Wan AI is Alibaba's open-source video generation platform that creates high-quality videos from text prompts, images, or audio inputs. Built on a diffusion transformer architecture, it supports text-to-video, image-to-video, and speech-to-video generation up to 1080p with synchronized audio and lip-sync. Wan works for marketers, content creators, filmmakers, and developers who want cinematic output without expensive proprietary tools. You can use the hosted web platform or self-host the open-weight models under Apache 2.0.

• Text-to-Video Generation: Turn written prompts into AI-generated video with Wan2.2's T2V models, supporting 480p and 720p output.
• Image-to-Video Generation: Animate still images with text-guided motion using the I2V-A14B model.
• Text-and-Image-to-Video: Generate video using both a written prompt and an input image with the TI2V-5B model, which supports 720p at 24 FPS.
• Speech-to-Video: Create videos driven by audio, reference images, and text prompts with the S2V-14B model. It also supports TTS through CosyVoice.
• Character Animation: Animate or replace characters using the Wan2.2-Animate-14B model, with support for pose, face, background, and mask inputs.
• MoE Architecture: Wan2.2 uses a Mixture-of-Experts architecture designed to increase model capacity while keeping computational costs manageable.
• 720p Video at 24 FPS: The TI2V-5B model supports 720p video generation at 24 frames per second.
• Open Model Ecosystem: Wan2.2 provides model weights and inference code and has integrations with ComfyUI and Diffusers.

• Strong Benchmark Performance: Scores 86.22% on VBench, beating Sora (84.28%) on complex motion and physics simulation.
• Open-Source Flexibility: Apache 2.0 license lets developers self-host, fine-tune, and use commercially without vendor lock-in.
• Cinematic Quality: Users report film-grade output with detailed hair physics, realistic motion, and multi-style support.
• Audio-Driven Video: The Wan2.2 family includes an S2V-14B model for generating video from speech and other inputs.
• Flexible Deployment: Developers can run Wan models locally and connect them with tools such as ComfyUI and Diffusers.

• High Hardware Requirements: Local generation can require substantial GPU memory, especially for the 14B models.
• Learning Curve: Advanced features and prompt engineering take time to master, and results depend heavily on prompt quality.
• Variable Generation Speed: Generation times can vary widely based on the model, resolution, GPU, and memory settings. Community tests show significant differences between standard and accelerated workflows.
• Demanding Local Setup: Installing dependencies, downloading large model files, configuring memory options, and troubleshooting compatibility can take time.

• Marketing Video Ads: Marketing teams create polished product demos and social media ads at low cost, keeping consistent style across a campaign without a full production budget.
• Cinematic Previsualization: Filmmakers and directors generate 5-second draft clips to test motion, lighting, and camera angles before committing to full production.
• Audio-driven Characters: Generate character videos driven by speech and audio, including supported lip-sync and motion workflows.
• Product Animation: E-commerce sellers animate static product photos into dynamic short clips for Instagram, TikTok, and product pages.
• Educational Content: Educators develop illustrated video lessons and demonstrations from text descriptions without needing video production skills.
• Developer Prototyping: Developers self-host the open-source model to build custom AI video features into their own apps and workflows.