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Ingredients
Overview :
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.
Target Users :
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, the open-source code and models provide a foundation for further development and integration into their own projects.
Use Cases
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.
Features
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.
How to Use
1. Clone the project's GitHub repository to your local machine.
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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, model path, seed value, and image file path for inference testing.
5. Review the generated video results, adjusting the prompts and other parameters as necessary to optimize the output.
6. Try using the Gradio Web UI for an online demonstration to experience all features supported by the project.
7. For users requiring training, wait for the project to release multi-stage training scripts and datasets, following the guidelines for training.
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