mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
181 lines
7.6 KiB
Python
181 lines
7.6 KiB
Python
from builtins import bool, float
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from typing import Dict, List, Literal, Optional, Union
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from pydantic import BaseModel, Field, validator
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from invokeai.app.invocations.primitives import ImageField
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from ...backend.model_management import BaseModelType
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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FieldDescriptions,
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InputField,
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Input,
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InvocationContext,
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OutputField,
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UIType,
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invocation,
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invocation_output,
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)
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CONTROL_ADAPTER_TYPES = Literal["ControlNet", "IP-Adapter", "T2I-Adapter"]
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CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
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CONTROLNET_RESIZE_VALUES = Literal[
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"just_resize",
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"crop_resize",
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"fill_resize",
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"just_resize_simple",
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]
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class ControlNetModelField(BaseModel):
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"""ControlNet model field"""
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model_name: str = Field(description="Name of the ControlNet model")
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base_model: BaseModelType = Field(description="Base model")
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class ControlField(BaseModel):
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control_type: CONTROL_ADAPTER_TYPES = Field(default="ControlNet", description="The type of control adapter")
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image: ImageField = Field(description="The control image")
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# control_model and ip_adapter_models are both optional
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# but must be on the two present
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# if control_type == "ControlNet", then mus be control_model
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# if control_type == "IP-Adapter", then must be ip_adapter_model
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control_model: Optional[ControlNetModelField] = Field(description="The ControlNet model to use")
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ip_adapter_model: Optional[str] = Field(description="The IP-Adapter model to use")
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image_encoder_model: Optional[str] = Field(description="The clip_image_encoder model to use")
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control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
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begin_step_percent: float = Field(
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default=0, ge=0, le=1, description="When the ControlNet 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 ControlNet is last applied (% of total steps)"
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)
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control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
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resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
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@validator("control_weight")
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def validate_control_weight(cls, v):
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"""Validate that all control weights in the valid range"""
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if isinstance(v, list):
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for i in v:
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if i < -1 or i > 2:
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raise ValueError("Control weights must be within -1 to 2 range")
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else:
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if v < -1 or v > 2:
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raise ValueError("Control weights must be within -1 to 2 range")
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return v
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@invocation_output("control_output")
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class ControlOutput(BaseInvocationOutput):
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"""node output for ControlNet info"""
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type: Literal["control_output"] = "control_output"
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# Outputs
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control: ControlField = OutputField(description=FieldDescriptions.control)
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@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet")
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class ControlNetInvocation(BaseInvocation):
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"""Collects ControlNet info to pass to other nodes"""
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type: Literal["controlnet"] = "controlnet"
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# Inputs
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image: ImageField = InputField(description="The control image")
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control_model: ControlNetModelField = InputField(
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default="lllyasviel/sd-controlnet-canny", description=FieldDescriptions.controlnet_model, input=Input.Direct
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)
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control_weight: Union[float, List[float]] = InputField(
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default=1.0, description="The weight given to the ControlNet", ui_type=UIType.Float
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)
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begin_step_percent: float = InputField(
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default=0, ge=-1, le=2, description="When the ControlNet 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 ControlNet is last applied (% of total steps)"
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)
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control_mode: CONTROLNET_MODE_VALUES = InputField(default="balanced", description="The control mode used")
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resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
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def invoke(self, context: InvocationContext) -> ControlOutput:
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return ControlOutput(
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control=ControlField(
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control_type="ControlNet",
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image=self.image,
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control_model=self.control_model,
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# ip_adapter_model is currently optional
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# must be either a control_model or ip_adapter_model
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# ip_adapter_model=None,
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control_weight=self.control_weight,
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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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control_mode=self.control_mode,
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resize_mode=self.resize_mode,
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),
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)
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IP_ADAPTER_MODELS = Literal[
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"models_ip_adapter/models/ip-adapter_sd15.bin",
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"models_ip_adapter/models/ip-adapter-plus_sd15.bin",
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"models_ip_adapter/models/ip-adapter-plus-face_sd15.bin",
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"models_ip_adapter/sdxl_models/ip-adapter_sdxl.bin"
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]
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IP_ADAPTER_IMAGE_ENCODER_MODELS = Literal[
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"models_ip_adapter/models/image_encoder/",
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"./models_ip_adapter/models/image_encoder/",
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"models_ip_adapter/sdxl_models/image_encoder/"
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]
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@invocation("ipadapter", title="IP-Adapter", tags=["ipadapter"], category="ipadapter")
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class IPAdapterInvocation(BaseInvocation):
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"""Collects IP-Adapter info to pass to other nodes"""
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type: Literal["ipadapter"] = "ipadapter"
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# Inputs
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image: ImageField = InputField(description="The control image")
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#control_model: ControlNetModelField = InputField(
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# default="lllyasviel/sd-controlnet-canny", description=FieldDescriptions.controlnet_model, input=Input.Direct
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#)
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ip_adapter_model: IP_ADAPTER_MODELS = InputField(default="./models_ip_adapter/models/ip-adapter_sd15.bin",
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description="The IP-Adapter model")
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image_encoder_model: IP_ADAPTER_IMAGE_ENCODER_MODELS = InputField(
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default="./models_ip_adapter/models/image_encoder/",
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description="The image encoder model")
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control_weight: Union[float, List[float]] = InputField(
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default=1.0, description="The weight given to the ControlNet", ui_type=UIType.Float
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)
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# begin_step_percent: float = InputField(
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# default=0, ge=-1, le=2, description="When the ControlNet 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 ControlNet is last applied (% of total steps)"
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# )
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# control_mode: CONTROLNET_MODE_VALUES = InputField(default="balanced", description="The control mode used")
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# resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
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def invoke(self, context: InvocationContext) -> ControlOutput:
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return ControlOutput(
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control=ControlField(
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control_type="IP-Adapter",
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image=self.image,
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# control_model is currently optional
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# must be either a control_model or ip_adapter_model
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# control_model=None,
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ip_adapter_model=self.ip_adapter_model,
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image_encoder_model=self.image_encoder_model,
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control_weight=self.control_weight,
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# rest are currently ignored
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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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#control_mode=self.control_mode,
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#resize_mode=self.resize_mode,
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),
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)
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