ForcingBooster is a framework that adaptively allocates denoising steps to individual video patches in autoregressive diffusion models, allowing easy patches to stop early while difficult patches receive more refinement. This dynamic scheduling reduces the total number of denoising steps by about 50% and improves generation efficiency with negligible quality loss. The method requires only a lightweight routing module and works with most existing few‑step autoregressive diffusion models.
arXiv:2608. 05237v1 Announce Type: cross Abstract: Current few-step autoregressive video diffusion models depend on previous fully denoised clean frames as context for all denoising steps of the current frame.
Recent advancements in low-light image enhancement have leveraged diffusion models for their strong ability to generate perceptually realistic, detailed images. Patch diffusion models further offer a...
arXiv:2609.01123v1 Announce Type: new Abstract: Recent advancements in low-light image enhancement have leveraged diffusion models for their strong ability to generate perceptually realistic, detaile...
arXiv:2608. 14706v1 Announce Type: cross Abstract: Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedules, limiting inference procedures from adapting to the data.
arXiv:2605.20624v2 Announce Type: replace-cross Abstract: Diffusion models provide powerful priors for zero-shot video inverse problems, but their real-time deployment is hindered by two inefficienci...
arXiv:2602.24208v2 Announce Type: replace-cross Abstract: Diffusion models achieve state-of-the-art video generation quality, but their inference remains expensive due to the large number of sequenti...
FilterFusion is a lightweight video denoising framework that blends temporally-aware learned features with a small bank of classical denoising filters. It uses a low-cost guidance signal to adaptively mix filter responses at the original resolution, achieving high quality on real sensor noise datasets while keeping the model to only 109K parameters. The approach delivers real‑time performance (55.9 FPS on a single CPU thread) and outperforms lightweight CNN baselines by a large margin in both speed and efficiency.
FastVR is a streaming video restoration framework that uses a one‑step diffusion model to achieve strong restoration quality and temporal consistency while processing 1080p video at 11 FPS on a single H20 GPU. It addresses efficiency bottlenecks by combining a lightweight VAE with chunk‑wise causal attention, and improves inference speed and restoration quality through velocity consistency regularization and continuous trajectory learning during training. Experiments demonstrate that FastVR outperforms diffusion baselines in efficiency and achieves state‑of‑the‑art performance on both synthetic and real‑world benchmarks.
The paper introduces DVG, a Dynamic Video Generation framework that allocates computation across time and space to accelerate diffusion-based video generation. By selecting content-aware acceleration strategies automatically, DVG achieves near-lossless speedups—up to 7× on HunyuanVideo models and 18× when combined with distillation—without manual tuning or retraining. The authors provide code on GitHub for reproducibility.
arXiv:2512.23709v3 Announce Type: replace Abstract: Diffusion-based video super-resolution (VSR) methods deliver strong perceptual quality but are often unsuitable for latency-sensitive scenarios due...
Manifold4D introduces a new denoising strategy for video re‑shooting that injects a rendered point‑cloud directly into the initial noise manifold, eliminating the need for the render to be an explicit conditioning stream during denoising. This approach allows the network to rely solely on the source video as a visual condition, improving camera‑control accuracy on the DAVIS‑Traj benchmark and Vista4D set, with significant reductions in rotation and translation errors while maintaining video fidelity. User studies confirm enhanced trajectory following and dynamic consistency, especially for large yaw amplitudes and even when the render is corrupted.
arXiv:2610.10457v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) achieve remarkable performance in video synthesis, but their iterative denoising process suffers from high inference late...