Structural Pruning for Diffusion Models (2305.10924v3)

Abstract: Generative modeling has recently undergone remarkable advancements, primarily propelled by the transformative implications of Diffusion Probabilistic Models (DPMs). The impressive capability of these models, however, often entails significant computational overhead during both training and inference. To tackle this challenge, we present Diff-Pruning, an efficient compression method tailored for learning lightweight diffusion models from pre-existing ones, without the need for extensive re-training. The essence of Diff-Pruning is encapsulated in a Taylor expansion over pruned timesteps, a process that disregards non-contributory diffusion steps and ensembles informative gradients to identify important weights. Our empirical assessment, undertaken across several datasets highlights two primary benefits of our proposed method: 1) Efficiency: it enables approximately a 50\% reduction in FLOPs at a mere 10\% to 20\% of the original training expenditure; 2) Consistency: the pruned diffusion models inherently preserve generative behavior congruent with their pre-trained models. Code is available at \url{https://github.com/VainF/Diff-Pruning}.

• The paper introduces Diff-Pruning, a novel method that reduces FLOPs by up to 50% with minimal impact on diffusion model performance.
• It employs Taylor expansion to selectively prune non-essential timesteps, avoiding the need for heavy retraining.
• The approach preserves key generative features across datasets, enabling efficient deployment in resource-constrained environments.

Structural Pruning for Diffusion Models

The paper introduces a novel approach to optimizing diffusion probabilistic models (DPMs) called Diff-Pruning. As an expert in generative modeling and diffusion models, the paper's significance lies in its technical approach to both reducing the computational cost of DPMs and addressing the complexities involved in maintaining model performance post-compression.

Overview

Diff-Pruning is an efficient structural pruning strategy developed to compress diffusion models without heavy retraining. It aims to specifically target and remove non-essential components in DPMs which often demand high computational resources both during training and inference. Diffusion Probabilistic Models have established their utility across a range of applications including image generation, edification, and other generative tasks, yet their computationally expensive nature makes them less practical for wide adoption, especially in environments constrained by resources.

Technical Contributions

The core mechanism of Diff-Pruning is the Taylor expansion over pruned timesteps, which allows the method to identify which diffusion steps can be pruned with minimal impact on the model's performance. The pruning approach is not arbitrary; by focusing on the timesteps that contribute least to the diffusion process, the algorithm is able to maintain the integrity of the original model's generative capabilities. This methodological rigor brings twofold benefits:

1. Efficiency: The proposed method achieves up to a 50% reduction in FLOPs, significantly decreasing the computational burden without requiring extensive retraining.
2. Consistency: The pruned models retain a high degree of generative similarity to pre-trained models, ensuring that the higher-level content and nuanced details in the generated samples remain intact.

In addition to addressing efficiency concerns, the method preserves the large-scale pretraining benefits within diffusion models, thereby enabling more accessible application in varied domains without sacrificing performance.

Results and Implications

The empirical evaluation of Diff-Pruning is conducted across multiple datasets and showcases significant reductions in computational costs while maintaining generative quality. For instance, the approach compresses models with only a small fraction of the original training cost yet preserves the fidelity of the generated outputs. In specific experiments, models trained on LSUN Church datasets demonstrate robust results with reduced FLOPs and comparable or improved FID scores relative to models trained from scratch, underscoring the method's effectiveness.

From a theoretical and practical standpoint, the introduction of Diff-Pruning proposes new avenues for the efficient deployment of generative models across constrained environments. It suggests the possibility of further structural optimizations in generative models beyond traditional approaches. The utilization of Taylor expansions to guide pruning decisions makes Diff-Pruning a baseline for future techniques aiming at efficient compression.

Future Directions

The paper sets the foundation for further developments in the efficient compression of diffusion models. Future research could explore extending the pruning methodology to a broader range of generative models, investigating more dynamic pruning strategies that could adjust adaptively during the generative process. Moreover, enhancing the robustness of these approaches to ensure consistency in real-world scenarios will be integral to maximizing the practical applications of DPMs.

In conclusion, Diff-Pruning represents a significant step towards making powerful generative models more computationally accessible while preserving their functional strengths. It models a baseline pruning approach that can inspire further innovations in the field of efficient diffusion models, potentially widening the scope of applications in resource-limited settings.