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Changing ImageToLatentsInvocation node to default to detected precision instead of fp16

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This commit is contained in:
Martin Kristiansen 2023-07-19 11:16:00 -04:00
parent 9f00e055ac
commit 2ec9dab595
1 changed files with 2 additions and 2 deletions
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4
invokeai/app/invocations/latent.py
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@ -22,7 +22,7 @@ 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_torch_device, torch_dtype
from ...backend.util.devices import choose_torch_device, torch_dtype, choose_precision
from ..models.image import ImageCategory, ImageField, ResourceOrigin
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
@ -492,7 +492,7 @@ class LatentsToImageInvocation(BaseInvocation):
tiled: bool = Field(
default=False,
description="Decode latents by overlaping tiles(less memory consumption)")
fp32: bool = Field(False, description="Decode in full precision")
fp32: bool = Field(default=choose_precision(choose_torch_device())=='float32', description="Decode in full precision")
metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
# Schema customisation