VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models
(2403.05438)Abstract
Text-to-image diffusion models (T2I) have demonstrated unprecedented capabilities in creating realistic and aesthetic images. On the contrary, text-to-video diffusion models (T2V) still lag far behind in frame quality and text alignment, owing to insufficient quality and quantity of training videos. In this paper, we introduce VideoElevator, a training-free and plug-and-play method, which elevates the performance of T2V using superior capabilities of T2I. Different from conventional T2V sampling (i.e., temporal and spatial modeling), VideoElevator explicitly decomposes each sampling step into temporal motion refining and spatial quality elevating. Specifically, temporal motion refining uses encapsulated T2V to enhance temporal consistency, followed by inverting to the noise distribution required by T2I. Then, spatial quality elevating harnesses inflated T2I to directly predict less noisy latent, adding more photo-realistic details. We have conducted experiments in extensive prompts under the combination of various T2V and T2I. The results show that VideoElevator not only improves the performance of T2V baselines with foundational T2I, but also facilitates stylistic video synthesis with personalized T2I. Our code is available at https://github.com/YBYBZhang/VideoElevator.
Overview
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VideoElevator is a plug-and-play method that enhances video generation quality by integrating text-to-image diffusion models to address frame quality and temporal consistency issues.
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It uses a two-step process: temporal motion refining with a Low-Pass Frequency Filter for reducing flickers and ensuring natural motion, and spatial quality elevating to infuse videos with high-quality details using cross-frame attention.
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Empirical evaluations show that VideoElevator improves performance on various text-to-video baselines when combined with text-to-image models like Stable Diffusion V1.5 and V2.1-base, enhancing frame quality and style fidelity.
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VideoElevator sets a new standard for text-to-video generation with rich detail and temporal consistency, promising future advancements in generative AI.
Enhancing Text-to-Video Generation with VideoElevator: A Dive into Versatile Text-to-Image Integration
Introduction to VideoElevator
The integration of text-to-image (T2I) diffusion models into the generation of videos has presented a promising avenue to address the lingering issue of low frame quality in text-to-video (T2V) diffusion models. Building on this concept, we introduce VideoElevator, a training-free and plug-and-play method designed to significantly enhance the quality of videos generated by existing T2V models through the superior capabilities of T2I diffusion models. VideoElevator stands out by explicitly decomposing the sampling process into two distinct components: temporal motion refining and spatial quality elevating. This strategic decomposition allows for a more refined approach in integrating T2I's impressive frame quality into video generation, ensuring both temporal consistency and high-definition spatial details.
Temporal Motion Refining
Temporal motion refining focuses on enhancing the temporal consistency of the video. The process begins with the application of a Low-Pass Frequency Filter (LPFF) along the temporal dimension of the video latent to mitigate high-frequency flickers. The refined video latent is then passed through a T2V-based SDEdit process to incorporate fine-grained motion, followed by a deterministic inversion to a noise distribution required by the T2I for further processing. This step is crucial for preserving the integrity of the natural motion within the video, a challenge that many existing models struggle to overcome fully.
Spatial Quality Elevating
Following the temporal refinement, spatial quality elevating aims to infuse the video with high-quality details leveraging the capabilities of T2I models. This is achieved by inflating the self-attention mechanism of the T2I model to allow for cross-frame attention, thereby ensuring appearance consistency across the video frames. The result is a series of frames that are not only temporally coherent but also rich in detail, closely matching the high-quality output characteristic of T2I-generated images.
Achievements and Evaluation
Empirical evaluations demonstrate VideoElevator's ability to significantly improve the performance of various T2V baselines when combined with different T2I models. This includes foundational T2I models such as Stable Diffusion V1.5 and V2.1-base, where VideoElevator was able to enhance frame quality, prompt consistency, and even align better with user-provided prompts. Furthermore, the application of VideoElevator with personalized T2I models facilitated a more faithful reproduction of various styles and aesthetic preferences, surpassing existing alternatives such as AnimateDiff in capturing style fidelity and detail richness.
Future Implications in AI
The introduction of VideoElevator signifies a leap forward in the quest for high-quality text-to-video generation. By meticulously breaking down the sampling step and effectively employing both T2V and T2I models, VideoElevator has set a new standard for generating videos that are not only rich in detail but also exhibit impressive temporal consistency. Looking forward, the potential for further exploration and advancement in combining these models promises exciting developments in the realm of generative AI, where the lines between reality and AI-generated content continue to blur.
In conclusion, VideoElevator heralds a new era in text-to-video generation, leveraging the strengths of text-to-image diffusion models to address critical challenges in video quality and consistency. Its successful integration of temporal motion refining and spatial quality elevating underlines the potential of methodical decomposition in enhancing generative models. As research progresses, the methodologies embedded within VideoElevator may well pave the way for future innovations in the dynamic and ever-evolving field of generative AI.
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