Musubi Tuner is a comprehensive LoRA training and inference toolkit for a wide range of diffusion model architectures, including HunyuanVideo, Wan, FramePack, FLUX variants, Qwen-Image, Z-Image, HiDream-O1, Ideogram4, Krea2, and Kandinsky5. It provides a unified pipeline of pre-caching (latents and text encoder outputs), training (LoRA, LoHa, LoKr), and generation, all accessible via CLI scripts and an optional Gradio GUI. The codebase is built on a shared `NetworkTrainer` base class with architecture-specific overrides, and includes advanced memory-saving techniques such as block-swap CPU offloading, FP8 quantization, and gradient checkpointing. While the repository is actively maintained and widely used (2035 stars), it exhibits medium technical drift due to vendored external model code, deprecated diffusers API usage, and a proliferation of near-duplicate architecture-specific scripts. The core reusable components—unified attention backend, offloading utilities, FP8 optimization, and LoRA network infrastructure—demonstrate solid engineering craftsmanship.