DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-transform (2406.01781v3)

Abstract: Generative modelling paradigms based on denoising diffusion processes have emerged as a leading candidate for conditional sampling in inverse problems. In many real-world applications, we often have access to large, expensively trained unconditional diffusion models, which we aim to exploit for improving conditional sampling. Most recent approaches are motivated heuristically and lack a unifying framework, obscuring connections between them. Further, they often suffer from issues such as being very sensitive to hyperparameters, being expensive to train or needing access to weights hidden behind a closed API. In this work, we unify conditional training and sampling using the mathematically well-understood Doob's h-transform. This new perspective allows us to unify many existing methods under a common umbrella. Under this framework, we propose DEFT (Doob's h-transform Efficient FineTuning), a new approach for conditional generation that simply fine-tunes a very small network to quickly learn the conditional $h$-transform, while keeping the larger unconditional network unchanged. DEFT is much faster than existing baselines while achieving state-of-the-art performance across a variety of linear and non-linear benchmarks. On image reconstruction tasks, we achieve speedups of up to 1.6$\times$, while having the best perceptual quality on natural images and reconstruction performance on medical images. Further, we also provide initial experiments on protein motif scaffolding and outperform reconstruction guidance methods.

• The paper introduces DEFT, a novel method that applies Doob's h-transform for efficient conditional sampling in diffusion models.
• The paper fine-tunes only a small ancillary network (4-9% of parameters) on pre-trained models, significantly lowering computational costs.
• The paper demonstrates up to a 1.6x speedup and enhanced image reconstruction quality, setting new benchmarks in conditional generative modeling.

Overview of DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform

The paper in question introduces a novel approach to the field of generative modeling via diffusion processes, namely DEFT (Doob's $h$-transform Efficient FineTuning). It targets the domain of conditional generative modeling, particularly focusing on refining the efficiency and efficacy of conditional sampling from pre-trained diffusion models. With the recent surge in popularity of diffusion models for various applications, particularly in generating high-quality images and solving inverse problems, optimizing these generative processes for conditional sampling has both theoretical and practical significance.

Theoretical Framework

Central to the paper is the unification of existing methods for conditional and sampling training under the well-established framework of Doob's $h$-transform. This mathematical tool, originating from the theory of stochastic differential equations (SDEs), allows the authors to transition seamlessly between unconditional and conditional diffusion models, proposing a framework where conditional processes are understood as special cases of unconditionally learned networks.

Methodology: DEFT

DEFT proposes a strikingly resource-efficient approach by bypassing the need for retraining larger pre-trained models. It achieves high-performance conditional sampling through the fine-tuning of a small, additional network on top of the existing unconditional diffusion model. The fine-tuning method only focuses on learning the generatively useful $h$-transform, which elegantly encapsulates the necessary transformations for conditional sampling, while keeping the larger model fixed. A significant advantage of this setup is the use of a smaller ancillary network, typically comprising just 4-9% of the parameters of the original model, resulting in reduced computational costs and time for the tuning process.

To attain this, DEFT leverages a stochastic control perspective, whereby minimum-energy control paths guide the conditional sampling process in a cost-effective manner, retaining high fidelity to the desired conditional outputs.

Experimental Evaluation

In their evaluation, the authors present compelling quantitative results demonstrating that DEFT not only accelerates the process (achieving up to 1.6 times speedup compared to baseline methods) but also leads in state-of-the-art performance across various established benchmarks. Crucially, in image reconstruction tasks, DEFT consistently achieves superior perceptual quality and reconstruction performance, proving effective for tasks ranging from natural image synthesis to more specialized medical image reconstruction problems.

Implications and Future Outlook

The implications of introducing DEFT are substantial for both practitioners and theoreticians in AI and machine learning. The approach promises enhancements in scenarios where conditional transformations are performed on pre-trained models locked behind inaccessible configurations, such as API-only environments. Furthermore, DEFT fosters a deeper understanding of the interplay between conditional transformations and generative processes under diffusion frameworks.

The paper sets the stage for the exploration of finer and more adaptive conditional finetuning methodologies, potentially catalyzing the development of more versatile AI models. Additionally, the connections drawn between Doob's $h$-transform and stochastic control might spark further investigations into bridging these theoretical domains with applied machine learning tasks, making efficient inroads into solution paths that respect both computation and precision constraints.

In conclusion, DEFT represents a significant advancement in conditional diffusion modeling, emphasizing efficiency, adaptability, and performance. By creatively applying established mathematical theories to contemporary machine learning challenges, this research not only pushes the boundaries of generative AI but also highlights the ongoing importance of cross-pollination between theory and practice in technological innovation.