Introduction
Alibaba has recently open-sourced Wan2.1, a powerful video generation model that has achieved state-of-the-art performance in the field of AI video generation. Released under the Apache 2.0 license, Wan2.1 is now available for developers worldwide through GitHub and HuggingFace platforms.
Key Features of Wan2.1
Wan2.1 stands out in the AI video generation landscape with several impressive capabilities:
- Superior Performance: Ranks #1 on the VBench leaderboard, outperforming both open-source and commercial models
- High Resolution Support: Capable of generating videos up to 720P resolution
- Low Hardware Requirements: Can run on consumer-grade GPUs with as little as 8GB VRAM
- Multilingual Text Support: Uniquely able to generate videos with both Chinese and English text/subtitles
- Natural Motion: Produces videos with natural movement, avoiding the distortions common in earlier AI video models
Multiple Task Support
Wan2.1 supports a variety of generation tasks:
| Task Type | Description |
|---|---|
| Text-to-Video (T2V) | Generates complete videos from text descriptions |
| Image-to-Video (I2V) | Creates dynamic videos from a single image |
| Video-Edit | AI optimization or modification of existing videos |
| Text-to-Image (T2I) | Generates high-quality images from text |
| Video-to-Audio (V2A) | Creates AI audio that matches video content |
Available Models
The open-source release includes four specific models across two parameter sizes:
- Wan2.1-I2V-14B-720P: 14B parameter model for generating high-definition 720P videos from images
- Wan2.1-I2V-14B-480P: 14B parameter model for generating 480P videos from images
- Wan2.1-T2V-14B: 14B parameter model for text-to-video generation, supporting both 480P and 720P resolutions
- Wan2.1-T2V-1.3B: A lightweight 1.3B parameter model that can run on almost any consumer GPU, requiring only 8.19GB VRAM to generate a 5-second 480P video
The 1.3B model is particularly noteworthy as it outperforms other 5B parameter models and even some larger models, making it an efficient option for developers with limited computational resources.
Technical Innovations
Wan2.1 incorporates several technical innovations:
3D Spatiotemporal VAE
Wan2.1 utilizes an advanced 3D spatiotemporal variational autoencoder (Wan-VAE) that achieves:
- More efficient video compression while maintaining temporal consistency
- Support for 1080P long videos without losing temporal information
- Faster processing and higher quality compared to traditional VAEs
- 2.5x faster video reconstruction speed on A800 GPUs compared to HunYuanVideo
Video Diffusion Transformer (DiT)
The model employs a mainstream video DiT structure with:
- Full Attention mechanism for effective modeling of long-term spatiotemporal dependencies
- Flow Matching framework combined with T5 encoder
- MLP for processing time embeddings
Data Processing
The training process involved a four-step data curation workflow focusing on:
- Basic dimensions
- Visual quality
- Motion quality
The pre-training process was divided into four stages, gradually increasing resolution and video duration to optimize training within computational constraints.
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How to Use
Developers can download and use the models through:
- GitHub: https://github.com/Wan-Video/Wan2.1
- HuggingFace: https://huggingface.co/Wan-AI
The models can be run locally using a Gradio Web interface for an interactive experience.
Wan2.1 also has been integrated with ComfyUI, allowing users to leverage the model within the ComfyUI workflow: https://comfyanonymous.github.io/ComfyUI_examples/wan/
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