continuous guidance.

Key Contributions

The authors propose novel adaptations of guidance mechanisms for discrete diffusion models introducing the concepts of discrete classifier-based guidance (D-CBG) and discrete classifier-free guidance (D-CFG). These allow for effective controllable generation in discrete settings by overcoming the lack of defined gradients in discrete data. The methodologies are straightforward yet effective, adapting known guidance mechanisms from continuous domains to the discrete space. The paper introduces:

1. Discrete Classifier-Free Guidance (D-CFG): This is adapted by formulating the tempered distribution that allows for control over the generation process without relying on an explicit classifier, by differentiating a single conditional model trained with occasional dropout of conditional information.
2. Discrete Classifier-Based Guidance (D-CBG): This method uses a classifier model to adjust the probability distribution during generation dynamically, applying a temperature parameter to manage guidance strength.

Additionally, the paper revisits uniform noise diffusion models. It refines training objectives for these models via a continuous-time variational approach, enhancing their applicability in domains with smaller vocabularies, which render them more amenable to guidance due to the ability to iteratively refine generated sequences.

Experimental Framework and Results

Experimental validations have been conducted across various domains including genomics, molecular design, and image generation. The experiments reveal the following:

• Genomic Sequence Generation: When applied to genomic data, both MDLM and UDLM demonstrate superior species-specific sequence generation compared to autoregressive (AR) models, especially under increased guidance control settings, which AR models struggled to accommodate without performance degradation.
• Molecular Property Optimization: Applying D-CFG and D-CBG to discrete diffusion models allows better optimization of molecular properties such as drug-likeness and ring count in molecules, showcasing more novel and diverse molecule generation than both AR and previous diffusion models.
• Image Generation: With guidance, diffusion models significantly improve image quality metrics on datasets like CIFAR-10, outperforming non-guided diffusion and AR models in terms of IS and FID metrics, especially under fast inference settings.

Theoretical Insights and Implications

The theoretical implications of this paper suggest substantial improvements in control over discrete data generation processes through novel guidance mechanisms. The continuous-time variational techniques provide insights into achieving tighter bounds and higher performance in diffusion models for discrete scenarios.

Practically, these models open avenues for high-quality, controllable generation across scientific and digital content creation fields. For example, in drug discovery, enhanced controllability of molecular generation can lead to better-targeted molecular designs with desired features, potentially accelerating the drug discovery process.

Future Directions

Future work could explore further generalization of these guidance mechanisms across broader classes of discrete datasets and examine their integration with other emerging techniques in machine learning. Additionally, collaborations with domain experts in genomics and chemistry might yield more targeted applications, unlocking potential advances in personalized medicine and materials science.

Overall, the paper presents impactful advancements in applying diffusion models to discrete data, marking significant steps towards more controllable and high-quality data generation across various scientific and technological domains.