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Become A Master Of SDXL Training With Kohya SS LoRAs - Combine Power Of Automatic1111 & SDXL LoRAs

Become A Master Of SDXL Training With Kohya SS LoRAs - Combine Power Of Automatic1111 & SDXL LoRAs

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