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Fix all comparisons against the DEFAULT_PRECISION constant. DEFAULT_PRECISION is a torch.dtype. Previously, it was compared to a str in a number of places where it would always resolve to False. This is a bugfix that results in a change to the default behavior. In practice, this will not change the behavior for many users, because it only causes a change in behavior if a users has configured float32 as their default precision.

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This commit is contained in:
Ryan Dick
2024-06-06 09:16:45 -04:00
committed by Kent Keirsey
parent a3cb5da130
commit 785bb1d9e4
6 changed files with 11 additions and 10 deletions
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4
invokeai/app/invocations/image_to_latents.py
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@ -12,7 +12,7 @@ from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
from invokeai.app.invocations.denoise_latents import DEFAULT_PRECISION
from invokeai.app.invocations.constants import DEFAULT_PRECISION
from invokeai.app.invocations.fields import (
FieldDescriptions,
ImageField,
@ -44,7 +44,7 @@ class ImageToLatentsInvocation(BaseInvocation):
input=Input.Connection,
)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
@staticmethod
def vae_encode(vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor) -> torch.Tensor: