Goodies taem!

Chat-GPT's "what's my tag?".py script!
In extra files section you can now find a zip with ChatGPT made python script that will allow you to analyze any number of folders containing images inside them and receive top X tags best matching average content of the images in each folder!

Open script in text editor and adjust 'CONFIG' settings as needed

BASE_DIR = "." # Current folder containing subfolders of images

LABELS_FILE = "tags.txt" # Candidate labels (names, styles, etc.)

OUTPUT_FILE = "results.txt" # Where results will be saved

TOP_K = 3 # Number of top picks per folder

Few notes:
- You NEED to have a .txt file containing candidate "tags". Those can anything you want, newline separated. They will be used to compare image content to.

Example: You have a painterly style image folder, but you don't know what it will train better on 'art' or 'painting'. Make tags.txt containing those two tags and run this script on that image folder!

- This is batch script, so it will run in current folder, and will access every subfolder inside current folder.

- This script pick tags exclusively! If you have similar content of images, picked tags will not repeat.

"In loving memory of ChatGPT-4.

Good night sweet prince."

So, here it is! The model Omnitron and Neotron wanted to be!
I won't go into training detail, otherwise this would be a full article.
But I will give a few notes and pointers:

- It's a "base" model so it's a little stiff. I will make single-tag finetune later, to add more noise and make it more creative.

- DO use negative prompts.

- This is not the model I was teasing. It's the latest one, but "base" version trained better. I have high hopes for noisy finetune.

- This has animals and mythical creatures in it (to a lesser extent) in a non-?????? context. They can be cute and 'whimsical', but this is still a NSFW model.

- It's a single gender model, mainly because SD1.5 blends shared tags and I couldn't be bothered coming up with two sets of pose tags (or maybe because I don't have any female datasets). It can probably still generate women (from whatever is left from Ultron underneath), but they have to be explicitly prompted and don't expect female nudity.

- Went back to 'art' tags from Omnitron, aka 'traditional art', 'illustration art' and so on. Because they train better than 'style'.

- It's Text Encoder trained, so '1boy' might come out a bit literal, I suggest avoiding it. Also, I botchered 'body hair', 'chest hair' tags - they work, but are destructive on the face (I'll try to fix it in tag finetune). Instead, use 'hairy' tag.

- Model was trained on my own tagging scheme. And even though I won't yet provide you with a full list of tags, there's really only one thing to know - it's a "dictionary word" tagging scheme minus 'a, an, the, own'. Word salad prompts do work. Tag ex: 'flexing biceps', 'one hand on hip', 'adult man', 'midair', 'on one leg', 'against wall', 'on floor' and so on. I will be using tags in my example prompts - take a peek, if you care enough.

- A few important tags:
'front view', 'left/right three quarter view', 'left/right side view', 'left/right back view' (left/right distinction was made, but I became convinced it doesn't matter in latent space, output ends up 50/50 random)

'eye-level shot', 'low-angle shot', 'high-angle shot', 'tilted left/right shot' ('worm's eye shot' and 'overhead shot' were included, output might not be as expected)

'close-up', 'medium close-up', 'medium shot', 'medium full shot', 'full shot' (these will depend on prompt focus and might be muted; 'long shot' and 'extreme close-up' were also in, but in few instances)

'young man', 'adult man', 'mature man', 'old man' (male subject tags - 'young' doesn't go below 18-20ish; as mentioned, 'boy' collaterally finetuned very young through Text Encoder alignment)

'skinny', 'athletic', 'muscular', 'hyper muscle', 'narrow waist', 'muscle chub', 'fat' (there was also 'average', but you might need negative prompt 'abs', 'muscle' to activate)

'clean shaved' -> 'unshaved face' -> 'stubble' -> 'facial hair' -> 'beard' -> 'long beard'

'laughing', 'smile', 'light smile', 'frown', 'furious' and many more (a bunch of facial expression training)

for hand/arm/leg poses single limb distinction was made as in 'one arm behind head'/'arms behind head', but multi-configuration is not guaranteed, as there just wasn't enough data to facilitate all variants.

fantasy concepts were in, but they should get stronger in single tag finetune. As of right now 'angel/demon/fairy/butterfly wings', 'ram/small/thick horns', 'elf/orc/goblin/dwarf/cyborg/robot/...' - most common stuff should work.

Honorary mention: 'short hair' - actual crew cut, which is usually hard to evoke in SD1.5 models.

Tags are too many to list. On top of that, large batches were subdivided into synonymous tags (something like 'adult man' was trained through ~10 various tags). But try to use tags expressed in normal dictionary words and you might hit what you're looking for. At some point, I might upload a full curated list of what got trained, but that's a lot of testing.

- Overall model came out a little too good, compared to what I normally come out of the woodworks (HA!) with. All thanks to ChatGPT-4, for spilling all the finetuning juice.

- Datasets were entirely ?????? in nature, not actual ?????? acts were included. Outside of Text Encoder randomly aligning onto some half-arsed imagery, I doubt any ?????? prompts will work.

- Training commenced on 15k images at 640x640 resolution.

- As per usual, if you have someone who buys stuff from you that's available for free - have a go at it.