Z Image Turbo LoRA training: Captioning tricks for character consistency

A Redditor (u/arthropal) shared detailed experiments training character LoRAs with Z Image Turbo (ZIT), a fast image generation model. They discovered that captioning certain traits directly fights character consistency. For animal-girl characters (cat girl, mouse girl), tagging ears and tails made the model treat them as changeable features rather than fixed attributes. The fix: omit those tags entirely. Similarly, for plus-sized human characters, captioning anything about body type caused the model to resist, as long as the dataset was clean (no thin versions sneaking in). The user employed a Qwen edit workflow to generate studio turnaround shots and candid poses from a single face, building a robust dataset. These lessons underscore a critical principle in LoRA training: stable character traits must be implicit in the training images, not explicitly labeled.

The most challenging case was a horror-themed character meant to juxtapose childhood innocence with supernatural gore. The initial LoRA tilted toward 'girl in Halloween costume' rather than truly disturbing horror. When the user created bloodier versions of the same character and retrained, the model refused to produce a child-like figure, instead outputting a young adult. The 'gravity well' of bloody imagery pulled the result away from the intended youthful look, even though the prompts included child-like cues. This highlights a trade-off in mixture-of-concepts LoRAs: strong visual signals (blood, gore) dominate subtler ones (youth, innocence) when captions don't explicitly separate them. The user acknowledges this is an open challenge, contributing field notes for the AI art community.