A Progressive Journey Through Working With AI Art – Part 6 – AI Is Boring

A few months ago I started a sort of series on going through using Stable Diffusion and AI Art.  I had some ideas for some more parts to this series, specifically on “Bad Results” and another possibly going into Text based AI.  I never got around to them.  Maybe I’ll touch on them a bit here.  The real point I want to make here…

I kind of find AI to be boring.

That’s the gist of it.  On the surface, it’s a really neat and interesting concept.  Maybe over time it gets better and actually becomes interesting.  But as it is now, I find it’s pretty boring and a little lame.  I know this is really contradictory to all the hype right now, though some of that ype may be dying a bit as well.  I barely see anything about AI art, it’s all “ChatGPT” now, and even that seems like it’s waiting a bit in popularity as people accept that it’s just, “Spicy Autocomplete”.

Maybe it’s just me though, maybe I’m missing some of the coverage because I am apathetic to the state of AI.  I also don’t think it’s going to be the end all be all creativity killer.  It’s just, not that creative.  It’s also incredibly unfulfilling, at least as a person who ostensibly is a “creator”.  I am sure boring bean counter types are frothing at the idea of an AI to generate all their logos or ad copy or stories so they can fire more people and suck in more money not having to pay people.  That’s a problem that’s probably going to get worse over time.  But the quality will drop.

But why is it boring?

Let’s look at the actual process, I’ll start with the image aspect, since I’ve used it the most.  You make a prompt, maybe you cut and paste in some modifier text to make sure it comes out not looking like a grotesque life-like Picasso, then you hit “generate”.  If you’re using an online service, you probably get like, 20-25 of these generations a month for free, or you get to pay some sort of subscription for more.  If you are doing it locally, you can just do it all you want.  And you’re going to need to do it a lot.  For every perfect and gorgeous looking image you get, you’re probably getting 20 really mediocre and weird looking images.  The subject won’t be looking the right direction, they will have extra limbs, or lack some limbs, the symmetry will be really goofy.  Any number of issues.  Often it’s just, a weird blob somewhere in the middle that feels like it didn’t fill in.  Also often, with people, the proportions will be all jacked up.  Weird sized head, arms or legs that are not quite the right length.

You get the idea.  This touches on the “bad results” I mentioned above.  Stable Diffusion is great at generating bad results.

It also, is really really bad at nuance.    The more nuance you need or want, the less likely you will get something useful.  Because it’s not actually “intelligent”.  It’s just, “making guesses”.  

You do a prompt for “The Joker”, you will probably get a random image of the Batman villain.  “The Joker, standing in a warehouse” might work after 3 or 4 tries, though it probably will give you plenty of images that are not quite “a warehouse.”  

But you want say, “The Joker, cackling madly while being strangled by Batman in a burning warehouse while holding the detonator for a bomb.”  You aren’t going to get jack shit.  that’s just, too much for AI to comprehend.  It fails really badly anytime you have multiple subjects, though sometimes you can get side by sides.  It fails even ore when those two people are interacting, or each doing individual things.  If you are really skilled you can do in painting and lots of generation to maybe get the above image, but at that point, you may as well just, draw it yourself.  Because it would probably take less time.  

In the end, as I mentioned, it’s also just, unfulfilling.  Maybe you spend all day playing with prompts and in painting and manage to get your Joker and Batman fighting image.  And so what?  You didn’t draw it, you didn’t create it, you pay as well have done a Google Image search or flipped through Batman comics to find a panel that matches this description.  You didn’t create anything, you hit refresh on a webpage for hours.

Even just, manually Photoshopping some other images together would be more fulfilling of an experience.  And the result will probably be better, since AI likes to give all these little tells.

Then there is text and ChatGPT.  I admit, I have not used it quite as much, but it seems to be mostly good at producing truthy Wiki-style articles.  It’s just the next generation Alexa/Siri at best.  It’s also really formulaic in it’s results.  It’s very, “this is a 5th grade report” in it’s structure for anything it writes.  Intro, three descriptive paragraphs, an outro restating your intro.  

