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中生成高保真图像而备受关注。其中，Stable Diffusion作为这一快速发展的领域中的领先开源模型脱颖而出。然而，微调这些模型的复杂性带来了多方面的挑战，从新方法集成到系统评估。为解决这些问题，本文介绍了LyCORIS（超越传统方法的LoRA及其他秩适应实现，用于Stable Diffusion）[https://github.com/KohakuBlueleaf/LyCORIS]，这是一个为Stable Diffusion提供多种微调方法的开源库。此外，我们提出了一个用于系统评估不同微调技术的全面框架。该框架采用多种指标，深入探讨微调的多个方面，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。通过这一综合方法，我们的工作为理解微调参数的细微影响提供了关键见解，架起了前沿研究与实际应用之间的桥梁。