HunyuanVideo-I2V: Tencent Hunyuan's Open-Source Image-to-Video Framework 192

What is HunyuanVideo-I2V?

HunyuanVideo-I2V is a new image-to-video framework open-sourced by the Tencent Hunyuan team on March 6, 2025. Built upon the previously open-sourced HunyuanVideo, it further extends the capability of image-to-video generation, allowing video creators to generate high-quality short videos from a single image and supporting customized effect training.

HunyuanVideo-I2V Features

• Image-to-video: HunyuanVideo-I2V can use a static image as the first frame of a video and generate matching video content based on user-input captions or descriptions.

• Multimodal Large Language Model (MLLM): The model employs a pre-trained multimodal large language model as the text encoder, significantly enhancing the understanding of the semantic content of input images.

• 3D Variational Autoencoder (3D VAE): A 3D VAE trained with CausalConv3D technology compresses videos and images from pixel space into a compact latent space, reducing the number of tokens in subsequent models.

• Dual-stream to Single-stream Hybrid Model Design: In the dual-stream stage, video and text tokens are processed independently through multiple Transformer blocks; in the single-stream stage, video and text tokens are concatenated for multimodal information fusion.

• Progressive Training Strategy: Gradually transition from low-resolution, short videos to high-resolution, long videos to improve model convergence speed.

• Prompt Rewriting Model: Addresses the issue of variable language styles and lengths in user prompts, converting user-input prompts into a format that is easier for the model to understand.

• Customizable LoRA Training: Supports training video generation models with specific effects using a small amount of data, such as "hair growth" or "character movements" effects.

• Efficient Inference: Provides single-GPU inference and multi-GPU sequence parallel inference, supporting faster inference speeds.

HunyuanVideo-I2V Technical Architecture

HunyuanVideo-I2V continues the design philosophy of HunyuanVideo in its overall architecture and optimizes it for image-to-video generation tasks. Its core lies in using image latent space concatenation technology to inject key information from reference images into the video generation process, while achieving cross-modal deep fusion of text and image information through MLLM.

How to Use HunyuanVideo-I2V

1. Installation and Dependencies

• Install PyTorch and other dependencies; specific installation guides can be found on the GitHub page.

• An NVIDIA GPU is recommended, with at least 80GB of VRAM required.

2. Download Pretrained Models

• Pretrained model weights can be downloaded from the GitHub page or Hugging Face page.

3. Inference Code

• Use the provided inference code (such as sample_video.py) to generate videos.

4. LoRA Training

• LoRA training code is provided, allowing users to train video generation models with specific effects using a small amount of data.

HunyuanVideo-I2V Application Scenarios

• Video Content Creation: Provides video creators with efficient, high-quality video generation tools.

• Film and Video Production: Generate cinematic-grade video content.

• Personalized Videos: Generate personalized videos based on user-uploaded images and descriptions.

Official Website:https://video.hunyuan.tencent.com/

GitHub:https://github.com/Tencent/HunyuanVideo-I2V

Hugging Face: https://huggingface.co/tencent/HunyuanVideo-I2V