the point here is to dissipate it across enough mediums as to be indiscernible from noisy background fluctuations regardless of existence, giving general-deniability to all mediums eventually, thru signal to noise ratio.

all security is just obscurity, eventually, where you are obscuring your private key's semi-prime's factors.

ga!

33 seats on a flight maybe. It's about one passenger aircraft flight, one way.

> 1. Different energy sources produce varyings of co2

Yes.

> 2. This likely does not include co2 to make the GPUs or machines

Definitely not, nobody does that.

Wish they did, in general I feel like a lot of beliefs around sustainability and environmentalism are wrong or backwards precisely because embodied energy is discounted; see e.g. stats on western nations getting cleaner, where a large - if not primary - driver of improved stats is just outsourcing manufacturing, so emissions are attributed to someone else.

Anyway, embodied energy isn't particularly useful here. Energy embodied in GPUs and machines amortizes over their lifetimes and should be counted against all the things those GPUs did, do and will do, of which the training in question is just a small part. Not including it isolates the analysis to contributions from the specific task per se, and makes the results applicable to different hardware/scenarios.

> 3. Humans involved are not added to this at all, and all of the impact they have on the environment

This metric is so ill-defined as to be arbitrary. Even more so with conjunction with 2, as you could plausibly include a million people into it.

> 4. No ability to predict future co2 from using this work.

Total, no. Contribution of compute alone given similar GPU-hours per ton of CO2eq, yes.

1. yes, this is the default co2 eq/ watts from the tool that is cited in the paper, but it's actually very hard to know the source of energy that aliments the cluster, so the numbers are only an order of magnitude rather than "real" numbers 2. 4. I found that https://huggingface.co/blog/sasha/ai-environment-primer gives a good broad overview (not only of the co2 eq, which is limited imo) of AI environmental impact

> Also if it really matters, then why do it at all? If we’re saying hey this is destroying the environmental and care, then maybe don’t do that work?

Although it may not the best way to quantify it, it gives a good overview of it. I would argue that it matters a lot to quantify and popularize the idea of such sections in any experimental ML papers (and should in my opinion be the default, as it is now for the reproducibility statement and ethical statement). People don't really know what an AI experiment represents. It may seem very abstract since everything happens in the "cloud", but it is pretty much physical: the clusters, the water consumption, the energy. And as someone who works in AI, I believe it's important to know what this represents, which these kinds of sections show clearly. It was the same in the DINOv2 paper or in the Llama paper.

That amount of compute was used for training. For inference (applying the watermarks), hopefully no more than a few seconds per image.

Llama 3 70B took 6.4M GPU hours to train, emitting 1900 tons of CO2 equivalent.

The embedder is only 1.1M parameters, so it should run extremely fast.

> ... for a repressive government ...

Why shouldn't a virtuous and transparent government (should one materialize somehow, somewhere) be interested in identifying leakers?

I stopped myself from making the printer analogy, but of course it's relevant, as is the fact that few seem to care. I personally hope some group strikes back to sanitize images watermarked this way, with no more difficulty than removing exif data.

Yeah, so it's impressive if this repo does what it claims and is robust to such manipulations.

I tried to run it but of course it failed with

    NVIDIA GeForce RTX 4090 with CUDA capability sm_89 is not compatible with the current PyTorch installation.
    The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.
    If you want to use the NVIDIA GeForce RTX 4090 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

> their models don't get Habsburg'd.

You mean develop a magnificent jawline, or continue to influence Austrian politics?

I wondered why they'd be doing this NOW and this makes perfect sense!!

>so their models don't get Habsburg'd.

Nice metaphor

Because downstream consumers of the media might want to know if an image has been created or manipulated by AI tools.

They would not want to train their next model on the output of the previous one...

Who says?

It's a form of Steganography https://en.wikipedia.org/wiki/Steganography

of course not, check their username.

Maybe. It depends how strong the watermark was, and how good the photo was at reproducing the image.

please tell us if it worked!

but that's what I'm saying, When you ask for exclamation marks after each word that must change the likelihoods of next token by quite a bit. You then remove the marks which hides the fact that you just changed every word without loss of meaning