LoRA Captioning Dilemma: How to Tag Character Traits Without Baking Them In

A Reddit user training a character LoRA (Low-Rank Adaptation for Stable Diffusion) is wrestling with captioning strategy. They have 25 images at 1024x1024 resolution, all consistent in style yet varying in pose, expression, and clothing—enough for a functional, if not fully flexible, LoRA. The core problem: how to caption without causing the model to bake in transient details (like a specific expression or hairstyle) or average them into a generic blob. The user understands the basics—tag camera angle, lighting, objects, background to prevent trigger word overfit—but is stuck on character-specific traits. Hairstyles and expressions define a character, yet explicitly tagging them might force the model to treat them as rigid attributes, reducing flexibility. Alternatively, leaving them untagged could cause the model to average all variations, resulting in a muddy, inconsistent output. The user wants a LoRA that responds naturally to different prompts without needing manual prompt engineering or hyper-specific fine-tuning. This is a classic tension in LoRA training: achieving versatility while preserving character identity.