How to Keep AI-Generated Characters Consistent Across Multiple Images

Maintaining a consistent character identity in AI image generation requires deliberate conditioning, as diffusion models have no persistent memory between generations. Methods range from fixed seeds and detailed prompts — which are free but unreliable for true identity — to image-prompt adapters and face-specific embeddings that transfer identity without training. The most reliable approach is a trained LoRA model, which requires hours of work but preserves both facial and non-facial features like costumes or body type. A practical workflow involves generating a single anchor image, using a reference adapter to produce 100–200 varied outputs, and selecting only the most consistent ones to train the LoRA. This loop of generate, filter, and retrain sidesteps the core problem of needing a real photoshoot to build a character dataset.