feat(nodes): update all invocations to use new invocation context

Update all invocations to use the new context. The changes are all fairly simple, but there are a lot of them.

Supporting minor changes:
- Patch bump for all nodes that use the context
- Update invocation processor to provide new context
- Minor change to `EventServiceBase` to accept a node's ID instead of the dict version of a node
- Minor change to `ModelManagerService` to support the new wrapped context
- Fanagling of imports to avoid circular dependencies

invokeai/app/invocations/baseinvocation.py

@@ -16,10 +16,16 @@ from pydantic import BaseModel, ConfigDict, Field, create_model
 from pydantic.fields import FieldInfo
 from pydantic_core import PydanticUndefined
 
-from invokeai.app.invocations.fields import FieldKind, Input
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    FieldKind,
+    Input,
+    InputFieldJSONSchemaExtra,
+    MetadataField,
+    logger,
+)
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
+from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.util.logging import InvokeAILogger
@@ -219,7 +225,7 @@ class BaseInvocation(ABC, BaseModel):
         """Invoke with provided context and return outputs."""
         pass
 
-    def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
+    def invoke_internal(self, context: InvocationContext, services: "InvocationServices") -> BaseInvocationOutput:
         """
         Internal invoke method, calls `invoke()` after some prep.
         Handles optional fields that are required to call `invoke()` and invocation cache.
@@ -244,23 +250,23 @@ class BaseInvocation(ABC, BaseModel):
                     raise MissingInputException(self.model_fields["type"].default, field_name)
 
         # skip node cache codepath if it's disabled
-        if context.services.configuration.node_cache_size == 0:
+        if services.configuration.node_cache_size == 0:
             return self.invoke(context)
 
         output: BaseInvocationOutput
         if self.use_cache:
-            key = context.services.invocation_cache.create_key(self)
+            key = services.invocation_cache.create_key(self)
-            cached_value = context.services.invocation_cache.get(key)
+            cached_value = services.invocation_cache.get(key)
             if cached_value is None:
-                context.services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
+                services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
                 output = self.invoke(context)
-                context.services.invocation_cache.save(key, output)
+                services.invocation_cache.save(key, output)
                 return output
             else:
-                context.services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
+                services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
                 return cached_value
         else:
-            context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
+            services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
             return self.invoke(context)
 
     id: str = Field(
@@ -513,3 +519,29 @@ def invocation_output(
         return cls
 
     return wrapper
+
+
+class WithMetadata(BaseModel):
+    """
+    Inherit from this class if your node needs a metadata input field.
+    """
+
+    metadata: Optional[MetadataField] = Field(
+        default=None,
+        description=FieldDescriptions.metadata,
+        json_schema_extra=InputFieldJSONSchemaExtra(
+            field_kind=FieldKind.Internal,
+            input=Input.Connection,
+            orig_required=False,
+        ).model_dump(exclude_none=True),
+    )
+
+
+class WithWorkflow:
+    workflow = None
+
+    def __init_subclass__(cls) -> None:
+        logger.warn(
+            f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
+        )
+        super().__init_subclass__()

invokeai/app/invocations/collections.py

@@ -7,7 +7,7 @@ from pydantic import ValidationInfo, field_validator
 from invokeai.app.invocations.primitives import IntegerCollectionOutput
 from invokeai.app.util.misc import SEED_MAX
 
-from .baseinvocation import BaseInvocation, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, invocation
 from .fields import InputField
 
 
@@ -27,7 +27,7 @@ class RangeInvocation(BaseInvocation):
             raise ValueError("stop must be greater than start")
         return v
 
-    def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
+    def invoke(self, context) -> IntegerCollectionOutput:
         return IntegerCollectionOutput(collection=list(range(self.start, self.stop, self.step)))
 
 
@@ -45,7 +45,7 @@ class RangeOfSizeInvocation(BaseInvocation):
     size: int = InputField(default=1, gt=0, description="The number of values")
     step: int = InputField(default=1, description="The step of the range")
 
-    def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
+    def invoke(self, context) -> IntegerCollectionOutput:
         return IntegerCollectionOutput(
             collection=list(range(self.start, self.start + (self.step * self.size), self.step))
         )
@@ -72,6 +72,6 @@ class RandomRangeInvocation(BaseInvocation):
         description="The seed for the RNG (omit for random)",
     )
 
-    def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
+    def invoke(self, context) -> IntegerCollectionOutput:
         rng = np.random.default_rng(self.seed)
         return IntegerCollectionOutput(collection=list(rng.integers(low=self.low, high=self.high, size=self.size)))

invokeai/app/invocations/compel.py

@@ -1,12 +1,18 @@
-from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Optional, Union
-from typing import List, Optional, Union
 
 import torch
 from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
+from invokeai.app.invocations.fields import (
-from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
+    ConditioningFieldData,
+    FieldDescriptions,
+    Input,
+    InputField,
+    OutputField,
+    UIComponent,
+)
+from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     ExtraConditioningInfo,
@@ -20,16 +26,14 @@ from ..util.ti_utils import extract_ti_triggers_from_prompt
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
 from .model import ClipField
 
+if TYPE_CHECKING:
+    from invokeai.app.services.shared.invocation_context import InvocationContext
 
-@dataclass
-class ConditioningFieldData:
-    conditionings: List[BasicConditioningInfo]
     # unconditioned: Optional[torch.Tensor]
 
 
@@ -44,7 +48,7 @@ class ConditioningFieldData:
     title="Prompt",
     tags=["prompt", "compel"],
     category="conditioning",
-    version="1.0.0",
+    version="1.0.1",
 )
 class CompelInvocation(BaseInvocation):
     """Parse prompt using compel package to conditioning."""
@@ -61,26 +65,18 @@ class CompelInvocation(BaseInvocation):
     )
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ConditioningOutput:
+    def invoke(self, context) -> ConditioningOutput:
-        tokenizer_info = context.services.model_manager.get_model(
+        tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
-            **self.clip.tokenizer.model_dump(),
+        text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
-            context=context,
-        )
-        text_encoder_info = context.services.model_manager.get_model(
-            **self.clip.text_encoder.model_dump(),
-            context=context,
-        )
 
         def _lora_loader():
             for lora in self.clip.loras:
-                lora_info = context.services.model_manager.get_model(
+                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
-                    **lora.model_dump(exclude={"weight"}), context=context
-                )
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
         for trigger in extract_ti_triggers_from_prompt(self.prompt):
@@ -89,11 +85,10 @@ class CompelInvocation(BaseInvocation):
                 ti_list.append(
                     (
                         name,
-                        context.services.model_manager.get_model(
+                        context.models.load(
                             model_name=name,
                             base_model=self.clip.text_encoder.base_model,
                             model_type=ModelType.TextualInversion,
-                            context=context,
                         ).context.model,
                     )
                 )
@@ -124,7 +119,7 @@ class CompelInvocation(BaseInvocation):
 
             conjunction = Compel.parse_prompt_string(self.prompt)
 
-            if context.services.configuration.log_tokenization:
+            if context.config.get().log_tokenization:
                 log_tokenization_for_conjunction(conjunction, tokenizer)
 
             c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
@@ -145,34 +140,23 @@ class CompelInvocation(BaseInvocation):
             ]
         )
 
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        conditioning_name = context.conditioning.save(conditioning_data)
-        context.services.latents.save(conditioning_name, conditioning_data)
 
-        return ConditioningOutput(
+        return ConditioningOutput.build(conditioning_name)
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
 
 
 class SDXLPromptInvocationBase:
     def run_clip_compel(
         self,
-        context: InvocationContext,
+        context: "InvocationContext",
         clip_field: ClipField,
         prompt: str,
         get_pooled: bool,
         lora_prefix: str,
         zero_on_empty: bool,
     ):
-        tokenizer_info = context.services.model_manager.get_model(
+        tokenizer_info = context.models.load(**clip_field.tokenizer.model_dump())
-            **clip_field.tokenizer.model_dump(),
+        text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
-            context=context,
-        )
-        text_encoder_info = context.services.model_manager.get_model(
-            **clip_field.text_encoder.model_dump(),
-            context=context,
-        )
 
         # return zero on empty
         if prompt == "" and zero_on_empty:
@@ -196,14 +180,12 @@ class SDXLPromptInvocationBase:
 
         def _lora_loader():
             for lora in clip_field.loras:
-                lora_info = context.services.model_manager.get_model(
+                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
-                    **lora.model_dump(exclude={"weight"}), context=context
-                )
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
         for trigger in extract_ti_triggers_from_prompt(prompt):
@@ -212,11 +194,10 @@ class SDXLPromptInvocationBase:
                 ti_list.append(
                     (
                         name,
-                        context.services.model_manager.get_model(
+                        context.models.load(
                             model_name=name,
                             base_model=clip_field.text_encoder.base_model,
                             model_type=ModelType.TextualInversion,
-                            context=context,
                         ).context.model,
                     )
                 )
@@ -249,7 +230,7 @@ class SDXLPromptInvocationBase:
 
             conjunction = Compel.parse_prompt_string(prompt)
 
-            if context.services.configuration.log_tokenization:
+            if context.config.get().log_tokenization:
                 # TODO: better logging for and syntax
                 log_tokenization_for_conjunction(conjunction, tokenizer)
 
@@ -282,7 +263,7 @@ class SDXLPromptInvocationBase:
     title="SDXL Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.0.0",
+    version="1.0.1",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -307,7 +288,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ConditioningOutput:
+    def invoke(self, context) -> ConditioningOutput:
         c1, c1_pooled, ec1 = self.run_clip_compel(
             context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=True
         )
@@ -364,14 +345,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
             ]
         )
 
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        conditioning_name = context.conditioning.save(conditioning_data)
-        context.services.latents.save(conditioning_name, conditioning_data)
 
-        return ConditioningOutput(
+        return ConditioningOutput.build(conditioning_name)
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
 
 
 @invocation(
@@ -379,7 +355,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     title="SDXL Refiner Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.0.0",
+    version="1.0.1",
 )
 class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -397,7 +373,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
     clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ConditioningOutput:
+    def invoke(self, context) -> ConditioningOutput:
         # TODO: if there will appear lora for refiner - write proper prefix
         c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>", zero_on_empty=False)
 
@@ -417,14 +393,9 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
             ]
         )
 
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        conditioning_name = context.conditioning.save(conditioning_data)
-        context.services.latents.save(conditioning_name, conditioning_data)
 
-        return ConditioningOutput(
+        return ConditioningOutput.build(conditioning_name)
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
 
 
 @invocation_output("clip_skip_output")
@@ -447,7 +418,7 @@ class ClipSkipInvocation(BaseInvocation):
     clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
     skipped_layers: int = InputField(default=0, description=FieldDescriptions.skipped_layers)
 
-    def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
+    def invoke(self, context) -> ClipSkipInvocationOutput:
         self.clip.skipped_layers += self.skipped_layers
         return ClipSkipInvocationOutput(
             clip=self.clip,

invokeai/app/invocations/controlnet_image_processors.py

@@ -25,18 +25,17 @@ from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
 from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
 
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, WithMetadata
+from invokeai.app.invocations.baseinvocation import WithMetadata
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField
+from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
 from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
+from invokeai.backend.model_management.models.base import BaseModelType
 
-from ...backend.model_management import BaseModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
@@ -121,7 +120,7 @@ class ControlNetInvocation(BaseInvocation):
         validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
         return self
 
-    def invoke(self, context: InvocationContext) -> ControlOutput:
+    def invoke(self, context) -> ControlOutput:
         return ControlOutput(
             control=ControlField(
                 image=self.image,
@@ -145,23 +144,14 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
         # superclass just passes through image without processing
         return image
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        raw_image = context.services.images.get_pil_image(self.image.image_name)
+        raw_image = context.images.get_pil(self.image.image_name)
         # image type should be PIL.PngImagePlugin.PngImageFile ?
         processed_image = self.run_processor(raw_image)
 
