Two-stage image pipeline offers reliable character consistency without LoRA training

Developers building AI apps that generate the same character across multiple scenes often struggle with face drift when using seeds or prompt-only methods. A two-stage pipeline addresses this by first generating one canonical base image, then using an image-edit model with that base as a reference for every subsequent scene. Unlike seeds, which only reproduce identical inputs, or LoRA fine-tuning, which requires curating dozens of images and running training jobs per character, this approach conditions each new generation on the actual reference pixels. The edit model is instructed only on what should change — pose, setting, lighting — while identity is preserved through the input image itself. This method is described as scalable for production apps where users create characters on demand, and works for non-human subjects such as animals, robots, or animated characters as well.