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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.
I really want to try this. Please add mobile iOS support!
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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.
> 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. That's awesome, I love it!
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…
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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.
Very creative and a fun way to interact with SD. I would encourage you to explore this idea further, as interest in SD might grow and people want to engage with the topic in an accessible way. I like the idea of hard-limiting play (1 quizz per day) but a small backlog of previous pictures could be nice to explore a little.
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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…
So I definitely see an issue with Stable Diffusion synthesizing CP in response to innocuous queries (in terms of optics—-the actual harm this would cause is unclear). That said, part of the problem with the general ignorance about machine learning and how it works is that there will be totally unreasonable demands for technical solutions to social problems. “Just make it impossible to generate CP” I’m sure will succe…
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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"…
Well that would be ~4000 people each with an Nvidia A100 equivalent, or more with less, this would be an open effort after all. Something similar to folding@home could be used. Obviously the software for that would need to be written, but I don't think the idea is unlikely. The power of the commons shouldn't be underestimated.
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"…
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Can you go into a bit more detail? What architecture did you use? Is the month training time really just training with mini batches with a constant learning rate? Or are these many failed attempts until you trained a successful model for a few days in the end? I particularly interested in the image generation part (the DDPM/SGM)
Yeah I did have a few false starts. Total time is more like 3 months vs 1 month for the final model. For small scale training I found it’s necessary to use a long lr warmup period, followed by constant lr. There’s code on my GitHub (glid3) edit: The architecture is identical to SD except I trained on 256px images with cosine noise schedule instead of linear. Using the cosine schedule makes the unet converge faster bu…