A practical guide to AI image generation, from diffusion to agents
danielball.com53 points by westsmith 4 days ago
53 points by westsmith 4 days ago
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
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?
Exactly, turn something nondeterministic into something more deterministic and AI does a great job at that.
Neat! I've followed you on Github. I like the idea of going to the past to see what works and adapting it for the LLM age. Nice work!
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!
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.
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?
Look into r/StableDiffusion and follow their prompts and download their ComfyUI workflows.
I have been asking opus 5.5 to write python to create logos. It is more consistent and easier to control than gen AI for this purpose
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