These models always create pixel art that looks like garbage because the people using it don't know enough about the medium to properly judge the output. This article is no exception. If you want pixel art for your project then learn or hire someone with that expertise
Are you saying people unfamiliar with the 'pixel arts' can't enjoy it their own way?
I don't think the author is posing as a professional. These models are like having a 3D printer at home. Very handy in a pinch, but what they make doesn't replace the real thing.
But if the user thinks it looks fine, then won't most players think the same?
I just don't get the complaints about "use proper art" when it comes to games. Most of the time the game has some other, far more glaring, visual issues (anti-aliasing, clipping, icons-not-matching-items).
Nice article. There's another option for pixel art that's recently been coming into vogue as tool usage improves in LLMs which is procedural graphics.
I actually experimented with creating a set of skills around rendering early AGI style EGA graphical scenes, and it worked surprisingly well.
So rather than storing full bitmaps, you instead store sequences of simple drawing instructions designed to recreate some specific scene - like you would an SVG.
As a result you end up sidestepping a lot of the problems with larger generative models, which can have issues with grid alignment, palette choices, and high-frequency noise.
This is very cool! I will try your method for background and tile creation in the asset-generation tool I've built [1]. Would your procedural method work for animations as well, like sprites?
How so? Judging from the examples the model does seem to take advantage of the language (creates and uses reusable elements) and AFAICT the language itself compiles down to "real" Sierra PIC commands so in theory it can be used with a Sierra PIC viewer/engine.
Yeah compiling to Sierra PIC is a unique advantage over asking the model for an image. But the claim im attacking is different. The claim in attacking is that you cant just tell it to produce images like this directly.
>As a result you end up sidestepping a lot of the problems with larger generative models, which can have issues with grid alignment, palette choices, and high-frequency noise.
All of these can be avoided with simple prompts and don’t require all this ceremony.
Convo then the artifact from the convo (sorry, apparently no way to share the pics but they are just captured from the artifact).
Thanks! Yeah, I started building it because I read an interview with the original creators of King’s Quest, Ken and Roberta Williams where they mentioned in passing that it was too expensive (in the 80s) from a storage perspective to store entire bitmaps for each scene, so instead they'd just store a series of very simple drawing instructions and render on demand.
I’m super excited to see more LLM-driven procedural graphics stuff in the future!
Related question: Whenever I try to run Image generation locally, be it with Flux or Qwen, I seem to be getting results that are about 1-2 years behind what ChatGPT can produce today - and take up to 5 minutes for a single image. Is this a hardware problem (running it on a decent windows gaming PC), a configuration problem or just the reality of what local models can deliver at the moment on consumer hardware?
You can get results a billion times better than the horrible, disgusting look of ChatGPT in less than a couple of seconds with a model like Krea or MOSS, and ComfyUI. With just a 8GB VRAM GPU or so...
Just visit Civitai, look at the pages for the base models, filter for images with the ComfyUI workflow in them, then you can just download and drag and drop them onto your local ComfyUI app.
Thanks, I've done that, but with mixed results. I've also tried comfyui wrappers and alternatives that try to hide the complexity and just set sensible defaults, but I'm beginning to suspect its ultimately a consumer hardware vs million dollar NVidia server question.
What are you trying to generate? I've also noticed that local models aren't as good as cloud models due to hardware but there are still ways of optimizing the process.
Are control nets not being used much anymore? I haven't seen anything that provides a similar level of composition control, and yet I do not see them being referenced much at all.
They are used a lot, and modern edit models kind of support base cases - depth, canny, openpose - as a reference image. I mean they understand "use image 2 as depth map", result indeed have same depth, etc. But interpretation sometimes arbitrary to model.
For the tasks where several control nets should be adhered (depth and canny and segmentation) dreaded SDXL is still unmatched. SDXL also the only one handling influence weight for each control net normally, this ability completely vanished on modern models
Control nets in early post-SDXL models like ZIT or Flux kind of work too, but still much worse in quality when combined. I suspect pure diffusion architecture of SDXL is really favorable to mixing control signals with precision, DITs probably can be trained to similar level for mixing signals - but it is much harder and never seen in practice. At least every post-SDXL model I tried is trained with single one in mind, judging from my attempts. This rant is from personal expirience.
I don't think the author is posing as a professional. These models are like having a 3D printer at home. Very handy in a pinch, but what they make doesn't replace the real thing.
The problem isn't that they look shitty to pixel artists or experts, they look shitty for anyone with a bit of observation.
It's junk art the same way Mc Donald's is junk food.
I think we could do a bit better here with the explanation of what looks bad to you..
I just don't get the complaints about "use proper art" when it comes to games. Most of the time the game has some other, far more glaring, visual issues (anti-aliasing, clipping, icons-not-matching-items).
I actually experimented with creating a set of skills around rendering early AGI style EGA graphical scenes, and it worked surprisingly well.
So rather than storing full bitmaps, you instead store sequences of simple drawing instructions designed to recreate some specific scene - like you would an SVG.
As a result you end up sidestepping a lot of the problems with larger generative models, which can have issues with grid alignment, palette choices, and high-frequency noise.
https://github.com/scpedicini/agi-stylist
https://kq-styles.specr.net
[1] https://github.com/acatovic/ai-game-studio
>As a result you end up sidestepping a lot of the problems with larger generative models, which can have issues with grid alignment, palette choices, and high-frequency noise.
All of these can be avoided with simple prompts and don’t require all this ceremony.
Convo then the artifact from the convo (sorry, apparently no way to share the pics but they are just captured from the artifact).
Convo: https://claude.ai/share/78cd63c8-ffdd-4569-8de2-ac8ab5c5cfe0
Artifact: https://claude.ai/artifact/3M6xiKJKz1oDxTKGWi8FUi
I’m super excited to see more LLM-driven procedural graphics stuff in the future!
Just visit Civitai, look at the pages for the base models, filter for images with the ComfyUI workflow in them, then you can just download and drag and drop them onto your local ComfyUI app.
For the tasks where several control nets should be adhered (depth and canny and segmentation) dreaded SDXL is still unmatched. SDXL also the only one handling influence weight for each control net normally, this ability completely vanished on modern models
Control nets in early post-SDXL models like ZIT or Flux kind of work too, but still much worse in quality when combined. I suspect pure diffusion architecture of SDXL is really favorable to mixing control signals with precision, DITs probably can be trained to similar level for mixing signals - but it is much harder and never seen in practice. At least every post-SDXL model I tried is trained with single one in mind, judging from my attempts. This rant is from personal expirience.