This is very cool. Fast image generation is nice, but imagine you can send 24x24x2 bytes = 1152 bytes and you can share a photo.

Applications: super low bandwidth projects such as radio transmission. Send a map or a photo of a location. Or a family photo.

For example with the settings on the picture, you can realistically send the image in 8 seconds (plus encoding and decoding on the phone).

Imagine that you could do the same with audio messages with sound ai models. I think there are very interesting data compression implications of these advanced statistical models.

Considering a duty cycle is also important. For Lora to work, a 1% on air transmission is enforced, so if you broadcast for 8 seconds, you should be silent for 792 seconds. Of course you can have different LoRa parameters that make it even more feasible. 9Kbps is feasible. You can play with your Lora parameters here:

https://unsigned.io/articles/2022_05_05_understanding-lora-parameters.html

Stable Cascade here:

https://github.com/Stability-AI/StableCascade

#m=image%2Fjpeg&dim=1074x992&blurhash=%3B25%3D9G%7EX-p-%3D.8M%7BIAM%7BM%7Bt7kCt7f6WCt7WBRjj%5BoLWBM%7BayWBM%7BRjofofayWBWBWVoJWAa%7DofjtRPRjkCWAoKt7ozofofo0WBNGofoLWBayofofRjWBfQayWBofofayayRkj%40t7WBbHt7ofayWB&x=cd7e895e45592c6b6d57bd9b57d9ec7563b2c4d307b8b1af6d36270808dd0821

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To get even more meta. LXMF from Reticulum project can transfer encrypted messages in QR codes. Maximum capacity of byte data (version 40) of QR code is 2953 bytes. So you can encode a picture, even two in a single QR code, or have better error correction. You can encode a medium quality picture in an encrypted QR code, stick it to a wall somewhere and only the recipient can read it.

The same with voice messages, both over LORA and QR codes!

The downside is that the endpoint needs to be able to process this AI models. On older smartphones this is going to be slow.

Space missions come to mind.

That's really interesting, there is probably entirely new field of compression possibilities that haven't been tapped yet.

This (what you describe here) is an extreme case, but I'm sure there are usecases for the same principles at higher rates. Once there is some kind of ubiquitous hw acceleration for crunching AI it will probably be used in most things.

It's not very new, the encoder-decoder architecture has been used for a long time now, so with neural networks you get compressed representation of data as a byproduct quite often.