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

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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
This commit is contained in:
psychedelicious
2024-01-13 23:23:16 +11:00
parent 97a6c6eea7
commit 7e5ba2795e
32 changed files with 716 additions and 1191 deletions
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8
invokeai/app/invocations/collections.py
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@ -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)))