Generation consistency measures whether quality, identity, style, structure, and instruction following remain dependable rather than appearing only in selected outputs. It matters because stochastic generation can create different results from the same or similar inputs.
Evaluation should preserve ordinary failures instead of showing only the strongest sample. Repeated generations, fixed and varied seeds, representative prompts, and success-rate criteria help estimate how much iteration a real workflow will require.
ELI5
Generation consistency means an AI model produces good, usable results regularly instead of only occasionally. A creator needs to know whether quality survives repeated attempts.
For example, if a portrait prompt works beautifully once but changes the person's face in most other clips, the model is not consistent for that job. Testing several ordinary runs gives a more honest picture than selecting the best one.
