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this post was submitted on 28 May 2024
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Before you can do that, you have to spend hours of computation to figure out a prompt and a set of variables that perfectly match the picture you want to transmit.
Sure, but this is just a more visual example of how compression using an ML model can work.
The time you spend reworking the prompt, or tweaking the steps/cfg/etc. is outside of the scope of this example.
And if we're really talking about creating a good pic it helps to use tools like control net/inpainting/etc... which could still be communicated to the receiving machine, but then you're starting to lose out on some of the compression by a factor of about 1KB for every additional additional time you need to run the model to get the correct picture.
You are removing the most computationally intensive part of the process in your example, that's making it sound easy, while adding it back shows that your process is not practical.
The first thing I said was, "the more you compress something, the more processing power you're going to need [to decompress it]"
I'm not removing the most computationally expensive part by any means and you are misunderstanding the process if you think that.
That's why I specified:
And again
Those 30-60+ second estimates are based on someone using an RTX 4090, the top end Consumer grade GPU of today. They could speed up the process by having multiple GPUs or even enterprise grade equipment, but that's why I mentioned that this depends on hardware.
So, yes, this very specific example is not practical for Neuralink (I even said as much in my original example), but this example still works very well for explaining a method that can allow you a compression rate of over 20,000x.
Yes you need power, energy, and time to generate the original image, and yes you need power, energy, and time to regenerate it on a different computer. But to transmit the information needed to regenerate that image you only need to convey a tiny message.