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It doesn't destroy performance for the simple reason that nowadays memory access has higher latency than pure compute. If you need to use compute to produce some data to be stored in memory, your overall throughput could very well be faster than without compression.
There have been a large amount of innovation on fast compression in recent years. Traditional compression tools like gzip or xz are geared towards higher compression ratio, but memory compression tends to favor speed. Check out those algorithms:
* lz4: https://lz4.github.io/lz4/
* Google's snappy: https://github.com/google/snappy
* Facebook's zstd in fast mode: http://facebook.github.io/zstd/#benchmarks
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iOS uses memory compression but not swap. iOS devices actually have special CPU instructions to speed up compression of up to page size increments specifically to aid in this model [1]
[1] https://github.com/apple-oss-distributions/xnu/blob/bb611c8f...
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Automatic1111's stable diffusion web gui works with apple silicon: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki...
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yeah, running the full decoder takes a while. though, since the "latent" is just 4 channels and pretty close to representing RGB, you can use a linear combination of latent channels and get a basic (grainy, low-res) preview image like this [0] without much trouble. I expect you could go further, and train a shallow conv-only decoder to get nicer preview results, but I'm not sure if anyone's bothered yet.
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