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bmaltais/kohya_ss

★ 12,262PythonAudience · generalComplexity · 4/5Setup · hard

TLDR

A browser-based graphical interface for fine-tuning Stable Diffusion image AI models using LoRA and Dreambooth, letting you train on your own images without writing command-line scripts.

Mindmap

mindmap
  root((kohya-ss GUI))
    Training Methods
      LoRA
      Dreambooth
      SDXL support
    Interface
      Gradio browser UI
      Form-based settings
      Config file saving
    Where to Run
      Local GPU
      Google Colab
      Runpod and Novita
      Docker
    Platforms
      Windows
      Linux
      macOS
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Things people build with this

USE CASE 1

Train a LoRA file on your own photos so Stable Diffusion can generate images of a specific person, art style, or object

USE CASE 2

Fine-tune an SDXL model on a custom image dataset using a form-based interface instead of command-line arguments

USE CASE 3

Run Stable Diffusion training on Google Colab or Runpod when you do not have a local GPU

Tech stack

PythonGradioStable Diffusion

Getting it running

Difficulty · hard Time to first run · 1h+

Local training requires a CUDA-capable GPU, cloud alternatives like Google Colab and Runpod work but require account setup and paid compute time.

In plain English

Kohya's GUI is a graphical user interface for training and customizing Stable Diffusion image generation models. Stable Diffusion is an AI system that creates images from text descriptions. Fine-tuning it, meaning training it on a set of your own images so it learns a specific person, art style, or subject, is a technically demanding process that normally requires running scripts from the command line with many configuration options. This project wraps those training scripts in a form-based interface built with Gradio, a Python library for creating browser-based panels. Instead of typing commands, users fill out fields for model paths, dataset locations, training settings, and output folders, then click a button to start the process. The GUI generates the appropriate command-line arguments automatically. It supports several training methods. LoRA (Low-Rank Adaptation) is the most popular: rather than retraining the entire model, it produces a small add-on file that teaches the base model a new concept. Dreambooth is another approach that trains the model to consistently recognize a specific subject. The GUI also supports SDXL training, which targets the higher-resolution SDXL variant of Stable Diffusion. The tool can run on a local machine with a supported GPU, or on cloud platforms like Google Colab, Runpod, and Novita for users who do not have the required hardware. Docker is also supported for developers who prefer containerized setups. Installation guides are available for Windows, Linux, and macOS. A configuration file allows users to save their preferred paths and settings so they do not have to re-enter them each time the GUI opens.

Copy-paste prompts

Prompt 1
Walk me through training a LoRA on 20 photos of my dog using kohya_ss on Windows, so Stable Diffusion can generate images of it
Prompt 2
What settings should I enter in kohya_ss to fine-tune an SDXL model on a dataset of 50 images without overfitting?
Prompt 3
How do I run kohya_ss on Google Colab to train a Dreambooth model without needing my own GPU? Give me the steps
Prompt 4
What is the difference between LoRA and Dreambooth training in kohya_ss, and when should I use each approach?
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