Fix latents.dtype before vae.decode() at ROCm devices in StableDiffusionPipelines

[tolgacangoz]

Co-authored-by: Bagheera [email protected]

What does this PR do?

This was talked about at a previous PR: #7858.

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@yiyixuxu @bghira

[bghira]

so i can actually hit this one on CUDA, but it only happens during training without autocast 🤔

the weights are in bf16 precision, not fp32. maybe this is what causes it?

[tolgacangoz]

Since I don't have a ROCm device, I can't work on this PR directly :/ What to do here?
Doesn't inferencing with bf16 produce a casting error?

[bghira]

inferencing is when the error occurs, but that's generally during training time, as the components can be initialised with different weight dtypes. it's not really clear why the scheduler can change the dtype other than certain calculations end up with the default torch dtype of float32 when one isn't specified, and i think autocast takes care of this. but we can't rely on autocast existing or being in use, because not all platforms support it - and future training might not use it at all, instead relying on bf16 optimiser states.

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[yiyixuxu]

@tolgacangoz
hey, sorry if I missed some context for this PR - why do we need to update this deprecated method?

[bghira]

because it has dtype inconsistency upon vae.decode especially when vae is upcast outside of the pipeline

[yiyixuxu]

@bghira is this used in training?
asking becaUse the method is deprecated and will be removed in the future