Text-to-image generative models have garnered immense attention for their ability to produce high-fidelity images from text prompts. Among these, Stable Diffusion distinguishes itself as a leading open-source model in this fast-growing field. However, the intricacies of fine-tuning these models pose multiple challenges from new methodology integration to systematic evaluation. Addressing these issues, this paper introduces LyCORIS (Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion) [https://github.com/KohakuBlueleaf/LyCORIS], an open-source library that offers a wide selection of fine-tuning methodologies for Stable Diffusion. Furthermore, we present a thorough framework for the systematic assessment of varied fine-tuning techniques. This framework employs a diverse suite of metrics and delves into multiple facets of fine-tuning, including hyperparameter adjustments and the evaluation with different prompt types across various concept categories. Through this comprehensive approach, our work provides essential insights into the nuanced effects of fine-tuning parameters, bridging the gap between state-of-the-art research and practical application.

翻译：文本到图像生成模型因其能够从文本提示生成高保真图像而备受关注。其中，Stable Diffusion作为这一快速发展领域中领先的开源模型脱颖而出。然而，微调这些模型的复杂性带来了从新方法集成到系统评估的多重挑战。针对这些问题，本文介绍了LyCORIS（超越传统方法的LoRA及其他秩适配实现在Stable Diffusion中的应用）[https://github.com/KohakuBlueleaf/LyCORIS]，这是一个提供多种Stable Diffusion微调方法的开源库。此外，我们提出了一个用于系统评估不同微调技术的完整框架。该框架采用了一套多样化的指标，并深入探究了微调的多个方面，包括超参数调整以及针对不同概念类别使用不同提示类型的评估。通过这种全面的方法，我们的工作为理解微调参数的细微影响提供了关键见解，弥合了前沿研究与实际应用之间的差距。