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arxiv: 2605.18190 · v1 · pith:FYT5NG75new · submitted 2026-05-18 · 💻 cs.LG · cs.CV

Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network

Grigory Bartosh , David Ruhe , Emiel Hoogeboom , Jonathan Heek , Thomas Mensink , Tim Salimans This is my paper

Pith reviewed 2026-05-20 12:52 UTC · model grok-4.3

classification 💻 cs.LG cs.CV
keywords diffusion modelssampling accelerationgenerative modelsefficient inferenceImageNetheavy-light networksfeature reuse
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The pith

Dual-Rate Diffusion speeds up sampling in diffusion models by interleaving a heavy context encoder with a light denoising network.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Standard diffusion sampling repeatedly evaluates a large neural network at every step, which is computationally expensive. The proposed method runs the heavy encoder only sparsely to extract high-dimensional features and reuses them with a lightweight model for the remaining steps. This reuse works because the features stay sufficiently stable and informative over short sequences of steps. On ImageNet the resulting samples match the quality of conventional approaches. The technique also pairs with distillation to achieve strong results with even fewer total steps.

Core claim

Dual-Rate Diffusion demonstrates that a heavy high-capacity context encoder can be evaluated at a lower rate than the denoising steps themselves. Its extracted features are fed to a light efficient denoising model that operates at the full rate. The separation allows the bulk of the computation to occur infrequently while still producing high-fidelity samples.

What carries the argument

The dual-rate interleaving schedule that evaluates the heavy context encoder sparsely and reuses its features in the light denoising model at every timestep.

Load-bearing premise

The high-dimensional features from the sparsely run heavy encoder remain informative and stable enough for the light model to reuse them across multiple steps without degrading output quality.

What would settle it

Measuring whether sample quality, measured by FID or human evaluation, declines noticeably when the heavy encoder is called less often than the schedule tested in the paper.

read the original abstract

Diffusion models achieve state-of-the-art generative performance but suffer from high computational costs during inference due to the repeated evaluation of a heavy neural network. In this work, we propose Dual-Rate Diffusion, a method to accelerate sampling by interleaving the execution of a heavy high-capacity context encoder and a light efficient denoising model. The context encoder is evaluated sparsely to extract high-dimensional features, which are effectively reused by the light denoising model at every step to refine the sample efficiently. This approach significantly accelerates inference without compromising sample quality. On ImageNet benchmarks, Dual-Rate Diffusion matches the performance of standard baselines while reducing computational cost by a factor of $2$-$4$. Furthermore, we demonstrate that our method is compatible with distillation techniques, such as Moment Matching Distillation, enabling further efficiency gains in few-step generation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces Dual-Rate Diffusion, an inference acceleration technique for diffusion models that interleaves sparse evaluations of a heavy high-capacity context encoder (to extract high-dimensional features) with frequent steps of a light efficient denoising model that reuses those features. The central empirical claim is that this yields sample quality matching standard baselines on ImageNet while reducing computational cost by a factor of 2-4; the method is also shown to be compatible with distillation techniques such as Moment Matching Distillation for further gains in few-step generation.

Significance. If the feature-reuse assumption holds under the reported conditions, the approach provides a practical, architecture-agnostic way to trade off compute for quality in diffusion sampling. The compatibility with existing distillation methods strengthens its potential impact for efficient generative modeling on standard benchmarks.

major comments (3)
  1. [§4, Table 1] §4 (Experiments), Table 1 and associated text: The reported FID scores for Dual-Rate Diffusion on ImageNet are presented as matching baselines at 2-4× lower cost, yet no error bars, standard deviations, or number of independent runs are provided. This makes it impossible to determine whether the observed equivalence lies within statistical variation of the baseline.
  2. [§3.2] §3.2 (Interleaving schedule): The frequency and placement of heavy encoder evaluations (the core design choice enabling the claimed speedup) are described at a high level but lack any ablation study or justification for the chosen interval. Without such analysis it is unclear whether the 2-4× factor is robust or specific to an unstated hyper-parameter setting.
  3. [§3.1 and §4.3] §3.1 and §4.3: No direct metrics (feature cosine similarity, drift norms, or per-step quality degradation) are reported to validate that high-dimensional features extracted by the sparsely evaluated heavy encoder remain sufficiently stable and informative when reused by the light denoiser across consecutive steps, especially in early diffusion timesteps where the input changes rapidly. This assumption is load-bearing for the central quality claim.
minor comments (2)
  1. [Figure 2] Figure 2: The architecture diagram would benefit from explicit annotation of which blocks are evaluated at the heavy versus light rate and the precise data flow of reused features.
  2. [§2] §2 (Related Work): The discussion of prior acceleration methods (e.g., caching, distillation) could more explicitly contrast the proposed interleaving strategy with existing feature-reuse or multi-rate techniques.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each of the major comments below and outline the revisions we intend to make to strengthen the paper.

