Learn how to personalize diffusion models using the DreamBooth methodology. You’ll prepare small, targeted datasets for training custom concepts and styles, and understand how prior-preservation loss helps maintain model generalization. You’ll also apply hyperparameter strategies to balance creativity with stability and practice managing checkpoints and merging techniques. These skills give you the ability to adapt diffusion models to unique styles and use cases, making fine-tuning directly relevant to real-world creative and professional projects.

Learn how to use ComfyUI to design and manage advanced workflows for diffusion models. You’ll set up the environment, navigate the node-based interface, and load custom fine-tuned models into your pipelines. You’ll also practice building complex generation workflows with extensions like ControlNet, giving you a flexible, visual way to experiment and produce consistent, high-quality results. These skills make workflow design more efficient and directly applicable to real-world creative and production settings.

Learn how to optimize fine-tuned diffusion models so they’re reliable in real production environments. You’ll adjust inference settings like steps, CFG scale, and batch size to balance speed, quality, and resource use, and practice testing how small tweaks can dramatically improve results. You’ll also adapt workflows for deployment, gaining practical skills to deliver outputs that are both efficient and production-ready. These techniques give you the ability to make informed trade-offs that directly impact performance in real-world projects.