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The comparison doesn't make much sense because for fair comparisons you have to measure decompressor size plus encoded image size. The decompressor here is super huge because it includes the whole AI model. Also, everyone needs to have the exact same copy of the model in the decompressor for it to work reliably.

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Only if decompressor and image are transmitted over the same channel at the same time, and you only have a small number of images. When compressing images for the web I don't care if a webp decompressor is smaller than a jpg or png decompressor, because the recipient already has all of those.

Of course stable diffusion's 4GB is much more extreme than Brotli's 120kb dictionary size, and would bloat a Browser's install size substantially. But for someone like Instagram or a Camera maker it could still make sense. Or imagine phones having the dictionary shipped in the OS to save just a couple kB on bad data connections.

Even if dictionaries were shipped, the biggest difficulty would be performance and resources. Most of these models require beefy compute and a large amount of VRAM that isn't likely to ever exist on end devices.

Unless that can be resolved it just doesn't make sense to use it as a (de)compressor.

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