         # currently can't see processed image in node UI without a showImage node,
         #    so for now setting image_type to RESULT instead of INTERMEDIATE so will get saved in gallery
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=processed_image)
-            image=processed_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.CONTROL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
 
         """Builds an ImageOutput and its ImageField"""
         processed_image_field = ImageField(image_name=image_dto.image_name)
@@ -180,7 +170,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
@@ -203,7 +193,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
@@ -232,7 +222,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
@@ -254,7 +244,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
@@ -277,7 +267,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
@@ -304,7 +294,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
@@ -321,7 +311,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.0"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.1"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
@@ -344,7 +334,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.0"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.1"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
@@ -371,7 +361,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
@@ -401,7 +391,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -417,7 +407,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
@@ -440,7 +430,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -469,7 +459,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -509,7 +499,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
@@ -551,7 +541,7 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""

invokeai/app/invocations/cv.py

@@ -5,23 +5,23 @@ import cv2 as cv
 import numpy
 from PIL import Image, ImageOps
 
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.invocations.fields import ImageField
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.invocations.primitives import ImageOutput
 
-from .baseinvocation import BaseInvocation, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, invocation
 from .fields import InputField, WithMetadata
 
 
-@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.0")
+@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.1")
 class CvInpaintInvocation(BaseInvocation, WithMetadata):
     """Simple inpaint using opencv."""
 
     image: ImageField = InputField(description="The image to inpaint")
     mask: ImageField = InputField(description="The mask to use when inpainting")
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
-        mask = context.services.images.get_pil_image(self.mask.image_name)
+        mask = context.images.get_pil(self.mask.image_name)
 
         # Convert to cv image/mask
         # TODO: consider making these utility functions
@@ -35,18 +35,6 @@ class CvInpaintInvocation(BaseInvocation, WithMetadata):
         # TODO: consider making a utility function
         image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=image_inpainted)
-            image=image_inpainted,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
-        )
 
-        return ImageOutput(
+        return ImageOutput.build(image_dto)
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/facetools.py

@@ -1,7 +1,7 @@
 import math
 import re
 from pathlib import Path
-from typing import Optional, TypedDict
+from typing import TYPE_CHECKING, Optional, TypedDict
 
 import cv2
 import numpy as np
@@ -13,13 +13,16 @@ from pydantic import field_validator
 import invokeai.assets.fonts as font_assets
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
-    InvocationContext,
+    WithMetadata,
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import InputField, OutputField, WithMetadata
+from invokeai.app.invocations.fields import ImageField, InputField, OutputField
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.services.image_records.image_records_common import ImageCategory
+
+if TYPE_CHECKING:
+    from invokeai.app.services.shared.invocation_context import InvocationContext
 
 
 @invocation_output("face_mask_output")
@@ -174,7 +177,7 @@ def prepare_faces_list(
 
 
 def generate_face_box_mask(
-    context: InvocationContext,
+    context: "InvocationContext",
     minimum_confidence: float,
     x_offset: float,
     y_offset: float,
@@ -273,7 +276,7 @@ def generate_face_box_mask(
 
 
 def extract_face(
-    context: InvocationContext,
+    context: "InvocationContext",
     image: ImageType,
     face: FaceResultData,
     padding: int,
@@ -304,37 +307,37 @@ def extract_face(
 
     # Adjust the crop boundaries to stay within the original image's dimensions
     if x_min < 0:
-        context.services.logger.warning("FaceTools --> -X-axis padding reached image edge.")
+        context.logger.warning("FaceTools --> -X-axis padding reached image edge.")
         x_max -= x_min
         x_min = 0
     elif x_max > mask.width:
-        context.services.logger.warning("FaceTools --> +X-axis padding reached image edge.")
+        context.logger.warning("FaceTools --> +X-axis padding reached image edge.")
         x_min -= x_max - mask.width
         x_max = mask.width
 
     if y_min < 0:
-        context.services.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
+        context.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
         y_max -= y_min
         y_min = 0
     elif y_max > mask.height:
-        context.services.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
+        context.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
         y_min -= y_max - mask.height
         y_max = mask.height
 
     # Ensure the crop is square and adjust the boundaries if needed
     if x_max - x_min != crop_size:
-        context.services.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
+        context.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
         diff = crop_size - (x_max - x_min)
         x_min -= diff // 2
         x_max += diff - diff // 2
 
     if y_max - y_min != crop_size:
-        context.services.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
+        context.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
         diff = crop_size - (y_max - y_min)
         y_min -= diff // 2
         y_max += diff - diff // 2
 
-    context.services.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
+    context.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
 
     # Crop the output image to the specified size with the center of the face mesh as the center.
     mask = mask.crop((x_min, y_min, x_max, y_max))
@@ -354,7 +357,7 @@ def extract_face(
 
 
 def get_faces_list(
-    context: InvocationContext,
+    context: "InvocationContext",
     image: ImageType,
     should_chunk: bool,
     minimum_confidence: float,
@@ -366,7 +369,7 @@ def get_faces_list(
 
     # Generate the face box mask and get the center of the face.
     if not should_chunk:
-        context.services.logger.info("FaceTools --> Attempting full image face detection.")
+        context.logger.info("FaceTools --> Attempting full image face detection.")
         result = generate_face_box_mask(
             context=context,
             minimum_confidence=minimum_confidence,
@@ -378,7 +381,7 @@ def get_faces_list(
             draw_mesh=draw_mesh,
         )
     if should_chunk or len(result) == 0:
-        context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
+        context.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
         width, height = image.size
         image_chunks = []
         x_offsets = []
@@ -397,7 +400,7 @@ def get_faces_list(
                 x_offsets.append(x)
                 y_offsets.append(0)
                 fx += increment
-                context.services.logger.info(f"FaceTools --> Chunk starting at x = {x}")
+                context.logger.info(f"FaceTools --> Chunk starting at x = {x}")
         elif height > width:
             # Portrait - slice the image vertically
             fy = 0.0
@@ -409,10 +412,10 @@ def get_faces_list(
                 x_offsets.append(0)
                 y_offsets.append(y)
                 fy += increment
-                context.services.logger.info(f"FaceTools --> Chunk starting at y = {y}")
+                context.logger.info(f"FaceTools --> Chunk starting at y = {y}")
 
         for idx in range(len(image_chunks)):
-            context.services.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
+            context.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
             result = result + generate_face_box_mask(
                 context=context,
                 minimum_confidence=minimum_confidence,
@@ -426,7 +429,7 @@ def get_faces_list(
 
         if len(result) == 0:
             # Give up
-            context.services.logger.warning(
+            context.logger.warning(
                 "FaceTools --> No face detected in chunked input image. Passing through original image."
             )
 
@@ -435,7 +438,7 @@ def get_faces_list(
     return all_faces
 
 
-@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.0")
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.1")
 class FaceOffInvocation(BaseInvocation, WithMetadata):
     """Bound, extract, and mask a face from an image using MediaPipe detection"""
 
@@ -456,7 +459,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.",
     )
 
-    def faceoff(self, context: InvocationContext, image: ImageType) -> Optional[ExtractFaceData]:
+    def faceoff(self, context: "InvocationContext", image: ImageType) -> Optional[ExtractFaceData]:
         all_faces = get_faces_list(
             context=context,
             image=image,
@@ -468,11 +471,11 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         )
 
         if len(all_faces) == 0:
-            context.services.logger.warning("FaceOff --> No faces detected. Passing through original image.")
+            context.logger.warning("FaceOff --> No faces detected. Passing through original image.")
             return None
 
         if self.face_id > len(all_faces) - 1:
-            context.services.logger.warning(
+            context.logger.warning(
                 f"FaceOff --> Face ID {self.face_id} is outside of the number of faces detected ({len(all_faces)}). Passing through original image."
             )
             return None
@@ -483,8 +486,8 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
 
         return face_data
 
-    def invoke(self, context: InvocationContext) -> FaceOffOutput:
+    def invoke(self, context) -> FaceOffOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
         result = self.faceoff(context=context, image=image)
 
         if result is None:
@@ -498,24 +501,9 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
             x = result["x_min"]
             y = result["y_min"]
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=result_image)
-            image=result_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
-        )
 
-        mask_dto = context.services.images.create(
+        mask_dto = context.images.save(image=result_mask, image_category=ImageCategory.MASK)
-            image=result_mask,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.MASK,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
 
         output = FaceOffOutput(
             image=ImageField(image_name=image_dto.image_name),
@@ -529,7 +517,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         return output
 
 
-@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.0")
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.1")
 class FaceMaskInvocation(BaseInvocation, WithMetadata):
     """Face mask creation using mediapipe face detection"""
 
@@ -556,7 +544,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
             raise ValueError('Face IDs must be a comma-separated list of integers (e.g. "1,2,3")')
         return v
 
-    def facemask(self, context: InvocationContext, image: ImageType) -> FaceMaskResult:
+    def facemask(self, context: "InvocationContext", image: ImageType) -> FaceMaskResult:
         all_faces = get_faces_list(
             context=context,
             image=image,
@@ -578,7 +566,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
 
             if len(intersected_face_ids) == 0:
                 id_range_str = ",".join([str(id) for id in id_range])
-                context.services.logger.warning(
+                context.logger.warning(
                     f"Face IDs must be in range of detected faces - requested {self.face_ids}, detected {id_range_str}. Passing through original image."
                 )
                 return FaceMaskResult(
@@ -613,28 +601,13 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
             mask=mask_pil,
         )
 
-    def invoke(self, context: InvocationContext) -> FaceMaskOutput:
+    def invoke(self, context) -> FaceMaskOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
         result = self.facemask(context=context, image=image)
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=result["image"])
-            image=result["image"],
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
-        )
 
-        mask_dto = context.services.images.create(
+        mask_dto = context.images.save(image=result["mask"], image_category=ImageCategory.MASK)
-            image=result["mask"],
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.MASK,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
 
         output = FaceMaskOutput(
             image=ImageField(image_name=image_dto.image_name),
@@ -647,7 +620,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.0"
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.1"
 )
 class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
     """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
@@ -661,7 +634,7 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
         description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.",
     )
 
-    def faceidentifier(self, context: InvocationContext, image: ImageType) -> ImageType:
+    def faceidentifier(self, context: "InvocationContext", image: ImageType) -> ImageType:
         image = image.copy()
 
         all_faces = get_faces_list(
@@ -702,22 +675,10 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
 
         return image
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
         result_image = self.faceidentifier(context=context, image=image)
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=result_image)
-            image=result_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
-        )
 
-        return ImageOutput(
+        return ImageOutput.build(image_dto)
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/fields.py

@@ -1,11 +1,13 @@
+from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Callable, Optional
+from typing import Any, Callable, List, Optional, Tuple
 
 from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter
 from pydantic.fields import _Unset
 from pydantic_core import PydanticUndefined
 
 from invokeai.app.util.metaenum import MetaEnum
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import BasicConditioningInfo
 from invokeai.backend.util.logging import InvokeAILogger
 
 logger = InvokeAILogger.get_logger()
@@ -255,6 +257,10 @@ class InputFieldJSONSchemaExtra(BaseModel):
 
 
 class WithMetadata(BaseModel):
+    """
+    Inherit from this class if your node needs a metadata input field.
+    """
+
     metadata: Optional[MetadataField] = Field(
         default=None,
         description=FieldDescriptions.metadata,
@@ -498,4 +504,53 @@ def OutputField(
             field_kind=FieldKind.Output,
         ).model_dump(exclude_none=True),
     )
+
+
+class ImageField(BaseModel):
+    """An image primitive field"""
+
+    image_name: str = Field(description="The name of the image")
+
+
+class BoardField(BaseModel):
+    """A board primitive field"""
+
+    board_id: str = Field(description="The id of the board")
+
+
+class DenoiseMaskField(BaseModel):
+    """An inpaint mask field"""
+
+    mask_name: str = Field(description="The name of the mask image")
+    masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
+
+
+class LatentsField(BaseModel):
+    """A latents tensor primitive field"""
+
+    latents_name: str = Field(description="The name of the latents")
+    seed: Optional[int] = Field(default=None, description="Seed used to generate this latents")
+
+
+class ColorField(BaseModel):
+    """A color primitive field"""
+
+    r: int = Field(ge=0, le=255, description="The red component")
+    g: int = Field(ge=0, le=255, description="The green component")
+    b: int = Field(ge=0, le=255, description="The blue component")
+    a: int = Field(ge=0, le=255, description="The alpha component")
+
+    def tuple(self) -> Tuple[int, int, int, int]:
+        return (self.r, self.g, self.b, self.a)
+
+
+@dataclass
+class ConditioningFieldData:
+    conditionings: List[BasicConditioningInfo]
+
+
+class ConditioningField(BaseModel):
+    """A conditioning tensor primitive value"""
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
     # endregion

invokeai/app/invocations/infill.py

@@ -6,15 +6,15 @@ from typing import Literal, Optional, get_args
 import numpy as np
 from PIL import Image, ImageOps
 
