feat(nodes): add datatypes module

invokeai/app/cli/commands.py

@@ -5,7 +5,8 @@ import argparse
 from typing import Any, Callable, Iterable, Literal, get_args, get_origin, get_type_hints
 from pydantic import BaseModel, Field
 
-from ..invocations.image import ImageField
+from invokeai.app.datatypes.image import ImageField
+
 from ..services.graph import GraphExecutionState
 from ..services.invoker import Invoker
 

invokeai/app/datatypes/exceptions.py

@@ -0,0 +1,3 @@
+class CanceledException(Exception):
+    """Execution canceled by user."""
+    pass

invokeai/app/datatypes/image.py

@@ -0,0 +1,26 @@
+from enum import Enum
+from typing import Optional
+from pydantic import BaseModel, Field
+
+
+class ImageType(str, Enum):
+    RESULT = "results"
+    INTERMEDIATE = "intermediates"
+    UPLOAD = "uploads"
+
+
+class ImageField(BaseModel):
+    """An image field used for passing image objects between invocations"""
+
+    image_type: str = Field(
+        default=ImageType.RESULT, description="The type of the image"
+    )
+    image_name: Optional[str] = Field(default=None, description="The name of the image")
+
+    class Config:
+        schema_extra = {
+            "required": [
+                "image_type",
+                "image_name",
+            ]
+        }

invokeai/app/invocations/cv.py

@@ -7,9 +7,9 @@ import numpy
 from PIL import Image, ImageOps
 from pydantic import Field
 
-from ..services.image_storage import ImageType
+from invokeai.app.datatypes.image import ImageField, ImageType
 from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput
+from .image import ImageOutput
 
 
 class CvInpaintInvocation(BaseInvocation):

invokeai/app/invocations/generate.py

@@ -8,12 +8,13 @@ from torch import Tensor
 
 from pydantic import Field
 
-from ..services.image_storage import ImageType
+from invokeai.app.datatypes.image import ImageField, ImageType
 from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput
+from .image import ImageOutput
 from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
 from ...backend.stable_diffusion import PipelineIntermediateState
-from ..util.util import diffusers_step_callback_adapter, CanceledException
+from ..datatypes.exceptions import CanceledException
+from ..util.step_callback import diffusers_step_callback_adapter
 
 SAMPLER_NAME_VALUES = Literal[
     tuple(InvokeAIGenerator.schedulers())

invokeai/app/invocations/image.py

@@ -7,27 +7,10 @@ import numpy
 from PIL import Image, ImageFilter, ImageOps
 from pydantic import BaseModel, Field
 
-from ..services.image_storage import ImageType
+from invokeai.app.datatypes.image import ImageField, ImageType
 from ..services.invocation_services import InvocationServices
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
 
-
-class ImageField(BaseModel):
-    """An image field used for passing image objects between invocations"""
-
-    image_type: str = Field(
-        default=ImageType.RESULT, description="The type of the image"
-    )
-    image_name: Optional[str] = Field(default=None, description="The name of the image")
-
-    class Config:
-        schema_extra = {
-            'required': [
-                'image_type',
-                'image_name',
-            ]
-        }
-        
 class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
     #fmt: off

invokeai/app/invocations/reconstruct.py

@@ -3,10 +3,10 @@ from typing import Literal, Union
 
 from pydantic import Field
 
-from ..services.image_storage import ImageType
+from invokeai.app.datatypes.image import ImageField, ImageType
 from ..services.invocation_services import InvocationServices
 from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput
+from .image import ImageOutput
 
 class RestoreFaceInvocation(BaseInvocation):
     """Restores faces in an image."""

invokeai/app/invocations/upscale.py

@@ -5,10 +5,10 @@ from typing import Literal, Union
 
 from pydantic import Field
 
-from ..services.image_storage import ImageType
+from invokeai.app.datatypes.image import ImageField, ImageType
 from ..services.invocation_services import InvocationServices
 from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput
+from .image import ImageOutput
 
 
 class UpscaleInvocation(BaseInvocation):

invokeai/app/services/image_storage.py

@@ -10,19 +10,12 @@ from queue import Queue
 from typing import Callable, Dict
 
 from PIL.Image import Image
-from invokeai.app.invocations.image import ImageField
+from invokeai.app.datatypes.image import ImageField, ImageType
 from invokeai.app.services.item_storage import PaginatedResults
 from invokeai.app.util.save_thumbnail import save_thumbnail
 
 from invokeai.backend.image_util import PngWriter
 
-
-class ImageType(str, Enum):
-    RESULT = "results"
-    INTERMEDIATE = "intermediates"
-    UPLOAD = "uploads"
-
-
 class ImageStorageBase(ABC):
     """Responsible for storing and retrieving images."""
 

invokeai/app/services/processor.py

@@ -4,7 +4,7 @@ from threading import Event, Thread
 from ..invocations.baseinvocation import InvocationContext
 from .invocation_queue import InvocationQueueItem
 from .invoker import InvocationProcessorABC, Invoker
-from ..util.util import CanceledException
+from ..datatypes.exceptions import CanceledException
 
 class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker_thread: Thread

invokeai/app/util/step_callback.py

@@ -1,14 +1,16 @@
 import torch
-from PIL import Image
 from ..invocations.baseinvocation import InvocationContext
 from ...backend.util.util import image_to_dataURL
 from ...backend.generator.base import Generator
 from ...backend.stable_diffusion import PipelineIntermediateState
 
-class CanceledException(Exception):
+def fast_latents_step_callback(
-    pass
+    sample: torch.Tensor,
-
+    step: int,
-def fast_latents_step_callback(sample: torch.Tensor, step: int, steps: int, id: str, context: InvocationContext, ):
+    steps: int,
+    id: str,
+    context: InvocationContext,
+):
     # TODO: only output a preview image when requested
     image = Generator.sample_to_lowres_estimated_image(sample)
 
@@ -21,15 +23,12 @@ def fast_latents_step_callback(sample: torch.Tensor, step: int, steps: int, id:
     context.services.events.emit_generator_progress(
         context.graph_execution_state_id,
         id,
-        {
+        {"width": width, "height": height, "dataURL": dataURL},
-            "width": width,
-            "height": height,
-            "dataURL": dataURL
-        },
         step,
         steps,
     )
 
+
 def diffusers_step_callback_adapter(*cb_args, **kwargs):
     """
     txt2img gives us a Tensor in the step_callbak, while img2img gives us a PipelineIntermediateState.
@@ -37,6 +36,8 @@ def diffusers_step_callback_adapter(*cb_args, **kwargs):
     """
     if isinstance(cb_args[0], PipelineIntermediateState):
         progress_state: PipelineIntermediateState = cb_args[0]
-        return fast_latents_step_callback(progress_state.latents, progress_state.step, **kwargs)
+        return fast_latents_step_callback(
+            progress_state.latents, progress_state.step, **kwargs
+        )
     else:
         return fast_latents_step_callback(*cb_args, **kwargs)