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Low-rank-Adaptation

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I hope this video gets added to the FAQ, wiki and stickies.

Appreciate very much.

https://youtu.be/mfaqqL5yOO4

content of the video

0:00 Introduction speech
1:07 How to install the LoRA extension to the Stable Diffusion Web UI
2:36 Preparation of training set images by properly sized cropping
2:54 How to crop images using Paint .NET, an open-source image editing software
5:02 What is Low-Rank Adaptation (LoRA)
5:35 Starting preparation for training using the DreamBooth tab - LoRA
6:50 Explanation of all training parameters, settings, and options
8:27 How many training steps equal one epoch
9:09 Save checkpoints frequency
9:48 Save a preview of training images after certain steps or epochs
10:04 What is batch size in training settings
11:56 Where to set LoRA training in SD Web UI
13:45 Explanation of Concepts tab in training section of SD Web UI
14:00 How to set the path for training images
14:28 Classification Dataset Directory
15:22 Training prompt - how to set what to teach the model
15:55 What is Class and Sample Image Prompt in SD training
17:57 What is Image Generation settings and why we need classification image generation in SD training
19:40 Starting the training process
21:03 How and why to tune your Class Prompt (generating generic training images)
22:39 Why we generate regularization generic images by class prompt
23:27 Recap of the setting up process for training parameters, options, and settings
29:23 How much GPU, CPU, and RAM the class regularization image generation uses
29:57 Training process starts after class image generation has been completed
30:04 Displaying the generated class regularization images folder for SD 2.1
30:31 The speed of the training process - how many seconds per iteration on an RTX 3060 GPU
31:19 Where LoRA training checkpoints (weights) are saved
32:36 Where training preview images are saved and our first training preview image
33:10 When we will decide to stop training
34:09 How to resume training after training has crashed or you close it down
36:49 Lifetime vs. session training steps
37:54 After 30 epochs, resembling images start to appear in the preview folder
38:19 The command line printed messages are incorrect in some cases
39:05 Training step speed, a certain number of seconds per iteration (IT)
39:25 Results after 5600 steps (350 epochs) - it was sufficient for SD 2.1
39:44 How I'm picking a checkpoint to generate a full model .ckpt file
40:23 How to generate a full model .ckpt file from a LoRA checkpoint .pt file
41:17 Generated/saved file name is incorrect, but it is generated from the correct selected .pt file
42:01 Doing inference (generating new images) using the text2img tab with our newly trained and generated model
42:47 The results of SD 2.1 Version 768 pixel model after training with the LoRA method and teaching a human face
44:38 Setting up the training parameters/options for SD version 1.5 this time
48:35 Re-generating class regularization images since SD 1.5 uses 512 pixel resolution
49:11 Displaying the generated class regularization images folder for SD 1.5
50:16 Training of Stable Diffusion 1.5 using the LoRA methodology and teaching a face has been completed and the results are displayed
51:09 The inference (text2img) results with SD 1.5 training
51:19 You have to do more inference with LoRA since it has less precision than DreamBooth
51:39 How to give more attention/emphasis to certain keywords in the SD Web UI
52:51 How to generate more than 100 images using the script section of the Web UI
54:46 How to check PNG info to see used prompts and settings
55:24 How to upscale using AI models
56:12 Fixing face image quality, especially eyes, with GFPGAN visibility
56:32 How to batch post-process
57:00 Where batch-generated images are saved
57:18 Conclusion and ending speech