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Hunyuancustom
Overview :
HunyuanCustom is a multimodal customized video generation framework designed to generate specific-topic videos based on user-defined conditions. The technology excels in identity consistency and support for multiple input modes, capable of processing text, images, audio, and video inputs, applicable to various scenarios such as virtual human advertising and video editing.
Target Users :
This product is suitable for video creators, advertising creative teams, and virtual human developers. HunyuanCustom enables creators to quickly generate high-quality customized videos through support for multiple input forms, meeting the needs of fields such as advertising and entertainment.
Use Cases
Generate a virtual human advertisement using images and audio, driving character dialogues through audio.
Replace characters in an existing video to achieve personalized video editing.
Create a singing avatar that can perform specified music works.
Features
Supports multimodal inputs: Can process text, images, audio, and video to achieve flexible customization.
Identity consistency: Maintains theme consistency in videos through the introduction of image ID enhancement modules and temporal cascading.
Audio-driven generation: Combines audio input to make characters in the generated video speak corresponding content.
Video object replacement: Allows replacing specified objects in a video to be consistent with the subject in the given image.
Supports single and multi-subject scenes: Suitable for single or multiple subject video generation needs.
Expands application scenarios: Can be used for virtual try-on, virtual human advertising, singing avatars, and more.
High-quality generation: Provides higher realism and text-video alignment compared to existing methods.
Parallel reasoning support: Enables efficient reasoning on multiple GPUs to speed up generation.
How to Use
1. Clone the code repository of HunyuanCustom.
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2. Install the required dependencies, including PyTorch and other libraries.
3. Download the pre-trained model and set environment variables.
4. Prepare the input files (images, audio, or video).
5. Run the generation script via command line, specifying inputs and conditions.
6. Wait for the model to generate the video and check the output results.
7. Adjust the inputs and parameters as needed to optimize the generated effects.
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