Region-Adaptive Sampling for Diffusion Transformers (2502.10389v1)

Abstract: Diffusion models (DMs) have become the leading choice for generative tasks across diverse domains. However, their reliance on multiple sequential forward passes significantly limits real-time performance. Previous acceleration methods have primarily focused on reducing the number of sampling steps or reusing intermediate results, failing to leverage variations across spatial regions within the image due to the constraints of convolutional U-Net structures. By harnessing the flexibility of Diffusion Transformers (DiTs) in handling variable number of tokens, we introduce RAS, a novel, training-free sampling strategy that dynamically assigns different sampling ratios to regions within an image based on the focus of the DiT model. Our key observation is that during each sampling step, the model concentrates on semantically meaningful regions, and these areas of focus exhibit strong continuity across consecutive steps. Leveraging this insight, RAS updates only the regions currently in focus, while other regions are updated using cached noise from the previous step. The model's focus is determined based on the output from the preceding step, capitalizing on the temporal consistency we observed. We evaluate RAS on Stable Diffusion 3 and Lumina-Next-T2I, achieving speedups up to 2.36x and 2.51x, respectively, with minimal degradation in generation quality. Additionally, a user study reveals that RAS delivers comparable qualities under human evaluation while achieving a 1.6x speedup. Our approach makes a significant step towards more efficient diffusion transformers, enhancing their potential for real-time applications.

• The paper presents Region-Adaptive Sampling (RAS), a novel strategy to significantly accelerate Diffusion Transformers by dynamically adjusting sampling ratios based on image regions, without additional training.
• RAS achieves substantial speed-ups (up to 2.36x for Stable Diffusion 3 and 2.51x for Lumina-Next-T2I) with minimal degradation in image quality, validated by metrics like FID and CLIP score.
• This method has practical implications for real-time applications and making sophisticated generative tasks more accessible on consumer-grade hardware.

Region-Adaptive Sampling for Diffusion Transformers

The paper "Region-Adaptive Sampling for Diffusion Transformers" presents an innovative method to improve the efficiency of Diffusion Models (DMs) by introducing a novel sampling strategy named Region-Adaptive Sampling (RAS). This method addresses the challenge of prolonged sample generation times, which traditionally hinder the real-time applicability of DMs. By manipulating the flexibility of Diffusion Transformers (DiTs), which allow for a variable number of tokens, RAS strategically alters sample ratios across different image regions, enhancing processing efficiency without requiring additional training.

Key Contributions

1. Novel Sampling Strategy: The core contribution of RAS lies in achieving significant computational acceleration in DMs by adapting the sampling process. This dynamic assignment of sampling ratios is based on the model’s focus on semantically significant image regions. The method emphasizes discriminating between regions that require more intensive sampling and those that do not, leveraging the flexibility of DiTs over conventional U-Net architectures.
2. Efficiency and Image Quality: The RAS method results in substantial speed-ups of up to 2.36x for Stable Diffusion 3 and 2.51x for Lumina-Next-T2I models. The proposed technique achieves these improvements with minimal degradation in image quality, as substantiated by human evaluations and quantitative measurements such as the Fréchet Inception Distance (FID) and CLIP score.
3. Technical Approach:
   • Region Identification: RAS relies on identifying key regions for intensive sampling, using standard deviation of predicted noises and other metrics.
   • Key and Value Caching: To maintain latent information about slower-changing regions, RAS incorporates a caching mechanism in the attention module, which retains and reuses previous outputs.
   • Error Resetting and Dynamic Sampling: The method includes strategies to periodically reset accumulated errors and dynamically manage sampling ratios based on the stability and similarity of inference steps.
4. Scalability: The experiments demonstrate that RAS can accommodate large diffusion needs, maintaining or improving upon baseline image qualities while utilizing less computational resources.

Implications and Future Directions

The development of RAS marks a significant contribution toward the real-time application of diffusion models in various fields such as image synthesis, super-resolution, and inpainting. This work underscores the potential of transformer-based architectures to overcome limitations inherent in traditional convolutional models, offering a path toward more flexible and scalable generative models.

Practically, RAS paves the way for enhanced performance in consumer-grade hardware, making sophisticated generative tasks more accessible. Theoretically, it prompts further exploration into region-focused sampling techniques, where future research might explore adaptive methods that dynamically tune sampling decisions based on evolving learning and perceptual criteria. There may also be opportunities to integrate similar approaches with other generative frameworks or extend this method toward three-dimensional and video generation tasks.

In conclusion, this research provides a sophisticated method for accelerating diffusion transformers, yielding significant improvements in generative model efficiency without compromising output quality. As computational resources continue to be a limiting factor in deploying advanced AI techniques, methods like RAS demonstrate that innovative sampling strategies can meaningfully counter these challenges.