In the late 1800s, being a gentleman or a gentlewoman wasn’t just about wearing the right clothes—it was a whole way of life. Men were expected to uphold a code of chivalry: they carried themselves with grace, were impeccably dressed, and always showed courtesy, especially toward women. It was a time when manners and etiquette were the pillars of society, and a gentleman’s honor was closely tied to his behavior in public and private. At the same time, gentlewomen were celebrated for their poise, education, and refined social skills. They were encouraged to be well-read, articulate, and gentle in their conduct, balancing a sense of inner strength with the expectations of decorum.
This era was marked by an elegant formality in social interactions. Both gentlemen and gentlewomen often met in carefully orchestrated settings like balls, tea parties, and church gatherings, where every detail—from the way they greeted one another to how they conducted conversations—was steeped in tradition. Courtship was seen as a delicate dance of respect and subtle hints, and even the smallest gesture carried significant meaning. It’s pretty fascinating to see how these cultural expectations helped shape social life, creating a legacy of etiquette and grace that still echoes in some traditions today. (Source ChatGPT)
The pattern is 8" wide and just over 10" tall. There are three 1/4" thick layers. I included a pattern for the stand as well.
I generate this AI image as a source to see as I put the pattern together.
Artists and designers have always needed source material and now
that has become much easier when AI can generate any number of
Here is the prompt I used to generate this image.
1890s gentleman and gentlewoman dressed up.
It is really not just a copy. For those who want to Geek out.
AI image generation typically starts with training a neural network on a massive collection of images paired with descriptive text. The model learns patterns, textures, and the relationships between visual elements and language. Once trained, the AI can take a text prompt and generate an image that reflects the input by synthesizing learned features from its training data. It’s a bit like teaching a robot to paint by showing it thousands of art pieces and then letting it mix and match techniques to create something new. This is similar to what human artists do when they study the works of the Masters and then slowly develop their own style.
There are different methods behind these models, with popular ones being Generative Adversarial Networks (GANs) and diffusion models. GANs pit two networks against each other—the generator tries to create realistic images, while the discriminator works to spot the fakes—leading to ever-improving results. Diffusion models, on the other hand, gradually refine a random noise image until it matches the text prompt. The process can seem like magic, but it’s really all about deep learning and statistical patterns. Laugh if you must, but these algorithms are doing some serious number-crunching under the hood!
The Diffusion model blows my mind a lot. It basically calculates the next pixel based on its training. So realistically every pixel in the photo was random until it achieved and calculated the best guess. That means that for the photo above there are close to half a million pixels that had to be calculated and processed many times. The number of calculations to resolve that image can easily be in the billions of floating-point operations. The image was generated in only a couple of minutes. Makes my head spin.
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