Kohya-ss
Training scripts for Stable Diffusion fine-tuning with LoRA and DreamBooth.
About
Behind much of the image generation community's LoRA training sits sd-scripts by kohya-ss, a collection of command line training and utility scripts for Stable Diffusion and related models. It supports LoRA training including Conv2d expansion, DreamBooth, full fine-tuning, textual inversion for SD and SDXL, inpainting model training, and ControlNet-LLLite, across SD 1.x and 2.x, SDXL, SD3 and SD3.5, FLUX.1, LUMINA, HunyuanImage-2.1, and Anima. Utility scripts handle model conversion, image tagging, and LoRA merging, and generation scripts are included for testing checkpoints. Requirements are Python 3.10, PyTorch 2.6 or later, and a CUDA capable NVIDIA GPU, and several third party GUI projects wrap the scripts for less technical users. Fine tuners producing custom LoRAs and checkpoints of their own subjects and styles make up the main audience. Released primarily under the Apache 2.0 license.
Should you use Kohya-ss?
Pick it when
Use it when you train LoRAs, DreamBooth models, or full fine-tunes for SD, SDXL, SD3, or FLUX.1 on an NVIDIA GPU and want the scripts most community presets and tutorials are written against, with merging and tagging tools included.
Look elsewhere when
It is command-line only and needs a CUDA-capable NVIDIA GPU. If you want a GUI, use kohya-gui or OneTrainer; for video models it does not apply, so use musubi-tuner or finetrainers.
Alternatives to Kohya-ss
- kohya-ss GUI
Gradio front end over these same scripts, so you keep sd-scripts behavior without memorizing command-line flags.
- OneTrainer
Integrated GUI for LoRA, DreamBooth, and embeddings across similar models, but AGPL-3.0 where sd-scripts is Apache-2.0.
- SimpleTuner
Multi-GPU and multi-resolution focus with a web UI, but a 12 GB VRAM floor and an AGPL-3.0 license.
- FluxGym
Simple web UI for FLUX LoRAs on 12 to 20 GB cards, built on sd-scripts; quicker to start, but FLUX only.
Reviews (0)
Leave a Review
No reviews yet. Be the first to review!
Details
- Category
- Model Training & Fine-Tuning
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Intermediate (3/5)
- License
- Apache-2.0
- Minimum VRAM
- 8 GB
- Added
- Apr 3, 2026