In this tutorial, you will learn how to install Automatic1111 Web UI for SDXL. How to use LoRAs with Automatic1111 SD Web UI. How to install Kohya SS GUI scripts to do Stable Diffusion training. How to train LoRAs on SDXL model with least amount of VRAM using settings. All of the details, tips and tricks of Kohya trainings. How to do x/y/z plot comparison to find your best LoRA checkpoint. And many more.
Tutorial GitHub Readme File ⤵️
0:00 Intro
2:15 Pre-requirements of this tutorial
3:20 1-Click installer for Automatic1111 Web UI
4:00 How to install Automatic1111 Web UI for SDXL and SD 1.5 models
4:15 How to checkout and verify your installed Python version
4:51 Which Automatic1111 Web UI command line arguments you need for SDXL
5:15 Where to find all command line arguments of Automatic1111 Web UI
5:45 Where to download SDXL model files and VAE file
6:17 Which folders you need to put model and VAE files
7:21 Detailed explanation of what is VAE (Variational Autoencoder) of Stable Diffusion
7:57 How to set your VAE and enable quick VAE selection options in Automatic1111
8:22 What does Automatic and None options mean in SD VAE selection
8:43 Why you shouldn't use embedded VAE of SD 1.0 model
9:38 Correct resolution of SDXL - how to use SDXL
9:58 How to install Kohya SS GUI script for SDXL training
12:29 What to do if your CMD is not progressing
13:19 When you need to use FP16 instead of BF16
13:55 How to install Kohya on RunPod or on a Unix system
14:35 How to start Kohya GUI after installation
15:18 What are Stable Diffusion LoRA and DreamBooth (rare token, class token, and more) training
15:45 How to select SDXL model for LoRA training in Kohya GUI
16:31 How to save and load your Kohya SS training configuration
16:41 How to use my own used configuration for this tutorial video training
17:56 How to prepare your training images for Kohya LoRA or DreamBooth SDXL training
19:17 What kind of training images you should use for training
20:57 What kind of regularization images you should use? The logic of using ground truth images
24:35 What is number of repeating in Kohya SS. Which number you need to pick
25:56 Where will be your LoRA checkpoints saved
26:31 How to verify your training images dataset properly composed
27:12 How to set your generated LoRA file names
27:46 Which training parameters you should use for SDXL LoRA training
27:56 Why select train batch size 1 and gradient accumulation steps 1
28:18 The logic of number of epochs
30:25 Detailed explanation of Kohya SS training. What each parameter and option do
31:03 Which learning rate for SDXL Kohya LoRA training
31:10 Why do I use Adafactor
32:39 The rest of training settings
33:56 Which Network Rank (Dimension) you need to select and why
34:44 How to fix if you get out of VRAM error - not enough memory
36:04 What is Network Alpha of Kohya LoRA
36:35 Don't forget Gradient Checkpointing
37:07 How to continue training with Kohya LoRA training
37:42 What does print training command do
37:59 How to calculate number of steps for each Epoch
38:12 How to calculate how many regularization images you need
38:43 When you should increase batch size when doing Stable Diffusion training?
39:27 How number of total steps (max training steps) are calculated in Kohya training
40:25 How you can generate your own regularization / classification images
41:45 How to manually edit generated Kohya training command and execute it
43:21 How to start training in Kohya
43:36 How to do training on your second GPU with Kohya SS
46:31 How much VRAM is SDXL LoRA training using with Network Rank (Dimension) 32
47:15 SDXL LoRA training speed of RTX 3060
47:25 How to fix image file is truncated error
48:05 How to reach and contact me if you get an error
48:50 VRAM usage and speed testing of different Network Rank
51:40 How to use absolute min VRAM to make it work
52:16 When is first checkpoint generated and where they are saved
53:12 How to continue training from saved state
53:55 Auto saved configuration files
55:42 How to use LoRAs with Automatic1111 Web UI
58:02 How to assign previews to your LoRA files / checkpoints
59:00 How to do x/y/z LoRA checkpoint comparison to find best LoRA model
1:03:10 How to understand if your LoRA model is overtrained / cooked or not
1:04:50 Testing our LoRAs stylization capability
1:07:07 How to generate studio shot quality images that you can use on LinkedIn, Twitter, Instagram and such
1:07:40 How to find best generated images with using an AI tool
1:11:42 How to utilize ChatGPT to find very good prompts
1:12:19 How to utilize high-res fix and LoRA inpainting to get amazing quality distant shot images
1:16:02 How to fix hands and face
1:19:52 How to use same training command I used
1:20:29 When you need to reduce weight / emphasis of the rare token
1:23:24 How to join our Discord community for help and tips