Training a text-to-video generation system

Plan a text-to-video training workflow: choose compatible model components, prepare captioned clips, measure preprocessing and training work, and evaluate both individual frames and motion over time.

A text-to-video model turns a description into a sequence of frames. A plausible frame is only part of the task: objects should persist, movement should agree with the prompt, and the scene should remain coherent as the camera or subjects move. These requirements matter in applications such as educational demonstrations, animation, and scene prototyping.

Video adds a time dimension to image generation. For a hypothetical clip of a person picking up a cup, evaluate whether the cup stays visible, the hand follows a plausible path, and the pickup actually occurs. A set of attractive but unrelated frames would fail that request.

Use Mochi 1 preview as the architectural reference in this lesson. Workload figures below are separate planning assumptions, not a report of its original training run.