TRaM-VSR, a Token Routing and Merging framework for adaptive token allocation, leveraging both context-aware video priors and network-level priors accelerates inference significantly while preserving state-of-the-art reconstruction quality and robust temporal consistency is proposed.
Abstract
Video super-resolution (VSR) using large-scale Diffusion Transformer (DiT) priors achieves exceptional perceptual quality but is often impractical due to the quadratic computational cost of processing dense spatio-temporal token sequences. Existing efficiency-oriented methods risk irreversible detail loss and temporal flickering, a vulnerability especially pronounced in one-step diffusion models. To address this, we propose TRaM-VSR, a Token Routing and Merging framework for adaptive token allocation, leveraging both context-aware video priors and network-level priors. First, token importance is estimated by fusing motion-sensitive temporal cues with semantic text similarity, isolating dynamic objects and structural boundaries. Next, this importance is further calibrated and adjusted by an offline planner to guide routing across optimally grouped network blocks. Technically, within each routed group, structurally critical tokens are processed in a high-fidelity local stream, while less informative tokens are aggregated into a compact global stream, both modulated by network depth and aligned with the multigranular nature of diffusion models. Extensive experiments show that TRaM-VSR accelerates inference significantly while preserving state-of-the-art reconstruction quality and robust temporal consistency. The code is available at https://github.com/Ree1s/TRaM-VSR.
The exceptional generative capabilities of modern diffusion models are fundamentally bottlenecked by the quadratic computational complexity of their attention mechanisms. While recent feature caching strategies attempt to accelerate inference by skipping layers at static intervals, they fail to account for the non-linear evolution of latent features, inevitably causing severe structural distortions and temporal flickering. To address this, we propose AST-ToMe (Adaptive Step-Aware Thresholding), a novel dynamic gating mechanism that utilizes a runtime L2 norm feature drift metric to adaptively determine whether to compute or reuse attention states. Furthermore, we extend AST-ToMe to video stream generation through a cross-frame state inheritance design. Experimental results demonstrate that for single-image synthesis, AST-ToMe achieves a 15.3% reduction in inference latency with near-lossless perceptual quality (LPIPS: 0.0023). In continuous video generation, our method not only accelerates inference but also serves as a robust temporal anchor. By effectively suppressing random stochastic variations, AST-ToMe successfully reduces Temporal Jitter from 0.2059 to 0.1865, paving a highly efficient path for stable, flicker-free video generation.
Shu-Zhi Zheng· International Conference on...· 0 citations
This work proposes Token Radius Attention (TRA), a training-free framework that maps query entropy to an analytic token budget and converts it into a temporally decayed radius without explicit key ranking and achieves 1.05x speedup with competitive generation quality.
Jiayu Chen, Zhi-Kun Jiang, Maoliang Li et al.· 0 citations
Diffusion Transformers (DiTs) achieve high-quality generation but are costly due to iterative sampling. Dynamic-resolution sampling reduces early-stage cost by denoising at low resolution; however, uniformly upsampling all latent tokens at resolution transitions incurs redundant computation and may degrade fine-detail consistency. Existing partial upsampling strategies typically rely on local latent structure cues or single-step statistics, making it difficult to jointly capture token-text semantic relevance and token-wise representation dynamics across diffusion steps. We propose AViTS, an adaptive spatiotemporal token selection framework for dynamic-resolution DiTs. AViTS models spatial importance via latent-text attention and temporal importance via token-level feature variation across diffusion timesteps, and fuses them to enable spatiotemporal importance-aware selective upsampling: it prioritizes resolution refinement for critical tokens while deferring less important ones, thereby reducing redundant high-resolution computation and improving the quality-efficiency trade-off. AViTS achieves up to 6.34x on FLUX and nearly 9x FLOPs reduction on Qwen-Image-Edit and FLUX.1-Kontext-dev, orthogonal to distillation, quantization, and feature caching, and reaching 14.76x with distilled models. Code: https://github.com/QHR69/AViTS
Haoran Qin, Zhen Yan, Shikang Zheng et al.· 0 citations
This work proposes Ms.Forcing, an efficient streaming video generation paradigm that adapts spatial granularity to each state's noise level and introduces Homogeneous-Noise-Level DMD, which assembles each fake video from clean predictions sharing the same source noise level, thereby reducing the mismatch between DMD training sequences and inference-time rollouts.
Zekun Li, Xiaoyan Cong, Hongyu Li et al.· arXiv.org· 0 citations
SPADE is presented, a training-free sparse-attention engine of three parts: a specification defining 3D blocking candidates and formalizing dynamic masks via Summarizer/Estimator expressions, and an executor with low-overhead index search, flash block-sparse attention, and kernel grouping.
Shanghao Liu, Renze Chen, Size Zheng et al.· 0 citations
Diffusion-based methods have achieved impressive performance in real-world image super-resolution (Real-ISR) by leveraging large pre-trained stable diffusion (SD) models as powerful generative priors. However, these methods still face two key limitations. First, existing SD-based one-step and multi-step Real-ISR approaches adopt a unified processing paradigm for all input samples, ignoring the varying restoration difficulty across images. Second, the aggressive resolution reduction of the VAE in SD models (e.g., 8x downsampling) leads to irreversible loss of fine-scale details, which cannot be recovered by the subsequent diffusion process. To address these limitations, we propose a Difficulty-aware Dynamic Routing (DDR) strategy that overcomes the rigid, one-size-fits-all processing paradigm. Specifically, we first design a difficulty estimator to predict the restoration cost of each input image, enabling automatic assignment to a network of appropriate capacity. Then, we construct a set of Real-ISR networks with varying model capacities by modulating the spatial downsampling ratio of the VAE in the SD backbone, thereby preserving more high-frequency information for challenging cases while maintaining efficiency for simpler inputs. Extensive experiments have demonstrated the superior efficiency and effectiveness of the proposed model compared to recent state-of-the-art methods.
Xue Wu, Kang Zhao, Kafeng Wang et al.· arXiv.org· 0 citations
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