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.
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.