read point-by-point responses

  1. Referee: [§4, Table 1] §4 (Experiments), Table 1 and associated text: The reported FID scores for Dual-Rate Diffusion on ImageNet are presented as matching baselines at 2-4× lower cost, yet no error bars, standard deviations, or number of independent runs are provided. This makes it impossible to determine whether the observed equivalence lies within statistical variation of the baseline.

    Authors: We agree with the referee that reporting statistical variation is essential for a robust comparison. In the revised manuscript, we will conduct additional experiments with multiple random seeds (at least three independent runs) and include error bars or standard deviations for the FID scores in Table 1 and the associated text. This will provide clearer evidence that the performance of Dual-Rate Diffusion is statistically comparable to the baselines. revision: yes

  2. Referee: [§3.2] §3.2 (Interleaving schedule): The frequency and placement of heavy encoder evaluations (the core design choice enabling the claimed speedup) are described at a high level but lack any ablation study or justification for the chosen interval. Without such analysis it is unclear whether the 2-4× factor is robust or specific to an unstated hyper-parameter setting.

    Authors: The interleaving schedule was determined based on empirical tuning to achieve a good balance between speed and quality, with the heavy encoder invoked at regular intervals that scale with the total number of sampling steps. To provide better justification, we will include an ablation study in the supplementary material examining different interleaving frequencies (such as every 1, 2, 4, and 8 steps) and their effects on both FID scores and computational speedup. This will demonstrate the robustness of the reported 2-4× acceleration factor. revision: yes

  3. Referee: [§3.1 and §4.3] §3.1 and §4.3: No direct metrics (feature cosine similarity, drift norms, or per-step quality degradation) are reported to validate that high-dimensional features extracted by the sparsely evaluated heavy encoder remain sufficiently stable and informative when reused by the light denoiser across consecutive steps, especially in early diffusion timesteps where the input changes rapidly. This assumption is load-bearing for the central quality claim.

    Authors: We recognize that direct empirical validation of the feature reuse assumption would bolster the central claims. Although the overall sample quality matching the baselines serves as indirect support, we will add new analyses in Section 4.3 and the appendix. Specifically, we will report metrics such as the cosine similarity of features from the heavy encoder across reuse intervals and measures of per-step denoising quality degradation, with particular attention to early timesteps. These additions will provide direct evidence for the stability of the reused features. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical architecture proposal

full rationale

The paper introduces Dual-Rate Diffusion as an architectural method that interleaves sparse evaluations of a heavy context encoder with frequent light denoising steps. Central claims rest on empirical ImageNet benchmark results showing matched FID at 2-4x lower cost, plus compatibility with distillation. No derivation chain, first-principles equations, or predictions are presented that reduce by construction to fitted inputs, self-citations, or ansatzes. The approach is self-contained as a practical design choice validated externally on standard benchmarks, with no load-bearing steps that collapse to the inputs themselves.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The method rests on the domain assumption that context features extracted at low frequency remain useful for many subsequent denoising steps; no free parameters or invented entities are explicitly named in the abstract.

axioms (1)
  • domain assumption Features produced by the heavy context encoder stay informative and stable enough to be reused by the light model across multiple denoising steps
    This premise is required for the sparse evaluation schedule to preserve sample quality; it is invoked implicitly when the abstract states that features are 'effectively reused'.

pith-pipeline@v0.9.0 · 5682 in / 1237 out tokens · 30573 ms · 2026-05-20T12:52:57.845337+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · 6 internal anchors

  1. [1]

