Train a LoRA file that teaches Stable Diffusion your art style using a small set of your own images, without a full model retrain.
Fine-tune FLUX.1 or SDXL with DreamBooth to generate images of a specific person, product, or character.
Convert and merge LoRA checkpoint files between model formats, or resize a LoRA to reduce its file size.
| kohya-ss/sd-scripts | priorlabs/tabpfn | feast-dev/feast | |
|---|---|---|---|
| Stars | 7,025 | 7,025 | 7,026 |
| Language | Python | Python | Python |
| Setup difficulty | hard | easy | moderate |
| Complexity | 4/5 | 2/5 | 4/5 |
| Audience | vibe coder | data | data |
Figures from each repo's GitHub metadata at analysis time.
Requires a CUDA-capable Nvidia GPU, training large models without one is not practical.
sd-scripts is a collection of Python scripts for training and fine-tuning AI image generation models. The primary focus is on Stable Diffusion and its variants, but the project also supports other modern image models including FLUX.1, SDXL, SD3, LUMINA, and HunyuanImage-2.1. The scripts let you take an existing image model and teach it new visual styles, subjects, or concepts using your own image data. The most common use case is LoRA training, a technique that adjusts a small part of the model rather than retraining the entire thing. This makes training feasible on consumer-grade hardware and produces files that can be layered on top of base models without permanently altering them. The project also supports deeper training approaches such as DreamBooth fine-tuning, Textual Inversion (teaching the model a new word that refers to a specific concept), and inpainting model training. Additional utilities cover practical tasks like converting models between different formats, tagging images automatically using a separate AI tagger (WD14), merging multiple LoRA files, and resizing them. Multi-resolution dataset handling lets you train on images of different sizes at once. Installation is available for both Windows and Linux, including WSL2 on Windows. Documentation for most training workflows is provided in both English and Japanese, reflecting the project's roots and the bilingual community around it. The README also describes a setup path for developers who want to use AI coding assistants like Claude within the repository. The project is actively maintained with frequent releases. Recent updates added Intel GPU support, improved fp16 training stability, and extended LoKr and LoHa training techniques to SDXL and the Anima model preview. No license terms are stated in the README excerpt provided.
A collection of Python scripts for training and fine-tuning AI image generation models like Stable Diffusion, FLUX, and SDXL on your own hardware using techniques like LoRA and DreamBooth.
Mainly Python. The stack also includes Python, PyTorch, CUDA.
Setup difficulty is rated hard, with roughly 1day+ to a first successful run.
Mainly vibe coder.
This repo across BitVibe Labs
Don't trust strangers blindly. Verify against the repo.