381–390 of 519 posts
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Interesting project but terrible naming.
To be clear to the original poster, the naming is terrible because of the nazi associations of the number 88, correct?
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The point is that there's is no practical limit on compression. You don't need "AI" or anything besides very basic statistics to get astronomical compression ratios. (See: "zip bomb".) The only practical limit is the amount of information entropy in the source material, and if you're going to claim that internet pictures are particularly information-dense I'd need some evidence, because I don't believe you.
Correct, however "compression is equivalent to general intelligence" ( http://prize.hutter1.net/hfaq.htm#compai ) and so in a sense, all learning is compression. In this case, SD applies a level of compression that is so high that the only way it can sustain information from its inputs is by capturing their underlying structure. This is a fundamentally deeper level of understanding than image codecs, which merely cap…
Both are just simple statistical relationships between parameters and random variables.
Hopefully related: If I'm a photographer wanting to improve resolution of my content for printing, what's my current best bet for upscaling? Is it realistic to make use of this on the command line, feeding it my own images? Or has someone wrapped it in an app or online service?
As a counter-recommendation, Topaz’s much-advertised Gigapixel AI is rarely useful. Their Denoise and Sharpen apps are good though.
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As a counter-recommendation, Topaz’s much-advertised Gigapixel AI is rarely useful. Their Denoise and Sharpen apps are good though.
I dunno - I've found it useful on a bunch of images[1] but I tend to try Pixelmator Pro first because that's a simple key combination to enlarge an image and 90% of the time it's Good Enough for my purposes. [1] The new Photo AI, on the other hand, is slow, clunky, and not infrequently glitches out wildly. But on the plus side it does combine sharpening and denoising into one workflow.
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Correct, however "compression is equivalent to general intelligence" ( http://prize.hutter1.net/hfaq.htm#compai ) and so in a sense, all learning is compression. In this case, SD applies a level of compression that is so high that the only way it can sustain information from its inputs is by capturing their underlying structure. This is a fundamentally deeper level of understanding than image codecs, which merely cap…
I fail to see the difference between "underlying structure" and "short-range visual features". Both are just simple statistical relationships between parameters and random variables.
Is there a good explanation of how to train this from scratch with a custom dataset[0]? I've been looking around the documentation on Huggingface, but all I could find was either how to train unconditional U-Nets[1], or how to use the pretrained Stable Diffusion model to process image prompts (which I already know how to do). Writing a training loop for CLIP manually wound up with me banging against all sorts of stra…
https://lambdalabs.com/blog/how-to-fine-tune-stable-diffusio...
In addition to removing NSFW images from the training set, this 2.0 release apparently also removed commercial artist styles and celebrities [1]. While it should be possible to fine tune this model to create them anyway using DreamBooth or a similar approach, they clearly went for the safe route after taking some heat. 1. https://twitter.com/emostaque/status/1595731407095140352?s=4...
In addition to removing NSFW images from the training set, this 2.0 release apparently also removed commercial artist styles and celebrities [1]. While it should be possible to fine tune this model to create them anyway using DreamBooth or a similar approach, they clearly went for the safe route after taking some heat. 1. https://twitter.com/emostaque/status/1595731407095140352?s=4...
I am a solo dev working on a creative content creation app to leverage the latest developments in AI. Demoing even the v1 of stable diffusion to the non-technical general users blows them away completely. Now that v2 is here, it’s clear we’re not able to keep pace in developing products to take advantage of it. The general public still is blown away by autosuggest in mobile OS keyboards. Very few really know how far…