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Intro

Why is LoRA a new standard?

Required resources

Overview of video parts 1. local GUI and 2. Google Colab Notebook

Part 1. local GUI training

PC hardware specs

Dataset preparation

Batch renaming filetypes to a new filetype

Batch renaming attempt #2

BIRME cropping

BLIP captioning

How to write good captions

Captioning example #2

Creating project folder structure using kohya_ss GUI

Training parameters

Why the training steps in the console are different from max train steps

Console output after training commences

What you get after training finishes

Testing the style LoRA in AUTOMATIC1111 webui

Using x/y/z plot in scripts to vary parameters

Checking x/y/z plot in photoshop

Testing the final epoch LoRA

Verifying LoRA's flexibility with prompting

Part 2. Anime style LoRA

Dataset preparation on redjuice/shiru artwork

Using AUTOMATIC1111 webui extension tagger

Modifying Danbooru style tags

Using the kohya LoRA DreamBooth colab notebook

Fixing the error that occurred

Preliminary testing of redjuice style LoRA

Checking x/y/z plot in photoshop

Combining character and style LoRAs

Description:

Learn how to train LoRA (Low-Rank Adaptation) for style in Stable Diffusion through this comprehensive tutorial video. Explore the process of creating custom style models using both local GUI and Google Colab methods. Discover techniques for dataset preparation, including batch renaming, cropping, and captioning. Master the use of kohya_ss GUI for project setup and training parameter configuration. Gain insights into testing and evaluating LoRA models using AUTOMATIC1111 webui and x/y/z plotting. Delve into anime-style LoRA creation, utilizing tools like the AUTOMATIC1111 webui extension tagger and modifying Danbooru-style tags. Troubleshoot common errors and learn to combine character and style LoRAs for enhanced results in AI-generated artwork.

Stable Diffusion - How to Train LoRA for Style - LoRA Part 1

kasukanra

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#Computer Science #Deep Learning #Stable Diffusion #Machine Learning #Art & Design #Visual Arts #Digital Art #LoRA (Low-Rank Adaptation)