SEINE Project Introduction
Overview
SEINE is an innovative video diffusion model designed for generating video transitions and predictions. Developed as part of the Vchitect video generation system, SEINE stands out for its ability to transform short video clips into longer videos, making it a valuable tool for content creators and researchers alike. This project is detailed in the arXiv paper (ICLR2024).
Key Features
- Video Diffusion Model: SEINE leverages advanced video diffusion techniques to create seamless transitions and video predictions, thus enhancing the quality and continuity of video content.
- Integration with Vchitect: As part of the Vchitect system, SEINE provides supportive capabilities for video generation efforts, complementing other tools such as the Text-to-Video framework, LaVie.
- Based on Stable Diffusion: The model builds on the stable diffusion framework, which is renowned for generating high-quality images and videos.
Getting Started
Preparing the Environment
To set up SEINE, users need to prepare their environment by installing the required software packages:
- Create a new environment using Conda with Python version 3.9.16.
- Activate the environment.
- Install dependencies from the provided
requirement.txtfile.
Model and Data Setup
The next steps involve downloading the SEINE model and the base model for text-to-image conversions. The models should be placed in a directory named pretrained, following the specific structure provided in the setup instructions.
Using SEINE
Image-to-Video (I2V) Conversion
By running a specific script with configurations detailed in sample_i2v.yaml, users can convert images into videos. The output videos are saved in a designated results directory. Users can customize video generation by altering parameters such as model checkpoints, text prompts, and input image paths.
Video Transition Inference
SEINE can also produce video transitions using a similar scripting approach with distinct configuration settings provided in sample_transition.yaml.
Project Results
- I2V Results: Demonstrations show the capability of SEINE to convert static images into dynamic video content seamlessly.
- Transition Results: The project showcases several examples where distinct images transition into one another to form continuous video sequences.
Research and Ethics
SEINE poses disclaimers regarding the ethical and legal use of the model. Users are advised to adhere to ethical standards and laws, focusing on non-harmful content generation.
Further Information
- Research Contributions: Contributions to the SEINE project are acknowledged from various open-source projects like LaVie and Stable Diffusion.
- Contact and Licensing: Queries related to the project can be directed to project contributors via email, while licensing terms are set under Apache-2.0, allowing both academic and free commercial usage.
For more detailed information, visit the SEINE Project Page.