VAE should have the ability to use bfloat16 if it is available to the user for LDMs that use a VAE, to save memory

[AmericanPresidentJimmyCarter]

Is your feature request related to a problem? Please describe.
Currently there are many casts to float32 so that the VAE can work without hitting zero values when in float16 mode. When in float16 mode or when dtype=torch.bfloat16 we should use the VAE in torch.bfloat16 if it is available in our environment. This will reduce VRAM usage by approximately half and in some cases increase performance.

Describe the solution you'd like

# ...somewhere, in a pipeline...

if self.vae.dtype == torch.float16 and torch.cuda.is_bf16_supported():
 self.vae.to(dtype=torch.bfloat16)

# Make sure the VAE is in float32 mode, as it overflows in float16.
# We don't need to do the upcasting and float32 dance if we have
# access to bfloat16, if which case we can just directly use the recast
# latents.
if self.vae.dtype == torch.bfloat16:
 latents = latents.to(dtype=torch.bfloat16)
elif self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
 self.upcast_vae()
 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)

[AmericanPresidentJimmyCarter]

Note that until torch 2.1 is released you still need to do the one single upscale interpolation step in float32 and then recast to bfloat16 after, but otherwise the VAE works fine in bfloat16.

[patrickvonplaten]

I like the idea of:

if self.vae.dtype == torch.bfloat16:
 latents = latents.to(dtype=torch.bfloat16)
elif self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
 self.upcast_vae()
 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)

Would you like to open a PR for this? :-)

[patrickvonplaten]

This means that the user has full control over the dtype by setting:

pipe.vae.to(torch.bfloat16)

which is nice!

[AmericanPresidentJimmyCarter]

Would you like to open a PR for this? :-)

I can look into doing a quick one after #4115 is in.

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