Concept LoRAs #
Concept LoRAs teach Stable Diffusion models to understand and generate specific objects, creatures, or abstract concepts that weren’t well-represented in the original training data. These adaptations modify the model’s understanding of semantic relationships and visual features associated with particular subjects. For example, a concept LoRA might help the model better generate specific architectural elements, unique creatures, or particular objects with consistent characteristics. Concept LoRAs typically require more diverse training data than style LoRAs (usually 50-100 images) to capture different angles, contexts, and variations of the subject matter. They work by adjusting both the model’s cross-attention and feed-forward layers to better recognize and reproduce the defining features of the target concept.