-from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
+from invokeai.app.invocations.fields import ColorField, ImageField
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.util.misc import SEED_MAX
 from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
 from invokeai.backend.image_util.lama import LaMA
 from invokeai.backend.image_util.patchmatch import PatchMatch
 
-from .baseinvocation import BaseInvocation, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, WithMetadata, invocation
-from .fields import InputField, WithMetadata
+from .fields import InputField
 from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
 
 
@@ -119,7 +119,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
     return si
 
 
-@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
+@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
 class InfillColorInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image with a solid color"""
 
@@ -129,33 +129,20 @@ class InfillColorInvocation(BaseInvocation, WithMetadata):
         description="The color to use to infill",
     )
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         solid_bg = Image.new("RGBA", image.size, self.color.tuple())
         infilled = Image.alpha_composite(solid_bg, image.convert("RGBA"))
 
         infilled.paste(image, (0, 0), image.split()[-1])
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=infilled)
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
 
-        return ImageOutput(
+        return ImageOutput.build(image_dto)
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
 
 
-@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
+@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
 class InfillTileInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image with tiles of the image"""
 
@@ -168,32 +155,19 @@ class InfillTileInvocation(BaseInvocation, WithMetadata):
         description="The seed to use for tile generation (omit for random)",
     )
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         infilled = tile_fill_missing(image.copy(), seed=self.seed, tile_size=self.tile_size)
         infilled.paste(image, (0, 0), image.split()[-1])
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=infilled)
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
 
-        return ImageOutput(
+        return ImageOutput.build(image_dto)
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
 
 
 @invocation(
-    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0"
+    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1"
 )
 class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image using the PatchMatch algorithm"""
@@ -202,8 +176,8 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
     downscale: float = InputField(default=2.0, gt=0, description="Run patchmatch on downscaled image to speedup infill")
     resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
+        image = context.images.get_pil(self.image.image_name).convert("RGBA")
 
         resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
 
@@ -228,77 +202,38 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
         infilled.paste(image, (0, 0), mask=image.split()[-1])
         # image.paste(infilled, (0, 0), mask=image.split()[-1])
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=infilled)
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
 
-        return ImageOutput(
+        return ImageOutput.build(image_dto)
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
 
 
-@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
+@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
 class LaMaInfillInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image using the LaMa model"""
 
     image: ImageField = InputField(description="The image to infill")
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         infilled = infill_lama(image.copy())
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=infilled)
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
 
-        return ImageOutput(
+        return ImageOutput.build(image_dto)
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
 
 
-@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
 class CV2InfillInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 
     image: ImageField = InputField(description="The image to infill")
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         infilled = infill_cv2(image.copy())
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=infilled)
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
 
-        return ImageOutput(
+        return ImageOutput.build(image_dto)
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/ip_adapter.py

@@ -7,7 +7,6 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator, model_valida
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
@@ -62,7 +61,7 @@ class IPAdapterOutput(BaseInvocationOutput):
     ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.1")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.2")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -93,9 +92,9 @@ class IPAdapterInvocation(BaseInvocation):
         validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
         return self
 
-    def invoke(self, context: InvocationContext) -> IPAdapterOutput:
+    def invoke(self, context) -> IPAdapterOutput:
         # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
-        ip_adapter_info = context.services.model_manager.model_info(
+        ip_adapter_info = context.models.get_info(
             self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
         )
         # HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
@@ -104,7 +103,7 @@ class IPAdapterInvocation(BaseInvocation):
         # is currently messy due to differences between how the model info is generated when installing a model from
         # disk vs. downloading the model.
         image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
-            os.path.join(context.services.configuration.get_config().models_path, ip_adapter_info["path"])
+            os.path.join(context.config.get().models_path, ip_adapter_info["path"])
         )
         image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
         image_encoder_model = CLIPVisionModelField(

invokeai/app/invocations/latent.py

@@ -3,7 +3,7 @@
 import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
-from typing import List, Literal, Optional, Union
+from typing import TYPE_CHECKING, List, Literal, Optional, Union
 
 import einops
 import numpy as np
@@ -23,21 +23,26 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
 from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
 
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType, WithMetadata
+from invokeai.app.invocations.fields import (
+    ConditioningField,
+    DenoiseMaskField,
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    LatentsField,
+    OutputField,
+    UIType,
+    WithMetadata,
+)
 from invokeai.app.invocations.ip_adapter import IPAdapterField
 from invokeai.app.invocations.primitives import (
-    DenoiseMaskField,
     DenoiseMaskOutput,
-    ImageField,
     ImageOutput,
-    LatentsField,
     LatentsOutput,
-    build_latents_output,
 )
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.util.controlnet_utils import prepare_control_image
-from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
 from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData, IPAdapterConditioningInfo
@@ -59,14 +64,15 @@ from ...backend.util.devices import choose_precision, choose_torch_device
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
-from .compel import ConditioningField
 from .controlnet_image_processors import ControlField
 from .model import ModelInfo, UNetField, VaeField
 
+if TYPE_CHECKING:
+    from invokeai.app.services.shared.invocation_context import InvocationContext
+
 if choose_torch_device() == torch.device("mps"):
     from torch import mps
 
@@ -102,7 +108,7 @@ class SchedulerInvocation(BaseInvocation):
         ui_type=UIType.Scheduler,
     )
 
-    def invoke(self, context: InvocationContext) -> SchedulerOutput:
+    def invoke(self, context) -> SchedulerOutput:
         return SchedulerOutput(scheduler=self.scheduler)
 
 
@@ -111,7 +117,7 @@ class SchedulerInvocation(BaseInvocation):
     title="Create Denoise Mask",
     tags=["mask", "denoise"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class CreateDenoiseMaskInvocation(BaseInvocation):
     """Creates mask for denoising model run."""
@@ -137,9 +143,9 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
         return mask_tensor
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
+    def invoke(self, context) -> DenoiseMaskOutput:
         if self.image is not None:
-            image = context.services.images.get_pil_image(self.image.image_name)
+            image = context.images.get_pil(self.image.image_name)
             image = image_resized_to_grid_as_tensor(image.convert("RGB"))
             if image.dim() == 3:
                 image = image.unsqueeze(0)
@@ -147,47 +153,37 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
             image = None
 
         mask = self.prep_mask_tensor(
-            context.services.images.get_pil_image(self.mask.image_name),
+            context.images.get_pil(self.mask.image_name),
         )
 
         if image is not None:
-            vae_info = context.services.model_manager.get_model(
+            vae_info = context.models.load(**self.vae.vae.model_dump())
-                **self.vae.vae.model_dump(),
-                context=context,
-            )
 
             img_mask = tv_resize(mask, image.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
             masked_image = image * torch.where(img_mask < 0.5, 0.0, 1.0)
             # TODO:
             masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
 
-            masked_latents_name = f"{context.graph_execution_state_id}__{self.id}_masked_latents"
+            masked_latents_name = context.latents.save(tensor=masked_latents)
-            context.services.latents.save(masked_latents_name, masked_latents)
         else:
             masked_latents_name = None
 
-        mask_name = f"{context.graph_execution_state_id}__{self.id}_mask"
+        mask_name = context.latents.save(tensor=mask)
-        context.services.latents.save(mask_name, mask)
 
-        return DenoiseMaskOutput(
+        return DenoiseMaskOutput.build(
-            denoise_mask=DenoiseMaskField(
             mask_name=mask_name,
             masked_latents_name=masked_latents_name,
-            ),
         )
 
 
 def get_scheduler(
-    context: InvocationContext,
+    context: "InvocationContext",
     scheduler_info: ModelInfo,
     scheduler_name: str,
     seed: int,
 ) -> Scheduler:
     scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
-    orig_scheduler_info = context.services.model_manager.get_model(
+    orig_scheduler_info = context.models.load(**scheduler_info.model_dump())
-        **scheduler_info.model_dump(),
-        context=context,
-    )
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
 
@@ -216,7 +212,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.1",
+    version="1.5.2",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -302,34 +298,18 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 raise ValueError("cfg_scale must be greater than 1")
         return v
 
-    # TODO: pass this an emitter method or something? or a session for dispatching?
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-        base_model: BaseModelType,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.model_dump(),
-            source_node_id=source_node_id,
-            base_model=base_model,
-        )
-
     def get_conditioning_data(
         self,
-        context: InvocationContext,
+        context: "InvocationContext",
         scheduler,
         unet,
         seed,
     ) -> ConditioningData:
-        positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        positive_cond_data = context.conditioning.get(self.positive_conditioning.conditioning_name)
         c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
         extra_conditioning_info = c.extra_conditioning
 
-        negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
+        negative_cond_data = context.conditioning.get(self.negative_conditioning.conditioning_name)
         uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
 
         conditioning_data = ConditioningData(
@@ -389,7 +369,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
     def prep_control_data(
         self,
-        context: InvocationContext,
+        context: "InvocationContext",
         control_input: Union[ControlField, List[ControlField]],
         latents_shape: List[int],
         exit_stack: ExitStack,
@@ -417,17 +397,16 @@ class DenoiseLatentsInvocation(BaseInvocation):
         controlnet_data = []
         for control_info in control_list:
             control_model = exit_stack.enter_context(
-                context.services.model_manager.get_model(
+                context.models.load(
                     model_name=control_info.control_model.model_name,
                     model_type=ModelType.ControlNet,
                     base_model=control_info.control_model.base_model,
-                    context=context,
                 )
             )
 
             # control_models.append(control_model)
             control_image_field = control_info.image
-            input_image = context.services.images.get_pil_image(control_image_field.image_name)
+            input_image = context.images.get_pil(control_image_field.image_name)
             # self.image.image_type, self.image.image_name
             # FIXME: still need to test with different widths, heights, devices, dtypes
             #        and add in batch_size, num_images_per_prompt?
@@ -463,7 +442,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
     def prep_ip_adapter_data(
         self,
-        context: InvocationContext,
+        context: "InvocationContext",
         ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]],
         conditioning_data: ConditioningData,
         exit_stack: ExitStack,
@@ -485,19 +464,17 @@ class DenoiseLatentsInvocation(BaseInvocation):
         conditioning_data.ip_adapter_conditioning = []
         for single_ip_adapter in ip_adapter:
             ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
-                context.services.model_manager.get_model(
+                context.models.load(
                     model_name=single_ip_adapter.ip_adapter_model.model_name,
                     model_type=ModelType.IPAdapter,
                     base_model=single_ip_adapter.ip_adapter_model.base_model,
-                    context=context,
                 )
             )
 
-            image_encoder_model_info = context.services.model_manager.get_model(
+            image_encoder_model_info = context.models.load(
                 model_name=single_ip_adapter.image_encoder_model.model_name,
                 model_type=ModelType.CLIPVision,
                 base_model=single_ip_adapter.image_encoder_model.base_model,
-                context=context,
             )
 
             # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
@@ -505,7 +482,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             if not isinstance(single_ipa_images, list):
                 single_ipa_images = [single_ipa_images]
 
-            single_ipa_images = [context.services.images.get_pil_image(image.image_name) for image in single_ipa_images]
+            single_ipa_images = [context.images.get_pil(image.image_name) for image in single_ipa_images]
 
             # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
             # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
@@ -532,7 +509,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
     def run_t2i_adapters(
         self,
-        context: InvocationContext,
+        context: "InvocationContext",
         t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
         latents_shape: list[int],
         do_classifier_free_guidance: bool,
@@ -549,13 +526,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         t2i_adapter_data = []
         for t2i_adapter_field in t2i_adapter:
-            t2i_adapter_model_info = context.services.model_manager.get_model(
+            t2i_adapter_model_info = context.models.load(
                 model_name=t2i_adapter_field.t2i_adapter_model.model_name,
                 model_type=ModelType.T2IAdapter,
                 base_model=t2i_adapter_field.t2i_adapter_model.base_model,
-                context=context,
             )
-            image = context.services.images.get_pil_image(t2i_adapter_field.image.image_name)
+            image = context.images.get_pil(t2i_adapter_field.image.image_name)
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
             if t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusion1:
@@ -642,30 +618,30 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         return num_inference_steps, timesteps, init_timestep
 
-    def prep_inpaint_mask(self, context, latents):
+    def prep_inpaint_mask(self, context: "InvocationContext", latents):
         if self.denoise_mask is None:
             return None, None
 
-        mask = context.services.latents.get(self.denoise_mask.mask_name)
+        mask = context.latents.get(self.denoise_mask.mask_name)
         mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
         if self.denoise_mask.masked_latents_name is not None:
-            masked_latents = context.services.latents.get(self.denoise_mask.masked_latents_name)
+            masked_latents = context.latents.get(self.denoise_mask.masked_latents_name)
         else:
             masked_latents = None
 
         return 1 - mask, masked_latents
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
+    def invoke(self, context) -> LatentsOutput:
         with SilenceWarnings():  # this quenches NSFW nag from diffusers
             seed = None
             noise = None
             if self.noise is not None:
-                noise = context.services.latents.get(self.noise.latents_name)
+                noise = context.latents.get(self.noise.latents_name)
                 seed = self.noise.seed
 
             if self.latents is not None:
-                latents = context.services.latents.get(self.latents.latents_name)
+                latents = context.latents.get(self.latents.latents_name)
                 if seed is None:
                     seed = self.latents.seed
 
@@ -691,27 +667,17 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 do_classifier_free_guidance=True,
             )
 
-            # Get the source node id (we are invoking the prepared node)
-            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
             def step_callback(state: PipelineIntermediateState):
-                self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
+                context.util.sd_step_callback(state, self.unet.unet.base_model)
 
             def _lora_loader():
                 for lora in self.unet.loras:
-                    lora_info = context.services.model_manager.get_model(
+                    lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
-                        **lora.model_dump(exclude={"weight"}),
-                        context=context,
-                    )
                     yield (lora_info.context.model, lora.weight)
                     del lora_info
                 return
 
-            unet_info = context.services.model_manager.get_model(
+            unet_info = context.models.load(**self.unet.unet.model_dump())
-                **self.unet.unet.model_dump(),
-                context=context,
-            )
             with (
                 ExitStack() as exit_stack,
                 ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
@@ -787,9 +753,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
             if choose_torch_device() == torch.device("mps"):
                 mps.empty_cache()
 
-            name = f"{context.graph_execution_state_id}__{self.id}"
+            name = context.latents.save(tensor=result_latents)
-            context.services.latents.save(name, result_latents)
+        return LatentsOutput.build(latents_name=name, latents=result_latents, seed=seed)
-        return build_latents_output(latents_name=name, latents=result_latents, seed=seed)
 
 
 @invocation(
@@ -797,7 +762,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.2.0",
+    version="1.2.1",
 )
 class LatentsToImageInvocation(BaseInvocation, WithMetadata):
     """Generates an image from latents."""
@@ -814,13 +779,10 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
     fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.latents.get(self.latents.latents_name)
 
-        vae_info = context.services.model_manager.get_model(
+        vae_info = context.models.load(**self.vae.vae.model_dump())
-            **self.vae.vae.model_dump(),
-            context=context,
-        )
 
         with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
             latents = latents.to(vae.device)
@@ -849,7 +811,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
                 vae.to(dtype=torch.float16)
                 latents = latents.half()
 
-            if self.tiled or context.services.configuration.tiled_decode:
+            if self.tiled or context.config.get().tiled_decode:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()
@@ -873,22 +835,9 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
         if choose_torch_device() == torch.device("mps"):
             mps.empty_cache()
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=image)
-            image=image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
 
-        return ImageOutput(
+        return ImageOutput.build(image_dto)
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
 
 
 LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
@@ -899,7 +848,7 @@ LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic",
     title="Resize Latents",
     tags=["latents", "resize"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class ResizeLatentsInvocation(BaseInvocation):
     """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
@@ -921,8 +870,8 @@ class ResizeLatentsInvocation(BaseInvocation):
     mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
     antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
 
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
+    def invoke(self, context) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.latents.get(self.latents.latents_name)
 
         # TODO:
         device = choose_torch_device()
@@ -940,10 +889,8 @@ class ResizeLatentsInvocation(BaseInvocation):
         if device == torch.device("mps"):
             mps.empty_cache()
 
-        name = f"{context.graph_execution_state_id}__{self.id}"
+        name = context.latents.save(tensor=resized_latents)
-        # context.services.latents.set(name, resized_latents)
+        return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
-        context.services.latents.save(name, resized_latents)
-        return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
 
 
 @invocation(
@@ -951,7 +898,7 @@ class ResizeLatentsInvocation(BaseInvocation):
     title="Scale Latents",
     tags=["latents", "resize"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class ScaleLatentsInvocation(BaseInvocation):
     """Scales latents by a given factor."""
@@ -964,8 +911,8 @@ class ScaleLatentsInvocation(BaseInvocation):
     mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
     antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
 
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
+    def invoke(self, context) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.latents.get(self.latents.latents_name)
 
         # TODO:
         device = choose_torch_device()
@@ -984,10 +931,8 @@ class ScaleLatentsInvocation(BaseInvocation):
         if device == torch.device("mps"):
             mps.empty_cache()
 
-        name = f"{context.graph_execution_state_id}__{self.id}"
+        name = context.latents.save(tensor=resized_latents)
-        # context.services.latents.set(name, resized_latents)
+        return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
-        context.services.latents.save(name, resized_latents)
-        return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
 
 
 @invocation(
@@ -995,7 +940,7 @@ class ScaleLatentsInvocation(BaseInvocation):
     title="Image to Latents",
     tags=["latents", "image", "vae", "i2l"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class ImageToLatentsInvocation(BaseInvocation):
     """Encodes an image into latents."""
@@ -1055,13 +1000,10 @@ class ImageToLatentsInvocation(BaseInvocation):
         return latents
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
+    def invoke(self, context) -> LatentsOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
-        vae_info = context.services.model_manager.get_model(
+        vae_info = context.models.load(**self.vae.vae.model_dump())
-            **self.vae.vae.model_dump(),
-            context=context,
-        )
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:
@@ -1069,10 +1011,9 @@ class ImageToLatentsInvocation(BaseInvocation):
 
         latents = self.vae_encode(vae_info, self.fp32, self.tiled, image_tensor)
 
-        name = f"{context.graph_execution_state_id}__{self.id}"
         latents = latents.to("cpu")
-        context.services.latents.save(name, latents)
+        name = context.latents.save(tensor=latents)
-        return build_latents_output(latents_name=name, latents=latents, seed=None)
+        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
 
     @singledispatchmethod
     @staticmethod
@@ -1092,7 +1033,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     title="Blend Latents",
     tags=["latents", "blend"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class BlendLatentsInvocation(BaseInvocation):
     """Blend two latents using a given alpha. Latents must have same size."""
@@ -1107,9 +1048,9 @@ class BlendLatentsInvocation(BaseInvocation):
     )
     alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
 
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
+    def invoke(self, context) -> LatentsOutput:
-        latents_a = context.services.latents.get(self.latents_a.latents_name)
+        latents_a = context.latents.get(self.latents_a.latents_name)
-        latents_b = context.services.latents.get(self.latents_b.latents_name)
+        latents_b = context.latents.get(self.latents_b.latents_name)
 
         if latents_a.shape != latents_b.shape:
             raise Exception("Latents to blend must be the same size.")
@@ -1163,10 +1104,8 @@ class BlendLatentsInvocation(BaseInvocation):
         if device == torch.device("mps"):
             mps.empty_cache()
 
-        name = f"{context.graph_execution_state_id}__{self.id}"
+        name = context.latents.save(tensor=blended_latents)
-        # context.services.latents.set(name, resized_latents)
+        return LatentsOutput.build(latents_name=name, latents=blended_latents)
-        context.services.latents.save(name, blended_latents)
-        return build_latents_output(latents_name=name, latents=blended_latents)
 
 
 # The Crop Latents node was copied from @skunkworxdark's implementation here:
@@ -1176,7 +1115,7 @@ class BlendLatentsInvocation(BaseInvocation):
     title="Crop Latents",
     tags=["latents", "crop"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 # TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
 # Currently, if the class names conflict then 'GET /openapi.json' fails.
@@ -1210,8 +1149,8 @@ class CropLatentsCoreInvocation(BaseInvocation):
         description="The height (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
     )
 
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
+    def invoke(self, context) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.latents.get(self.latents.latents_name)
 
         x1 = self.x // LATENT_SCALE_FACTOR
         y1 = self.y // LATENT_SCALE_FACTOR
@@ -1220,10 +1159,9 @@ class CropLatentsCoreInvocation(BaseInvocation):
 
         cropped_latents = latents[..., y1:y2, x1:x2]
 
-        name = f"{context.graph_execution_state_id}__{self.id}"
+        name = context.latents.save(tensor=cropped_latents)
-        context.services.latents.save(name, cropped_latents)
 
-        return build_latents_output(latents_name=name, latents=cropped_latents)
+        return LatentsOutput.build(latents_name=name, latents=cropped_latents)
 
 
 @invocation_output("ideal_size_output")

invokeai/app/invocations/math.py

@@ -8,7 +8,7 @@ from pydantic import ValidationInfo, field_validator
 from invokeai.app.invocations.fields import FieldDescriptions, InputField
 from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
 
-from .baseinvocation import BaseInvocation, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, invocation
 
 
 @invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")
@@ -18,7 +18,7 @@ class AddInvocation(BaseInvocation):
     a: int = InputField(default=0, description=FieldDescriptions.num_1)
     b: int = InputField(default=0, description=FieldDescriptions.num_2)
 
-    def invoke(self, context: InvocationContext) -> IntegerOutput:
+    def invoke(self, context) -> IntegerOutput:
         return IntegerOutput(value=self.a + self.b)
 
 
@@ -29,7 +29,7 @@ class SubtractInvocation(BaseInvocation):
     a: int = InputField(default=0, description=FieldDescriptions.num_1)
     b: int = InputField(default=0, description=FieldDescriptions.num_2)
 
-    def invoke(self, context: InvocationContext) -> IntegerOutput:
+    def invoke(self, context) -> IntegerOutput:
         return IntegerOutput(value=self.a - self.b)
 
 
@@ -40,7 +40,7 @@ class MultiplyInvocation(BaseInvocation):
     a: int = InputField(default=0, description=FieldDescriptions.num_1)
     b: int = InputField(default=0, description=FieldDescriptions.num_2)
 
-    def invoke(self, context: InvocationContext) -> IntegerOutput:
+    def invoke(self, context) -> IntegerOutput:
         return IntegerOutput(value=self.a * self.b)
 
