Experiments

We’ll be relying on the seed to produce consistent images for comparison, so let’s see what happens if we run the same prompt three times

Very minor Differences! Looks like we can reliably use this for our experiment!

Prompt Separators

First, let's look at how separators could affect a simple prompt (We wont get into how NLP parses lists and grammar, or how to trick it into creating pseudo words / compound words)

Welp, that convinces me, separator doesn't do much of anything in this context!

Prompt Weights

Regular Weight Variation

Next, let's look at weights. We're using an image prompt, so that image gets a default weight of .25, unspecified weights get a default value of 1.

Negative Weights

Next let's examine a few interesting weights! Note: The total of all weights needs to be non negative or you will get an error.

Image Weights

Image Examples

Aspect Ratios

Uplight And Stop

If you’ve ever upscaled your image, you may find it super grainy, full of noise, or drastically different. Some people use external upscalers, but you can also attempt playing with the –uplight and –stop parameters. Let’s take a look! We’ll use the prompt /imagine Utah Teapot --ar 16:9 --seed 1 play with various stop and uplight combinations. We’ll specifically look at variation #2 (The top right image on the grid).

Seeds

// TODO: Simple example showing how simply incrementing the result doesn't create simple variations, but sameseed does

Default Prompt Weights

Is the same as

Pseudo Compound Words

So what does MJ think of random words we make up? Can it interpret custom compound words such as blightwood or terratron? Maybe! Lets see how words can be smooshed together and how MJ might interpret them. For this, we wont be using the image prompt.

// TODO: UtahTeapot vs Utah Teapot vs Utah-Teapot vs Utah+Teapot (notice the lack of space - makes a big difference in MJs understanding)