Fine-Tuning a Diffusion Model with Your Photos

Overview

Learn how to fine-tune a diffusion model using your own photos in this comprehensive 52-minute video tutorial. Explore the Flux Schnell model, select appropriate GPUs, and navigate through the fine-tuning process using LoRA. Discover tips for running the model in Google Colab, setting up tensorboard logging, and generating images with your custom LoRA adapter. Gain insights into the inner workings of diffusion models, including basic concepts, latent space diffusion, and Variational Autoencoders. Delve into the FLUX model architecture, understanding components like CLIP, T5, transformers, and VAE. Examine diffusion steps, model size considerations, and noise removal approaches. Access additional resources and support through provided links to enhance your learning experience.

Syllabus

Introduction to Fine-tuning Diffusion Models
Video Overview
Flux Schnell and Flux Dev Overview
Picking a GPU for fine-tuning Flux
Fine-tuning notebooks for diffusion models
Installation
Choosing photos for a training dataset
Running inference before fine-tuning generating images
Tips for running in Google Colab
Running fine-tuning of Flux Schnell using LoRA
Setting up tensorboard logging
Inspecting the training results
Generating images with your LoRA adapter
Explaining how diffusion models like FLUX work
Basic diffusion models
Diffusion in “latent space”
How Variational Autoencoders work
FLUX model architecture - putting it all together CLIP, T5, transformer, VAE
Diffusion steps, Model size, Noise Removal Approaches Flow, Guided generation
Video Resources trelis.com/ADVANCED-vision

Taught by

Trelis Research

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