241–250 of 519 posts
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Not exactly, since the abuse needed to actually happen for the derivative images to be possible to generate.
Is Stable Diffusion only able to generate images of things that have actually happened?
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…
And it's still not available in my language on iOS... :( (Norwegian)
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Porn has driven many tech advances. I predict that models trained on specific porn genres will appear as soon as training a good model is doable for under $5000. They’ll get here much quicker if we get video to that mark first.
> Porn has driven many tech advances. This is an urban myth.
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I have... ~11TBs of free disk space and a 1080ti. Obviously nowhere close to being able to crunch all of Wikimedia Commons, but I'm also not trying to beat Stability AI at their own game. I just want to move the arguments people have about art generators beyond "this is unethical copyright laundering" and "the model is taking reference just like a real human".
To put things in perspective, the dataset it's trained on is ~240TB and Stability has over ~4000 Nvidia A100 (which is much faster than a 1080ti). Without those ingredients, you're highly unlikely to get a model that's worth using (it'll produce mostly useless outputs). That argument also makes little sense when you consider that the model is a couple gigabytes itself, it can't memorize 240TB of data, so it "learned"…
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…
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…
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…
> Writing a training loop for CLIP manually wound up with me banging against all sorts of strange roadblocks and missing bits of documentation, and I still don't have it working. There is working training code for openCLIP https://github.com/mlfoundations/open_clip But training multi-modal text-to-image models is still a _very_ new thing, in terms of the software world. Given that, my experience has been that it's ne…
I just thought about this, so bare in mind that I don't know much of the technical implications of this, but: Couldn't we train a very good model by distributing the dataset along with the computing power using something similar to folding@home?
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The main reason why Stable Diffusion is worried about NSFW is that people will use it to generate disgusting amounts of CSAM. If LAION-5B or OpenAI's CLIP have ever seen CSAM - and given how these datasets are literally just scraped off the Internet, they have - then they're technically distributing it. Imagine the "AI is just copying bits of other people's art" argument, except instead of statutory damages of up to…
Is artificially generated CSAM that doesn't actually involve children in its production not an improvement over the status quo?
I don't think the fact that it's artificially generated has any bearing for some important purposes.
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…
I'm in a kind of same boat. I think indie games are the way to show true potential of SD. Hence, I'm working on http://diffudle.com/ which is a mix of Wheel Of Fortune + Stable Diffusion + Wordle. I Can't figure it out but feels to me like its lacking something.