Application of the low-rank adaptation method on the example of fine-tuning a latent diffusion model

Abstract

This article explores the Low-Rank Adaptation (LoRA) method, a fast fine-tuning technique for large-parameter neural networks, and its potential application in various fields, with a focus on architecture, construction, and structural mechanics. The study applies LoRA to fine-tune a Latent Diffusion Model (LDM) for generating images of buildings in various architectural styles, serving as an illustrative example of LoRA’s effectiveness for adapting large models to specialized tasks.  

Large-scale neural networks, such as Latent Diffusion Models (LDMs) and Large Language Models (LLMs), have shown significant potential in various fields, but their training from scratch is computationally expensive and time-consuming. Fine-tuning offers a more efficient approach by adapting pre-trained models to specific tasks and data. LoRA further enhances efficiency by adding a small number of parameters to the model instead of adjusting all weights. LoRA uses low-rank matrix representations to reduce the number of trainable parameters during fine-tuning. By introducing smaller matrices for each layer and training them on new data, LoRA significantly speeds up the fine-tuning process and reduces computational costs.  

The study demonstrates the application of LoRA for fine-tuning the LDM Stable Diffusion 1.5 to generate images of buildings in various architectural styles using the OneTrainer tool. The results show that fine-tuning Stable Diffusion 1.5 using LoRA effectively generates high-quality images of buildings in specified architectural styles, highlighting LoRA’s potential for adapting large models to specialized tasks. The use of a validation dataset is emphasized for preventing overfitting and determining the optimal stopping point for training, ensuring the model's generalizability.

This research contributes to the broader exploration of LoRA’s applicability for fine-tuning large neural networks in various domains. While the study focuses on LDMs for architectural applications, the underlying principles and demonstrated effectiveness of LoRA extend to other types of large models, such as LLMs, for addressing specialized tasks in different fields.