tokenflow
Maintainer: cjwbw
1
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
TokenFlow is a framework that enables consistent video editing using a pre-trained text-to-image diffusion model, without any further training or finetuning. It builds upon key observations that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. The method propagates diffusion features based on inter-frame correspondences to preserve the spatial layout and dynamics of the input video, while adhering to the target text prompt. This approach contrasts with similar models like consisti2v, which focuses on enhancing visual consistency for I2V generation, and stable-video-diffusion, which aims to generate high-quality videos from text.
Model inputs and outputs
TokenFlow is designed for structure-preserving video editing. The model takes in a source video and a target text prompt, and generates a new video that adheres to the prompt while preserving the spatial layout and dynamics of the input.
Inputs
- Video: The input video to be edited
- Inversion Prompt: A text description of the input video (optional)
- Diffusion Prompt: A text description of the desired output video
- Negative Diffusion Prompt: Words or phrases to avoid in the output video
Outputs
- Edited Video: The output video that reflects the target text prompt while maintaining the consistency of the input video
Capabilities
TokenFlow leverages a pre-trained text-to-image diffusion model to enable text-driven video editing without additional training. It can be used to make localized and global edits that change the texture of existing objects or augment the scene with semi-transparent effects (e.g., smoke, fire, snow).
What can I use it for?
The TokenFlow framework can be useful for a variety of video editing applications, such as:
- Video Augmentation: Enhancing existing videos by adding new elements like visual effects or changing the appearance of objects
- Video Retouching: Improving the quality and consistency of videos by addressing issues like lighting, texture, or composition
- Video Personalization: Customizing videos to match a specific style or theme by aligning the content with a target text prompt
Things to try
One key aspect of TokenFlow is its ability to preserve the spatial layout and dynamics of the input video while editing. This can be particularly useful for creating seamless and natural-looking video edits. Experiment with a variety of text prompts to see how the model can transform the visual elements of a video while maintaining the overall structure and flow.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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