Explore the powerful features of Wan 2.1, an open-source AI video generation model based on Diffusion Transformer and Wan-VAE, supporting various tasks like T2V, I2V, and more.
Based on Diffusion Transformer, integrating the innovative Wan-VAE architecture, supporting multiple tasks such as T2V and I2V.
Excels in authoritative benchmarks like VBench (comprehensive score 84.7%+), especially adept at handling complex dynamics, spatial relationships, and multi-object interactions.
The lightweight 1.3B model requires only about 8GB VRAM, running smoothly on mainstream consumer GPUs, significantly lowering the barrier to entry.
Not limited to T2V/I2V, also supports diverse creative needs like video editing, restoration, extension, and audio generation (V2A).
Pioneers clear generation of bilingual (Chinese/English) text within videos, supporting various font effects, greatly expanding application boundaries.
Novel 3D spatio-temporal VAE significantly improves encoding/decoding efficiency and quality, supports high-resolution long video processing, balancing speed and VRAM.
Follows the Apache 2.0 license, fully opening model code and weights, actively embracing the community to jointly advance technology and application deployment.
Accurately generate realistic video streams containing large body movements, object rotations, scene changes, and camera movements.
Example: Simulating a dynamic shot of a snowmobiler speeding and kicking up snow on a snowy landscape.
Accurately simulate real-world physical laws to generate intuitive object interactions and dynamic effects.
Example: A panda performs difficult skateboarding tricks on city streets, including jumps, spins, and grinds, with smooth, natural movements showcasing exquisite skill.
Deliver visual quality comparable to movies, generating video frames with rich textures, realistic lighting, and diverse styles.
Example: A close-up cinematic shot capturing the face of a transforming spy.
Based on Wan-Edit technology, supports diverse video editing operations for fine-tuning content.
Example: Replacing the background or adding elements while preserving the main structure of the video.
Breakthrough support for directly generating clear, dynamic bilingual (Chinese/English) text within video frames, applicable with various fonts and effects.
Prompt Example (Ink Art): "On a red New Year paper background, a drop of ink slowly spreads, forming a blurry, natural character "福" (Fu - blessing), with ink color fading from dark to light, showcasing Eastern aesthetics."
Example: Adding dynamic slogans or annotations to a product demo video.
Not only generates visuals but also intelligently matches or generates sound effects and background music (V2A) consistent with the content and rhythm.
Prompt Example (Ice Cube Drop): "Close-up shot, ice cubes fall from a height into a glass, producing cracking sounds and liquid sloshing sounds..." (Generates matching sound effects)
Example: Automatically generating background music fitting the plot and atmosphere for an animated short film.
Wan 2.1 offers model variants with different parameter scales and functionalities to meet various needs from rapid validation to high-quality creation, all open-sourced under the Apache 2.0 license.
1.3 Billion Parameters
Text-to-Video (T2V), focusing on 480p resolution. Optimized for consumer GPUs with low VRAM requirements (approx. 8GB).
14 Billion Parameters
Text-to-Video (T2V), providing excellent quality, supporting 480p/720p resolution, with unique bilingual text generation capabilities.
14 Billion Parameters
Image-to-Video (I2V), generating video by combining image references and text prompts, available in 480p and 720p high-quality variants.
14 Billion Parameters
First&Last-Frame-to-Video (FLF2V), intelligently synthesizes transitions between start and end frames to generate smooth video, supporting multi-GPU acceleration.
🚀 Alibaba Tongyi Lab launches the first 14 billion parameter First&Last-Frame-to-Video large model! Fully open source, providing digital artists with unprecedented creative efficiency and flexibility.
Generate cinematic, high-fidelity video content with rich details and realistic physics.
Accurately capture and generate complex object movements, camera motions, and natural dynamic interactions.
Unique in-video bilingual text generation capability adds more possibilities to content creation.
Advanced Wan-VAE technology brings faster processing speed and better resource utilization efficiency.
Open source combined with consumer hardware support allows everyone to experience cutting-edge AI video technology.
Benefit from contributions, optimizations, and integrations from global developers, fostering continuous ecosystem growth.
Wan 2.1 AI is based on the mainstream Diffusion Transformer (DiT) paradigm and introduces the innovative 3D Spatio-Temporal Variational Autoencoder (Wan-VAE) for efficient video data processing. It also employs Flow Matching techniques and understands text prompts via a T5 encoder, integrating text and visual information using cross-attention mechanisms.
Hardware requirements depend on the model version. The 1.3B T2V model is very consumer GPU-friendly, requiring only about 8GB VRAM minimum. The 14B models (T2V, I2V, FLF2V) require more powerful hardware, recommending professional-grade GPUs with 24GB or more VRAM (like A100, RTX 4090), potentially needing multi-GPU setups for efficient inference.
Wan 2.1 AI performs excellently on benchmarks like VBench, often considered superior or comparable to closed-source models like Sora in certain metrics (e.g., motion smoothness, subject consistency). Its main advantages lie in being open source, consumer hardware support (1.3B model), and unique bilingual text generation. Sora and Veo 2 are likely closed-source, possibly focusing on specific aesthetic qualities or longer video generation, but Wan 2.1 AI offers greater flexibility and efficiency.
While Wan 2.1 AI can generate high-quality videos, like all generative models, output quality can have some instability, occasionally producing artifacts, distortions, or poor detail control (especially in complex scenes or specific styles like portraits). Other limitations include: relatively slower generation speed for larger models, high hardware requirements, and content safety/ethical risks common to open-source models (e.g., lack of built-in watermarking).
You can visit the official GitHub repository for source code, model weights, and detailed usage instructions. The models are also integrated into popular platforms like Hugging Face Hub, Diffusers, ComfyUI, etc., allowing users to directly call or deploy them locally. The community also provides many tutorials and tools.
Wan 2.1 AI code and model weights are open-sourced under the Apache 2.0 license. This means users are free to use, modify, and distribute it, including for commercial purposes, provided they comply with the license terms (e.g., retaining copyright notices and disclaimers).