Low-Rank Adaptation (LoRA) is a novel technique introduced by Microsoft in 2021 for fine-tuning large language models (LLMs). LoRA is an efficient adaptation strategy that introduces no additional inference latency and substantially reduces the number of trainable parameters for downstream tasks while maintaining model quality. Although LoRA was initially proposed for LLMs, it also can be applied elsewhere. Inspired by the Stable Diffusion paper published