Features: - Full data privacy - nothing is sent to the cloud - Clean and easy to use UI - One click installer - No dependencies needed - Multiple image sizes - Optimized for M1/M2 Chips - Runs locally on your computer
Seriously though, I imagine this is less a case of whether this specific implementation permits pornography, but whether any porn was included in the dataset it was trained on. No matter how good AI is, it only knows what it knows.
It mostly understands what naked people look like, but the images I've generated involve a lot of accidental body horror. You get a lot of people with extra arms, weird eyes, or body parts in the wrong places. The fact that they explicitly removed porn from the training set comes through pretty clearly in the model.
I suspect it could be improved a lot with some specialized retraining. As far as I know, nobody has done that work yet.
The official implementation has a second model that detects pornography, and replaces outputs including it with a picture of this dude https://www.youtube.com/watch?v=dQw4w9WgXcQ (Not kidding). Removing that is a really simple a one line change in the official script.
I was amused by this when reading the source. Here’s the function that loads the replacement.
https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a...
It looks like removing line 309 of the same file would disable the check, but I haven’t tried it.
But the second point here is also wrong: the whole reason these models are interesting is because they can generate things they haven't seen before - the corpus of knowledge represents some type of abstract understanding of how words relate to things that theoretically does encode a little bit of the mechanisms behind it.
For example, it theoretically should be able to reconstruct human like poses it has never seen before provided it has examples of what humans look like and something which transposes to an approximate value - an obvious example in the context of the original question would be building photorealistic versions of a sketched concept (since somewhere in it's model is an axis which traces from "artistic depiction of a human" to "photograph of a human" in terms of style content).
Of course, most people aren't very good at drawing realistic human poses - it's a learned skill. But the magic of deep learning is really that eventually it doesn't need to be - we would hopefully be able to train a model which can be easily copied and distributed which represents the skill, and SD is a big step in that direction (whether it's a local maxima remains to be seen - it's dramatic, but is it versatile?)
It's not good at penises or vaginas, just breasts and butts. I can find what I like pornographically without ai. But the ducking and dodging around nudity and sexuality is childish and tiresome and we ought to discuss this topic as disinterestedly and nonchalantly as we do the other things it excels or struggles at. Penises and vaginas are not somehow more vile than other things.
Sure enough, sexual content ought to be age-gated and there is potential for abuse (slapping a person's face onto explicit imagery without their consent isn't cool yall). But are we working toward AGI or not? Because at some point it's gonna have to know about the birds and the bees.
:) made me chuckle. AGI would need to know how to be evil, as well, right?
[1] https://www.allure.com/story/vagina-vulva-difference-planned...
we're creating the speakwrite machine.
No, it's essentially generating mashups of its training data, which can be very interesting.
So a model that hasn't been trained on a lot of porn will of course do a very bad job at generating porn.
It requires a good steer and prompting is a clumsy tool for fine tuning - it's adequate for initialization but we lack words for every shade of meaning, and phrase weighting is pretty clumsy too, because words have a blend of meaning.
The very fact that the model is interpolating between things in the latent space probably explains why its images haven't been explored by human artists before: because there is a disconnect between the latent space of the model and genuine "latent space" of human artistic endeavor, which is an interplay between the laws of physics and the aesthetic interests of humans. I think these models know very little about either of those things and thus generate some pretty interesting novelty.
Aesthetic choices like colour and shapes and composition combine with literal representations, facial emotions, symbolic meanings and so on. AI art so far feels quite shallow by this metric, usually only hitting a couple of notes. But sometimes it can play those couple of notes very sweetly.
Its like if Photoshop broke itself when you tried to modify or create anything nude or provocative, as the default
That would just be weird and thats what these AI software devs have done
so everyone patches that contrived feature flag, but nobody knows if they patched it
The filter should be easy to remove and there are already people who simply removed the filter.
https://laion-aesthetic.datasette.io/laion-aesthetic-6pls/im...
This is just a small fraction of the imagery and it does include pornography.
I respect that if you're online and reading HN, you're probably mature enough to handle seeing pornography. So if you're curious to see some of the training data that made it in: choose from the dropdown "-column-" and change it to "punsafe" and set the value in the adjacent field to "1", then press Apply.
Obviously this will show pornography on your screen.
An article which talks about the imagery and how this browser came about is here: https://waxy.org/2022/08/exploring-12-million-of-the-images-...
Still, probably not too hard to build for iPad or iOS.
Another comment mentions RAM capabilities. Unfortunately that’s tied to the storage tiers instead of being something you can pick separately, so if you want 16 GB of RAM you have to buy the 1 TB or 2 TB models. Meaning for a 12.9” iPad Pro, if you want 16 GB you’re looking at an $1800 tablet. Not ideal.
M1 ultra was compared to RTX 3090 which was a larger stretch.
The M1 max deliver about 10.5 tflops The M1 ultra about 21 tflops.
The desktop RTX 3080 delivers about 30 tflops and RTX 3090 about 40.
Apple’s comparison graph showed the speed of the M1s vs. RTXs at increasing power levels, with the M1s being more efficient at the same watt levels (which is probably true). However, since the graph stopped before the RTX GPUs reached full potential, the graph was somewhat misleading.
The M1 max and Ultra have extra video processing modules that make them faster than the RTX GPUs at some video tasks though.