Recent advancements in diffusion frameworks have significantly enhanced video editing, achieving high fidelity and strong alignment with textual prompts. However, conventional approaches using image diffusion models fall short in handling video dynamics, particularly for challenging temporal edits like motion adjustments. While current video diffusion models produce high-quality results, adapting them for efficient editing remains difficult due to the heavy computational demands that prevent the direct application of previous image editing techniques. To overcome these limitations, we introduce FADE—a training-free yet highly effective video editing approach that fully leverages the inherent priors from pre-trained video diffusion models via frequency-aware factorization. Rather than simply using these models, we first analyze the attention patterns within the video model to reveal how video priors are distributed across different components. Building on these insights, we propose a factorization strategy to optimize each component’s specialized role. Furthermore, we devise spectrum-guided modulation to refine the sampling trajectory with frequency domain cues, preventing information leakage and supporting efficient, versatile edits while preserving the basic spatial and temporal structure. Extensive experiments on real-world videos demonstrate that our method consistently delivers high-quality, realistic and temporally coherent editing results both qualitatively and quantitatively. Code is available at https://github.com/EternalEvan/FADE.

Image enhancement finds wide-ranging applications in real-world scenarios due to complex environments and the inherent limitations of imaging devices. Recent diffusion-based methods yield promising outcomes but necessitate prolonged and computationally intensive iterative sampling. In response, we propose InstaRevive, a straightforward yet powerful image enhancement framework that employs score-based diffusion distillation to harness potent generative capability and minimize the sampling steps. To fully exploit the potential of the pre-trained diffusion model, we devise a practical and effective diffusion distillation pipeline using dynamic noise control to address inaccuracies in updating direction during score matching. Our noise control strategy enables a dynamic diffusing scope, facilitating precise learning of denoising trajectories within the diffusion model and ensuring accurate distribution matching gradients during training. Additionally, to enrich guidance for the generative power, we incorporate textual prompts via image captioning as auxiliary conditions, fostering further exploration of the diffusion model. Extensive experiments substantiate the efficacy of our framework across a diverse array of challenging tasks and datasets, unveiling the compelling efficacy and efficiency of InstaRevive in delivering high-quality and visually appealing results.

Building on the success of universal language models in natural language processing (NLP), researchers have recently sought to develop methods capable of tackling a broad spectrum of visual tasks within a unified foundation framework. However, existing universal vision models face significant challenges when adapting to the rapidly expanding scope of downstream tasks. These challenges stem not only from the prohibitive computational and storage expenses associated with training such models but also from the complexity of their workflows, which makes efficient adaptations difficult. Moreover, these models often fail to deliver the required performance and versatility for a broad spectrum of applications, largely due to their incomplete visual generation and perception capabilities, limiting their generalizability and effectiveness in diverse settings. In this paper, we present VisionHub, a novel universal vision model designed to concurrently manage multiple visual restoration and perception tasks, while offering streamlined transferability to downstream tasks. Our model leverages the frozen denoising U-Net architecture from Stable Diffusion as the backbone, fully exploiting its inherent potential for both visual restoration and perception. To further enhance the model's flexibility, we propose the incorporation of lightweight task-plugins and the task router, which are seamlessly integrated onto the U-Net backbone. This architecture enables VisionHub to efficiently handle various vision tasks according to user-provided natural language instructions, all while maintaining minimal storage costs and operational overhead. Extensive experiments across 11 different vision tasks showcase both the efficiency and effectiveness of our approach. Remarkably, VisionHub achieves competitive performance across a variety of benchmarks, including 53.3% mIoU on ADE20K semantic segmentation, 0.253 RMSE on NYUv2 depth estimation, and 74.2 AP on MS-COCO pose estimation.