kohya-ss/musubi-tuner

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2103 stars · 316 forks observed · 2026-10-19

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Musubi Tuner is a set of Python scripts for training LoRA (Low-Rank Adaptation) adapters for video and image generation model architectures such as HunyuanVideo, Wan2.1/2.2, FramePack, FLUX.1 Kontext, FLUX.2, Qwen-Image, and Z-Image. It handles dataset configuration, latent/text-encoder caching, accelerated training, and inference with the fine-tuned models.

Use cases

• train a lora for hunyuanvideo video generation
• fine-tune wan 2.1 with my own video clips
• train a lora on qwen-image
• low-rank adaptation training for flux.1 kontext
• cache latents and train a video model on a single gpu
• fine-tune image generation models with custom datasets

When to choose

• you want to train LoRA adapters for supported video/image diffusion architectures
• you need GPU-efficient fine-tuning with pre-caching of latents and text embeddings
• you are comfortable with command-line training scripts and accelerate configuration

When to avoid

• you need a GUI-based training tool
• you want to fine-tune architectures not on the supported list
• you need an officially supported training pipeline from the model authors

Facets

library · maturity active

llm-training machine-learning deep-learning cli machine-learning deep-learning image-processing artificial-intelligence windows python lora-training fine-tuning hunyuanvideo wan flux qwen-image diffusion-models video-generation video linux macos gpu

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