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Zero To Hero Generative AI - Become A Master

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  1. Lecture 1 : How To Install Python, Setup Virtual Environment VENV, Set Default Python System Path & Install Git
  2. Lecture 2 : Essential AI Tools and Libraries: A Guide to Python, Git, C++ Compile Tools, FFmpeg, CUDA, PyTorch
  3. Lecture 3 : Zero to Hero ControlNet Tutorial: Stable Diffusion Web UI Extension | Complete Feature Guide
  4. Lecture 4 : How To Find Best Stable Diffusion Generated Images By Using DeepFace AI - DreamBooth / LoRA Training
  5. Lecture 5 : Generate Studio Quality Realistic Photos By Kohya LoRA Stable Diffusion Training - Full Tutorial
  6. Lecture 6 : The END of Photography - Use AI to Make Your Own Studio Photos, FREE Via DreamBooth Training
  7. Lecture 7 : How To Use Stable Diffusion X-Large (SDXL) On Google Colab For Free
  8. Lecture 8 : Stable Diffusion XL (SDXL) Locally On Your PC - 8GB VRAM - Easy Tutorial With Automatic Installer
  9. Lecture 9 : Ultimate RunPod Tutorial For Stable Diffusion - Automatic1111 - Data Transfers, Extensions, CivitAI
  10. Lecture 10 : How To Use SDXL On RunPod Tutorial. Auto Installer & Refiner & Amazing Native Diffusers Based Gradio
  11. Lecture 11 : ComfyUI Tutorial - How to Install ComfyUI on Windows, RunPod & Google Colab | Stable Diffusion SDXL
  12. Lecture 12 : First Ever SDXL Training With Kohya LoRA - Stable Diffusion XL Training Will Replace Older Models
  13. Lecture 13 : How To Use SDXL in Automatic1111 Web UI - SD Web UI vs ComfyUI - Easy Local Install Tutorial / Guide
  14. Lecture 14 : Mind-Blowing Deepfake Tutorial: Turn Anyone into Your Favorite Movie Star! PC & Google Colab - roop
  15. Lecture 15 : How to use Stable Diffusion X-Large (SDXL) with Automatic1111 Web UI on RunPod - Easy Tutorial
  16. Lecture 16 : Become A Master Of SDXL Training With Kohya SS LoRAs - Combine Power Of Automatic1111 & SDXL LoRAs
  17. Lecture 17 : How To Do SDXL LoRA Training On RunPod With Kohya SS GUI Trainer & Use LoRAs With Automatic1111 UI
  18. Lecture 18 : How to Do SDXL Training For FREE with Kohya LoRA - Kaggle - NO GPU Required - Pwns Google Colab
  19. Lecture 19 : How Use Stable Diffusion, SDXL, ControlNet, LoRAs For FREE Without A GPU On Kaggle Like Google Colab
  20. Lecture 20 : Turn Videos Into Animation With Just 1 Click - ReRender A Video Tutorial
  21. Lecture 21 : Turn Videos Into Animation / 3D Just 1 Click - ReRender A Video Tutorial - Installer For RunPod
  22. Lecture 22 : Double Your Stable Diffusion Inference Speed with RTX Acceleration TensorRT: A Comprehensive Guide
  23. Lecture 23 : How to Install & Run TensorRT on RunPod, Unix, Linux for 2x Faster Stable Diffusion Inference Speed
  24. Lecture 24 : SOTA Image PreProcessing Scripts For Stable Diffusion Training - Auto Subject Crop & Face Focus
  25. Lecture 25 : Fooocus Stable Diffusion Web UI - Use SDXL Like You Are Using Midjourney - Easy To Use High Quality
  26. Lecture 26 : How To Do Stable Diffusion XL (SDXL) DreamBooth Training For Free - Utilizing Kaggle - Easy Tutorial
  27. Lecture 27 : Essential AI Tools and Libraries: A Guide to Python, Git, C++ Compile Tools, FFmpeg, CUDA, PyTorch
Lesson 26 of 27
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Lecture 26 : How To Do Stable Diffusion XL (SDXL) DreamBooth Training For Free – Utilizing Kaggle – Easy Tutorial

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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 ⤵️
https://www.patreon.com/posts/kohya-sdxl-lora-88397937

Tutorial GitHub Readme File ⤵️
https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Tutorials/Full-Stable-Diffusion-XL-SDXL-DreamBooth-Training-Tutorial-On-Kaggle.md

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