Overview

Learn the intricacies of Stable Diffusion Textual Inversion (TI) and Text Embeddings using the Automatic1111 Web UI in this comprehensive tutorial video. Explore topics such as setting up the Automatic1111 environment, understanding command line arguments, managing Stable Diffusion models and VAE files, and configuring training settings. Dive deep into the technical aspects of tokens, vectors, and embedding learning rates. Discover how to prepare and preprocess training datasets, use prompt templates, and leverage filewords for effective training. Gain insights into monitoring training progress, avoiding overtraining, and comparing different AI training techniques. Master the use of generated embeddings, test various checkpoints, and learn how to upscale images using AI. By the end of this tutorial, you'll have a thorough understanding of Textual Inversion and its application in Stable Diffusion image generation.

Syllabus

Introduction to #StableDiffusion #TextualInversion Embeddings
Which commit of the #Automatic1111 Web UI we are using and how to checkout / switch to specific commit of any Git project
Used command line arguments of Automatic1111 webui-user.bat file
Automatic1111 command line arguments
How to and where to put Stable Diffusion models and VAE files in Automatic1111 installation
Why do we use latest VAE file and what does VAE file do
Training settings of Automatic1111
All about names of text embeddings
What is initialization text of textual inversion training
Embedding inspector extension of Automatic1111
How to set number of vectors per token when doing Textual Inversion training
Technical and detailed explanation of tokens and their numerical weights vectors in Stable Diffusion
How the prompts getting tokenized - turned into tokens - by using tokenizer extension
Setting number of training vectors
Where embedding files are saved in automatic1111 installation
All about preprocess images before TI training
Training tab of textual inversion
What to and how to set embedding learning rate
What are the Batch size and Gradient accumulation steps and how to set them
How to set training learning rate according to Batch size and Gradient accumulation steps
What are prompt templates, what are they used for, how to set and use them in textual inversion training
What are filewords and how they are used in training in automatic1111 web ui
How to edit image captions when doing textual inversion training
From training images pool, how and why did i choose some of them and not all of them
Why I did add noise to the backgrounds of some training dataset images
How should be your training dataset. What is a good training dataset
Save TI training checkpoints
Which latent sampling method is best
Training started
Overclock GPU to get 10% training speed up
Where to find TI training preview images
Where to see used final prompts during training
How to use inspect_embedding_training script to determine overtraining of textual inversion
What is training loss
Technical difference of Textual Inversion, DreamBooth, LoRA and HyperNetworks training
Over 200 epochs and already got very good sample preview images
How to set newest VAE file as default in the settings of automatic1111 web ui
How to use generated embeddings checkpoint files
How to test different checkpoints via X/Y plot and embedding files name generator script
How to upscale image by using AI
How to use multiple embeddings in a prompt

Taught by

Software Engineering Courses - SE Courses

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