Low-Rank Adaptation (LoRa) allows a single base AI model to be efficiently fine-tuned into multiple, distinct specialist models. This is a powerful strategy for companies needing varied editing capabilities, such as for different client aesthetics, without the high cost of training and maintaining separate large models.
For creating specific image editing capabilities with AI, a small, curated dataset of "before and after" examples yields better results than a massive, generalized collection. This strategy prioritizes data quality and relevance over sheer volume, leading to more effective model fine-tuning for niche tasks.