Iterative Generation is a workflow approach that uses the output of one generation as the input or reference for subsequent generations, creating chains of refinement where each step builds on and improves upon the previous result. It treats AI generation as a progressive refinement process rather than expecting perfect results from single attempts.

This approach is particularly effective for complex creative goals where the path to the final result isn't clear from the start. By generating initial outputs, evaluating them, and then using them as references or starting points for modified generations, creators can explore directions, discover unexpected possibilities, and progressively zero in on successful results. Iterative generation workflows often combine text prompts with image-to-image techniques, inpainting, and variation generation to refine specific aspects while preserving successful elements.

Understanding iterative generation as a deliberate workflow strategy helps creators approach AI tools with appropriate expectations and working methods. Rather than viewing unsuccessful first attempts as failures, iterative workflows frame them as necessary steps in a refinement process, encouraging systematic exploration and progressive improvement toward creative goals.