  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.
