HunyuanVideo
HunyuanVideo is one of the heavier open-weight video models covered in the local video models index, part of the video generation pillar. It is a text-to-video model first, with image-to-video generation available as well, and it sits at the demanding end of what you can run at home.
What it does
You give it a prompt describing a scene and it produces a short clip. The model is built on the diffusion family of approaches, applied to video rather than single frames: it denoises a compressed representation that covers all the frames at once, so motion is planned as part of the generation instead of being stitched on afterwards. That is the main reason clips from it tend to hold together, with camera moves and subject motion that stay consistent across the length of the clip.
Given a still image plus a prompt, it can animate that image instead of inventing the scene from scratch. That path is useful when you already have a composition you like from an image model and only want to decide how it moves.
Prompts behave much like image prompts with motion added. Describing the subject, the setting, the light and then the movement or camera behaviour separately gets more predictable results than one long run-on sentence.
What it needs
This is the part to be honest about: the larger HunyuanVideo builds ask for more memory than most consumer hardware has comfortably, and which build you can run is the first thing to settle. The weights are large, and video generation adds a second cost on top of that, because every frame in the clip has to be held in memory while it is being denoised. Longer or larger clips push memory use up steeply.
In practice that means VRAM is the binding constraint, and full-precision weights are out of reach for most single-card machines. Quantization is how people actually fit it: reduced-precision builds of the weights circulate widely, and they trade some quality and some fidelity in fine detail for a model that will load at all. Offloading parts of the model to system RAM between steps is the other common tactic, and it works, slowly.
Before you commit to a download this size, it is worth reading will this model fit in memory and checking your card against the GPU pages. A card that runs image models happily can still fall over on video.
Where it fits
Reach for HunyuanVideo when clip quality and motion coherence matter more than iteration speed, and when you have the memory to spare or the patience for offloading.
- Stable Video Diffusion is the lighter image-to-video option and a better starting point if you mostly want to animate stills.
- AnimateDiff works on top of an existing image model, so it reuses checkpoints and styles you already have.
- LTX-Video is the one to try when you want fast turnaround and many attempts per hour.
- Wan sits in similar territory to HunyuanVideo, also large, also text-to-video with an image-to-video path.
Running it
ComfyUI is where most people run it. The node graph model suits video work, because you end up wiring the same generation into different conditioning and post-processing paths and rerunning only the parts that changed. It also supports loading reduced-precision weights and offloading model components, which is exactly what you need here.
Expect setup to take longer than an image model. The weights, the text encoder and the video autoencoder are separate downloads, and they have to be placed where the runtime expects them before anything will load. Keep them somewhere deliberate rather than scattered; organising model files covers a layout that survives multiple runtimes.