/kohya-trainer

Adapted from https://note.com/kohya_ss/n/nbf7ce8d80f29 for easier cloning

Primary LanguagePython

Kohya Trainer V4 Colab UI - VRAM 12GB

Best way to train Stable Diffusion model for peeps who didn't have good GPU

Adapted to Google Colab based on Kohya Guide
Adapted to Google Colab by Linaqruf
You can find latest notebook update here

What is this?

Q: So what's differences between Kohya Trainer and other Stable Diffusion trainer out there?

A: Kohya Trainer have some new features like

  1. Using the U-Net learning
  2. Automatic captioning/tagging for every image automatically with BLIP/DeepDanbooru
  3. Implemented NovelAI Aspect Ratio Bucketing Tool so you don't need to crop image dataset 512x512 ever again
  • Use the output of the second-to-last layer of CLIP (Text Encoder) instead of the last layer.
  • Learning at non-square resolutions (Aspect Ratio Bucketing) .
  • Extend token length from 75 to 225.
  1. By preparing a certain number of images (several hundred or more seems to be desirable), you can make learning even more flexible than with DreamBooth.
  2. It also support Hypernetwork learning
  3. NEW! 23/11 - Implemented Waifu Diffusion 1.4 Tagger for alternative DeepDanbooru to auto-tagging

Q: And what's differences between this notebook and other dreambooth notebook out there?

A: We're adding Quality of Life features such as:

  • Install gallery-dl to scrap images, so you can get your own dataset fast with google bandwidth
  • Huggingface Integration, here you can login to huggingface-hub and upload your trained model/dataset to huggingface

Credit

Kohya | Just for my part