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invoke-training

A library for training custom Stable Diffusion models (fine-tuning, LoRA training, textual inversion, etc.) that can be used in InvokeAI.

Warning

invoke-training is still under active development, and breaking changes are likely. Full backwards compatibility will not be guranteed until v1.0.0. In the meantime, I recommend pinning to a specific commit hash.

Documentation

https://invoke-ai.github.io/invoke-training/

Training Modes

  • Stable Diffusion
    • LoRA
    • DreamBooth LoRA
    • Textual Inversion
  • Stable Diffusion XL
    • LoRA
    • DreamBooth LoRA
    • Textual Inversion
    • LoRA and Textual Inversion

More training modes coming soon!

Installation

For more installation details, see the Installation section of the documentation.

# A recent version of pip is required, so first upgrade pip:
python -m pip install --upgrade pip

# Editable install:
pip install -e ".[test]" --extra-index-url https://download.pytorch.org/whl/cu121

Quick Start

invoke-training pipelines can be configured and launched from either the CLI or the GUI.

CLI

Run training via the CLI with type-checked YAML configuration files for maximum control:

invoke-train --cfg-file src/invoke_training/sample_configs/sd_lora_pokemon_1x8gb.yaml

GUI

Run training via the GUI for a simpler starting point.

invoke-train-ui

Features

Training progress can be monitored with Tensorboard: Screenshot of the Tensorboard UI showing validation images. Validation images in the Tensorboard UI.

All trained models are compatible with InvokeAI:

Screenshot of the InvokeAI UI with an example of a Yoda pokemon generated using a Pokemon LoRA model. Example image generated with the prompt "A cute yoda pokemon creature." and the trained Pokemon LoRA.

Contributing

Contributors are welcome. For developer guidance, see the Contributing section of the documentation.

invoke-training's People

Contributors

ryanjdick avatar hipsterusername avatar

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