Train your ControlNet with diffusers: end-to-end workflow for Stable Diffusion conditioning

AI Impact Summary

This piece documents an end-to-end workflow for training a ControlNet-conditioned diffusion model using the Hugging Face diffusers stack. It covers using a SPIGA facial-landmarks model to generate conditioning masks from the FaceSynthetics dataset, crafting a dataset with ground-truth images, conditioning images, and captions, and running train_controlnet.py with stabilityai/stable-diffusion-2-1-base as the base model. The guide also details practical GPU considerations (8 GB VRAM minimum, A100 example) and performance tips (xformers memory-efficient attention, Weights & Biases for experiment tracking, and a 3-epoch vs 1-epoch training trade-off that led to overfitting). This is valuable for teams building domain-specific generative capabilities but signals substantial compute and data preparation effort, plus the risk of overfitting to synthetic data and 3D-looking artifacts if not tuned.