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| File Name: | Stable Diffusion & DreamBooth & LoRA – Zero To Hero |
| Content Source: | https://www.udemy.com/course/stable-diffusion-dreambooth-lora-zero-to-hero/ |
| Genre / Category: | Drawing & Painting |
| File Size : | 2.9GB |
| Publisher: | udemy |
| Updated and Published: | July 14, 2023 |
Learn How To Install & Use Stable Diffusion Via Automatic1111 Web UI & Realistic DreamBooth & Kohya LoRA Training
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
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