Hunyuan Image-Based Video - Tencent Hunyuan's open-source image-based video model

What is the Hunyuan Tusheng video?

Hunyuan Image-Generated Video is an open-source image-generated video model launched by Tencent Hunyuan. Users can upload an image and add a brief description to generate a 5-second short video. The model supports features such as lip-syncing, motion-driven animation, and automatic background sound effect generation. The model is suitable for various characters and scenes, including realistic, animated, and CGI works, with a total of 13 billion parameters. The Tencent Hunyuan Image-Generated Video model is available on Tencent Cloud, and users can experience it through the Hunyuan AI Video official website. The Hunyuan Image-Generated Video model is also open-sourced on mainstream developer communities such as GitHub and HuggingFace, including weights, inference code, and LoRA training code, allowing developers to train their own LoRA and other derivative models.

The main functions of Hunyuan Tusheng video

• Image-generated videoUsers only need to upload an image and enter a brief description, and the model can convert the static image into a 5-second short video, while also supporting automatic generation of background sound effects.
• Audio driver functionUsers can upload photos of people, input text or audio, and the model can accurately match the lip movements, making the people in the photos "speak" or "sing" and presenting facial expressions that match the tone of voice.
• Action-driven functionAfter users upload images and select an action template, the model can make the person in the image perform actions such as dancing, waving, and doing gymnastics. It is suitable for short video creation, game character animation, and film and television production.
• High-quality video outputSupports 2K high-definition image quality, suitable for various characters and scenes including realistic, anime, and CGI.

Technical principles of Hunyuan image generation video

• Image-to-video generation frameworkHunyuanVideo-I2V integrates information from reference images into the video generation process through image latent stitching technology. The input image is first processed by a pre-trained multimodal large-scale language model (MLLM) to generate semantic image tokens, which are then stitched together with the video latent tokens to achieve cross-modal full attention computation.
• Multimodal Large Language Model (MLLM)The model employs an MLLM with a decoder-only structure as the text encoder, significantly enhancing its ability to understand the semantic content of the input image. Compared to traditional CLIP or T5 models, MLLM performs better in image detail description and complex reasoning, enabling a more effective deep fusion of image and textual descriptive information.
• 3D Variational Autoencoder (3D VAE)To efficiently process video and image data, HunyuanVideo-I2V uses the CausalConv3D technique to train a 3D VAE, compressing video and images in pixel space into a compact latent space. This design significantly reduces the number of tokens in subsequent models, enabling training at the original resolution and frame rate.
• Hybrid Model Design for Two-Stream to One-Stream ConversionIn the dual-stream phase, video and text tokens are processed independently through multiple Transformer blocks to avoid mutual interference; in the single-stream phase, video and text tokens are concatenated for multimodal information fusion. This design captures the complex interactions between visual and semantic information, improving the coherence and semantic consistency of the generated video.
• Progressive training strategyThe model employs a progressive training strategy, gradually transitioning from low-resolution, short videos to high-resolution, long videos. This improves the model's convergence speed and ensures high-quality generated videos at different resolutions.
• Prompt word rewriting modelTo address the issue of varying language styles and lengths of user prompts, HunyuanVideo-I2V introduces a prompt rewriting module, which can convert user-input prompts into a format that is easier for the model to understand, thereby improving the generation results.
• Customizable LoRA trainingThe model supports LoRA (Low-Rank Adaptation) training, allowing developers to train video generation models with specific effects using a small amount of data, such as "hair growth" or "human movement" effects.

Project address of Hunyuan Tusheng Video

• Github repository: https://github.com/Tencent/HunyuanVideo-I2V
• Huggingface model library:https://huggingface.co/tencent/HunyuanVideo-I2V

How to use Hunyuan Image Generation Video

• Experience it through the Hunyuan AI Video official websiteUsers can access directlyTencent Hunyuan AI Video Official WebsiteSelect "Image to Video," upload an image, and enter a brief description to generate a 5-second short video.
• Using Tencent Cloud APIEnterprises and developers can apply for API interfaces through Tencent Cloud to achieve more efficient video generation and customized development.
• Local deployment of open source modelsFor users who require higher customization, Tencent's Hunyuan Image-Generated Video Model has been open-sourced on GitHub, supporting local deployment and customized development.
• Hardware Requirements
  • GPUNVIDIA graphics card, supports CUDA, minimum 60GB video memory (for generating 720p video), recommended 80GB video memory.
  • operating systemLinux (official testing environment).
  • CUDA versionI recommend CUDA 11.8 or 12.0.

Application scenarios of Hunyuan image-generated video

• Creative video generationGenerate short videos by uploading images and descriptions.
• Special effects productionCustomized special effects, such as hair growth and character movements, can be achieved through LoRA training.
• Animation and game developmentQuickly generate character animations and reduce production costs.