Lecture 1: How To Install Python, Setup Virtual Environment VENV, Set Default Python System Path & Install Git

0:00 Very comprehensive guide to Python installation on Windows

1:11 What is CMD - Command Prompt

1:56 How to open a cmd window and use it

2:04 How to run cmd as administrator

2:17 What is Git and why do we need Git

2:35 How to download and install Git

3:30 Why do we need Git large and how to download and install Git large

3:50 Why do we need specific Python versions

4:03 How to download and install any Python version

4:32 How to verify if Python installed or not

4:55 How to customize Python installation

5:17 Python add path checkbox during installation

6:20 How to verify your Python installed version

6:35 How to change or set system environment variables path of Python

7:15 How to install another Python version - multiple Python installations

8:30 How to change default Python version when having multiple Python installations

9:30 How to use specific Python installation when having multiple Python

9:35 What is Python venv and why do we need it

10:40 How to start cmd inside certain directory

10:55 How to compose a Python venv

11:19 How to activate Python venv

11:58 How to compose a venv from different Python version

13:39 Demo of installed package separation from other Python installations inside venv

14:17 Where to find installed packages in Python installation folder

14:50 How to write a bash script to automatically activate Python venv and start a cmd

15:24 How to view extensions of files in Windows

15:43 The script itself to activate venv and start cmd

17:11 How to install Stable Diffusion Automatic1111 web UI

17:30 How to use Git clone to download entire project from GitHub repo

 

Lecture 2: Zero to Hero ControlNet Tutorial: Stable Diffusion Web UI Extension | Complete Feature Guide

0:00 Introduction to most advanced zero to hero ControlNet tutorial

2:55 How to install Stable Diffusion Automatic1111 Web UI from scratch

5:05 How to see extensions of files like .bat

6:15 Where to find command line arguments of Automatic1111 and what are they

6:46 How to run Stable Diffusion and ControlNet on a weak GPU

7:37 Where to put downloaded Stable Diffusion model files

8:29 How to give different folder path as the model path - you can store models on another drive

9:15 How to start using Stable Diffusion via Automatic1111 Web UI

10:00 Command line interface freezing behaviour

10:13 How to improve image generation of Stable Diffusion with better VAE file

11:39 Default VAE vs best VAE comparison

11:50 How to set quick shortcuts for VAE and Clip Skip for Automatic1111 Web UI

12:30 How to upgrade xFormers to the latest version in Automatic1111

13:40 What is xFormers and other optimizers

14:26 How to install ControlNet extension of Automatic1111 Web UI

18:00 How to download ControlNet models

19:40 How to use custom Stable Diffusion models with Automatic1111 Web UI

21:24 How to update ControlNet extension to the latest version

22:53 Set this true, allow other scripts to control ControlNet extension

24:37 How to make amazing QR code images with ControlNet

30:59 Best settings for QR code image generation

31:44 What is Depth ControlNet option and how to use it

33:28 Depth_leres++ of ControlNet

34:15 Depth_zoe of ControlNet

34:22 Official information of Depth maps

34:49 ControlNet Normal map

35:34 Normal Midas map

36:05 Official information of Normal maps

34:49 ControlNet Canny model

37:42 Official information of Canny

37:55 ControlNet MLSD straight lines model

39:08 Official information of MLSD straight lines

39:18 ControlNet Scribble model

40:28 How to use your own scribble images and turn them into amazing artworks

40:45 When to select none in pre-processor section

41:20 My prompt is more important

41:36 ControlNet is more important

42:01 Official information of Scribble

42:11 ControlNet Softedge model

43:12 Official information of SoftEdge

43:22 ControlNet Segmentation (Seg) model

43:55 How to modify your prompt to properly utilize segmentation

44:10 Association of prompt with segments the ControlNet finds

44:41 How to turn your wall into a painting with ControlNet

45:33 Why I selected none preprocessor

43:06 Official information of segmentation (Seg)

46:16 Open pose module of ControlNet

46:40 How to install and use OpenPose editor

50:58 Official information of OpenPose

51:08 ControlNet Lineart model

51:36 Preprocessor preview bug

54:21 Real lineart into amazing art example

56:34 How to generate amazing logo images by using Lineart of ControlNet

58:16 Difference between just resize, crop and resize, and resize and fill

59:02 ControlNet Shuffle model

1:00:50 Official information of Shuffle

1:02:36 What is multi-ControlNet and how to use it

1:04:05 Instruct pix2pix of ControlNet

1:06:00 Inpainting feature of ControlNet

1:07:49 ControlNet inpainting vs Automatic1111 inpainting

1:07:59 How to get true power of inpainting of ControlNet (hint: with tiling)

1:09:00 How to upscale and add details to the images with inpainting + tiling

1:09:30 The tile color fix + sharp to obtain even better results

1:10:35 Tile color fix + sharp vs old tile resample result comparison

1:11:20 How to use generative fill feature of Photoshop in ControlNet to remove objects

1:12:58 How to outpaint (zoom out feature of midjourney 5.2) image with ControlNet

1:14:17 The logic of outpainting

1:14:40 How to continue outpainting easily

1:16:06 Tiling of ControlNet - ultimate game changer for upscaling

1:17:19 How to turn your image into a fully stylized image with tiling without training

1:20:57 Reference only feature of ControlNet

1:22:29 Official information of Reference mode

1:22:39 Style Transfer (T2IA) of ControlNet

1:26:54 How to install and use ControlNet on RunPod

 

Lecture 3: How To Find Best Stable Diffusion Generated Images By Using DeepFace AI - DreamBooth / LoRA Training

0:00 Introduction to what DeepFace does and how we are going to utilize it

0:58 Let's say you have generated 2000 images how to get good ones

1:17 This approach can be used for professional business purposes

1:32 If you are new to Stable Diffusion or image generation

2:17 Beginning with composing venv to install DeepFace

3:18 The training dataset images I have used for this tutorial

3:57 I have generated over 3000 images

4:06 The prompts I have used to generate images - how to use PNG info to find used prompts

5:23 How to write and use DeepFace best images finding script

9:18 How to use the script demonstration after you written and set it

11:20 Explanation of the values displayed during the script runtime

12:18 Sorted images from best to worst

 

Lecture 4: Generate Studio Quality Realistic Photos By Kohya LoRA Stable Diffusion Training - Full Tutorial

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

 

Lecture 5: The END of Photography - Use AI to Make Your Own Studio Photos, FREE Via DreamBooth Training

0:00 Dreambooth training with Automatic1111 Web UI

1:44 How to install DreamBooth extension of Automatic1111 Web UI

2:37 Automatic installer script for DreamBooth extension

3:20 Manual installation of DreamBooth extension

3:30 How to use older / certain version of Auto1111 or DreamBooth with git checkout

4:30 Main manual installation part of DreamBooth extension

4:57 How to manually update previously installed DreamBooth extension to the latest version

5:44 How to install requirements of DreamBooth extension

7:15 How to use DreamBooth extension

7:25 How to compose your training model in DreamBooth extension

7:35 Best base model and settings for realism training in DreamBooth

7:51 Where to find installed Python ,xFormers, Torch, Auto1111 versions

8:10 How to solve frozen / non-progressing CMD window

8:23 Where the DreamBooth generated training files (native diffusers) are stored

8:37 Where the Stable Diffusion training files are stored

8:57 Select training model and start setting parameters for best realism

9:07 How to continue training later a time

9:38 Which configuration (settings tab) for best realism and best training

12:14 Concept tab settings

12:28 How to prepare your training images dataset with my human cropping script and pre-processing

13:43 What kind of training images you should have for DreamBooth training

14:52 Continue back setting parameters for concepts tab

15:02 Everything about classification / regularization images used during Dreambooth / LoRA training

16:07 Used pre-prepared real images based classification images for this tutorial

16:55 How to generate classification images by using the trained model

17:22 How to generate images with Automatic1111 forever until cancelled

18:09 How to use image captions with DreamBooth extension via filewords

18:25 How to automatically generate captions for training or class images

18:35 How to use BLIP or deepbooru for captioning

19:25 What happens when image caption is read, what is the final output of instance prompt

19:59 How to set class images per instance

20:32 What is the benefit of using real photos as classification images

21:42 How to start training after setting all configuration

23:05 Training started, displayed messages on CMD

23:47 When it generates new classification images

25:52 What if if you don't have such powerful GPU for such quality training

26:55 How to do x/y/z checkpoint comparison to find best checkpoint

28:43 How checkpoints are named when saved - 1 epoch step count

30:05 The best VAE file I use for best quality

30:36 How to open x/y/z plot comparison results and evaluate them

33:20 How sort thousands of generated image with the best similarity thus quality

34:39 How to improve generated image quality via 2 different inpainting methodology

36:56 Improve results with inpainting + ControlNet

38:50 What is important to get good quality images after inpainting