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fix(latent): remove temporary workaround for lack of TAESD tiling support.

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Now available in diffusers 0.21: https://github.com/huggingface/diffusers/pull/4627
This commit is contained in:
Kevin Turner 2023-09-13 09:40:06 -07:00
parent 090db1ab3a
commit d219167849
1 changed files with 8 additions and 27 deletions
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35
invokeai/app/invocations/latent.py
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@ -48,7 +48,6 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from ...backend.util.devices import choose_precision, choose_torch_device
from ...backend.util.logging import InvokeAILogger
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import (
BaseInvocation,
@ -608,19 +607,10 @@ class LatentsToImageInvocation(BaseInvocation):
vae.to(dtype=torch.float16)
latents = latents.half()
try:
if self.tiled or context.services.configuration.tiled_decode:
vae.enable_tiling()
else:
vae.disable_tiling()
except AttributeError as err:
# FIXME: This is a TEMPORARY measure until AutoencoderTiny gets tiling support from https://github.com/huggingface/diffusers/pull/4627
if err.name.endswith("_tiling"):
InvokeAILogger.getLogger(self.__class__.__name__).debug(
"ignoring tiling error for %s", vae.__class__, exc_info=err
)
else:
raise
if self.tiled or context.services.configuration.tiled_decode:
vae.enable_tiling()
else:
vae.disable_tiling()
# clear memory as vae decode can request a lot
torch.cuda.empty_cache()
@ -783,19 +773,10 @@ class ImageToLatentsInvocation(BaseInvocation):
vae.to(dtype=torch.float16)
# latents = latents.half()
try:
if tiled:
vae.enable_tiling()
else:
vae.disable_tiling()
except AttributeError as err:
# FIXME: This is a TEMPORARY measure until AutoencoderTiny gets tiling support from https://github.com/huggingface/diffusers/pull/4627
if err.name.endswith("_tiling"):
InvokeAILogger.getLogger(ImageToLatentsInvocation.__name__).debug(
"ignoring tiling error for %s", vae.__class__, exc_info=err
)
else:
raise
if tiled:
vae.enable_tiling()
else:
vae.disable_tiling()
# non_noised_latents_from_image
image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)