LTX-Video
LTX-Video is a video model designed around turnaround time. The goal is clips that arrive quickly enough to treat generation as an iterative process rather than a commitment. It is one of the entries in the video models index, part of the video pillar.
What it does
LTX-Video generates a clip from a text prompt, and it can also work from a starting image when you want to pin down the opening frame. The distinguishing feature is not what it can be asked for, it is how long you wait for the answer.
The speed comes from architecture rather than from cutting corners at run time. The model works in a heavily compressed latent representation of video, so each step of the diffusion process moves much less data than it would if frames were handled at full size. Fewer bytes per step means more clips per hour.
That changes how you work with it. With a slow model you write a careful prompt, start a generation and go and do something else, and a mistake costs you the whole wait. With a fast one you generate, look, adjust one thing and generate again, which is how people actually find the shot they wanted. Prompting for video rewards that loop, because motion is much harder to predict from a description than composition is.
What it needs
Speed is not the same as being light. The model still holds a sequence of frames during inference, so VRAM remains the constraint that decides whether you can run it comfortably.
In general terms:
- Memory use rises with clip length and frame size together, the same as other video models.
- Reduced-precision weights are the usual route to fitting it on a smaller card, and matter more here than for image work because there is more to hold.
- The time saving shows up per generation, so it compounds across a session rather than making any single clip trivially cheap.
A card with generous memory gives you longer clips and bigger frames; a mid-range card usually gets you short clips at a modest size. Our GPU pages cover the specifics per card.
Where it fits
Reach for LTX-Video when you are exploring. Finding a good clip usually takes several attempts, and a model that answers quickly lets you spend those attempts in one sitting. It is also a good fit when you want to go straight from an idea to a clip without producing a still first.
Choose Stable Video Diffusion when you already have the exact frame you want animated and control of composition matters more than throughput. Choose AnimateDiff when the clip needs to carry the style of an image checkpoint and LoRAs you have already built around.
Both text-to-video and image-to-video apply here, depending on how you start.
Running it
ComfyUI is where most people run it, using the graph layout the model ships guidance for: load the weights, write the prompt, optionally supply a starting image, and decode the result out to frames. It is a conventional generation graph, and the node vocabulary is the same one you would use for any other model in that runtime.
Because generations are quick, it is worth setting up a workflow you can rerun with one change at a time, and keeping the prompts that worked. The bottleneck stops being the hardware and starts being your own note-keeping.