Getting started with local video generation

Local video generation means running a model on your own machine that turns a prompt, or a still image, into a short moving clip. This page is the entry point to the video pillar: what the field looks like, what it asks of your hardware, and how to get a first result without wasting a weekend.

Be clear about the scale first. Local video models produce clips measured in seconds, not scenes, not sequences, and certainly not long-form footage. People make longer pieces by generating many short clips and editing them together, but the model itself only ever gives you a fragment at a time. If you are expecting to type a paragraph and receive a finished film, video generation of any kind will disappoint you, local or not.

What makes video harder than images

A video model is doing image generation over and over, once per frame, and then solving a second problem on top: the frames have to agree with each other. A face that shifts between frames, a background that changes colour halfway through, a hand that grows a finger and loses it again, all of these are failures unique to video. Models handle temporal consistency with extra structure, attention that reaches across frames and latents that hold a whole clip rather than a single picture, and that structure costs memory.

That is why VRAM is a harder wall here than it is in image generation. In image work the model weights dominate what you need to fit; if the weights fit, you are usually fine. In video the model has to hold state for every frame in the clip at once, so your memory use climbs with clip length as well as with resolution. Running out mid-generation is the ordinary failure mode, not an edge case.

Memory bandwidth matters more too. Every denoising step touches the whole clip's worth of latent data instead of one frame's worth, so the same diffusion process that felt quick on a still image feels slow when it is moving a much larger block of numbers back and forth. A card that generates images comfortably can still feel sluggish on video.

The shapes a video model comes in

Most local video work falls into three shapes, covered in more depth under techniques:

Text to video: a prompt goes in, a clip comes out. The most open-ended and the least controllable, because you are asking the model to invent both the subject and its motion.
Image to video: you supply a starting still and the model animates it. You have already decided what the scene looks like, so the model only has to decide how it moves. This is where most people get usable results soonest.
Video to video: an existing clip goes in and comes out restyled, upscaled, or extended. Frame interpolation belongs to this family, generating in-between frames to make short or choppy output play more smoothly.

Many models support more than one of these, and a typical workflow chains them: generate a still, animate it, then interpolate or upscale the result.

What you need

No single number answers this, but the shape of the requirement is consistent.

Video wants noticeably more memory than image generation at the same resolution, because of the per-frame state described above.
Clip length trades against resolution. If a clip will not fit, you shorten it or you shrink it, and those are usually your only two levers besides quantization.
Generation time scales with the number of frames. Doubling the length of a clip roughly doubles the work, on top of however long a single frame would have taken.

If you are choosing or assessing hardware, start with GPUs for local AI and the build guides. To work out whether a specific model will run on the machine you already own, will this model fit in memory walks through the arithmetic.

Where the models are

The video models index covers the families worth knowing: Stable Video Diffusion for image to video, AnimateDiff as a motion layer over existing image models, LTX-Video for fast generation on modest hardware, and the HunyuanVideo and Wan families, published in several sizes, so the memory you have decides which build you run. Each page lists what the model does, what it needs, and what it is bad at.

A realistic first attempt

Start with image to video. Take a still you already like, ideally one you generated yourself so you know the prompt that produced it, and ask a model to animate it. Keep the clip as short as the workflow allows. Short clips fail faster, and failing fast is the whole point at this stage.

Most local video work happens in ComfyUI, because video pipelines are naturally graphs: load model, encode image, sample, decode frames, assemble. If you have already used it for image work, the video nodes will feel familiar.

Expect several attempts. Motion is much harder to steer with words than composition is, and the gap between a clip that technically generated and a clip you would show someone is wide. Your first few results will drift, flicker, or barely move at all. Change one thing at a time, keep the settings that worked, and treat each run as a sample rather than a final render. Once a short clip behaves, push the length or the resolution up, one step at a time, until inference stops fitting in memory. That point is your ceiling, and knowing where it sits is more useful than any published figure.