Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models (2405.05252v1)

Abstract: Diffusion Models (DMs) have exhibited superior performance in generating high-quality and diverse images. However, this exceptional performance comes at the cost of expensive architectural design, particularly due to the attention module heavily used in leading models. Existing works mainly adopt a retraining process to enhance DM efficiency. This is computationally expensive and not very scalable. To this end, we introduce the Attention-driven Training-free Efficient Diffusion Model (AT-EDM) framework that leverages attention maps to perform run-time pruning of redundant tokens, without the need for any retraining. Specifically, for single-denoising-step pruning, we develop a novel ranking algorithm, Generalized Weighted Page Rank (G-WPR), to identify redundant tokens, and a similarity-based recovery method to restore tokens for the convolution operation. In addition, we propose a Denoising-Steps-Aware Pruning (DSAP) approach to adjust the pruning budget across different denoising timesteps for better generation quality. Extensive evaluations show that AT-EDM performs favorably against prior art in terms of efficiency (e.g., 38.8% FLOPs saving and up to 1.53x speed-up over Stable Diffusion XL) while maintaining nearly the same FID and CLIP scores as the full model. Project webpage: https://atedm.github.io.

• The paper demonstrates AT-EDM, a method that dynamically prunes tokens using attention maps to reduce computational load by up to 38.8%.
• It leverages Generalized Weighted Page Rank and Denoising-Steps-Aware Pruning to adjust token importance without retraining the model.
• Results show a 1.53x speed improvement with maintained FID and CLIP scores, highlighting its practical benefits in resource-constrained settings.

Exploring AT-EDM: Efficient Image Generation with Diffusion Models

Diffusion Models (DMs) have transformed the landscape of image generation with their superior performance. Yet, their computational demands can be prohibitive. Addressing these challenges, a paper introduced the Attention-driven Training-free Efficient Diffusion Model (AT-EDM), a novel framework designed to enhance the efficiency of DMs without the need for retraining.

Introduction to AT-EDM

AT-EDM stands out by employing a special technique that prunes redundant tokens dynamically during runtime using attention maps, preserving the integrity and quality of the generated images. This approach avoids the extensive computational expense typically involved with retraining models, making it a scalable solution for real-world applications.

Key Components of AT-EDM

AT-EDM consists of two primary innovative strategies:

1. Token Pruning Using Generalized Weighted Page Rank (G-WPR):
   • Attention Maps: Utilizes attention maps to assess and rank the importance of tokens.
   • Pruning Process: Less important tokens are dynamically pruned in a way that computation during image generation is significantly reduced without affecting output quality.
2. Denoising-Steps-Aware Pruning (DSAP):
   • Variable Pruning Intensity: Adjusts the intensity of pruning based on the phase of the denoising process, allowing more aggressive pruning in later stages where image details are more defined.
   • Quality Preservation: By adapting the pruning process over different denoising steps, DSAP helps maintain high-quality image generation.

Practical Implications and Performance

The practical implications of AT-EDM are quite promising. In tests, AT-EDM achieved a reduction in computation (measured in FLOPs) by up to 38.8% and speed improvements of about 1.53 times, all while maintaining similar FID and CLIP scores to full models. These metrics are crucial as they indicate that the image quality and relevance (in terms of textual description match) are retained even with the reduced computational load.

This efficiency does not come at the cost of generative quality. The evaluation demonstrates that images generated under this new model maintain their aesthetic and contextual integrity, aligning well with their textual descriptions—a testament to the effectiveness of the token pruning and recovery approach.

Future Directions

The introduction of AT-EDM might encourage further research in training-free approaches for enhancing DMs. Considerations for future studies could include:

• Expanding Application Scope: Exploring how these techniques perform across different types of DMs, including those geared towards other forms of media like video or audio.
• Hybrid Approaches: Combining AT-EDM with other efficiency-improving strategies to enhance performance further.
• Real-World Deployment: Testing the model in real-world applications on mobile and other edge devices could yield insights into practical deployments and usability.

Conclusion

AT-EDM presents a compelling advancement in the domain of efficient image generation using DMs. By intelligently pruning and recovering tokens through a nuanced understanding of model attention mechanisms, it opens up new possibilities for deploying advanced image generation models in resource-constrained environments. While the immediate results are promising, the broader impact and potential applications of such a framework remain an exciting area for future exploration.