Training Stable Diffusion with DreamBooth via Diffusers — best practices and hyperparameters

AI Impact Summary

DreamBooth training with Diffusers is feasible but delicate: it requires careful tuning of learning rate, training steps, and prior preservation to prevent overfitting, especially for faces. The post demonstrates the impact of using different schedulers (DDIM vs PNDM), text encoder fine-tuning, and memory-optimization techniques (8-bit Adam, fp16, gradient accumulation), plus hardware considerations (2x 40 GB A100s, 24 GB RAM for the text encoder). For engineering teams, this implies selecting the right training script (train_dreambooth.py) and configuring ops to balance image quality, training time, and cost while ensuring compliance with license and safety considerations.