Wan2.1

Wan2.1 is an open-source large-scale video generative model developed by Wan-Video. It represents a state-of-the-art approach to video generation, supporting both text-to-video and image-to-video tasks with impressive quality, temporal consistency, and creative control. With over 16,500 GitHub stars and 3,000 forks, it has quickly become a reference model in the open-source video generation community.

The project is hosted at github.com/Wan-Video/Wan2.1. Wan2.1 builds on the diffusion model paradigm, scaling it to high-resolution video generation with advanced architectural improvements for temporal modeling and computational efficiency.

• Text-to-Video - Generate high-quality videos from natural language descriptions with consistent motion and scene composition.
• Image-to-Video - Animate static images into video sequences while preserving the original content and style.
• High-Resolution Output - Produces videos at competitive resolutions with fine detail preservation.
• Temporal Consistency - Advanced temporal modeling ensures smooth motion across frames without flickering or artifacts.
• VACE Integration - Optional VACE module for enhanced video composition and editing capabilities.
• Gradio Web Interface - Built-in Gradio-based UI for easy experimentation and demonstration.

Wan2.1 is based on a diffusion transformer architecture optimized for video generation. The model processes video data as sequences of latent patches, using spatial-temporal attention mechanisms to capture both the visual content of individual frames and the motion dynamics across frames. The architecture is designed for efficient scaling, allowing the model to handle longer video sequences and higher resolutions without proportional increases in computational cost.

The model uses a two-stage training approach: pre-training on large-scale video datasets for fundamental motion understanding, followed by fine-tuning on higher-quality data for visual fidelity and creative control. The wan/ directory contains the core model implementation with configurable architecture parameters.

VACE (Video Adapter for Compositional Editing) is an optional enhancement module for Wan2.1 that provides advanced video composition and editing capabilities. It allows for fine-grained control over generated video content, including object insertion, background replacement, style transfer, and region-specific editing.

The VACE module was added to Wan2.1 to extend beyond simple text-to-video generation, enabling practical video production workflows where specific elements of a scene need to be controlled or modified independently.

git clone https://github.com/Wan-Video/Wan2.1.git
cd Wan2.1
pip install -r requirements.txt
# Download model weights (see INSTALL.md)

Detailed installation instructions are available in INSTALL.md. Wan2.1 requires a GPU with at least 24GB VRAM for the full model. The Gradio web interface can be launched with python gradio/app.py.

Wan2.1 has attracted a vibrant community of developers and researchers building on top of the model. Community projects include fine-tuned variants for specific use cases, integrations with popular AI frameworks, and creative applications ranging from short film production to educational content creation. The project's README highlights community contributions and the ecosystem of tools built around Wan2.1.