 
@@ -51,7 +51,7 @@ class DivideInvocation(BaseInvocation):
     a: int = InputField(default=0, description=FieldDescriptions.num_1)
     b: int = InputField(default=0, description=FieldDescriptions.num_2)
 
-    def invoke(self, context: InvocationContext) -> IntegerOutput:
+    def invoke(self, context) -> IntegerOutput:
         return IntegerOutput(value=int(self.a / self.b))
 
 
@@ -69,7 +69,7 @@ class RandomIntInvocation(BaseInvocation):
     low: int = InputField(default=0, description=FieldDescriptions.inclusive_low)
     high: int = InputField(default=np.iinfo(np.int32).max, description=FieldDescriptions.exclusive_high)
 
-    def invoke(self, context: InvocationContext) -> IntegerOutput:
+    def invoke(self, context) -> IntegerOutput:
         return IntegerOutput(value=np.random.randint(self.low, self.high))
 
 
@@ -88,7 +88,7 @@ class RandomFloatInvocation(BaseInvocation):
     high: float = InputField(default=1.0, description=FieldDescriptions.exclusive_high)
     decimals: int = InputField(default=2, description=FieldDescriptions.decimal_places)
 
-    def invoke(self, context: InvocationContext) -> FloatOutput:
+    def invoke(self, context) -> FloatOutput:
         random_float = np.random.uniform(self.low, self.high)
         rounded_float = round(random_float, self.decimals)
         return FloatOutput(value=rounded_float)
@@ -110,7 +110,7 @@ class FloatToIntegerInvocation(BaseInvocation):
         default="Nearest", description="The method to use for rounding"
     )
 
-    def invoke(self, context: InvocationContext) -> IntegerOutput:
+    def invoke(self, context) -> IntegerOutput:
         if self.method == "Nearest":
             return IntegerOutput(value=round(self.value / self.multiple) * self.multiple)
         elif self.method == "Floor":
@@ -128,7 +128,7 @@ class RoundInvocation(BaseInvocation):
     value: float = InputField(default=0, description="The float value")
     decimals: int = InputField(default=0, description="The number of decimal places")
 
-    def invoke(self, context: InvocationContext) -> FloatOutput:
+    def invoke(self, context) -> FloatOutput:
         return FloatOutput(value=round(self.value, self.decimals))
 
 
@@ -196,7 +196,7 @@ class IntegerMathInvocation(BaseInvocation):
             raise ValueError("Result of exponentiation is not an integer")
         return v
 
-    def invoke(self, context: InvocationContext) -> IntegerOutput:
+    def invoke(self, context) -> IntegerOutput:
         # Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
         if self.operation == "ADD":
             return IntegerOutput(value=self.a + self.b)
@@ -270,7 +270,7 @@ class FloatMathInvocation(BaseInvocation):
             raise ValueError("Root operation resulted in a complex number")
         return v
 
-    def invoke(self, context: InvocationContext) -> FloatOutput:
+    def invoke(self, context) -> FloatOutput:
         # Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
         if self.operation == "ADD":
             return FloatOutput(value=self.a + self.b)

invokeai/app/invocations/metadata.py

@@ -5,15 +5,20 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
 from invokeai.app.invocations.controlnet_image_processors import ControlField
-from invokeai.app.invocations.fields import FieldDescriptions, InputField, MetadataField, OutputField, UIType
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    InputField,
+    MetadataField,
+    OutputField,
+    UIType,
+)
 from invokeai.app.invocations.ip_adapter import IPAdapterModelField
 from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
-from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 
 from ...version import __version__
@@ -59,7 +64,7 @@ class MetadataItemInvocation(BaseInvocation):
     label: str = InputField(description=FieldDescriptions.metadata_item_label)
     value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any)
 
-    def invoke(self, context: InvocationContext) -> MetadataItemOutput:
+    def invoke(self, context) -> MetadataItemOutput:
         return MetadataItemOutput(item=MetadataItemField(label=self.label, value=self.value))
 
 
@@ -76,7 +81,7 @@ class MetadataInvocation(BaseInvocation):
         description=FieldDescriptions.metadata_item_polymorphic
     )
 
-    def invoke(self, context: InvocationContext) -> MetadataOutput:
+    def invoke(self, context) -> MetadataOutput:
         if isinstance(self.items, MetadataItemField):
             # single metadata item
             data = {self.items.label: self.items.value}
@@ -95,7 +100,7 @@ class MergeMetadataInvocation(BaseInvocation):
 
     collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection)
 
-    def invoke(self, context: InvocationContext) -> MetadataOutput:
+    def invoke(self, context) -> MetadataOutput:
         data = {}
         for item in self.collection:
             data.update(item.model_dump())
@@ -213,7 +218,7 @@ class CoreMetadataInvocation(BaseInvocation):
         description="The start value used for refiner denoising",
     )
 
-    def invoke(self, context: InvocationContext) -> MetadataOutput:
+    def invoke(self, context) -> MetadataOutput:
         """Collects and outputs a CoreMetadata object"""
 
         return MetadataOutput(

invokeai/app/invocations/model.py

@@ -10,7 +10,6 @@ from ...backend.model_management import BaseModelType, ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
@@ -102,7 +101,7 @@ class LoRAModelField(BaseModel):
     title="Main Model",
     tags=["model"],
     category="model",
-    version="1.0.0",
+    version="1.0.1",
 )
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
@@ -110,13 +109,13 @@ class MainModelLoaderInvocation(BaseInvocation):
     model: MainModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
     # TODO: precision?
 
-    def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
+    def invoke(self, context) -> ModelLoaderOutput:
         base_model = self.model.base_model
         model_name = self.model.model_name
         model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,
@@ -203,7 +202,7 @@ class LoraLoaderOutput(BaseInvocationOutput):
     clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
-@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.0")
+@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.1")
 class LoraLoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
@@ -222,14 +221,14 @@ class LoraLoaderInvocation(BaseInvocation):
         title="CLIP",
     )
 
-    def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
+    def invoke(self, context) -> LoraLoaderOutput:
         if self.lora is None:
             raise Exception("No LoRA provided")
 
         base_model = self.lora.base_model
         lora_name = self.lora.model_name
 
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             base_model=base_model,
             model_name=lora_name,
             model_type=ModelType.Lora,
@@ -285,7 +284,7 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
     title="SDXL LoRA",
     tags=["lora", "model"],
     category="model",
-    version="1.0.0",
+    version="1.0.1",
 )
 class SDXLLoraLoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
@@ -311,14 +310,14 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
         title="CLIP 2",
     )
 
-    def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
+    def invoke(self, context) -> SDXLLoraLoaderOutput:
         if self.lora is None:
             raise Exception("No LoRA provided")
 
         base_model = self.lora.base_model
         lora_name = self.lora.model_name
 
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             base_model=base_model,
             model_name=lora_name,
             model_type=ModelType.Lora,
@@ -384,7 +383,7 @@ class VAEModelField(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
 
-@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
+@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.1")
 class VaeLoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
@@ -394,12 +393,12 @@ class VaeLoaderInvocation(BaseInvocation):
         title="VAE",
     )
 
-    def invoke(self, context: InvocationContext) -> VAEOutput:
+    def invoke(self, context) -> VAEOutput:
         base_model = self.vae_model.base_model
         model_name = self.vae_model.model_name
         model_type = ModelType.Vae
 
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             base_model=base_model,
             model_name=model_name,
             model_type=model_type,
@@ -449,7 +448,7 @@ class SeamlessModeInvocation(BaseInvocation):
     seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
     seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")
 
-    def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
+    def invoke(self, context) -> SeamlessModeOutput:
         # Conditionally append 'x' and 'y' based on seamless_x and seamless_y
         unet = copy.deepcopy(self.unet)
         vae = copy.deepcopy(self.vae)
@@ -485,6 +484,6 @@ class FreeUInvocation(BaseInvocation):
     s1: float = InputField(default=0.9, ge=-1, le=3, description=FieldDescriptions.freeu_s1)
     s2: float = InputField(default=0.2, ge=-1, le=3, description=FieldDescriptions.freeu_s2)
 
-    def invoke(self, context: InvocationContext) -> UNetOutput:
+    def invoke(self, context) -> UNetOutput:
         self.unet.freeu_config = FreeUConfig(s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2)
         return UNetOutput(unet=self.unet)

invokeai/app/invocations/noise.py

@@ -4,15 +4,13 @@
 import torch
 from pydantic import field_validator
 
-from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField
+from invokeai.app.invocations.fields import FieldDescriptions, InputField, LatentsField, OutputField
-from invokeai.app.invocations.latent import LatentsField
 from invokeai.app.util.misc import SEED_MAX
 
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
@@ -67,9 +65,9 @@ class NoiseOutput(BaseInvocationOutput):
     width: int = OutputField(description=FieldDescriptions.width)
     height: int = OutputField(description=FieldDescriptions.height)
 
-
+    @classmethod
-def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
+    def build(cls, latents_name: str, latents: torch.Tensor, seed: int) -> "NoiseOutput":
-    return NoiseOutput(
+        return cls(
             noise=LatentsField(latents_name=latents_name, seed=seed),
             width=latents.size()[3] * 8,
             height=latents.size()[2] * 8,
@@ -114,7 +112,7 @@ class NoiseInvocation(BaseInvocation):
         """Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
         return v % (SEED_MAX + 1)
 
-    def invoke(self, context: InvocationContext) -> NoiseOutput:
+    def invoke(self, context) -> NoiseOutput:
         noise = get_noise(
             width=self.width,
             height=self.height,
@@ -122,6 +120,5 @@ class NoiseInvocation(BaseInvocation):
             seed=self.seed,
             use_cpu=self.use_cpu,
         )
-        name = f"{context.graph_execution_state_id}__{self.id}"
+        name = context.latents.save(tensor=noise)
-        context.services.latents.save(name, noise)
+        return NoiseOutput.build(latents_name=name, latents=noise, seed=self.seed)
-        return build_noise_output(latents_name=name, latents=noise, seed=self.seed)

invokeai/app/invocations/onnx.py

@@ -37,7 +37,7 @@ from .baseinvocation import (
     invocation_output,
 )
 from .controlnet_image_processors import ControlField
-from .latent import SAMPLER_NAME_VALUES, LatentsField, LatentsOutput, build_latents_output, get_scheduler
+from .latent import SAMPLER_NAME_VALUES, LatentsField, LatentsOutput, get_scheduler
 from .model import ClipField, ModelInfo, UNetField, VaeField
 
 ORT_TO_NP_TYPE = {
@@ -63,7 +63,7 @@ class ONNXPromptInvocation(BaseInvocation):
     prompt: str = InputField(default="", description=FieldDescriptions.raw_prompt, ui_component=UIComponent.Textarea)
     clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
 
-    def invoke(self, context: InvocationContext) -> ConditioningOutput:
+    def invoke(self, context) -> ConditioningOutput:
         tokenizer_info = context.services.model_manager.get_model(
             **self.clip.tokenizer.model_dump(),
         )
@@ -201,7 +201,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
 
     # based on
     # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
+    def invoke(self, context) -> LatentsOutput:
         c, _ = context.services.latents.get(self.positive_conditioning.conditioning_name)
         uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
         graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
@@ -342,7 +342,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
     )
     # tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
         latents = context.services.latents.get(self.latents.latents_name)
 
         if self.vae.vae.submodel != SubModelType.VaeDecoder:
@@ -417,7 +417,7 @@ class OnnxModelLoaderInvocation(BaseInvocation):
         description=FieldDescriptions.onnx_main_model, input=Input.Direct, ui_type=UIType.ONNXModel
     )
 
