Wan
Wan is a family of open-weight video models, one of the larger options in the local video models index and part of the video generation pillar. Like the other big model on this list it does text-to-video as its main job, with an image-to-video path alongside it, and it expects a generous amount of memory.
What it does
From a text prompt, Wan generates a short clip: it works in the diffusion tradition, denoising a compressed latent that spans the whole clip rather than producing frames one at a time. Because motion is part of what gets denoised, the result usually reads as one continuous shot instead of a sequence of stills that happen to be adjacent.
Hand it a still image and a prompt and it will animate that image, which is the mode most people use when they have already settled on a composition in an image model and want to keep it.
The weights have been published in more than one size. That matters more here than it does for most models, because the smaller build is within reach of hardware that cannot load the larger one at all, at some cost in detail and prompt adherence. If you are unsure which you can run, start with the smaller one, get a workflow you trust, then decide whether the larger one is worth the wait.
Wan is also notable for handling prompts in more than one language, so you are not forced into English phrasing to get reliable results.
What it needs
Say it plainly: the larger Wan weights ask for more memory than most consumer hardware has comfortably. Video generation spends memory on two things at once, the model itself and the frames being denoised, and the second cost grows with clip length and frame size. It is easy to load a model successfully and then run out of memory halfway through a longer generation.
VRAM is the number that decides whether you can play, and there is no substitute for having enough of it. Quantization is the usual way people fit these weights onto a single card: reduced-precision builds circulate for both sizes, and they make the difference between a model that runs and one that does not. Offloading model components to system RAM is the fallback when quantization alone is not enough, at a real cost to speed.
Work out the budget before downloading anything: will this model fit in memory walks through the arithmetic, and the GPU pages cover what the common cards have to work with.
Where it fits
Wan suits you if you want a general text-to-video model that can also animate stills, you are willing to wait on each inference run, and you have either a large card or the patience to work around a small one.
- Stable Video Diffusion is lighter and image-to-video only, a reasonable first stop.
- AnimateDiff plugs into image checkpoints you already have, which keeps downloads small and styles familiar.
- LTX-Video is built around fast iteration, so it is the better choice when you want many attempts.
- HunyuanVideo is the closest comparison on this list, similar in shape and similar in appetite.
Running it
ComfyUI is the practical home for Wan locally. Its graph layout lets you keep the text-to-video and image-to-video paths side by side in one workflow and swap only the conditioning, and it handles reduced-precision weights and component offloading without extra tooling.
Plan for a multi-part download. The model weights, the text encoder and the video autoencoder arrive separately, and the runtime only finds them if each lands in the directory it expects. Pick a layout and stick to it; organising model files suggests one that holds up when you add a second runtime later.