Stable Diffusion Cannot Do This Until You Add LoRA | By Dr Mohan Dash

Описание: #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:    • Diffusion Based Image Generation with Diff...  

All code used in this video is available here: https://github.com/mohan696matlab/Dif...

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🔗Links🔗
LinkedIn:   / balyogi-mohan-dash  
GitHub: https://github.com/mohan696matlab
Google Scholar: https://scholar.google.com/citations?...

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Here is the cleaned-up version with only the chapter titles and timestamps:

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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