johoba
The AI inference cold start problem
Mastering Stable Diffusion Image Generation with Associative Prompting
Show HN: Generate photorealistic images using Stable Diffusion and ControlNet
Show HN: Iterative architectural design using generative AI
Hi everyone,I'm Joachim, and together with my co-founder, we're building Visoid - a tool for rendering architectural designs in an iterative manner. We firmly believe that the future of this type of rendering, especially during the ideation stage of a project, will be heavily reliant on generative AI.Traditionally, you would send your designs to a professional and potentially wait several days...
Lessons from My First Month as an Indie Hacker
I write, and why you should too
I do some prompt engineering on a daily basis, and I find that the challenge is often less about the writing and more about ensuring that the output is what I want, and consistently so.
Trying to think of some useful features for this tool, I'm thinking that some improved tooling for testing output could be useful. One thing is to validate the output, but also to be able to run the same query in bulk to check that the output always validates correctly. The model often outputs something correctly one time, and incorrectly another time, for the same prompt.
Just some thoughts from my side!
I tried to run it with the following query:
Hyper-realistic, aluminum panels, shiny windows, blue sky with clouds, lush nature, brick driveway to garage, grass garden, morning light, modern design, architecture design, environment, Cinematography, mega scans, cinematic, hyper-realistic, photo real, cinematic composition, highly detailed, vray,8k render, 16 mm lens, DSLR
And the only thing it changed, was by adding "--ar 3:2". Not really that much of an improvement over the original prompt. The above prompt is maybe not that bad already but for this tool to be useful, I think it should be able to improve even the good ones to be even better.