mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
257 lines
11 KiB
Python
257 lines
11 KiB
Python
from typing import Any, Literal, Optional, Union
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from pydantic import BaseModel, ConfigDict, Field
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from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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invocation,
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invocation_output,
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)
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from invokeai.app.invocations.controlnet_image_processors import (
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CONTROLNET_MODE_VALUES,
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CONTROLNET_RESIZE_VALUES,
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)
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from invokeai.app.invocations.fields import (
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FieldDescriptions,
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ImageField,
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InputField,
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MetadataField,
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OutputField,
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UIType,
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)
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from invokeai.app.invocations.model import ModelIdentifierField
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from ...version import __version__
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class MetadataItemField(BaseModel):
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label: str = Field(description=FieldDescriptions.metadata_item_label)
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value: Any = Field(description=FieldDescriptions.metadata_item_value)
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class LoRAMetadataField(BaseModel):
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"""LoRA Metadata Field"""
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model: ModelIdentifierField = Field(description=FieldDescriptions.lora_model)
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weight: float = Field(description=FieldDescriptions.lora_weight)
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class IPAdapterMetadataField(BaseModel):
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"""IP Adapter Field, minus the CLIP Vision Encoder model"""
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image: ImageField = Field(description="The IP-Adapter image prompt.")
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ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model.")
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weight: Union[float, list[float]] = Field(description="The weight given to the IP-Adapter")
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begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
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end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")
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class T2IAdapterMetadataField(BaseModel):
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image: ImageField = Field(description="The control image.")
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processed_image: Optional[ImageField] = Field(default=None, description="The control image, after processing.")
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t2i_adapter_model: ModelIdentifierField = Field(description="The T2I-Adapter model to use.")
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weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
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begin_step_percent: float = Field(
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default=0, ge=0, le=1, description="When the T2I-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 T2I-Adapter is last applied (% of total steps)"
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)
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resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
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class ControlNetMetadataField(BaseModel):
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image: ImageField = Field(description="The control image")
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processed_image: Optional[ImageField] = Field(default=None, description="The control image, after processing.")
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control_model: ModelIdentifierField = Field(description="The ControlNet 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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@invocation_output("metadata_item_output")
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class MetadataItemOutput(BaseInvocationOutput):
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"""Metadata Item Output"""
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item: MetadataItemField = OutputField(description="Metadata Item")
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@invocation("metadata_item", title="Metadata Item", tags=["metadata"], category="metadata", version="1.0.1")
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class MetadataItemInvocation(BaseInvocation):
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"""Used to create an arbitrary metadata item. Provide "label" and make a connection to "value" to store that data as the value."""
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label: str = InputField(description=FieldDescriptions.metadata_item_label)
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value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any)
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def invoke(self, context: InvocationContext) -> MetadataItemOutput:
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return MetadataItemOutput(item=MetadataItemField(label=self.label, value=self.value))
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@invocation_output("metadata_output")
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class MetadataOutput(BaseInvocationOutput):
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metadata: MetadataField = OutputField(description="Metadata Dict")
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@invocation("metadata", title="Metadata", tags=["metadata"], category="metadata", version="1.0.1")
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class MetadataInvocation(BaseInvocation):
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"""Takes a MetadataItem or collection of MetadataItems and outputs a MetadataDict."""
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items: Union[list[MetadataItemField], MetadataItemField] = InputField(
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description=FieldDescriptions.metadata_item_polymorphic
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)
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def invoke(self, context: InvocationContext) -> MetadataOutput:
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if isinstance(self.items, MetadataItemField):
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# single metadata item
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data = {self.items.label: self.items.value}
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else:
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# collection of metadata items
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data = {item.label: item.value for item in self.items}
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# add app version
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data.update({"app_version": __version__})
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return MetadataOutput(metadata=MetadataField.model_validate(data))
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@invocation("merge_metadata", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.1")
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class MergeMetadataInvocation(BaseInvocation):
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"""Merged a collection of MetadataDict into a single MetadataDict."""
