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https://github.com/invoke-ai/InvokeAI
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Added per-step setting of IP-Adapter weights (for param easing, etc.)
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@ -1,6 +1,8 @@
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import os
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from builtins import bool, float
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from pydantic import BaseModel, Field
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from typing import Dict, List, Literal, Optional, Union
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from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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@ -33,9 +35,14 @@ class IPAdapterField(BaseModel):
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image: ImageField = Field(description="The IP-Adapter image prompt.")
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ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
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image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
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weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
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begin_step_percent: float = Field(default=0.0, ge=0, le=1.0)
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end_step_percent: float = Field(default=1.0, ge=0, le=1.0)
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weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
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# weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
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begin_step_percent: float = Field(
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default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
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)
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end_step_percent: float = Field(
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default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
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)
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@invocation_output("ip_adapter_output")
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@ -55,7 +62,11 @@ class IPAdapterInvocation(BaseInvocation):
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title="IP-Adapter Model",
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input=Input.Direct,
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)
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weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
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# weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
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weight: Union[float, List[float]] = InputField(default=1, ge=0, description="The weight given to the IP-Adapter",
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ui_type=UIType.Float, title="Weight")
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begin_step_percent: float = InputField(
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default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
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)
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@ -81,7 +92,6 @@ class IPAdapterInvocation(BaseInvocation):
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model_name=image_encoder_model_name,
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base_model=BaseModelType.Any,
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)
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return IPAdapterOutput(
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ip_adapter=IPAdapterField(
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image=self.image,
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