>Dreambooth is the best training method for Stable Diffusion. In this tutorial, I show how to install the Dreambooth extension of Automatic1111 Web UI from scratch. Additionally, I demonstrate my months of work on the realism workflow, which enables you to produce studio-quality images of yourself through #Dreambooth training. Furthermore, I share my automatic installer script for the DreamBooth extension.

Source GitHub Readme File ⤵️

Automatic Installer Scripts ⤵️

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The generative AI along with LLMs are going to cause huge unemployment. Looks like #photography is going to be one of the early goners.

Moreover if you are having hard time to install and use DreamBooth, this tutorial is the best place that will teach you both automatically and manually installing the extension.

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Technology & Science: News, Tips, Tutorials, Tricks, Best Applications, Guides, Reviews ⤵️
https://www.youtube.com/playlist?list=PL_pbwdIyffsnkay6X91BWb9rrfLATUMr3 />
Playlist of #StableDiffusion Tutorials, Automatic1111 and Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Pix2Pix, Img2Img ⤵️
https://www.youtube.com/playlist?list=PL_pbwdIyffsmclLl0O144nQRnezKlNdx3 />
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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