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collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection)
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def invoke(self, context: InvocationContext) -> MetadataOutput:
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data = {}
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for item in self.collection:
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data.update(item.model_dump())
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return MetadataOutput(metadata=MetadataField.model_validate(data))
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GENERATION_MODES = Literal[
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"txt2img", "img2img", "inpaint", "outpaint", "sdxl_txt2img", "sdxl_img2img", "sdxl_inpaint", "sdxl_outpaint"
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]
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@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="2.0.0")
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class CoreMetadataInvocation(BaseInvocation):
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"""Collects core generation metadata into a MetadataField"""
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generation_mode: Optional[GENERATION_MODES] = InputField(
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default=None,
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description="The generation mode that output this image",
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)
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positive_prompt: Optional[str] = InputField(default=None, description="The positive prompt parameter")
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negative_prompt: Optional[str] = InputField(default=None, description="The negative prompt parameter")
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width: Optional[int] = InputField(default=None, description="The width parameter")
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height: Optional[int] = InputField(default=None, description="The height parameter")
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seed: Optional[int] = InputField(default=None, description="The seed used for noise generation")
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rand_device: Optional[str] = InputField(default=None, description="The device used for random number generation")
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cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
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cfg_rescale_multiplier: Optional[float] = InputField(
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default=None, description=FieldDescriptions.cfg_rescale_multiplier
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)
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steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
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scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
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seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis")
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seamless_y: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the Y axis")
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clip_skip: Optional[int] = InputField(
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default=None,
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description="The number of skipped CLIP layers",
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)
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model: Optional[ModelIdentifierField] = InputField(default=None, description="The main model used for inference")
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controlnets: Optional[list[ControlNetMetadataField]] = InputField(
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default=None, description="The ControlNets used for inference"
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)
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ipAdapters: Optional[list[IPAdapterMetadataField]] = InputField(
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default=None, description="The IP Adapters used for inference"
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)
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t2iAdapters: Optional[list[T2IAdapterMetadataField]] = InputField(
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default=None, description="The IP Adapters used for inference"
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)
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loras: Optional[list[LoRAMetadataField]] = InputField(default=None, description="The LoRAs used for inference")
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strength: Optional[float] = InputField(
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default=None,
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description="The strength used for latents-to-latents",
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)
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init_image: Optional[str] = InputField(
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default=None,
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description="The name of the initial image",
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)
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vae: Optional[ModelIdentifierField] = InputField(
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default=None,
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description="The VAE used for decoding, if the main model's default was not used",
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)
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# High resolution fix metadata.
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hrf_enabled: Optional[bool] = InputField(
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default=None,
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description="Whether or not high resolution fix was enabled.",
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)
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# TODO: should this be stricter or do we just let the UI handle it?
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hrf_method: Optional[str] = InputField(
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default=None,
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description="The high resolution fix upscale method.",
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)
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hrf_strength: Optional[float] = InputField(
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default=None,
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description="The high resolution fix img2img strength used in the upscale pass.",
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)
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# SDXL
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positive_style_prompt: Optional[str] = InputField(
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default=None,
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description="The positive style prompt parameter",
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)
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negative_style_prompt: Optional[str] = InputField(
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default=None,
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description="The negative style prompt parameter",
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)
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# SDXL Refiner
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refiner_model: Optional[ModelIdentifierField] = InputField(
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default=None,
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description="The SDXL Refiner model used",
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)
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refiner_cfg_scale: Optional[float] = InputField(
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default=None,
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description="The classifier-free guidance scale parameter used for the refiner",
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)
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refiner_steps: Optional[int] = InputField(
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default=None,
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description="The number of steps used for the refiner",
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)
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refiner_scheduler: Optional[str] = InputField(
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default=None,
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description="The scheduler used for the refiner",
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)
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refiner_positive_aesthetic_score: Optional[float] = InputField(
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default=None,
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description="The aesthetic score used for the refiner",
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)
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refiner_negative_aesthetic_score: Optional[float] = InputField(
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default=None,
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description="The aesthetic score used for the refiner",
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)
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refiner_start: Optional[float] = InputField(
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default=None,
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description="The start value used for refiner denoising",
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)
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def invoke(self, context: InvocationContext) -> MetadataOutput:
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"""Collects and outputs a CoreMetadata object"""
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as_dict = self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
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as_dict["app_version"] = __version__
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return MetadataOutput(metadata=MetadataField.model_validate(as_dict))
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model_config = ConfigDict(extra="allow")
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