-    def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:
+    def invoke(self, context) -> ONNXModelLoaderOutput:
         base_model = self.model.base_model
         model_name = self.model.model_name
         model_type = ModelType.ONNX

invokeai/app/invocations/param_easing.py

@@ -41,7 +41,7 @@ from matplotlib.ticker import MaxNLocator
 
 from invokeai.app.invocations.primitives import FloatCollectionOutput
 
-from .baseinvocation import BaseInvocation, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, invocation
 from .fields import InputField
 
 
@@ -62,7 +62,7 @@ class FloatLinearRangeInvocation(BaseInvocation):
         description="number of values to interpolate over (including start and stop)",
     )
 
-    def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
+    def invoke(self, context) -> FloatCollectionOutput:
         param_list = list(np.linspace(self.start, self.stop, self.steps))
         return FloatCollectionOutput(collection=param_list)
 
@@ -110,7 +110,7 @@ EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]
     title="Step Param Easing",
     tags=["step", "easing"],
     category="step",
-    version="1.0.0",
+    version="1.0.1",
 )
 class StepParamEasingInvocation(BaseInvocation):
     """Experimental per-step parameter easing for denoising steps"""
@@ -130,7 +130,7 @@ class StepParamEasingInvocation(BaseInvocation):
     # alt_mirror: bool = InputField(default=False, description="alternative mirroring by dual easing")
     show_easing_plot: bool = InputField(default=False, description="show easing plot")
 
-    def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
+    def invoke(self, context) -> FloatCollectionOutput:
         log_diagnostics = False
         # convert from start_step_percent to nearest step <= (steps * start_step_percent)
         # start_step = int(np.floor(self.num_steps * self.start_step_percent))
@@ -149,19 +149,19 @@ class StepParamEasingInvocation(BaseInvocation):
         postlist = list(num_poststeps * [self.post_end_value])
 
         if log_diagnostics:
-            context.services.logger.debug("start_step: " + str(start_step))
+            context.logger.debug("start_step: " + str(start_step))
-            context.services.logger.debug("end_step: " + str(end_step))
+            context.logger.debug("end_step: " + str(end_step))
-            context.services.logger.debug("num_easing_steps: " + str(num_easing_steps))
+            context.logger.debug("num_easing_steps: " + str(num_easing_steps))
-            context.services.logger.debug("num_presteps: " + str(num_presteps))
+            context.logger.debug("num_presteps: " + str(num_presteps))
-            context.services.logger.debug("num_poststeps: " + str(num_poststeps))
+            context.logger.debug("num_poststeps: " + str(num_poststeps))
-            context.services.logger.debug("prelist size: " + str(len(prelist)))
+            context.logger.debug("prelist size: " + str(len(prelist)))
-            context.services.logger.debug("postlist size: " + str(len(postlist)))
+            context.logger.debug("postlist size: " + str(len(postlist)))
-            context.services.logger.debug("prelist: " + str(prelist))
+            context.logger.debug("prelist: " + str(prelist))
-            context.services.logger.debug("postlist: " + str(postlist))
+            context.logger.debug("postlist: " + str(postlist))
 
         easing_class = EASING_FUNCTIONS_MAP[self.easing]
         if log_diagnostics:
-            context.services.logger.debug("easing class: " + str(easing_class))
+            context.logger.debug("easing class: " + str(easing_class))
         easing_list = []
         if self.mirror:  # "expected" mirroring
             # if number of steps is even, squeeze duration down to (number_of_steps)/2
@@ -172,7 +172,7 @@ class StepParamEasingInvocation(BaseInvocation):
 
             base_easing_duration = int(np.ceil(num_easing_steps / 2.0))
             if log_diagnostics:
-                context.services.logger.debug("base easing duration: " + str(base_easing_duration))
+                context.logger.debug("base easing duration: " + str(base_easing_duration))
             even_num_steps = num_easing_steps % 2 == 0  # even number of steps
             easing_function = easing_class(
                 start=self.start_value,
@@ -184,14 +184,14 @@ class StepParamEasingInvocation(BaseInvocation):
                 easing_val = easing_function.ease(step_index)
                 base_easing_vals.append(easing_val)
                 if log_diagnostics:
-                    context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
+                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
             if even_num_steps:
                 mirror_easing_vals = list(reversed(base_easing_vals))
             else:
                 mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
             if log_diagnostics:
-                context.services.logger.debug("base easing vals: " + str(base_easing_vals))
+                context.logger.debug("base easing vals: " + str(base_easing_vals))
-                context.services.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
+                context.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
             easing_list = base_easing_vals + mirror_easing_vals
 
         # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
@@ -226,12 +226,12 @@ class StepParamEasingInvocation(BaseInvocation):
                 step_val = easing_function.ease(step_index)
                 easing_list.append(step_val)
                 if log_diagnostics:
-                    context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
+                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
 
         if log_diagnostics:
-            context.services.logger.debug("prelist size: " + str(len(prelist)))
+            context.logger.debug("prelist size: " + str(len(prelist)))
-            context.services.logger.debug("easing_list size: " + str(len(easing_list)))
+            context.logger.debug("easing_list size: " + str(len(easing_list)))
-            context.services.logger.debug("postlist size: " + str(len(postlist)))
+            context.logger.debug("postlist size: " + str(len(postlist)))
 
         param_list = prelist + easing_list + postlist
 

invokeai/app/invocations/primitives.py

@@ -1,16 +1,26 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
-from pydantic import BaseModel, Field
 
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
+from invokeai.app.invocations.fields import (
+    ColorField,
+    ConditioningField,
+    DenoiseMaskField,
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    LatentsField,
+    OutputField,
+    UIComponent,
+)
+from invokeai.app.services.images.images_common import ImageDTO
 
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
@@ -49,7 +59,7 @@ class BooleanInvocation(BaseInvocation):
 
     value: bool = InputField(default=False, description="The boolean value")
 
-    def invoke(self, context: InvocationContext) -> BooleanOutput:
+    def invoke(self, context) -> BooleanOutput:
         return BooleanOutput(value=self.value)
 
 
@@ -65,7 +75,7 @@ class BooleanCollectionInvocation(BaseInvocation):
 
     collection: list[bool] = InputField(default=[], description="The collection of boolean values")
 
-    def invoke(self, context: InvocationContext) -> BooleanCollectionOutput:
+    def invoke(self, context) -> BooleanCollectionOutput:
         return BooleanCollectionOutput(collection=self.collection)
 
 
@@ -98,7 +108,7 @@ class IntegerInvocation(BaseInvocation):
 
     value: int = InputField(default=0, description="The integer value")
 
-    def invoke(self, context: InvocationContext) -> IntegerOutput:
+    def invoke(self, context) -> IntegerOutput:
         return IntegerOutput(value=self.value)
 
 
@@ -114,7 +124,7 @@ class IntegerCollectionInvocation(BaseInvocation):
 
     collection: list[int] = InputField(default=[], description="The collection of integer values")
 
-    def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
+    def invoke(self, context) -> IntegerCollectionOutput:
         return IntegerCollectionOutput(collection=self.collection)
 
 
@@ -145,7 +155,7 @@ class FloatInvocation(BaseInvocation):
 
     value: float = InputField(default=0.0, description="The float value")
 
-    def invoke(self, context: InvocationContext) -> FloatOutput:
+    def invoke(self, context) -> FloatOutput:
         return FloatOutput(value=self.value)
 
 
@@ -161,7 +171,7 @@ class FloatCollectionInvocation(BaseInvocation):
 
     collection: list[float] = InputField(default=[], description="The collection of float values")
 
-    def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
+    def invoke(self, context) -> FloatCollectionOutput:
         return FloatCollectionOutput(collection=self.collection)
 
 
@@ -192,7 +202,7 @@ class StringInvocation(BaseInvocation):
 
     value: str = InputField(default="", description="The string value", ui_component=UIComponent.Textarea)
 
-    def invoke(self, context: InvocationContext) -> StringOutput:
+    def invoke(self, context) -> StringOutput:
         return StringOutput(value=self.value)
 
 
@@ -208,7 +218,7 @@ class StringCollectionInvocation(BaseInvocation):
 
     collection: list[str] = InputField(default=[], description="The collection of string values")
 
-    def invoke(self, context: InvocationContext) -> StringCollectionOutput:
+    def invoke(self, context) -> StringCollectionOutput:
         return StringCollectionOutput(collection=self.collection)
 
 
@@ -217,18 +227,6 @@ class StringCollectionInvocation(BaseInvocation):
 # region Image
 
 
-class ImageField(BaseModel):
-    """An image primitive field"""
-
-    image_name: str = Field(description="The name of the image")
-
-
-class BoardField(BaseModel):
-    """A board primitive field"""
-
-    board_id: str = Field(description="The id of the board")
-
-
 @invocation_output("image_output")
 class ImageOutput(BaseInvocationOutput):
     """Base class for nodes that output a single image"""
@@ -237,6 +235,14 @@ class ImageOutput(BaseInvocationOutput):
     width: int = OutputField(description="The width of the image in pixels")
     height: int = OutputField(description="The height of the image in pixels")
 
+    @classmethod
+    def build(cls, image_dto: ImageDTO) -> "ImageOutput":
+        return cls(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
+
 
 @invocation_output("image_collection_output")
 class ImageCollectionOutput(BaseInvocationOutput):
@@ -247,7 +253,7 @@ class ImageCollectionOutput(BaseInvocationOutput):
     )
 
 
-@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
+@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.1")
 class ImageInvocation(
     BaseInvocation,
 ):
@@ -255,8 +261,8 @@ class ImageInvocation(
 
     image: ImageField = InputField(description="The image to load")
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         return ImageOutput(
             image=ImageField(image_name=self.image.image_name),
@@ -277,7 +283,7 @@ class ImageCollectionInvocation(BaseInvocation):
 
     collection: list[ImageField] = InputField(description="The collection of image values")
 
-    def invoke(self, context: InvocationContext) -> ImageCollectionOutput:
+    def invoke(self, context) -> ImageCollectionOutput:
         return ImageCollectionOutput(collection=self.collection)
 
 
@@ -286,32 +292,24 @@ class ImageCollectionInvocation(BaseInvocation):
 # region DenoiseMask
 
 
-class DenoiseMaskField(BaseModel):
-    """An inpaint mask field"""
-
-    mask_name: str = Field(description="The name of the mask image")
-    masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
-
-
 @invocation_output("denoise_mask_output")
 class DenoiseMaskOutput(BaseInvocationOutput):
     """Base class for nodes that output a single image"""
 
     denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
 
+    @classmethod
+    def build(cls, mask_name: str, masked_latents_name: Optional[str] = None) -> "DenoiseMaskOutput":
+        return cls(
+            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=masked_latents_name),
+        )
+
 
 # endregion
 
 # region Latents
 
 
-class LatentsField(BaseModel):
-    """A latents tensor primitive field"""
-
-    latents_name: str = Field(description="The name of the latents")
-    seed: Optional[int] = Field(default=None, description="Seed used to generate this latents")
-
-
 @invocation_output("latents_output")
 class LatentsOutput(BaseInvocationOutput):
     """Base class for nodes that output a single latents tensor"""
@@ -322,6 +320,14 @@ class LatentsOutput(BaseInvocationOutput):
     width: int = OutputField(description=FieldDescriptions.width)
     height: int = OutputField(description=FieldDescriptions.height)
 
+    @classmethod
+    def build(cls, latents_name: str, latents: torch.Tensor, seed: Optional[int] = None) -> "LatentsOutput":
+        return cls(
+            latents=LatentsField(latents_name=latents_name, seed=seed),
+            width=latents.size()[3] * 8,
+            height=latents.size()[2] * 8,
+        )
+
 
 @invocation_output("latents_collection_output")
 class LatentsCollectionOutput(BaseInvocationOutput):
@@ -333,17 +339,17 @@ class LatentsCollectionOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.0"
+    "latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.1"
 )
 class LatentsInvocation(BaseInvocation):
     """A latents tensor primitive value"""
 
     latents: LatentsField = InputField(description="The latents tensor", input=Input.Connection)
 
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
+    def invoke(self, context) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.latents.get(self.latents.latents_name)
 
-        return build_latents_output(self.latents.latents_name, latents)
+        return LatentsOutput.build(self.latents.latents_name, latents)
 
 
 @invocation(
@@ -360,35 +366,15 @@ class LatentsCollectionInvocation(BaseInvocation):
         description="The collection of latents tensors",
     )
 
-    def invoke(self, context: InvocationContext) -> LatentsCollectionOutput:
+    def invoke(self, context) -> LatentsCollectionOutput:
         return LatentsCollectionOutput(collection=self.collection)
 
 
-def build_latents_output(latents_name: str, latents: torch.Tensor, seed: Optional[int] = None):
-    return LatentsOutput(
-        latents=LatentsField(latents_name=latents_name, seed=seed),
-        width=latents.size()[3] * 8,
-        height=latents.size()[2] * 8,
-    )
-
-
 # endregion
 
 # region Color
 
 
-class ColorField(BaseModel):
-    """A color primitive field"""
-
-    r: int = Field(ge=0, le=255, description="The red component")
-    g: int = Field(ge=0, le=255, description="The green component")
-    b: int = Field(ge=0, le=255, description="The blue component")
-    a: int = Field(ge=0, le=255, description="The alpha component")
-
-    def tuple(self) -> Tuple[int, int, int, int]:
-        return (self.r, self.g, self.b, self.a)
-
-
 @invocation_output("color_output")
 class ColorOutput(BaseInvocationOutput):
     """Base class for nodes that output a single color"""
@@ -411,7 +397,7 @@ class ColorInvocation(BaseInvocation):
 
     color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=255), description="The color value")
 
-    def invoke(self, context: InvocationContext) -> ColorOutput:
+    def invoke(self, context) -> ColorOutput:
         return ColorOutput(color=self.color)
 
 
@@ -420,18 +406,16 @@ class ColorInvocation(BaseInvocation):
 # region Conditioning
 
 
-class ConditioningField(BaseModel):
-    """A conditioning tensor primitive value"""
-
-    conditioning_name: str = Field(description="The name of conditioning tensor")
-
-
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""
 
     conditioning: ConditioningField = OutputField(description=FieldDescriptions.cond)
 
+    @classmethod
+    def build(cls, conditioning_name: str) -> "ConditioningOutput":
+        return cls(conditioning=ConditioningField(conditioning_name=conditioning_name))
+
 
 @invocation_output("conditioning_collection_output")
 class ConditioningCollectionOutput(BaseInvocationOutput):
@@ -454,7 +438,7 @@ class ConditioningInvocation(BaseInvocation):
 
     conditioning: ConditioningField = InputField(description=FieldDescriptions.cond, input=Input.Connection)
 
-    def invoke(self, context: InvocationContext) -> ConditioningOutput:
+    def invoke(self, context) -> ConditioningOutput:
         return ConditioningOutput(conditioning=self.conditioning)
 
 
@@ -473,7 +457,7 @@ class ConditioningCollectionInvocation(BaseInvocation):
         description="The collection of conditioning tensors",
     )
 
-    def invoke(self, context: InvocationContext) -> ConditioningCollectionOutput:
+    def invoke(self, context) -> ConditioningCollectionOutput:
         return ConditioningCollectionOutput(collection=self.collection)
 
 

invokeai/app/invocations/prompt.py

@@ -7,7 +7,7 @@ from pydantic import field_validator
 
 from invokeai.app.invocations.primitives import StringCollectionOutput
 
-from .baseinvocation import BaseInvocation, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, invocation
 from .fields import InputField, UIComponent
 
 
@@ -29,7 +29,7 @@ class DynamicPromptInvocation(BaseInvocation):
     max_prompts: int = InputField(default=1, description="The number of prompts to generate")
     combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator")
 
-    def invoke(self, context: InvocationContext) -> StringCollectionOutput:
+    def invoke(self, context) -> StringCollectionOutput:
         if self.combinatorial:
             generator = CombinatorialPromptGenerator()
             prompts = generator.generate(self.prompt, max_prompts=self.max_prompts)
@@ -91,7 +91,7 @@ class PromptsFromFileInvocation(BaseInvocation):
                     break
         return prompts
 
-    def invoke(self, context: InvocationContext) -> StringCollectionOutput:
+    def invoke(self, context) -> StringCollectionOutput:
         prompts = self.promptsFromFile(
             self.file_path,
             self.pre_prompt,

invokeai/app/invocations/sdxl.py

@@ -4,7 +4,6 @@ from ...backend.model_management import ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
@@ -30,7 +29,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
-@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.0")
+@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.1")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
@@ -39,13 +38,13 @@ class SDXLModelLoaderInvocation(BaseInvocation):
     )
     # TODO: precision?
 
-    def invoke(self, context: InvocationContext) -> SDXLModelLoaderOutput:
+    def invoke(self, context) -> SDXLModelLoaderOutput:
         base_model = self.model.base_model
         model_name = self.model.model_name
         model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,
@@ -116,7 +115,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
     title="SDXL Refiner Model",
     tags=["model", "sdxl", "refiner"],
     category="model",
-    version="1.0.0",
+    version="1.0.1",
 )
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
@@ -128,13 +127,13 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     )
     # TODO: precision?
 
-    def invoke(self, context: InvocationContext) -> SDXLRefinerModelLoaderOutput:
+    def invoke(self, context) -> SDXLRefinerModelLoaderOutput:
         base_model = self.model.base_model
         model_name = self.model.model_name
         model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,

invokeai/app/invocations/strings.py

@@ -5,7 +5,6 @@ import re
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
@@ -33,7 +32,7 @@ class StringSplitNegInvocation(BaseInvocation):
 
     string: str = InputField(default="", description="String to split", ui_component=UIComponent.Textarea)
 
-    def invoke(self, context: InvocationContext) -> StringPosNegOutput:
+    def invoke(self, context) -> StringPosNegOutput:
         p_string = ""
         n_string = ""
         brackets_depth = 0
@@ -77,7 +76,7 @@ class StringSplitInvocation(BaseInvocation):
         default="", description="Delimiter to spilt with. blank will split on the first whitespace"
     )
 
-    def invoke(self, context: InvocationContext) -> String2Output:
+    def invoke(self, context) -> String2Output:
         result = self.string.split(self.delimiter, 1)
         if len(result) == 2:
             part1, part2 = result
@@ -95,7 +94,7 @@ class StringJoinInvocation(BaseInvocation):
     string_left: str = InputField(default="", description="String Left", ui_component=UIComponent.Textarea)
     string_right: str = InputField(default="", description="String Right", ui_component=UIComponent.Textarea)
 
-    def invoke(self, context: InvocationContext) -> StringOutput:
+    def invoke(self, context) -> StringOutput:
         return StringOutput(value=((self.string_left or "") + (self.string_right or "")))
 
 
@@ -107,7 +106,7 @@ class StringJoinThreeInvocation(BaseInvocation):
     string_middle: str = InputField(default="", description="String Middle", ui_component=UIComponent.Textarea)
     string_right: str = InputField(default="", description="String Right", ui_component=UIComponent.Textarea)
 
-    def invoke(self, context: InvocationContext) -> StringOutput:
+    def invoke(self, context) -> StringOutput:
         return StringOutput(value=((self.string_left or "") + (self.string_middle or "") + (self.string_right or "")))
 
 
@@ -126,7 +125,7 @@ class StringReplaceInvocation(BaseInvocation):
         default=False, description="Use search string as a regex expression (non regex is case insensitive)"
     )
 
-    def invoke(self, context: InvocationContext) -> StringOutput:
+    def invoke(self, context) -> StringOutput:
         pattern = self.search_string or ""
         new_string = self.string or ""
         if len(pattern) > 0:

invokeai/app/invocations/t2i_adapter.py

@@ -5,13 +5,11 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator, model_valida
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
 from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField
-from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.backend.model_management.models.base import BaseModelType
 
@@ -91,7 +89,7 @@ class T2IAdapterInvocation(BaseInvocation):
         validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
         return self
 
-    def invoke(self, context: InvocationContext) -> T2IAdapterOutput:
+    def invoke(self, context) -> T2IAdapterOutput:
         return T2IAdapterOutput(
             t2i_adapter=T2IAdapterField(
                 image=self.image,

invokeai/app/invocations/tiles.py

@@ -8,13 +8,12 @@ from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     Classification,
-    InvocationContext,
+    WithMetadata,
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import Input, InputField, OutputField, WithMetadata
+from invokeai.app.invocations.fields import ImageField, Input, InputField, OutputField
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.backend.tiles.tiles import (
     calc_tiles_even_split,
     calc_tiles_min_overlap,
@@ -58,7 +57,7 @@ class CalculateImageTilesInvocation(BaseInvocation):
         description="The target overlap, in pixels, between adjacent tiles. Adjacent tiles will overlap by at least this amount",
     )
 
-    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
+    def invoke(self, context) -> CalculateImageTilesOutput:
         tiles = calc_tiles_with_overlap(
             image_height=self.image_height,
             image_width=self.image_width,
@@ -101,7 +100,7 @@ class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
         description="The overlap, in pixels, between adjacent tiles.",
     )
 
-    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
+    def invoke(self, context) -> CalculateImageTilesOutput:
         tiles = calc_tiles_even_split(
             image_height=self.image_height,
             image_width=self.image_width,
@@ -131,7 +130,7 @@ class CalculateImageTilesMinimumOverlapInvocation(BaseInvocation):
     tile_height: int = InputField(ge=1, default=576, description="The tile height, in pixels.")
     min_overlap: int = InputField(default=128, ge=0, description="Minimum overlap between adjacent tiles, in pixels.")
 
-    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
+    def invoke(self, context) -> CalculateImageTilesOutput:
         tiles = calc_tiles_min_overlap(
             image_height=self.image_height,
             image_width=self.image_width,
@@ -176,7 +175,7 @@ class TileToPropertiesInvocation(BaseInvocation):
 
     tile: Tile = InputField(description="The tile to split into properties.")
 
-    def invoke(self, context: InvocationContext) -> TileToPropertiesOutput:
+    def invoke(self, context) -> TileToPropertiesOutput:
         return TileToPropertiesOutput(
             coords_left=self.tile.coords.left,
             coords_right=self.tile.coords.right,
@@ -213,7 +212,7 @@ class PairTileImageInvocation(BaseInvocation):
     image: ImageField = InputField(description="The tile image.")
     tile: Tile = InputField(description="The tile properties.")
 
-    def invoke(self, context: InvocationContext) -> PairTileImageOutput:
+    def invoke(self, context) -> PairTileImageOutput:
         return PairTileImageOutput(
             tile_with_image=TileWithImage(
                 tile=self.tile,
@@ -249,7 +248,7 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
         description="The amount to blend adjacent tiles in pixels. Must be <= the amount of overlap between adjacent tiles.",
     )
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
         images = [twi.image for twi in self.tiles_with_images]
         tiles = [twi.tile for twi in self.tiles_with_images]
 
@@ -265,7 +264,7 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
         # existed in memory at an earlier point in the graph.
         tile_np_images: list[np.ndarray] = []
         for image in images:
-            pil_image = context.services.images.get_pil_image(image.image_name)
+            pil_image = context.images.get_pil(image.image_name)
             pil_image = pil_image.convert("RGB")
             tile_np_images.append(np.array(pil_image))
 
@@ -288,18 +287,5 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
         # Convert into a PIL image and save
         pil_image = Image.fromarray(np_image)
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=pil_image)
-            image=pil_image,
+        return ImageOutput.build(image_dto)
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/upscale.py

@@ -8,13 +8,13 @@ import torch
 from PIL import Image
 from pydantic import ConfigDict
 
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.invocations.fields import ImageField
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
 from invokeai.backend.util.devices import choose_torch_device
 
-from .baseinvocation import BaseInvocation, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, invocation
 from .fields import InputField, WithMetadata
 
 # TODO: Populate this from disk?
@@ -30,7 +30,7 @@ if choose_torch_device() == torch.device("mps"):
     from torch import mps
 
 
-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.0")
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.1")
 class ESRGANInvocation(BaseInvocation, WithMetadata):
     """Upscales an image using RealESRGAN."""
 
