A practical guide to AI image generation, from diffusion to agents

You mentioned in your README that it was tested with GLM-5.3 but this is not a multi-modal model. Did you mean GLM-5.3-Flash?

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.

https://github.com/scpedicini/agi-stylist

https://kq-styles.specr.net

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?

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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