Ingredients

A project that combines custom photos with video using a video diffusion transformer.

CommonProductVideoVideo CreationCustomization

Ingredients is a research project utilizing advanced video diffusion transformer technology to integrate photos of specific identities into video creation, providing powerful tools for video customization. Initiated by feizc, the project is currently in the research phase, with a recommendation to explore more mature products. Its key advantage lies in achieving video fusion with multi-ID photos, bringing personalization and innovation to video creation. The project is open-source under the Apache-2.0 license and currently has 34 stars on GitHub.

Provides a simple testing script
infer.py
for ease of inference testing.
Includes evaluation metrics code and data for comparing results in multi-ID customization tasks.
Maintains high standards for prompt quality; users are encouraged to refer to relevant links to enhance output effectiveness.
Supports online demonstrations through the Gradio Web UI
integrating all currently supported features.
Will soon release multi-stage training scripts and multi-ID text-video datasets to support further training and research.

The primary audience consists of video creators
researchers
and developers who wish to personalize video content by incorporating specific photos
enhancing the individuality of their video productions. For researchers
this project offers a new direction for exploring video diffusion transformers and multi-ID customization. For developers

Video creators can utilize this project to incorporate photos of specific individuals into videos
adding unique styles and identity features.
Researchers can use the project's model and code to further explore the applications and optimizations of video diffusion transformers in video customization.
Developers can integrate the project's models into their own video editing software to offer users personalized video creation capabilities.

1. Clone the project's GitHub repository to your local machine.
2. Create and activate a conda environment
installing dependencies according to the setup requirements in the README.
3. Download the model weight files and place them in the specified directory.
4. Use the infer.py script to input the prompt

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Bounce Rate

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