What is Kohya?

Kohya is a free, open-source program that helps you fine-tune AI image generators like Stable Diffusion. It lets you teach the model new styles or subjects by training small “LoRA” add-on files instead of re-training the whole network.

Let's break it down

  • Free, open-source: Anyone can download, use, and change the code without paying.
  • Program: A piece of software you run on your computer.
  • Fine-tune: Adjust an already-trained AI so it gets better at a specific task.
  • AI image generators: Tools (e.g., Stable Diffusion) that create pictures from text prompts.
  • Stable Diffusion: A popular model that turns words into images.
  • LoRA (Low-Rank Adaptation): Tiny add-on files (usually a few megabytes) that modify the big model’s behavior without needing huge compute.
  • Add-on files: Extra data you load together with the main model to change its output.

Why does it matter?

Because it lets artists, hobbyists, and developers customize powerful image-generation AIs without needing expensive hardware or deep machine-learning expertise. You can create a personal style or niche subject quickly and share it with others.

Where is it used?

  • Custom art styles: A comic artist trains a LoRA to make the AI draw in their unique line work.
  • Brand-specific graphics: A marketing team creates a LoRA that matches their logo colors and visual language for automated ad creation.
  • Educational projects: Teachers use Kohya to show students how AI can be adapted with small datasets.
  • Game asset generation: Indie developers fine-tune the model to produce textures that fit their game’s aesthetic.

Good things about it

  • Requires far less GPU memory than full model training.
  • Fast training cycles; you can see results in a few hours.
  • Works with the popular Stable Diffusion ecosystem, so outputs are immediately usable.
  • Community-driven: many tutorials, scripts, and pre-made LoRAs are shared online.
  • Keeps the original model unchanged, so you can switch between multiple LoRAs easily.

Not-so-good things

  • Still needs a decent GPU (8 GB+ VRAM) to run efficiently.
  • Quality depends heavily on the quantity and diversity of your training images; poor data yields poor results.
  • Limited to tasks that fit the LoRA approach; you can’t completely overhaul the model’s core knowledge.
  • The command-line interface can be intimidating for absolute beginners.