Back to Course

Zero To Hero Generative AI - Become A Master

0% Complete
0/0 Steps
  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 5 of 27
In Progress

Lecture 5 : Generate Studio Quality Realistic Photos By Kohya LoRA Stable Diffusion Training – Full Tutorial

Select languageDADENLENFRHIINITJANOPTESSVTHZH

#Kohya SS web GUI DreamBooth #LoRA training full tutorial. You don’t need technical knowledge to follow this tutorial. In this tutorial I have explained how to generate professional photo studio quality portrait / self images for free with Stable Diffusion training.

Our Discord server ⤵️
https://bit.ly/SECoursesDiscord

If I have been of assistance to you and you would like to show your support for my work, please consider becoming a patron on 🥰 ⤵️
https://www.patreon.com/SECourses

Technology & Science: News, Tips, Tutorials, Tricks, Best Applications, Guides, Reviews ⤵️

Playlist of #StableDiffusion Tutorials, Automatic1111 and Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Pix2Pix, Img2Img ⤵️

Gist file used in tutorial ⤵️
https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Tutorials/Generate-Studio-Quality-Realistic-Photos-By-Kohya-LoRA-Stable-Diffusion-Training-Full-Tutorial.md

How to install Python and Git tutorial ⤵️

Master DreamBooth tutorial to learn rare tokens, instance prompt, class prompt and such ⤵️

How to fix distant faces with inpainting ⤵️

How to install and use Automatic1111 Web UI for Stable Diffusion ⤵️
1 : https://youtu.be/AZg6vzWHOTA
2 : https://youtu.be/AZg6vzWHOTA

How many classification images performs best for DreamBooth training ⤵️

How LoRA training actually works tutorial ⤵️

Watch this tutorial to understand how token thing actually works ⤵️

0:00 Introduction to Kohya LoRA Training and Studio Quality Realistic AI Photo Generation
2:40 How to download and install Kohya’s GUI to do Stable Diffusion training
5:04 How to install newer cuDNN dll files to increase training speed
6:43 How to upgrade to the latest version previously installed Kohya GUI
7:02 How to start Kohya GUI via cmd
8:00 How to set DreamBooth LoRA training parameters correctly
8:10 How to use previously downloaded models to do Kohya LoRA training
8:35 How to download Realistic Vision V2 model
8:49 How to do training with Stable Diffusion 2.1 512px and 768px versions
9:44 Instance / activation and class prompt settings
10:18 What kind of training dataset you should use
11:46 Explanation of number of repeats in Kohya DreamBooth LoRA training
13:34 How to set best VAE file for better image generation quality
13:52 How to generate classification / regularization images via Automatic1111 Web UI
16:53 How to prepare captions to images and when you do need image captions
17:48 What kind of regularization images I have used
18:04 How to set training folders
18:57 Best LoRA Training settings for minimum amount of VRAM having GPUs
21:47 How to save state of training and continue later
22:44 How to save and load Kohya Training settings
23:31 How to calculate 1 epoch step count when considering repeating count
24:41 How to decide how many epochs when repeating count considered
26:00 Explanation of command line parameters displayed during training
28:19 Caption extension changing
29:24 After when we will get a checkpoint and checkpoints will be saved where
29:57 How to use generated LoRA safetensors files in SD Automatic1111 Web UI
30:45 How to activate LoRA in Stable Diffusion web UI
31:30 How to do x/y/z checkpoint comparison of LoRA checkpoints to find best model
33:29 How to improve face quality of generated images with high res fix
36:00 18 Different training parameters experiments I have made and their results comparison
36:42 How to test 18 different LoRA checkpoints with x/y/z plot
39:18 How to properly set number of epochs and save checkpoints when reducing repeating count
40:36 How to use checkpoints of Kohya DyLora, LoCon, LyCORIS/LoCon, LoHa in Automatic1111 Web UI
42:12 How to install Torch 1.13 instead of 1.12 and newer xFormers compatible with this version
43:06 How to make Kohya scripts to use your second GPU instead of your primary GPU

Dreambooth LoRA training is a method for training large language models (LLMs) to generate images from text descriptions. It is a combination of two techniques: Dreambooth and LoRA.

Dreambooth is a method for generating images from text descriptions by iteratively updating the image to match the text description. It works by first generating a random image, then using a text-to-image model to generate a new image that is closer to the text description. This process is repeated until the image is sufficiently close to the text description.

LoRA is a method for improving the performance of Dreambooth by using a latent representation of the image. LoRA works by first generating a latent representation of the image. This latent representation is then used to train a text-to-image model.