What I used for training:
In-build preprocess images tab (it is awesome for how simple it is).
"Split oversized images into two" and Deepbooru for caption
In the end I got 168 images for training with some splits included in that number.

Trained on 512x512 Number of vectors per token 24
Template was just:

[filewords], [name]

Since my previous attempts at hypernetwork sucked I started cautiously
Learn rates:
2e-05 till 70k steps
0.005 till 89k steps
0.0001 till the end

What I used for previews:
Left side:
Steps: 40, Sampler: Euler, CFG scale: 8, Seed: Random until I got something nice, 
Size: 512x512, Model hash: 925997e9 Tags from pictures at the top extracted by deepdanbooru 
And some slight additions to get something similiar ( I suck at prompting).
Right side:
Proompts from /hdg/. Pictures on top have post number.
Tried matching parameters but obviously resolution had to be 512
And I couldn't be bothered to use different model or clip skip.
So the image may be different but the result is still shown as first pic

Notes:
-For data set I just picked a little bit of everything.From danbooru. Both nsfw and sfw stuff.
-I left previews without any input from txt2img tab (Read parameters (prompt, etc...) from txt2img tab). Script uses random tags from your pictures.And this is still enough to see if model is going in a good direction.
-I tried hypernetwork first but it went nowhere. For now I think artstyle is better done with embed. As you monitor the embed you should start to see some things appear at some point and it gradually shows more and more. See transition from original > 42.5k steps (because of low learn rate I used) > 60k.For hypernetwork you will get nice results quickly, but it is much harder to see if it is going in a right direction when it spits out pure and unrelated garbage between decent things.I will try hypernetwork next and do something like this for a hypernetwork.
-I noticed deepdanbooru is very self sufficient, but there was one picture that put in a lot of stuff that wasn't relevant.If you feel like it go through txt files and see if everything is ok. You can also probably ignore it if you use a sufficiently big learning data and those errors will get diluted by correctly tagged images.
-If you are trying your first embedding I would probably stick to 0.0001 learn rate and monitor if it works constantly. If you see through the output it generates that it failed then copy an embedding from earlier step you can find in your textual_inversion folder log back to your embedding folder and try again with lower learn rate. You fail when you see completely psychodelic blobs of slime or something like this:





Also if you get those pictures very quickly regardless of your learn rate then you need to check this in settings:


And remove this from that folder. Just move it up a directory. I WASTED MY FIRST DAY BECAUSE OF THIS.