Given how shit the education system is anymore, I guess it’s not that surprising this feels impressive.

Another issue is that it’s so sterile in it’s responses.  There were some things going around about how to “hack” it into making it say dirty things, but most of that has been ironed out.  I mean, I don’t NEED my AI chat bot to swear, or say mean things, but it goes to such extremes to avoid this that it kind of sucks the fun out of things.  For example, i asked it “Who would win in a cage match battle to the death between Siri, Cortana, and Alexa” and it tells me it can’t answer that because it’s violent.

It’s AI constructs.  I just want to get a stupid silly response.  Please pretend that you’re talking about fictional entities having a fictional fight.  Meanwhile, on this site Character.ai, which lets you create AI bots with personalities of people, real and fake, I have antagonized a Tom Riddle bot into trying to kill me, repeatedly.

It’s funny as shit.  It’s way more compelling and fun than WikipediaBotGPT.

I also tried to make some code with ChatGPT.  It does, ok-ish, with popular languages like Java, HTML, Python etc.  I asked it to make some LSL (Linden Scripting Language) code and it made something that follows the proper structure, but does nothing.  I got similar results trying to get it to create Pong in NES Assembly that I could compile into a ROM.  I had a friend look over the code and help me follow it and it basically gave the 2nd paddle nothing to do.  It also didn’t seem to actually draw out any graphics anywhere.  Which is another issue with the code part.  It can produce single file responses.  It’s not going to produce base code, and some sprites to go with it, and maybe some imported files that do other things.  

(The code below does not do anything)

; Pong game for NES
; Written in 6502 Assembly

; Constants
P1_PADDLE_Y = $20  ; P1 paddle vertical position
P2_PADDLE_Y = $80  ; P2 paddle vertical position
BALL_RADIUS = $03  ; Ball radius

; Variables
P1_SCORE = $00      ; P1 score
P2_SCORE = $00      ; P2 score
BALL_X = $80        ; Ball horizontal position
BALL_Y = $60        ; Ball vertical position
BALL_DX = $01       ; Ball horizontal velocity
BALL_DY = $01       ; Ball vertical velocity

; Main loop
    ; Read input
    LDA $4016   ; Read controller 1 input
    AND #%00000001  ; Mask A button
    BEQ .P1_DOWN    ; If A button not pressed, go to P1_DOWN
    INC P1_PADDLE_Y ; If A button pressed, move P1 paddle up
    LDA $4016   ; Read controller 1 input
    AND #%00000010  ; Mask B button
    BEQ .P1_STILL   ; If B button not pressed, go to P1_STILL
    DEC P1_PADDLE_Y ; If B button pressed, move P1 paddle down
    ; Do nothing

    ; Update P1 paddle position
    STA $2003  ; Set PPU address to P1 paddle Y position
    STA $2004

    ; Update P2 paddle position
    STA $2003  ; Set PPU address to P2 paddle Y position
    STA $2004

    ; Update ball position

    ; Check for collision with top or bottom wall
    CMP #%00000011
    SBC #%00000100
    EOR #%11111111
    EOR #%11111111
    CMP #%11111100
    ADC #%00000100
    EOR #%11111111
    EOR #%11111111

    ; Check for collision with P1 paddle
    CMP #%00000100
    CMP #%00000100+BALL_RADIUS
    BCC .

Like generating the Joker/Batman image, it’s just not that smart.  It’s auto-completing a response based on probabilities.  It doesn’t understand how to actually break down code into parts, or what other files may be needed to make the code work.

A lot of the problem in general I think, is the more you use these tools, the more the trick becomes glaringly obvious.  The repetition in results, both images and text, really how how completely unintelligent, the “Artificial intelligence” is.  It’s just regurgitating the same things, over and over, with slightly different phrasings.

