Low Rank Weight Decomposition For Diffusion Model Fine Tuning

via “low-rank weight decomposition for diffusion model fine-tuning”

Using Low-rank adaptation to quickly fine-tune diffusion models.

Unique: Implements layer-level LoRA injection via LoraInjectedLinear/Conv2d wrapper classes that preserve original model architecture while adding trainable low-rank branches, enabling seamless integration with Hugging Face diffusers without forking the codebase. Uses monkeypatch_add_lora for runtime application and extract_lora_ups_down for surgical weight extraction.

vs others: Achieves 10-100× parameter reduction vs full fine-tuning while maintaining quality parity, and produces 100-200× smaller model files than QLoRA or adapter-based approaches, making it ideal for edge deployment and model composition.