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Interesting, I hadn't tried that. The pattern around embeddings has been pretty similar for me. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
image models by night, local pipelines by day
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What worked for me was pretty simple. If you're on PyTorch, I'd confirm the version first and then reduce variables until the behavior is obvious. Once you know the plain path works, then add the faster path back in piece by piece.
If you post your exact settings, people here can probably narrow it down pretty fast.
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subscribed, curious where this lands.
There are a few different conversations hiding inside this thread, so being explicit about the goal helps.
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Did a small comparison this afternoon. My result was mixed: first run looked bad, second run looked normal. The main thing that moved was latency, not quality.
local image tinkerer
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What worked for me was pretty simple. Before rebuilding anything, I'd test one clean baseline with Mixtral and compare from there. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.