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🌟 Master Stable Diffusion XL Training on Kaggle for Free! 🌟 Welcome to this comprehensive tutorial where I'll be guiding you through the exciting world of setting up and training Stable Diffusion XL (SDXL) with Kohya on a free Kaggle account. This video is your one-stop resource for learning everything from initiating a Kaggle session with dual T4 GPUs to fine-tuning your SDXL model for optimal performance. #Kaggle #StableDiffusion #SDXL Notebook ⤵️ Tutorial GitHub Readme File ⤵️ 0:00 Introduction To The Kaggle Free SDXL DreamBooth Training Tutorial 2:01 How to register Kaggle account and login 2:26 Where to and how to download Kaggle training notebook for Kohya GUI 2:47 How to import / load downloaded Kaggle Kohya GUI training notebook 3:08 How to enable GPUs and Internet on your Kaggle session 3:52 How to start your Kaggle session / cloud machine 4:02 How to see your Kaggle given free hardware features 4:18 How to install Kohya GUI on a Kaggle notebook 4:46 How to know when the Kohya GUI installation has been completed on a Kaggle notebook 5:00 How to download regularization images before starting training 5:22 Introduction to the classification dataset that I prepared 6:35 How to setup and enter your token to use Kohya Web UI on Kaggle 8:20 How to load pre-prepared configuration json file on Kohya GUI 8:48 How to do Dataset Preparation after configuration loaded 8:59 How to upload your training dataset to your Kaggle session 9:12 Properties of my training images dataset 9:22 What kind of training dataset is good and why 10:06 How to upload any data to Kaggle and use it on your notebook 10:20 How to use previously composed Kaggle dataset in your new Kaggle session 10:34 How to get path of session included dataset 10:44 Why do I train with 100 repeating and 1 epoch 10:54 Explanation of 1 epoch and how to calculate epochs 11:23 How to set path of regularization images 11:33 How to set instance prompt and why we set it to a rare token 11:46 How to set destination directory and model output into temp disk space 12:29 How to set Kaggle temporary models folder path 13:07 How many GB temporary space do Kaggle provides us for free 13:23 Which parameters you need to set on Kohya GUI before starting training 13:33 How to calculate the N number of save every N steps parameter to save checkpoints 13:45 How to calculate total number of steps that your Kohya Stable Diffusion going to take 14:10 If I want to take 5 checkpoints what number of steps I need calculation 14:33 How to download saved configuration json file 14:43 Click start training and training starts 14:55 Can we combine both GPU VRAM and use as a single VRAM 15:05 How we are setting the base model that it will do training 15:55 The SDXL full DreamBooth training speed we get on a free Kaggle notebook 16:51 Can you close your browser or computer during training 17:54 Can we download models during training 18:26 Training has been completed 18:57 How to prevent last checkpoint to be saved 2 times 19:30 How to download generated checkpoints / model files 21:11 How you will know the download status when downloading from Kaggle working directory 22:03 How to upload generated checkpoints / model files into Hugging Face for blazing fast upload and download 25:02 Where to find Hugging Face uploaded models after upload has been completed 26:54 Explanation of why generated last 2 checkpoints are duplicate 27:27 Hugging Face upload started and the amazing speed of the upload 27:49 All uploads have been completed now how to download them 29:02 Download speed from Hugging Face repository 29:17 How to terminate your Kaggle session 29:36 Where to see how much GPU time you have left for free on Kaggle for that week 29:46 How to make a fresh installation of Automatic1111 SD Web UI 31:05 How to download Hugging Face uploaded models with wget very fast 31:57 Which settings to set on a freshly installed Automatic1111 Web UI, e.g. VAE quick selection 32:07 How to install after detailer (adetailer) extension to improve faces automatically 32:51 Why you should add --no-half-vae to your command line arguments 33:05 How to start / restart Automatic1111 Web UI 33:37 How switch to the development branch of Automatic1111 Web UI to use latest version 34:24 Where to download amazing prompts list for DreamBooth trained models 35:07 How to use PNG info to quickly load prompts 35:52 How to do x/y/z checkpoint comparison to find the best checkpoint of your SDXL DreamBooth training 38:09 How to make SDXL work faster on weak GPUs 38:37 How to analyze results of x/y/z checkpoint comparison to decide best checkpoint 42:06 How to obtain better images 42:20 How to install TensorRT and use it to generate images very fast with same quality 44:41 How to use amazing prompt list as a list txt file
