chore: minor cleanup after merge & flake8

invokeai/app/invocations/latent.py

@@ -22,18 +22,18 @@ from invokeai.app.invocations.metadata import CoreMetadata
 from invokeai.app.invocations.primitives import (
     ImageField,
     ImageOutput,
-    LatentsField,
-    LatentsOutput,
     InpaintMaskField,
     InpaintMaskOutput,
+    LatentsField,
+    LatentsOutput,
     build_latents_output,
 )
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 
-from ...backend.model_management.models import BaseModelType
 from ...backend.model_management.lora import ModelPatcher
+from ...backend.model_management.models import BaseModelType
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
     ConditioningData,
@@ -45,16 +45,7 @@ from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import Post
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import choose_precision, choose_torch_device
 from ..models.image import ImageCategory, ResourceOrigin
-from .baseinvocation import (
-    BaseInvocation,
-    FieldDescriptions,
-    Input,
-    InputField,
-    InvocationContext,
-    UIType,
-    tags,
-    title,
-)
+from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, UIType, tags, title
 from .compel import ConditioningField
 from .controlnet_image_processors import ControlField
 from .model import ModelInfo, UNetField, VaeField
@@ -65,7 +56,7 @@ DEFAULT_PRECISION = choose_precision(choose_torch_device())
 SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
 
-@title("Create inpaint mask")
+@title("Create Inpaint Mask")
 @tags("mask", "inpaint")
 class CreateInpaintMaskInvocation(BaseInvocation):
     """Creates mask for inpaint model run."""
@@ -85,12 +76,11 @@ class CreateInpaintMaskInvocation(BaseInvocation):
 
     def prep_mask_tensor(self, mask_image):
         if mask_image.mode != "L":
-            # FIXME: why do we get passed an RGB image here? We can only use single-channel.
             mask_image = mask_image.convert("L")
         mask_tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
         if mask_tensor.dim() == 3:
             mask_tensor = mask_tensor.unsqueeze(0)
-        #if shape is not None:
+        # if shape is not None:
         #    mask_tensor = tv_resize(mask_tensor, shape, T.InterpolationMode.BILINEAR)
         return mask_tensor
 
@@ -107,7 +97,7 @@ class CreateInpaintMaskInvocation(BaseInvocation):
         mask = self.prep_mask_tensor(
             context.services.images.get_pil_image(self.mask.image_name),
         )
-        
+
         if image is not None:
             vae_info = context.services.model_manager.get_model(
                 **self.vae.vae.dict(),
@@ -779,12 +769,8 @@ class ImageToLatentsInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        # image = context.services.images.get(
-        #     self.image.image_type, self.image.image_name
-        # )
         image = context.services.images.get_pil_image(self.image.image_name)
 
-        # vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
         vae_info = context.services.model_manager.get_model(
             **self.vae.vae.dict(),
             context=context,