@@ -42,9 +42,9 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
 
     model_config = ConfigDict(protected_namespaces=())
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
-        models_path = context.services.configuration.models_path
+        models_path = context.config.get().models_path
 
         rrdbnet_model = None
         netscale = None
@@ -88,7 +88,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
             netscale = 2
         else:
             msg = f"Invalid RealESRGAN model: {self.model_name}"
-            context.services.logger.error(msg)
+            context.logger.error(msg)
             raise ValueError(msg)
 
         esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
@@ -111,19 +111,6 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
         if choose_torch_device() == torch.device("mps"):
             mps.empty_cache()
 
-        image_dto = context.services.images.create(
+        image_dto = context.images.save(image=pil_image)
-            image=pil_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
 
-        return ImageOutput(
+        return ImageOutput.build(image_dto)
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/services/events/events_base.py

@@ -55,7 +55,7 @@ class EventServiceBase:
         queue_item_id: int,
         queue_batch_id: str,
         graph_execution_state_id: str,
-        node: dict,
+        node_id: str,
         source_node_id: str,
         progress_image: Optional[ProgressImage],
         step: int,
@@ -70,7 +70,7 @@ class EventServiceBase:
                 "queue_item_id": queue_item_id,
                 "queue_batch_id": queue_batch_id,
                 "graph_execution_state_id": graph_execution_state_id,
-                "node_id": node.get("id"),
+                "node_id": node_id,
                 "source_node_id": source_node_id,
                 "progress_image": progress_image.model_dump() if progress_image is not None else None,
                 "step": step,

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -5,11 +5,11 @@ from threading import BoundedSemaphore, Event, Thread
 from typing import Optional
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.invocations.baseinvocation import InvocationContext
 from invokeai.app.services.invocation_queue.invocation_queue_common import InvocationQueueItem
 from invokeai.app.services.invocation_stats.invocation_stats_common import (
     GESStatsNotFoundError,
 )
+from invokeai.app.services.shared.invocation_context import InvocationContextData, build_invocation_context
 from invokeai.app.util.profiler import Profiler
 
 from ..invoker import Invoker
@@ -131,16 +131,20 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         # which handles a few things:
                         # - nodes that require a value, but get it only from a connection
                         # - referencing the invocation cache instead of executing the node
-                        outputs = invocation.invoke_internal(
+                        context_data = InvocationContextData(
-                            InvocationContext(
+                            invocation=invocation,
-                                services=self.__invoker.services,
+                            session_id=graph_id,
-                                graph_execution_state_id=graph_execution_state.id,
-                                queue_item_id=queue_item.session_queue_item_id,
-                                queue_id=queue_item.session_queue_id,
-                                queue_batch_id=queue_item.session_queue_batch_id,
                             workflow=queue_item.workflow,
+                            source_node_id=source_node_id,
+                            queue_id=queue_item.session_queue_id,
+                            queue_item_id=queue_item.session_queue_item_id,
+                            batch_id=queue_item.session_queue_batch_id,
                         )
+                        context = build_invocation_context(
+                            services=self.__invoker.services,
+                            context_data=context_data,
                         )
+                        outputs = invocation.invoke_internal(context=context, services=self.__invoker.services)
 
                         # Check queue to see if this is canceled, and skip if so
                         if self.__invoker.services.queue.is_canceled(graph_execution_state.id):

invokeai/app/services/model_manager/model_manager_base.py

@@ -5,11 +5,12 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from logging import Logger
 from pathlib import Path
-from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
+from typing import Callable, List, Literal, Optional, Tuple, Union
 
 from pydantic import Field
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.shared.invocation_context import InvocationContextData
 from invokeai.backend.model_management import (
     AddModelResult,
     BaseModelType,
@@ -21,9 +22,6 @@ from invokeai.backend.model_management import (
 )
 from invokeai.backend.model_management.model_cache import CacheStats
 
-if TYPE_CHECKING:
-    from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext
-
 
 class ModelManagerServiceBase(ABC):
     """Responsible for managing models on disk and in memory"""
@@ -49,8 +47,7 @@ class ModelManagerServiceBase(ABC):
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        node: Optional[BaseInvocation] = None,
+        context_data: Optional[InvocationContextData] = None,
-        context: Optional[InvocationContext] = None,
     ) -> ModelInfo:
         """Retrieve the indicated model with name and type.
         submodel can be used to get a part (such as the vae)

invokeai/app/services/model_manager/model_manager_default.py

@@ -11,6 +11,8 @@ from pydantic import Field
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
 from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.shared.invocation_context import InvocationContextData
 from invokeai.backend.model_management import (
     AddModelResult,
     BaseModelType,
@@ -30,7 +32,7 @@ from invokeai.backend.util import choose_precision, choose_torch_device
 from .model_manager_base import ModelManagerServiceBase
 
 if TYPE_CHECKING:
-    from invokeai.app.invocations.baseinvocation import InvocationContext
+    pass
 
 
 # simple implementation
@@ -86,13 +88,16 @@ class ModelManagerService(ModelManagerServiceBase):
         )
         logger.info("Model manager service initialized")
 
+    def start(self, invoker: Invoker) -> None:
+        self._invoker: Optional[Invoker] = invoker
+
     def get_model(
         self,
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        context: Optional[InvocationContext] = None,
+        context_data: Optional[InvocationContextData] = None,
     ) -> ModelInfo:
         """
         Retrieve the indicated model. submodel can be used to get a
@@ -100,9 +105,9 @@ class ModelManagerService(ModelManagerServiceBase):
         """
 
         # we can emit model loading events if we are executing with access to the invocation context
-        if context:
+        if context_data is not None:
             self._emit_load_event(
-                context=context,
+                context_data=context_data,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
@@ -116,9 +121,9 @@ class ModelManagerService(ModelManagerServiceBase):
             submodel,
         )
 
-        if context:
+        if context_data is not None:
             self._emit_load_event(
-                context=context,
+                context_data=context_data,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
@@ -263,22 +268,25 @@ class ModelManagerService(ModelManagerServiceBase):
 
     def _emit_load_event(
         self,
-        context: InvocationContext,
+        context_data: InvocationContextData,
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
         model_info: Optional[ModelInfo] = None,
     ):
-        if context.services.queue.is_canceled(context.graph_execution_state_id):
+        if self._invoker is None:
+            return
+
+        if self._invoker.services.queue.is_canceled(context_data.session_id):
             raise CanceledException()
 
         if model_info:
-            context.services.events.emit_model_load_completed(
+            self._invoker.services.events.emit_model_load_completed(
-                queue_id=context.queue_id,
+                queue_id=context_data.queue_id,
-                queue_item_id=context.queue_item_id,
+                queue_item_id=context_data.queue_item_id,
-                queue_batch_id=context.queue_batch_id,
+                queue_batch_id=context_data.batch_id,
-                graph_execution_state_id=context.graph_execution_state_id,
+                graph_execution_state_id=context_data.session_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
@@ -286,11 +294,11 @@ class ModelManagerService(ModelManagerServiceBase):
                 model_info=model_info,
             )
         else:
-            context.services.events.emit_model_load_started(
+            self._invoker.services.events.emit_model_load_started(
-                queue_id=context.queue_id,
+                queue_id=context_data.queue_id,
-                queue_item_id=context.queue_item_id,
+                queue_item_id=context_data.queue_item_id,
-                queue_batch_id=context.queue_batch_id,
+                queue_batch_id=context_data.batch_id,
-                graph_execution_state_id=context.graph_execution_state_id,
+                graph_execution_state_id=context_data.session_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,

invokeai/app/services/shared/graph.py

@@ -13,7 +13,6 @@ from invokeai.app.invocations import *  # noqa: F401 F403
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InvocationContext,
     invocation,
     invocation_output,
 )
@@ -202,7 +201,7 @@ class GraphInvocation(BaseInvocation):
     # TODO: figure out how to create a default here
     graph: "Graph" = InputField(description="The graph to run", default=None)
 
-    def invoke(self, context: InvocationContext) -> GraphInvocationOutput:
+    def invoke(self, context) -> GraphInvocationOutput:
         """Invoke with provided services and return outputs."""
         return GraphInvocationOutput()
 
@@ -228,7 +227,7 @@ class IterateInvocation(BaseInvocation):
     )
     index: int = InputField(description="The index, will be provided on executed iterators", default=0, ui_hidden=True)
 
-    def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
+    def invoke(self, context) -> IterateInvocationOutput:
         """Produces the outputs as values"""
         return IterateInvocationOutput(item=self.collection[self.index], index=self.index, total=len(self.collection))
 
@@ -255,7 +254,7 @@ class CollectInvocation(BaseInvocation):
         description="The collection, will be provided on execution", default=[], ui_hidden=True
     )
 
-    def invoke(self, context: InvocationContext) -> CollectInvocationOutput:
+    def invoke(self, context) -> CollectInvocationOutput:
         """Invoke with provided services and return outputs."""
         return CollectInvocationOutput(collection=copy.copy(self.collection))
 

invokeai/app/services/shared/invocation_context.py

@@ -6,8 +6,7 @@ from PIL.Image import Image
 from pydantic import ConfigDict
 from torch import Tensor
 
-from invokeai.app.invocations.compel import ConditioningFieldData
+from invokeai.app.invocations.fields import ConditioningFieldData, MetadataField, WithMetadata
-from invokeai.app.invocations.fields import MetadataField, WithMetadata
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
 from invokeai.app.services.images.images_common import ImageDTO
@@ -245,13 +244,15 @@ class ConditioningInterface:
             )
             return name
 
-        def get(conditioning_name: str) -> Tensor:
+        def get(conditioning_name: str) -> ConditioningFieldData:
             """
             Gets conditioning data by name.
 
             :param conditioning_name: The name of the conditioning data to get.
             """
-            return services.latents.get(conditioning_name)
+            # TODO(sm): We are (ab)using the latents storage service as a general pickle storage
+            # service, but it is typed as returning tensors, so we need to ignore the type here.
+            return services.latents.get(conditioning_name) # type: ignore [return-value]
 
         self.save = save
         self.get = get

invokeai/app/util/step_callback.py

@@ -1,25 +1,18 @@
-from typing import Protocol
+from typing import TYPE_CHECKING
 
 import torch
 from PIL import Image
 
-from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException, ProgressImage
-from invokeai.app.services.invocation_queue.invocation_queue_base import InvocationQueueABC
-from invokeai.app.services.shared.invocation_context import InvocationContextData
 
 from ...backend.model_management.models import BaseModelType
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.util.util import image_to_dataURL
 
-
+if TYPE_CHECKING:
-class StepCallback(Protocol):
+    from invokeai.app.services.events.events_base import EventServiceBase
-    def __call__(
+    from invokeai.app.services.invocation_queue.invocation_queue_base import InvocationQueueABC
-        self,
+    from invokeai.app.services.shared.invocation_context import InvocationContextData
-        intermediate_state: PipelineIntermediateState,
-        base_model: BaseModelType,
-    ) -> None:
-        ...
 
 
 def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=None):
@@ -38,11 +31,11 @@ def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=
 
 
 def stable_diffusion_step_callback(
-    context_data: InvocationContextData,
+    context_data: "InvocationContextData",
     intermediate_state: PipelineIntermediateState,
     base_model: BaseModelType,
-    invocation_queue: InvocationQueueABC,
+    invocation_queue: "InvocationQueueABC",
-    events: EventServiceBase,
+    events: "EventServiceBase",
 ) -> None:
     if invocation_queue.is_canceled(context_data.session_id):
         raise CanceledException