TokenFlow: Consistent Diffusion Features for Consistent Video Editing (2307.10373v3)

Abstract: The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos. Webpage: https://diffusion-tokenflow.github.io/

Analysis of "TokenFlow: Consistent Diffusion Features for Consistent Video Editing"

The paper presents a novel framework, TokenFlow, designed to enhance text-driven video editing utilizing a pre-trained text-to-image diffusion model. The authors address a prominent gap in video generation quality and control compared to image models by enabling high-quality, semantically consistent edits across video frames while maintaining original spatial layouts and motions.

Core Contributions

1. TokenFlow Technique: The primary advancement is TokenFlow, which ensures semantic consistency in edited videos by enforcing diffusion feature correspondences across frames. This involves propagating features through inter-frame correspondences derived from the diffusion feature space of the original video.
2. System Design and Process: TokenFlow integrates with any off-the-shelf text-to-image editing method without needing additional training or fine-tuning. The framework consists of two main components: keyframe sampling with joint editing to achieve global consistency, and feature propagation to handle fine-grained temporal consistency.
3. Empirical Analysis: The paper provides an empirical paper on diffusion features across video frames, illustrating the correlation between feature and RGB consistency, a novel insight pivotal for the proposed method.
4. State-of-the-Art Results: Demonstrations include editing of various real-world videos, achieving superior temporal consistency compared to existing methods. This showcases the efficacy of the proposed approach in maintaining coherence in generated video content.

Technical Details

• Diffusion Models and Stable Diffusion:

Leveraging the capabilities of diffusion probabilistic models, specifically using Stable Diffusion, the paper incorporates deterministic sampling via DDIM inversion to process video frames. The self-attention mechanism of these models plays a crucial role in achieving temporal consistency through attention extension and token correspondences.

• Token Propagation Mechanism:

A key innovation is propagating the features of keyframes to other frames using pre-computed nearest neighbor fields based on the original video's feature tokens. This process ensures that the edited video maintains a consistent representation across time.

Implications and Future Directions

The implications of this research are significant for fields involving automated video editing, where maintaining temporal consistency is critical. By reducing the need for extensive training or fine-tuning and allowing integration with existing editing techniques, TokenFlow provides a practical approach to improving video coherence.

Future developments may involve exploring more complex motion dynamics and handling structural modifications in video edits. Extending these ideas might also enhance large-scale generative video models, providing opportunities for advancements in how AI interprets and generates dynamic content.

In conclusion, this paper introduces a robust framework that addresses a critical challenge in video editing using AI, offering a method that bridges the gap between image and video generation capabilities. The insights on diffusion feature space hold potential for further exploration in developing sophisticated generative models in AI.