Wan

Wan is Alibaba Cloud's open and advanced large-scale video generative model suite, available under the Apache 2.0 license. It is actively maintained and developed as a family of releases rather than a fixed point-in-time version, with multiple model variants and specialized tools expanding the suite over time. The project is organized as a GitHub organization with multiple repositories, with the primary models Wan2.1 and Wan2.2 representing the latest generation. This is a full-featured video generation ecosystem designed for both research and practical production use.

At its core, Wan delivers text-to-video, image-to-video, video editing, text-to-image, and video-to-audio capabilities in a single model suite. This breadth of features means a single model can handle multiple different creative workflows without needing to chain together separate specialized tools. The video editing capability in particular sets it apart from many open video models that only support pure generation: it allows controlled modification of existing video content. Wan also includes VACE (video creation and editing), First-Last-Frame-to-Video generation, and various control mechanisms contributed by the community, making it a genuinely extensible platform.

The model comes in two main sizes. The 1.3B variant requires only about 8.19 GB of VRAM, making it practical for consumer graphics cards like an RTX 4090 or similar. The larger 14B model targets higher-end systems with more memory, trading off resource efficiency for potentially higher quality output. On the smaller model, you can generate a 5-second 480P video on an RTX 4090 in about 4 minutes. Output resolutions include 480P (standard), 720P (supported with varying stability depending on the model variant), and 1080P encoding and decoding capability. This means you can work at standard HD quality locally without cloud infrastructure.

A genuinely distinctive feature of Wan is that it can generate both Chinese and English text directly inside the video frames. Most video generation models struggle with or cannot render readable text at all, so this is a meaningful capability for anyone creating videos with on-screen graphics, captions, or subtitles in either language. The technical implementation uses an advanced VAE (variational autoencoder) that encodes and decodes unlimited-length 1080P video while preserving temporal information, which is the underlying reason it can handle this text rendering more robustly than simpler architectures.

Under the hood, Wan uses a Flow Matching framework based on Diffusion Transformers, a modern approach to generative modeling. This architecture choice influences both the quality of the output and the stability of generation across different model sizes. The model is trained and maintained by Alibaba Cloud's research team, so you are getting the benefit of significant corporate R&D investment behind a genuinely open Apache 2.0 license, not a permissively-licensed fork or a research-only release with restrictions.

Installation and usage follow standard open-source Python practices. You clone the repository, install dependencies (PyTorch and related packages), download the model weights, and run the generation code. The setup is straightforward enough for anyone comfortable with Python and pip, though the initial model weight downloads are substantial. Unlike purely cloud-based solutions, you run everything locally once set up, giving you full control and privacy over your generations. There is no API quota, no monthly fees, and no usage limits beyond your local hardware.

For text-to-video specifically, you provide a text prompt and optionally control parameters like resolution and duration. The model generates frames progressively, and the quality improves with the larger model variant. For image-to-video, you supply a static image as the starting frame and a text prompt describing how you want it to animate. The video editing mode works differently: you provide a source video and a prompt describing the change you want to make, and Wan modifies the content while preserving the structure. This is particularly useful for correcting parts of a generated video or making targeted adjustments without regenerating the entire sequence. The First-Last-Frame mode is a specialized approach where you specify how the video should start and end, and Wan fills in the coherent frames between those endpoints. This gives you strong compositional control over the output without needing to iterate through multiple full generations.

Compared to other open video models, Wan occupies a strong middle ground. Stable Video Diffusion focuses on image-to-video with shorter, more limited outputs. CogVideoX and HunyuanVideo (both Chinese models like Wan) offer different capabilities and efficiency profiles; the choice between them depends on whether you prioritize text rendering, specific output resolutions, or particular control modalities. AnimateDiff takes a different architectural approach via attention mechanisms rather than full diffusion models. Mochi is newer and represents recent research in the space but has a different license and capability set. Wan's particular strengths are the combination of text rendering, video editing, a small efficient variant for consumer GPUs, and active development across multiple model sizes.

The project's maintenance and evolution matter practically. Wan releases new model variants and capabilities on a regular cadence, which means you are not stuck with a static version. This also means the community contributes extensions, optimizations, and integrations over time. Many open video model projects stall after an initial release; Wan's org structure and multiple repositories suggest sustained development momentum.

Who should use Wan depends on your specific needs. If you need to generate videos with readable text overlaid, especially in Chinese, this is one of the few open models that delivers that. If you have a consumer GPU and want to experiment with video generation without cloud costs, the 1.3B model fits that profile well. If you are building video generation into a product or workflow and want full control and no external dependencies, the open-source, locally-runnable architecture is a major advantage. If you need high-volume production use and can afford larger GPUs, the 14B model scales your quality without requiring a proprietary API. The video editing capability makes it suitable for workflows where you want to modify rather than only generate from scratch.

Wan is a mature, well-maintained open video model suite that covers diverse use cases including text-to-video, image-to-video, video editing, and multilingual text rendering, runs efficiently on consumer hardware at small scale, and uniquely handles text in both Chinese and English. The active development and multiple capabilities make it a natural choice for anyone building on open video generation technology who needs breadth and control.