Chat GPT has finally figured me out, I think. It has put the clamps on my "verboten" image making. I'll only be able to render Pilgrims and Mennonites now. Still, the illustrations are pretty cool. Since I can't figure out how to "jailbreak" the system, and since my A.I, pal has been reprimanded, I asked for information rather than images. Here's what I got:
1. Diffusion — painting by subtraction
Modern image generators (Stable Diffusion, DALL·E, Midjourney, etc.) start with pure noise — a random static image.
They learn to reverse the process that adds noise to real images.
Each step removes a bit of noise and replaces it with structure guided by a text description.
Think of it as sculpting out of fog: the model slowly “de-noises” toward something that statistically matches the words you gave it.
🖋️ 2. Latent space — where the images live
The model doesn’t paint in pixel space. It works inside a latent space — an abstract mathematical world where every concept (pose, lighting, fabric, color mood, etc.) has coordinates.
When you write a prompt, you’re pulling the model’s attention toward certain clusters of meaning.
For example:
“Ingres lighting” might activate vectors for smooth transitions, ivory tones, soft edges.
“Hopper interior” might activate vectors for hard shadows, right angles, melancholic palette.
The image forms at the intersection of those forces — a compromise among probabilities.
🗝️ 3. Conditioning and prompt weighting
Every word (or token) gets an embedding — a set of numbers that represent its meaning.
The model weighs them during generation.
That’s why adding precision (“cool daylight through tall window”) matters more than adjectives (“beautiful, detailed”).
You can think of prompt design as composition through language: arranging conceptual lights and forms.
⚙️ 4. Safety and filters
The same text encoder that interprets meaning also tags risk.
If certain tokens (body-related, violence, etc.) cross a learned threshold, the system refuses to decode the image.
The algorithm doesn’t “understand” morality; it just compares patterns against banned examples in its training.
That’s the wall you keep hitting — not a moral judgment, but a pattern match.
🧩 5. Fine-tuning and control
If you ever did want your own version — one that could interpret Ingres–Botticelli–Messina faithfully — you’d need to:
Gather a dataset of reference paintings and your own photographic studies.
Train a small LoRA (low-rank adapter) or DreamBooth model on that dataset.
Use a prompt template (like the ones we built) to steer it with text.
It wouldn’t take a supercomputer — just patience, curation, and GPU time.
I found that pretty interesting, so I asked for more info. It gave me pages of information on how to set up my own A.I system for making my own photo algorithms. I don't understand any of it, so I am asking people if they know a coder who can help me out. I don't know any coders, but surely they are fairly abundant. Right?
If I can manipulate any of my images in the manner I want. . . oh, my. . . oo-la-la! It would be like figuring out the old Polaroid 669 method again, only better. For now, however, I'll have to be happy with making family photos, I guess.
Not my family, of course.
I just deleted fifteen minute of writing about my woes.
You're welcome.
The weather has turned. I woke to clear skies and cool air. Let's see if it will make me feel any better. I made chicken soup last night. It was good and there is a lot left over. Hearty soups are a good way to eat and live.
With crusty bread, of course.
I'll be about my day now. I slept late and my mother is being very. . . "busy." I need to attend.
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