21–30 of 55 posts
Drawing of the inside of a cylinder.
That's usually bad enough. Then try to specify size, and specify things you want to place inside the cylinder relative to the specified size.
(e.g. try to approximate an O'Neill cylinder)
I love generative AI models, but they're really bad at that, and this one is no exception, but the speed makes playing around with prompt variations to try to see if I get somewhere a lot easier (I'm not getting anywhere...)
> Instant image generation that responds as users type – a first in the field Stable Diffusion Turbo has been able to do this for more than a year, even on my "mere" RTX 3080.
The models seem to have gotten to a point where even something I can run locally will give decent results in a reasonable time. What is currently "the best" (both from an output quality and ease of installation perspective) setup to just play with local a) image generation, b) image editing?
What am I missing?
I wasn't able to get many decent results after playing with the demo for some time. I guess my question is...what exactly is this for? I was able to get substantially better results about 2 years ago running SD 2 locally on a gaming laptop. Sure, the images took 30 seconds or so each, but the quality was better than I could get in the demo. Not sure what the point of instantly generating a ton of bad quality images i…
What does consumer-grade mean in this context - is this referring to an M1 MacBook or a tower full of GPUs? I couldn't find in the paper or README.
From the paper :
> We train using the AdamW [26] optimizer with a batch size of 5 and gradient accumulation over 20 steps on a single NVIDIA A100 GPU
So it's "consumer-grade" because it's available to anyone, not just businesses.
My favorite test of image models: Drawing of the inside of a cylinder. That's usually bad enough. Then try to specify size, and specify things you want to place inside the cylinder relative to the specified size. (e.g. try to approximate an O'Neill cylinder) I love generative AI models, but they're really bad at that, and this one is no exception, but the speed makes playing around with prompt variations to try to se…
What does consumer-grade mean in this context - is this referring to an M1 MacBook or a tower full of GPUs? I couldn't find in the paper or README.
One Nvidia A100. From the paper : > We train using the AdamW [26] optimizer with a batch size of 5 and gradient accumulation over 20 steps on a single NVIDIA A100 GPU So it's "consumer-grade" because it's available to anyone, not just businesses.
My favorite test of image models: Drawing of the inside of a cylinder. That's usually bad enough. Then try to specify size, and specify things you want to place inside the cylinder relative to the specified size. (e.g. try to approximate an O'Neill cylinder) I love generative AI models, but they're really bad at that, and this one is no exception, but the speed makes playing around with prompt variations to try to se…
Careful how much you say that. I'm sure there's more than a few AI engineers willing to use some 3d graphics program to add a hundred thousand views of the inside of randomly generated shapes to the training set.