RhymeFlow: Training-Free Acceleration for Video Generation with Asynchronous Denoising Flow Scheduling

• URL: http://arxiv.org/abs/2606.06309v1
• Date: Thu, 04 Jun 2026 15:49:37 GMT
• Title: RhymeFlow: Training-Free Acceleration for Video Generation with Asynchronous Denoising Flow Scheduling
• Abstract summary: Video generation models based on Diffusion Transformers (DiTs) have achieved remarkable performance in video synthesis.<n>DiTs suffer from high inference latency and computational costs due to the quadratic complexity of 3D attention.<n>We introduce bftextRhymeFlow, a training-free framework that decouples the denoising trajectories of different frames.
• Score: 51.279397568734424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video generation models based on Diffusion Transformers (DiTs) have achieved remarkable performance in video synthesis, yet they suffer from high inference latency and computational costs due to the quadratic complexity of 3D attention. Existing acceleration methods primarily reduce computational complexity within each individual denoising steps through techniques such as sparse attention and KV-caching. However, they rigidly adhere to the inherent constraint of the standard diffusion pipeline: every frame in the target video sequence must be subjected to a complete, dense denoising process across all diffusion timesteps. We observe that due to the corresponding contents and motions among adjacent frames, when keyframes with critical semantic transitions are anchored, the intermediate states of others often follow more predictable trajectories, which indicates that such uniform, dense denoising process is inherently redundant for natural video data. To this end, we introduce \textbf{RhymeFlow}, a training-free framework that decouples the denoising trajectories of different frames. Specifically, we first identify a sparse set of pivotal key frames that dominate the latent semantic evolution. Then, only these keyframes undergo dense, step-by-step denoising to ensure structural integrity, while non-keyframes progressively skip denoising steps to minimize computational cost. Since skipped intermediate states of non-keyframes break the temporal coherence in keyframe denoising steps, leading to visual degradation, we further introduce a latent trajectory projection module, which enables keyframes to interact with a complete and temporally consistent sequence representation. Extensive experiments on current DiT-based video generation models demonstrate our method outperforms existing baselines with higher inference speed and better visual quality.

Related papers

• In-Context Forcing: Uncovering Context Effects in Autoregressive Video Diffusion [52.18702395026718]
  We introduce In-Context Forcing, a progressive autoregressive paradigm that utilizes contexts with decreasing noise levels.<n>By applying less masking to distant frames and more masking to adjacent ones, this approach provides adaptive guidance.<n>Our method significantly outperforms state-of-the-art approaches in both visual fidelity and inference speed.
  arXiv  Detail & Related papers  (2026-08-05T13:54:31Z)
• EcoVideo: Entropy-Orchestrated Video Generation Paradigm in Cloud-Edge Dynamics [15.798324066707105]
  We propose EcoVideo, an entropy-orchestrated framework for dynamic inter-frame decoupling.<n>We show improved quality-efficiency trade-offs and up to 2.9x end-to-end speedup in low-bandwidth, compute-limited edge settings.
  arXiv  Detail & Related papers  (2026-06-29T16:51:56Z)
• Accelerating Diffusion-based Video Editing via Heterogeneous Caching: Beyond Full Computing at Sampled Denoising Timestep [37.62908191585867]
  HetCache is a training-free diffusion acceleration framework for video-to-video (MV2V) generation and editing.<n>It reduces redundant attention operations while maintaining editing consistency and fidelity.<n> Experiments show that HetCache achieves a noticeable acceleration, including a 2.67$times$ latency speedup and FLOPs reduction over commonly used foundation models.
  arXiv  Detail & Related papers  (2026-03-25T12:53:31Z)
• Causality in Video Diffusers is Separable from Denoising [38.11368818072302]
  Causality underlies many complex generative processes, including videos, language, and robot trajectories.<n>Current causal diffusion models entangle temporal reasoning with iterative denoising, applying causal attention across all layers, at every denoising step, and over the entire context.<n>We show that the causal reasoning in these models is separable from the multi-step denoising process.<n>Motivated by these findings, we introduce Separable Causal Diffusion (SCD), a new architecture that explicitly decouples once-per-frame temporal reasoning, via a causal transformer encoder, from multi-step frame-wise rendering
  arXiv  Detail & Related papers  (2026-02-10T18:57:21Z)
• LiteAttention: A Temporal Sparse Attention for Diffusion Transformers [1.3471268811218626]
  LiteAttention exploits temporal coherence to enable evolutionary computation skips across the denoising sequence.<n>We implement a highly optimized LiteAttention kernel on top of FlashAttention and demonstrate substantial speedups on production video diffusion models.
  arXiv  Detail & Related papers  (2025-11-14T08:26:55Z)
• DiTVR: Zero-Shot Diffusion Transformer for Video Restoration [48.97196894658511]
  DiTVR is a zero shot video restoration framework that couples a diffusion transformer with trajectory aware attention and a flow consistent sampler.<n>Our attention mechanism aligns tokens along optical flow trajectories, with particular emphasis on vital layers that exhibit the highest sensitivity to temporal dynamics.<n>The flow guided sampler injects data consistency only into low-frequency bands, preserving high frequency priors while accelerating cache.
  arXiv  Detail & Related papers  (2025-08-11T09:54:45Z)
• Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion [116.40704026922671]
  First-in-first-out (FIFO) video diffusion, built on a pre-trained text-to-video model, has recently emerged as an effective approach for tuning-free long video generation.<n>We propose Ouroboros-Diffusion, a novel video denoising framework designed to enhance structural and content (subject) consistency.
  arXiv  Detail & Related papers  (2025-01-15T18:59:15Z)
• Motion-Aware Video Frame Interpolation [49.49668436390514]
  We introduce a Motion-Aware Video Frame Interpolation (MA-VFI) network, which directly estimates intermediate optical flow from consecutive frames. It not only extracts global semantic relationships and spatial details from input frames with different receptive fields, but also effectively reduces the required computational cost and complexity.
  arXiv  Detail & Related papers  (2024-02-05T11:00:14Z)
• TimeLens: Event-based Video Frame Interpolation [54.28139783383213]
  We introduce Time Lens, a novel indicates equal contribution method that leverages the advantages of both synthesis-based and flow-based approaches. We show an up to 5.21 dB improvement in terms of PSNR over state-of-the-art frame-based and event-based methods.
  arXiv  Detail & Related papers  (2021-06-14T10:33:47Z)
• Efficient Semantic Video Segmentation with Per-frame Inference [117.97423110566963]
  In this work, we process efficient semantic video segmentation in a per-frame fashion during the inference process. We employ compact models for real-time execution. To narrow the performance gap between compact models and large models, new knowledge distillation methods are designed.
  arXiv  Detail & Related papers  (2020-02-26T12:24:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.