Diffusion Based Image Generation with Diffuser (Full Course) - Stable Diffusion Cannot Do This Until You Add LoRA | By Dr Mohan Dash

#stablediffusion #aiart #python #generativeai #diffusion #pytorch I am Dr. Balyogi Mohan Dash, and I welcome you to Intelligent Machines! Struggling to get Stable Diffusion to generate exactly what you imagine instead of vague or incorrect results? This video solves that problem by showing how diffusion models can be precisely personalized using modern fine-tuning techniques. This tutorial takes a deep, practical look at diffusion model personalization, starting with a clear comparison of four major fine-tuning methods: DreamBooth, Textual Inversion, HyperNetworks, and LoRA (Low-Rank Adaptation). Each method is explained with its strengths, limitations, and real-world trade-offs. The focus then shifts to why LoRA has become the dominant approach for custom concepts: fast training, minimal storage, and strong results with very few images. Using a real-world example, the video demonstrates how to teach Stable Diffusion what a platypus actually looks like, a concept the base model struggles with. You will see how to load pre-trained LoRA weights using the Hugging Face Diffusers library, control LoRA strength during inference, apply textual inversion for better negative prompting, and finally train a custom LoRA model from scratch using your own dataset. Step-by-step walkthrough of training a custom LoRA model 📘 Full playlist on diffusion: https://youtube.com/playlist?list=PLoSULBSCtofearln-pGND44nr69FE9eIM&si=LATKNLGwu0NULoly All code used in this video is available here: https://github.com/mohan696matlab/Diffusion_Gen_AI_Course Subscribe to the channel to follow this complete course and master diffusion models from theory to practical implementation. 🔗Links🔗 LinkedIn: https://www.linkedin.com/in/balyogi-mohan-dash/ GitHub: https://github.com/mohan696matlab Google Scholar: https://scholar.google.com/citations?user=jzcIElIAAAAJ&hl=en WhatsApp inquiries are currently closed. Please reach out via email for any questions: [email protected] Here is the cleaned-up version with only the chapter titles and timestamps: __ Related Videos: Stable Diffusion: Text-to-Image, Image-to-Image & Inpainting Explained https://youtu.be/pucHB7i2IFE Image-to-Image Diffusion Tutorial | Diffusion Course | By Dr Mohan Dash https://youtu.be/-nSAel8dXFo --- 00:00 Problem of Generating Specific Images with Diffusion 00:10 Overview of Diffusion Model Fine-Tuning Methods 00:20 Comparison of DreamBooth, Textual Inversion, Hypernetworks, and LoRA 01:44 DreamBooth Method Explained 03:24 Textual Inversion Explained 04:05 Hypernetworks Explained 04:42 LoRA Method and Its Advantages 05:33 Focus on LoRA and Practical Workflow 06:02 Baseline Stable Diffusion Results on Platypus 06:52 Loading and Using a LoRA Model 07:27 Controlling LoRA Strength During Inference 08:36 Textual Inversion for Negative Prompts 10:25 Preparing a Dataset for LoRA Training 11:11 Model Components and Training Setup 12:48 Dataset and DataLoader Construction 14:01 Latent and Text Embedding Processing 15:13 Adding and Configuring LoRA Weights 16:26 Optimizer and Training Preparation 17:19 LoRA Training Loop Explained 19:45 Saving LoRA Checkpoints 20:26 Inference with Trained LoRA Models 21:10 Effects of LoRA Scale and Training Steps 21:38 Style Transfer with Trained LoRA 21:58 Summary and Conclusion