This specialized course focuses on mastering DreamBooth and LoRA to train custom Stable Diffusion models for personalized AI image generation. You will learn how to train Stable Diffusion on your own face or any subject, allowing you to generate consistent, high-quality AI artwork with full creative control.

The course covers DreamBooth training locally and via Google Colab, including low-VRAM optimization techniques (6GB–10GB VRAM), faster training workflows, and multi-concept training. You will understand how to structure datasets, choose training parameters, and avoid common mistakes that reduce model quality.

In addition, you will learn how to convert Diffusers models to CKPT format for compatibility with Automatic1111 WebUI and other interfaces. The course also explores the differences between DreamBooth, Embeddings (Textual Inversion), and LoRA models—helping you choose the right approach depending on your hardware and project goals.

You’ll also integrate Img2Img and pose control techniques to create dynamic compositions and consistent character poses.

By the end of this course, you will confidently train, optimize, and deploy custom Stable Diffusion models for professional AI art, branding, content creation, and character design.