Tencent Drops HunyuanVideo-I2V, An AI Model That Produces Smooth, Stable Videos

Aleha Noor

1 year ago

Tencent Drops HunyuanVideo-I2V, An AI Model That Produces Smooth, Stable Videos

Tencent recently released HunyuanVideo-I2V, a new AI video tool that can turn any single image into a smooth, flowing video. What makes this tool special is how it keeps everything looking natural – the objects stay consistent, and the movements look real from start to finish. Unlike older AI video generators that often make flickery videos, HunyuanVideo-I2V creates stable animations that stay true to the original image.

The AI understands what’s in your picture and adds realistic movement based on how those objects would naturally move in real life. It’s like bringing your photos to life!

How HunyuanVideo-I2V Works
Example Videos Generated by HunyuanVideo-I2V
Requirements for Running HunyuanVideo-I2V
How to Get Started With HunyuanVideo-I2V
Getting the Best Results Out of HunyuanVideo-I2V
ComfyUI Implementation
Wrapping Up

How HunyuanVideo-I2V Works

HunyuanVideo-I2V builds on Tencent’s existing video technology and uses a clever “token replace” method to keep the original image details while adding motion. The system uses a special type of AI called a Multimodal Large Language Model with a Decoder-Only design to understand both images and text descriptions.

When you input an image, the system breaks it down into “semantic image tokens” that capture what’s in the picture. These tokens combine with video information, allowing the AI to create a movement that makes sense for your specific image.

Example Videos Generated by HunyuanVideo-I2V

Requirements for Running HunyuanVideo-I2V

To generate videos using HunyuanVideo-I2V, there are specific hardware requirements. The model supports a resolution of 720p and requires a GPU with CUDA support. The minimum GPU memory required is 60GB for 720p resolution, but for better generation quality, a GPU with 80GB of memory is recommended. The model has been tested on a single 80G GPU and is compatible with Linux operating systems.

How to Get Started With HunyuanVideo-I2V

If you want to try HunyuanVideo-I2V, here’s how to get started:

1. Clone the repository:

git clone https://github.com/tencent/HunyuanVideo-I2V

cd HunyuanVideo-I2V

2. Create and activate a conda environment:

conda create -n HunyuanVideo-I2V python==3.11.9

conda activate HunyuanVideo-I2V

3. Install PyTorch and other dependencies:

conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia

4. Install pip dependencies and flash attention for acceleration:

python -m pip install -r requirements.txt

python -m pip install ninja

python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3

Additionally, a pre-built Docker image is available for easier setup:

docker pull hunyuanvideo/hunyuanvideo-i2v:cuda_12

docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo-i2v --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged hunyuanvideo/hunyuanvideo-i2v:cuda_12

Getting the Best Results Out of HunyuanVideo-I2V

HunyuanVideo-I2V can create videos up to 720p resolution and about 5 seconds long (129 frames). For best results, keep your text descriptions simple and focus on the main subject (what the video is about), the action (what’s happening), the background (optional) and the camera angle (optional). Don’t go overboard with details! Long, complicated descriptions can confuse the AI and cause weird transitions in your video.

1. Making Stable Videos

If you want smooth, gentle movement in your videos, use these settings:

cd HunyuanVideo-I2V

python3 sample_image2video.py \ --prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \ --i2v-image-path ./demo/imgs/0.jpg \ --model HYVideo-T/2 \ --i2v-mode \ --i2v-resolution 720p \ --i2v-stability \ --infer-steps 50 \ --video-length 129 \ --flow-reverse \ --flow-shift 7.0 \ --seed 0 \ --embedded-cfg-scale 6.0 \ --use-cpu-offload \ --save-path ./results

The key here is turning on the stability flag (–i2v-stability) and using a lower flow shift value (7.0).

2. Making Dynamic Videos

If you want more energetic movement:

cd HunyuanVideo-I2V

python3 sample_image2video.py \ --prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \ --i2v-image-path ./demo/imgs/0.jpg \ --model HYVideo-T/2 \ --i2v-mode \ --i2v-resolution 720p \ --infer-steps 50 \ --video-length 129 \ --flow-reverse \ --flow-shift 17.0 \ --seed 0 \ --embedded-cfg-scale 6.0 \ --use-cpu-offload \ --save-path ./results

Notice how the stability flag is removed and the flow shift is higher (17.0). This creates more dramatic motion – great for action scenes or natural elements like water or trees.

ComfyUI Implementation

HunyuanVideo-I2V has a ComfyUI implementation ready, with GGUFs available for easy integration. The ComfyUI wrapper nodes for HunyuanVideo allow for early access and testing of potential new features.

Wrapping Up

HunyuanVideo-I2V marks a big step forward in AI-generated media. By turning static images into natural-looking videos, it opens up new creative possibilities for many industries. In the future, we might see:

Longer videos beyond the current 5-second limit
Better control over specific movements
Adding sound to the videos
Versions that run on regular computers

As it becomes more accessible, more people will be able to bring their images to life in ways that were previously impossible or required specialized skills.

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