A Progressive Journey Working With AI Art – Part 5 – Training the AI

I’ve had a bit of a pause on this series, for a few reasons, mostly just, the process is slow. One of the interesting things you can do with Stable Diffusion, is train your own models. The thing is, training models takes time. A LOT of time. I have only trained Embeddings, I believe Hyperwork Training takes even longer, and I am still not entirely sure what the difference is, despite researching it a few times. The results I’ve gotten have been hit and miss, and for reasons I have not entirely pinned down, it seems to have gotten worse over time.

So how does it work. Basically, at least in the Automatic1111 version of SD I’ve been using, you create the Embedding file, along with the prompt you want to use to trigger it. My Advice on this, make the trigger, something unique. If I train a person, like a celebrity, for example, I will add an underscore between first and last name, and use the full name, so it will differentiate from any built in models for that person. I am not famous, but as an example, “Ramen Junkie” would become Ramen_Junkie” for example. So when I want to trigger it, I can do something like, “A photograph of ramen_junkie in a forest”.

This method definitely works.

Some examples, If I use Stable Diffusion with “Lauren Mayberry” from CHVRCHES, I get an image like this:

Which certainly mostly looks like her, but it’s clearly based on some older images. After training a model for “Lauren_Mayberry” using some more recent photos from the current era, I can get images like this:

Which are a much better match, especially for how she looks now.

Anyway, after setting up the prompt and embedding file name, you preprocess the images, which mostly involves pointing the system at a folder of images so it can crop them to 512×512. There are some options here, I usually let it do reversed images, so it gets more data, and for people, I will use the auto focal point deal, where it, theoretically picks out faces.

The last step is the actual training. Select the created Embedding from the drop down, enter the folder of the preprocessed images, then hit “Train Embedding”. This takes a LONG time. In my experience, on my pretty beefy machine, it takes 11-12 hours. I almost always leave this to run overnight, because it also puts a pretty heavy load on everything, so anything except basic web browsing or writing is going to not work at all. Definitely not any sort of gaming.

The main drawback of the long time is, it often fails. I’m not entirely sure WHY it sometimes fails. Sometimes you get bad results, which I can understand, but the failing just leaves cryptic error messages, usually involving CUDA. I also believe sometimes it crashes the PC, because occasionally I check on it in the morning and the PC has clearly rebooted (no open windows, Steam/etc all start up). I generally keep my PC up to date, so it’s not a Windows Update problem. Sometimes if the same data set fails repeatedly I’ll go through and delete some of the less ideal images, in case there is some issue with the data set.

Speaking of Data Sets, the number needed is not super clear either. I’ve done a few with a dozen images, I’ve done some with 500 images. Just to see what kind of different results I can get. The larger data sets actually seemed to produce worse results. I suspect that larger data sets don’t give it enough to pull out the nuances of the lesser number of images. Also, at least one large data set I tried was just a series of still frames from a video, and the results there were ridiculously cursed. My point is mostly, a good middle ground seems to be 20-30 base images, with similar but not identical styles. For people, clear faces helps a lot.

I have tried to do training on specific styles but I have not had any luck on that one yet. I’m thinking maybe my data sets on styles are not “regular” enough or something. I may still experiment a bit with this, I’ve only tried a few data sets. For example I tried to train one on the G1 Transformers Cartoon, Floro Dery art style, but it just kept producing random 3D style robots.

For people, I also trained it on myself, which I may use a bit more for examples in a future post. It came out mostly OK, other than AI Art me is a lot skinnier and a lot better dressed. I have no idea, but every result is wearing a suit. I did not ask for a suit and I don’t think any of the training images were wearing a suit. Also, you might look at them and think “the hair is all over”, but I am real bad about fluctuating from “Recent hair cut” to “desperately needs a haircut” constantly. The hair is almost the MOST accurate part.

Anyway, a few more samples of Stable Diffusion Images built using training data.

A Progressive Journey Through Working With AI Art – Part 4 – Better Prompts

The next step in my journey to better AI Art, was better prompts. Which also has sort of landed me on just using one complex prompt I found and modifying it as needed, which works very well. I started off by adding more descriptive words to the basic prompts. Including Camera models which was suggested by quite a few people.

