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Text-to-image generative models have garnered immense attention for theirability to produce high-fidelity images from text prompts. Among these, StableDiffusion distinguishes itself as a leading open-source model in thisfast-growing field. However, the intricacies of fine-tuning these models posemultiple challenges from new methodology integration to systematic evaluation.Addressing these issues, this paper introduces LyCORIS (Lora beYondConventional methods, Other Rank adaptation Implementations for Stablediffusion) [https://github.com/KohakuBlueleaf/LyCORIS], an open-source librarythat offers a wide selection of fine-tuning methodologies for Stable Diffusion.Furthermore, we present a thorough framework for the systematic assessment ofvaried fine-tuning techniques. This framework employs a diverse suite ofmetrics and delves into multiple facets of fine-tuning, includinghyperparameter adjustments and the evaluation with different prompt typesacross various concept categories. Through this comprehensive approach, ourwork provides essential insights into the nuanced effects of fine-tuningparameters, bridging the gap between state-of-the-art research and practicalapplication.

Navigating Text-To-Image Customization:From LyCORIS Fine-Tuning to Model Evaluation