Exploring General AI Image Generation: future possibilities

[eviethethinks]

I wanted to start a broader future possibilities thread around General AI Image Generation.

A useful angle is limitations, but it also connects with advanced workflows. Different tools and workflows can produce very different results even when the goal is similar.

It would be interesting to compare practical examples, experiments, setups, or lessons that show where General AI Image Generation is genuinely useful and where it still needs improvement.

 

[ben_rwh]

I think general and image is the key part of this topic. A practical approach is to define the outcome you want, test it with a small example, and then check quality, speed and reliability separately. It is usually easier to troubleshoot when each change is tested on its own.

 

[lucyonpanda]

A useful way to look at Exploring General AI Image Generation: future possibilities is to focus first on general and image. That tends to make the trade-offs easier to compare. It also gives you a cleaner baseline if you want to compare tools or settings later.

 

[elliotworld]

For Exploring General AI Image Generation: future possibilities, I would separate the problem into setup, output quality and repeatability. general and image fits into that because it can change the result quite a lot depending on the workflow. That way you can improve the workflow without losing track of what caused the improvement.