fix(app): fixed InputField default values

Author: rikublock

invokeai/app/invocations/create_denoise_mask.py

@@ -6,7 +6,6 @@
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import DEFAULT_PRECISION
 from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField
 from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
 from invokeai.app.invocations.model import VAEField
@@ -29,11 +28,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
     image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
     mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
-    fp32: bool = InputField(
-        default=DEFAULT_PRECISION == torch.float32,
-        description=FieldDescriptions.fp32,
-        ui_order=4,
-    )
+    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32, ui_order=4)
 
     def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
         if mask_image.mode != "L":

invokeai/app/invocations/create_gradient_mask.py

@@ -7,7 +7,6 @@
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-from invokeai.app.invocations.constants import DEFAULT_PRECISION
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
     FieldDescriptions,
@@ -76,11 +75,7 @@ class CreateGradientMaskInvocation(BaseInvocation):
         ui_order=7,
     )
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=8)
-    fp32: bool = InputField(
-        default=DEFAULT_PRECISION == torch.float32,
-        description=FieldDescriptions.fp32,
-        ui_order=9,
-    )
+    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32, ui_order=9)
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> GradientMaskOutput:

invokeai/app/invocations/image_to_latents.py

@@ -13,7 +13,7 @@
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -49,7 +49,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     # NOTE: tile_size = 0 is a special value. We use this rather than `int | None`, because the workflow UI does not
     # offer a way to directly set None values.
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
-    fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
+    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32)
 
     @staticmethod
     def vae_encode(

invokeai/app/invocations/latents_to_image.py

@@ -12,7 +12,7 @@
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -51,7 +51,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     # NOTE: tile_size = 0 is a special value. We use this rather than `int | None`, because the workflow UI does not
     # offer a way to directly set None values.
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
-    fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
+    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32)
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput: