VDE accelerates rectified flow models like Flux by 3.22x with LPIPS of 0.069 via velocity decomposition into parallel/orthogonal components plus periodic full-pass anchoring.
Less is enough: Training-free video diffusion acceleration via runtime-adaptive caching
13 Pith papers cite this work. Polarity classification is still indexing.
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TACache accelerates rectified flow sampling up to 4.14x for text-to-image and 2.11x for text-to-video via offline skip scheduling from cumulative variation thresholds and online velocity reconstruction using historical orthogonal directions.
1.x-Distill achieves better quality and diversity than prior few-step distillation methods at 1.67 and 1.74 effective NFEs on SD3 models with up to 33x speedup.
SyncCache accelerates DiT-based audio-driven portrait animation up to 4.12x via spatially-asymmetric probing and modality-decoupled caching while preserving near-lossless quality and audio sync.
A test-time Kalman-style dual-state tracker of the output-to-input feature ratio enables error-bounded computation skipping in video diffusion without offline calibration.
LearniBridge learns shared low-rank calibration updates via LoRA to enable high-ratio feature caching in DiTs using only 3-5 samples, delivering up to 5.87x acceleration on FLUX with quality gains on video models.
RhymeFlow is a training-free acceleration framework that decouples denoising trajectories across video frames by dense processing of semantic keyframes and asynchronous skipping for non-keyframes, augmented by a latent trajectory projection module to maintain consistency.
ReCache learns recomputation schedules via policy gradients to maximize quality under a target compute budget for any caching mechanism in diffusion models.
HSA assigns variable denoising steps to spatiotemporal tokens in DiTs based on velocity dynamics, with KV-cache sync and cached Euler updates, outperforming prior caching methods on quality-runtime tradeoffs for T2V and I2V generation.
A video transfer pipeline augments simulated VLA data into realistic videos while preserving actions, yielding consistent performance gains on robot benchmarks such as 8% on Robotwin 2.0.
NUMINA improves counting accuracy in text-to-video diffusion models by up to 7.4% via a training-free identify-then-guide framework on the new CountBench dataset.
Sol Video Inference Engine uses parallel skill agents to optimize cache, sparse attention, token pruning, quantization, and kernel fusion, delivering over 2x end-to-end acceleration with near-lossless quality on three video models.
PixelWizard decouples global structure from fine details via a spatiotemporal anchor and introduces Noise-Span Aligned Shortcut Training with biased sampling to achieve over 10x faster sampling for high-fidelity 2K/4K video generation.
citing papers explorer
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VDE: Training-Free Accelerating Rectified Flow Model via Velocity Decomposition and Estimation
VDE accelerates rectified flow models like Flux by 3.22x with LPIPS of 0.069 via velocity decomposition into parallel/orthogonal components plus periodic full-pass anchoring.
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Accelerating Rectified Flow Models via Trajectory-Aware Caching
TACache accelerates rectified flow sampling up to 4.14x for text-to-image and 2.11x for text-to-video via offline skip scheduling from cumulative variation thresholds and online velocity reconstruction using historical orthogonal directions.
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1.x-Distill: Breaking the Diversity, Quality, and Efficiency Barrier in Distribution Matching Distillation
1.x-Distill achieves better quality and diversity than prior few-step distillation methods at 1.67 and 1.74 effective NFEs on SD3 models with up to 33x speedup.
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SyncCache: Exploiting Asymmetric Dynamics for Fast Audio-Driven Portrait Animation
SyncCache accelerates DiT-based audio-driven portrait animation up to 4.12x via spatially-asymmetric probing and modality-decoupled caching while preserving near-lossless quality and audio sync.
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NaviCache: Test-Time Self-Calibration Caching for Video Generation
A test-time Kalman-style dual-state tracker of the output-to-input feature ratio enables error-bounded computation skipping in video diffusion without offline calibration.
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LearniBridge: Learnable Calibration of Feature Caching for Diffusion Models Acceleration
LearniBridge learns shared low-rank calibration updates via LoRA to enable high-ratio feature caching in DiTs using only 3-5 samples, delivering up to 5.87x acceleration on FLUX with quality gains on video models.
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RhymeFlow: Training-Free Acceleration for Video Generation with Asynchronous Denoising Flow Scheduling
RhymeFlow is a training-free acceleration framework that decouples denoising trajectories across video frames by dense processing of semantic keyframes and asynchronous skipping for non-keyframes, augmented by a latent trajectory projection module to maintain consistency.
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ReCache: Learning Budget-Aware Caching Schedules for Diffusion Models via REINFORCE
ReCache learns recomputation schedules via policy gradients to maximize quality under a target compute budget for any caching mechanism in diffusion models.
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Not All Tokens Need 40 Steps: Heterogeneous Step Allocation in Diffusion Transformers for Efficient Video Generation
HSA assigns variable denoising steps to spatiotemporal tokens in DiTs based on velocity dynamics, with KV-cache sync and cached Euler updates, outperforming prior caching methods on quality-runtime tradeoffs for T2V and I2V generation.
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Seeing Realism from Simulation: Efficient Video Transfer for Vision-Language-Action Data Augmentation
A video transfer pipeline augments simulated VLA data into realistic videos while preserving actions, yielding consistent performance gains on robot benchmarks such as 8% on Robotwin 2.0.
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When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models
NUMINA improves counting accuracy in text-to-video diffusion models by up to 7.4% via a training-free identify-then-guide framework on the new CountBench dataset.
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Sol Video Inference Engine: Agent-Native Full-Stack Acceleration Framework for Efficient Video Generation
Sol Video Inference Engine uses parallel skill agents to optimize cache, sparse attention, token pruning, quantization, and kernel fusion, delivering over 2x end-to-end acceleration with near-lossless quality on three video models.
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PixelWizard: Towards Efficient High-Fidelity Video Generation at Ultra-Large Spatial Resolution
PixelWizard decouples global structure from fine details via a spatiotemporal anchor and introduces Noise-Span Aligned Shortcut Training with biased sampling to achieve over 10x faster sampling for high-fidelity 2K/4K video generation.