  • “In the Style of Manga”
  • “An oil Painting Of”
  • “A Pencil Sketch of”
  • “in the style of [artist]
  • “Realistic”
  • “Hyper-realistic”
  • Canon 5D

This worked better. But I started looking around on the Stable diffusion Sub-Reddits for good prompts to use. I came across the following Prompt:

, (humorous illustration, hyperrealistic, big depth of field, colors, night club scenery, 3d octane render, 4k, concept art, hyperdetailed, hyperrealistic, trending on artstation:1.1)

text, b&w, (cartoon, 3d, bad art, poorly drawn, close up, blurry, disfigured, deformed, extra limbs:1.5)

Which I have used and adapted quite a lot. Essentially, everything in front of the first Comma is your actual prompt. This is essentially, what I have been doing. Everything after refines things a lot. You can also change the background by editing the “night club scenery” bit.

Anyway, the rest of the post is sharing some more pics based on this prompt.

Prompt: “Tracer from Overwatch” +

As normal, really iffy on the hands, but still some neat concepts that could actually be skins in the game.

Prompt: Godzilla +

Prompt: Several different Batman Prompts (Batman Fighting, Batman Overlooking Gotham, Batman Battling Joker)

Prompt: The Joker +

These are some of my favorites so far. I am not a huge Joker Fan really, but they do a REALLY good job of portraying the more modern crazy that is The Joker. I actually left a few off because frankly, they are super creepy, but really are nice.

Prompt: Professor Layton

Again, it has no idea who Layton is, but still seems to do really well with the Aethetic of Layton. Which is kind of odd honestly.

Prompt: An Adorable Pixar Kitten

Feels like Pixar styled art is cheater mode a bit but these came out pretty good as well.

Three Prompts with similar results, A Norwegian Landscape, The Lord of the Rings, and Arya Stark,

It’s kind of crazy just how much better the results have gotten from previous attempts, especially just like, 6 months ago or something, when I started playing with this concept using online tools. That said, it also gets old pretty quick, and you end up with a lot of “Weird shit” output, extra limbs, weird proportions, extra elbows, odd faces. I can see how it might be useful to produce some generic banner backdrops and whatnot. I also can see it just getting even better, very rapidly. If hands can be figured out, that would be a real game changer.

A Progressive Journey Through Working With AI Art – Part 3 – Running Natively with Automatic1111

After experimenting with online sources, then running Stable Diffusion locally using Windows Subsystem for Linux, I wanted more, and better, because I knew my machine was capable of much more. So I looked into alternatives and found Automatic1111’s Stable Diffusion variant.

The core take away here, is how this is like night and day in performance and quality.

Previously, with WSL, I would run batches of prompts and seeds and maybe get a few okish results. Also, any dimensions larger than the base 512×512 would crash the thing and I’d get nothing. Basically, it definitely was not exploiting the full potential here. It also completely dogged my entire rig down while building an image, which took maybe 5-10 minutes for the actual processing to work.

It still dogs the machine down, but not nearly as much as it had been. And it takes like 10-20 SECONDS to produce an image. The image quality is also like 1000 times better, though not still without that “AI Art Wonkeyness” like these weird square cows.

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A Progressive Journey Through Working With AI Art – Part 2 – Running Locally

My initial foray into doing AI Art locally involved Stable Diffusion, which can be found here. This path ended up being sort of, multi staged. Initially I set it all up to run off the command line through PowerShell and Python. I ran through several prompts aver a few days off and on with iffy results (above). At some point I closed it down. When I came back, I couldn’t get it to run again. I couldn’t figure out WHY and so I gave up and nuked it to start fresh. (The problem was I forgot to use the Python Virtual Environment, DUUUUH).

Part 1 can be found here.

Side Note, Unfortunately, I don’t know the prompts for many of these because my initial runs just produced file names with numbers for file names.

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