Region-Adaptive Sampling for Diffusion Transformers
Ziming Liu, Yifan Yang, Chengruidong Zhang, Yiqi Zhang, Lili Qiu, Yang, You, Yuqing Yang
TL;DR
This paper introduces RAS, a training-free, region-adaptive sampling method for diffusion transformers that dynamically focuses computation on semantically meaningful regions, significantly speeding up image generation with minimal quality loss.
Contribution
RAS is a novel, training-free sampling strategy for diffusion transformers that leverages model focus continuity to accelerate image generation.
Findings
Achieves up to 2.51x speedup on benchmark models.
Maintains comparable image quality with minimal degradation.
Enhances potential for real-time diffusion transformer applications.
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…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
1. **Novel and practical idea exploiting DiT properties.** The paper leverages a property unique to transformer-based diffusion backbones (variable token handling) to enable spatially non-uniform sampling — a conceptually simple but impactful direction compared to uniform timestep reduction. 2. **Well-engineered, end-to-end system.** RAS contains multiple pragmatic components (metric, starvation prevention, error resets, KV caching, kernel fusing) that together address obvious failure mode
1. **Comparisons to strongest baselines could be deeper.** The baseline is rectified flow (uniform timestep reduction) and some cached-layer methods; however, recent fast-sampling / distillation or scheduler adaptations (and combined methods) may offer competitive alternatives. It is unclear if RAS can be combined with all such methods or whether combined evaluation was performed. The paper claims orthogonality but empirical combination results are limited. 2. **Kernel detail.** The paper
1. The idea of exploiting spatial heterogeneity in the sampling process is straightforward and has the potential to be highly impactful. 2. A significant advantage of RAS is that it is a training-free method. This makes it easy to apply to a wide range of pre-trained diffusion models without the need for costly retraining. 3. The authors provide a set of experiments on state-of-the-art Diffusion Transformers. The reported speedups are substantial, and the quality of the generated images is well-
1. The manuscript's clarity is a major concern. The writing is difficult to follow, with grammatical errors and awkward phrasings. I strongly recommend a thorough revision of the entire manuscript by a native English speaker or a professional editing service. 2. More experiments about comparison with existing works are needed.
This paper proposes a new sampling method on diffusion, called RAS. RAS aims to dynamically update the image reigons during the denoising steps based on the deviation as the metric. That menas that RAS can help the diffusion model concentrate on semantically meaningful areas, and reuse cached noise for others. The idea seems interesting, however, it misses a lot of necessary details. Please see the weaknesses.
I have carefully read this paper, and the writing issues are significant. 1. the mis-use of the format of reference. I think the authors mis-use the \citep and \citet. The reference format in this paper is totally wrong, making it difficult for me to recognize the main content. 2. writing typos. Line 107, "As shown in Figure 5." ; Line 130 and 146 "anda". Caption of Table 2, "Full experiment results are available in Figure 2". 3. missing reference. Line 215, "Layernorm and MLP". 4. non-stan
1. The insight presented in this paper is both novel and thought-provoking, and the experimental results demonstrate strong and consistent performance across various settings. 2. The proposed method is simple yet effective. Its straightforward and elegant design ensures ease of implementation and makes it readily applicable to real-world scenarios. 3. The approach is grounded in clear and well-motivated observations about redundancy and regional variation in diffusion models. This strong conce
1. Heuristic Nature and Limited Theoretical Justification. While the proposed method is empirically effective, its design is primarily heuristic. The use of noise standard deviation as an indicator of regional importance lacks strong theoretical grounding or formal justification. This raises concerns about the generalizability of the approach to different architectures, datasets, or diffusion formulations. 2. Sensitivity to Hyperparameters and Stability Issues. The effectiveness of the proposed
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Taxonomy
TopicsBlind Source Separation Techniques