  In this paper, we show different fine-tuning methods for Stable Diffusion XL;
this includes inference steps, and caption customization for each image to
align with generating images in the style of a commercial 2D icon training set.
We also show how important it is to properly define what "high-quality" really
is especially for a commercial-use environment. As generative AI models
continue to gain widespread acceptance and usage, there emerge many different
ways to optimize and evaluate them for various applications. Specifically
text-to-image models, such as Stable Diffusion XL and DALL-E 3 require distinct
evaluation practices to effectively generate high-quality icons according to a
specific style. Although some images that are generated based on a certain
style may have a lower FID score (better), we show how this is not absolute in
and of itself even for rasterized icons. While FID scores reflect the
similarity of generated images to the overall training set, CLIP scores measure
the alignment between generated images and their textual descriptions. We show
how FID scores miss significant aspects, such as the minority of pixel
differences that matter most in an icon, while CLIP scores result in misjudging
the quality of icons. The CLIP model's understanding of "similarity" is shaped
by its own training data; which does not account for feature variation in our
style of choice. Our findings highlight the need for specialized evaluation
metrics and fine-tuning approaches when generating high-quality commercial
icons, potentially leading to more effective and tailored applications of
text-to-image models in professional design contexts.
