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stable-diffusion-2-1

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

Appreciate very much.

Full Stable Diffusion related playlist that explains both on google colab and PC by Automatic1111 Web UI :
➡️ https://www.youtube.com/playlist?list=PL_pbwdIyffsmclLl0O144nQRnezKlNdx3

🎦 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