    M. Deng, H. Li, T. Li, Y. Du, and K. He. Generative modeling via drifting.arXiv preprint arXiv:2602.04770,

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    URLhttps://sander.ai/2024/09/02/ spectral-autoregression.html. T. Dockhorn, A. Vahdat, and K. Kreis. Genie: Higher-order denoising diffusion solvers.Advances in Neural Information Processing Systems, 35:30150–30166,

    work page 2024

  3. [3]

    Z. Geng, M. Deng, X. Bai, J. Z. Kolter, and K. He. Mean flows for one-step generative modeling.arXiv preprint arXiv:2505.13447,

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [4]

    Habibian, A

    A. Habibian, A. Ghodrati, N. Fathima, G. Sautiere, R. Garrepalli, F. Porikli, and J. Petersen. Clockwork diffusion: Efficient generation with model-step distillation.arXiv preprint arXiv:2312.08128,

    work page arXiv

  5. [5]

    Classifier-Free Diffusion Guidance

    J. Ho and T. Salimans. Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598,

    work page internal anchor Pith review Pith/arXiv arXiv

  6. [6]

    Ryoo, and Tian Xie

    11 Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network K. Kahatapitiya, H. Liu, S. He, D. Liu, M. Jia, C. Zhang, M. S. Ryoo, and T. Xie. Adaptive caching for faster video generation with diffusion transformers.arXiv preprint arXiv:2411.02397,

    work page arXiv

  7. [7]

    Consistency traject ory models: Learning probability flow ode trajectory of diffusion

    D. Kim, C.-H. Lai, W.-H. Liao, N. Murata, Y. Takida, T. Uesaka, Y. He, Y. Mitsufuji, and S. Ermon. Consistency trajectory models: Learning probability flow ode trajectory of diffusion.arXiv preprint arXiv:2310.02279,

    work page arXiv

  8. [8]

    D. P. Kingma and J. Ba. Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980,

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [9]

    Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro. Diffwave: A versatile diffusion model for audio synthesis.arXiv preprint arXiv:2009.09761,

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  10. [10]

    H. Liu, W. Zhang, J. Xie, F. Faccio, M. Xu, T. Xiang, M. Z. Shou, J.-M. Perez-Rua, and J. Schmidhuber. Faster diffusion via temporal attention decomposition.arXiv preprint arXiv:2404.02747, 2024a. J. Liu, J. Geddes, Z. Guo, H. Jiang, and M. K. Nandwana. Smoothcache: A universal inference acceleration technique for diffusion transformers.arXiv preprint arX...

    work page arXiv

  11. [11]

    Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models

    C. Lu and Y. Song. Simplifying, stabilizing and scaling continuous-time consistency models.arXiv preprint arXiv:2410.11081,

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    Generative modelling with inverse heat dissipation

    S. Rissanen, M. Heinonen, and A. Solin. Generative modeling with inverse heat dissipation.arXiv preprint arXiv:2206.13397,

    work page arXiv

  13. [13]

    T. Yin, M. Gharbi, T. Park, R. Zhang, E. Shechtman, F. Durand, and W. T. Freeman. Improved distribution matching distillation for fast image synthesis.Advances in neural information processing systems, 37:47455–47487, 2024a. T. Yin, T. Michaeli, G. Menjat, S. Rosenoz, M. Cohen, L. Van Gool, B. Poole, and J. Ho. One-step diffusion with distribution matchin...

    work page arXiv 2025

  14. [14]

    (2025) and Salimans et al

    For both standard diffusion and distillation, we closely follow the setups from Hoogeboom et al. (2025) and Salimans et al. (2024), respectively. We always randomly flip images horizontally with a probability of0.5. When using extra data augmentation, we also apply random translation with a probability of0.4. We use a cosine noise schedule (Nichol and Dhariwal,

    work page 2025

  15. [15]

    For sampling, we use the standard ancestral sampling algorithm adopted for Dual-Rate Diffusion (see Algorithm 1)

    for all experiments. For sampling, we use the standard ancestral sampling algorithm adopted for Dual-Rate Diffusion (see Algorithm 1). During sampling, we also apply clipping of thexpredictions to the range[−1, 1]. For the experiments with standard diffusion, we use classifier-free guidance (Ho and Salimans, 2022). To support this, during training, we dro...

    work page 2022