FORA: Fast-Forward Caching in Diffusion Transformer Acceleration (2407.01425v1)

Abstract: Diffusion transformers (DiT) have become the de facto choice for generating high-quality images and videos, largely due to their scalability, which enables the construction of larger models for enhanced performance. However, the increased size of these models leads to higher inference costs, making them less attractive for real-time applications. We present Fast-FORward CAching (FORA), a simple yet effective approach designed to accelerate DiT by exploiting the repetitive nature of the diffusion process. FORA implements a caching mechanism that stores and reuses intermediate outputs from the attention and MLP layers across denoising steps, thereby reducing computational overhead. This approach does not require model retraining and seamlessly integrates with existing transformer-based diffusion models. Experiments show that FORA can speed up diffusion transformers several times over while only minimally affecting performance metrics such as the IS Score and FID. By enabling faster processing with minimal trade-offs in quality, FORA represents a significant advancement in deploying diffusion transformers for real-time applications. Code will be made publicly available at: https://github.com/prathebaselva/FORA.

FORA: Fast-Forward Caching in Diffusion Transformer Acceleration

The landscape of generative models has witnessed significant growth with the introduction of diffusion transformers (DiT), which offer enhanced scalability compared to traditional U-Net-based diffusion models. Despite the promising capabilities of DiT models, their expansive size poses considerable challenges for real-time applications due to elevated inference costs. The paper introduces a novel approach, Fast-Forward Caching (FORA), targeting the acceleration of DiT models by leveraging the repetitive nature of their diffusion process. This essay explores the technical details, results, and implications of the FORA framework.

Technical Overview

FORA proposes a caching mechanism designed specifically for DiT models to improve inference efficiency without necessitating any model retraining. The mechanism capitalizes on the high similarity observed in the intermediate outputs across consecutive denoising steps during the diffusion process. By caching and reusing these intermediate outputs from the attention and Multi-Layer Perceptron (MLP) layers, FORA can significantly reduce computational overhead.

The mechanism introduces a static caching strategy governed by a single hyperparameter, termed the cache interval $N$, which dictates the frequency of feature recomputation and caching. The process involves computing and caching the features at regular intervals and then reusing these cached features for subsequent steps until the next caching event is triggered. This cyclic recomputation, caching, and reuse strategy continues throughout the reverse diffusion process, maintaining a balance between computational efficiency and output quality.

Empirical Results

The authors conducted experiments using widely recognized datasets such as ImageNet and MS-COCO to validate the effectiveness of the FORA method. For class-conditional and text-conditional image generation tasks, FORA demonstrated an impressive acceleration—up to 8.07 times speed increase—with minimal impact on the quality metrics like Fréchet Inception Distance (FID) and Inception Score (IS).

In class-conditional image generation experiments on the ImageNet dataset, the use of a static cache interval $N = 3$ emerged as an optimal balance, providing significant speedups without compromising generative quality. Comparable improvements in efficiency were also observed in text-conditional tasks, where FORA accelerated sampling time substantially while maintaining quality measured by zero-shot FID-30K scores.

Theoretical and Practical Implications

On the theoretical front, FORA's contributions emphasize the benefits of employing a caching mechanism in the context of inherently repetitive generative processes. By addressing the computational bottlenecks associated with DiT models, FORA not only enhances the practicality of large diffusion models for real-time applications but also paves the way for broader applications of diffusion transformers in fields demanding expedited generative capabilities.

From a practical standpoint, FORA's plug-and-play nature makes it an attractive solution for integrating into existing DiT models without necessitating retraining. This characteristic is particularly valuable in practical deployments where training costs and energy consumption are critical considerations. The method's compatibility with fast samplers further underscores its potential to reduce the carbon footprint associated with high-performance generative models by decreasing inference effort.

Future Directions

While the paper presents a significant stride towards efficient diffusion transformers, it also opens avenues for future research. One limitation identified is the static nature of the caching strategy, which may not fully exploit the nuanced similarity patterns in feature maps across different diffusion stages. Future work could explore dynamic caching mechanisms that adapt in real-time, potentially yielding further efficiency gains.

In conclusion, FORA represents a compelling advancement in the field of diffusion model acceleration by offering a balanced approach to reducing computational demands while preserving image quality. Its training-free and seamlessly integrable design positions it as a facilitator for expanding the deployment of diffusion transformers in dynamic, real-world applications requiring rapid generative responses.