feat(anima): Anima Controlnets + Inpainting Adapter

invokeai/app/invocations/anima_lllite.py

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+"""Anima ControlNet-LLLite invocation for model-level inpaint conditioning."""
+
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    InputField,
+    OutputField,
+)
+from invokeai.app.invocations.model import ModelIdentifierField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType
+
+
+class AnimaLLLiteField(BaseModel):
+    """An Anima ControlNet-LLLite conditioning field (e.g. inpaint adapter)."""
+
+    image_name: str = Field(description="The name of the conditioning image (the initial/raster image)")
+    mask_name: str | None = Field(
+        default=None,
+        description="The name of the inpaint mask image (white = inpaint area)",
+    )
+    control_model: ModelIdentifierField = Field(description="The Anima ControlNet-LLLite adapter model")
+    weight: float = Field(
+        default=1.0,
+        ge=-10.0,
+        le=10.0,
+        description="The strength of the LLLite adapter",
+    )
+    begin_step_percent: float = Field(
+        default=0.0,
+        ge=0.0,
+        le=1.0,
+        description="When the adapter is first applied (% of total steps)",
+    )
+    end_step_percent: float = Field(
+        default=1.0,
+        ge=0.0,
+        le=1.0,
+        description="When the adapter is last applied (% of total steps)",
+    )
+
+
+@invocation_output("anima_lllite_output")
+class AnimaLLLiteOutput(BaseInvocationOutput):
+    """Anima ControlNet-LLLite output containing adapter configuration."""
+
+    control: AnimaLLLiteField = OutputField(description="Anima ControlNet-LLLite conditioning")
+
+
+@invocation(
+    "anima_lllite",
+    title="Anima ControlNet-LLLite",
+    tags=["image", "anima", "control", "controlnet", "inpaint"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class AnimaLLLiteInvocation(BaseInvocation):
+    """Configure an Anima ControlNet-LLLite adapter for model-level conditioning.
+
+    Takes a conditioning image (the initial/raster image), an optional inpaint
+    mask (white = area to inpaint), and a LLLite adapter model. Inpainting
+    adapters (4-channel conditioning) require a mask; other adapters ignore it.
+    """
+
+    image: ImageField = InputField(
+        description="The conditioning image (the initial/raster image for inpainting)",
+    )
+    mask: Optional[ImageField] = InputField(
+        default=None,
+        description="The inpaint mask (white = area to inpaint). Required by inpainting adapters.",
+    )
+    control_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.controlnet_model,
+        title="Control Model",
+        ui_model_base=BaseModelType.Anima,
+        ui_model_type=ModelType.ControlNet,
+    )
+    weight: float = InputField(
+        default=1.0,
+        ge=-10.0,
+        le=10.0,
+        description="Strength of the LLLite adapter.",
+    )
+    begin_step_percent: float = InputField(
+        default=0.0,
+        ge=0.0,
+        le=1.0,
+        description="When the adapter is first applied (% of total steps)",
+    )
+    end_step_percent: float = InputField(
+        default=1.0,
+        ge=0.0,
+        le=1.0,
+        description="When the adapter is last applied (% of total steps)",
+    )
+
+    def invoke(self, context: InvocationContext) -> AnimaLLLiteOutput:
+        return AnimaLLLiteOutput(
+            control=AnimaLLLiteField(
+                image_name=self.image.image_name,
+                mask_name=self.mask.image_name if self.mask is not None else None,
+                control_model=self.control_model,
+                weight=self.weight,
+                begin_step_percent=self.begin_step_percent,
+                end_step_percent=self.end_step_percent,
+            )
+        )