revert and disable auto-formatting of invocations

invokeai/app/invocations/generate.py

@@ -26,45 +26,18 @@ class TextToImageInvocation(BaseInvocation):
 
     # Inputs
     # TODO: consider making prompt optional to enable providing prompt through a link
+    # fmt: off
     prompt: Optional[str] = Field(description="The prompt to generate an image from")
-    seed: int = Field(
+    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
-        default=-1,
+    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
-        ge=-1,
+    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
-        le=np.iinfo(np.uint32).max,
+    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image", )
-        description="The seed to use (-1 for a random seed)",
+    cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    )
+    sampler_name: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The sampler to use" )
-    steps: int = Field(
+    seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
-        default=10, gt=0, description="The number of steps to use to generate the image"
-    )
-    width: int = Field(
-        default=512,
-        multiple_of=64,
-        gt=0,
-        description="The width of the resulting image",
-    )
-    height: int = Field(
-        default=512,
-        multiple_of=64,
-        gt=0,
-        description="The height of the resulting image",
-    )
-    cfg_scale: float = Field(
-        default=7.5,
-        gt=0,
-        description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt",
-    )
-    sampler_name: SAMPLER_NAME_VALUES = Field(
-        default="k_lms", description="The sampler to use"
-    )
-    seamless: bool = Field(
-        default=False,
-        description="Whether or not to generate an image that can tile without seams",
-    )
     model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(
+    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
-        default=False,
+    # fmt: on
-        description="Whether or not to produce progress images during generation",
-    )
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(

invokeai/app/invocations/image.py

@@ -260,13 +260,14 @@ class LerpInvocation(BaseInvocation):
 
 class InverseLerpInvocation(BaseInvocation):
     """Inverse linear interpolation of all pixels of an image"""
-
+    #fmt: off
     type: Literal["ilerp"] = "ilerp"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to lerp")
     min: int = Field(default=0, ge=0, le=255, description="The minimum input value")
     max: int = Field(default=255, ge=0, le=255, description="The maximum input value")
+    #fmt: on
     
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(

invokeai/app/invocations/prompt.py

@@ -7,7 +7,8 @@ from .baseinvocation import BaseInvocationOutput
 
 class PromptOutput(BaseInvocationOutput):
     """Base class for invocations that output a prompt"""
-
+    #fmt: off
     type: Literal["prompt"] = "prompt"
 
     prompt: str = Field(default=None, description="The output prompt")
+    #fmt: on

invokeai/app/invocations/reconstruct.py

@@ -11,14 +11,13 @@ from .image import ImageField, ImageOutput
 
 class RestoreFaceInvocation(BaseInvocation):
     """Restores faces in an image."""
-
+    #fmt: off
     type:  Literal["restore_face"] = "restore_face"
 
     # Inputs
     image: Union[ImageField, None] = Field(description="The input image")
-    strength: float = Field(
+    strength:                float = Field(default=0.75, gt=0, le=1, description="The strength of the restoration"  )
-        default=0.75, gt=0, le=1, description="The strength of the restoration"
+    #fmt: on
-    )
     
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(

invokeai/app/invocations/upscale.py

@@ -13,13 +13,14 @@ from .image import ImageField, ImageOutput
 
 class UpscaleInvocation(BaseInvocation):
     """Upscales an image."""
-
+    #fmt: off
     type: Literal["upscale"] = "upscale"
 
     # Inputs
     image: Union[ImageField, None] = Field(description="The input image", default=None)
     strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
     level: Literal[2, 4] = Field(default=2, description="The upscale level")
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(

invokeai/app/services/item_storage.py

@@ -9,13 +9,13 @@ T = TypeVar("T", bound=BaseModel)
 
 class PaginatedResults(GenericModel, Generic[T]):
     """Paginated results"""
-
+    #fmt: off
     items: list[T] = Field(description="Items")
     page: int = Field(description="Current Page")
     pages: int = Field(description="Total number of pages")
     per_page: int = Field(description="Number of items per page")
     total: int = Field(description="Total number of items in result")
-
+    #fmt: on
 
 class ItemStorageABC(ABC, Generic[T]):
     _on_changed_callbacks: list[Callable[[T], None]]