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90 lines
3.9 KiB
Python
90 lines
3.9 KiB
Python
from builtins import float
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from typing import List, Literal, Union
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from pydantic import field_validator, model_validator
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from typing_extensions import Self
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from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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from invokeai.app.invocations.fields import InputField
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from invokeai.app.invocations.ip_adapter import (
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CLIP_VISION_MODEL_MAP,
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IPAdapterField,
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IPAdapterInvocation,
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IPAdapterOutput,
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)
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from invokeai.app.invocations.model import ModelIdentifierField
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from invokeai.app.invocations.primitives import ImageField
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from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.model_manager.configs.ip_adapter import IPAdapter_Checkpoint_FLUX_Config
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from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType
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@invocation(
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"flux_ip_adapter",
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title="FLUX IP-Adapter",
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tags=["ip_adapter", "control"],
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category="conditioning",
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version="1.0.0",
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)
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class FluxIPAdapterInvocation(BaseInvocation):
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"""Collects FLUX IP-Adapter info to pass to other nodes."""
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# FLUXIPAdapterInvocation is based closely on IPAdapterInvocation, but with some unsupported features removed.
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image: ImageField = InputField(description="The IP-Adapter image prompt(s).")
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ip_adapter_model: ModelIdentifierField = InputField(
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description="The IP-Adapter model.",
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title="IP-Adapter Model",
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ui_model_base=BaseModelType.Flux,
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ui_model_type=ModelType.IPAdapter,
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)
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# Currently, the only known ViT model used by FLUX IP-Adapters is ViT-L.
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clip_vision_model: Literal["ViT-L"] = InputField(description="CLIP Vision model to use.", default="ViT-L")
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weight: Union[float, List[float]] = InputField(
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default=1, description="The weight given to the IP-Adapter", title="Weight"
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)
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begin_step_percent: float = InputField(
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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 = InputField(
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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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@field_validator("weight")
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@classmethod
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def validate_ip_adapter_weight(cls, v: float) -> float:
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validate_weights(v)
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return v
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@model_validator(mode="after")
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def validate_begin_end_step_percent(self) -> Self:
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validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
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return self
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def invoke(self, context: InvocationContext) -> IPAdapterOutput:
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# Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
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ip_adapter_info = context.models.get_config(self.ip_adapter_model.key)
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assert isinstance(ip_adapter_info, IPAdapter_Checkpoint_FLUX_Config)
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# Note: There is a IPAdapterInvokeAIConfig.image_encoder_model_id field, but it isn't trustworthy.
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image_encoder_starter_model = CLIP_VISION_MODEL_MAP[self.clip_vision_model]
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image_encoder_model_id = image_encoder_starter_model.source
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image_encoder_model_name = image_encoder_starter_model.name
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image_encoder_model = IPAdapterInvocation.get_clip_image_encoder(
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context, image_encoder_model_id, image_encoder_model_name
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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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ip_adapter_model=self.ip_adapter_model,
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image_encoder_model=ModelIdentifierField.from_config(image_encoder_model),
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weight=self.weight,
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target_blocks=[], # target_blocks is currently unused for FLUX IP-Adapters.
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begin_step_percent=self.begin_step_percent,
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end_step_percent=self.end_step_percent,
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mask=None, # mask is currently unused for FLUX IP-Adapters.
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),
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)
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