mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
c7f80cd163
* add control net to useRecallParams * got recall controlnets working * fix metadata viewer controlnet * fix type errors * fix controlnet metadata viewer * add ip adapter to metadata * added ip adapter to recall parameters * got ip adapter recall working, still need to fix type errors * fix type issues * clean up logs * python formatting * cleanup * fix(ui): only store `image_name` as ip adapter image * fix(ui): use nullish coalescing operator for numbers Need to use the nullish coalescing operator `??` instead of false-y coalescing operator `||` when the value being check is a number. This prevents unintended coalescing when the value is zero and therefore false-y. * feat(ui): fall back on default values for ip adapter metadata * fix(ui): remove unused schema * feat(ui): re-use existing schemas in metadata schema * fix(ui): do not disable invocationCache --------- Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
200 lines
8.6 KiB
Python
200 lines
8.6 KiB
Python
from typing import Optional
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from pydantic import 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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InputField,
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InvocationContext,
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OutputField,
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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 ControlField
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from invokeai.app.invocations.ip_adapter import IPAdapterModelField
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from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
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from invokeai.app.invocations.primitives import ImageField
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from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
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from ...version import __version__
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class LoRAMetadataField(BaseModelExcludeNull):
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"""LoRA metadata for an image generated in InvokeAI."""
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lora: LoRAModelField = Field(description="The LoRA model")
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weight: float = Field(description="The weight of the LoRA model")
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class IPAdapterMetadataField(BaseModelExcludeNull):
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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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weight: float = Field(description="The weight of the IP-Adapter model")
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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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class CoreMetadata(BaseModelExcludeNull):
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"""Core generation metadata for an image generated in InvokeAI."""
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app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
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generation_mode: str = Field(
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description="The generation mode that output this image",
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)
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created_by: Optional[str] = Field(description="The name of the creator of the image")
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positive_prompt: str = Field(description="The positive prompt parameter")
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negative_prompt: str = Field(description="The negative prompt parameter")
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width: int = Field(description="The width parameter")
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height: int = Field(description="The height parameter")
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seed: int = Field(description="The seed used for noise generation")
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rand_device: str = Field(description="The device used for random number generation")
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cfg_scale: float = Field(description="The classifier-free guidance scale parameter")
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steps: int = Field(description="The number of steps used for inference")
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scheduler: str = Field(description="The scheduler used for inference")
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clip_skip: Optional[int] = Field(
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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: MainModelField = Field(description="The main model used for inference")
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controlnets: list[ControlField] = Field(description="The ControlNets used for inference")
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ipAdapters: list[IPAdapterMetadataField] = Field(description="The IP Adapters used for inference")
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loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
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vae: Optional[VAEModelField] = Field(
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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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# Latents-to-Latents
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strength: Optional[float] = Field(
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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] = Field(default=None, description="The name of the initial image")
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# SDXL
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positive_style_prompt: Optional[str] = Field(default=None, description="The positive style prompt parameter")
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negative_style_prompt: Optional[str] = Field(default=None, description="The negative style prompt parameter")
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# SDXL Refiner
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refiner_model: Optional[MainModelField] = Field(default=None, description="The SDXL Refiner model used")
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refiner_cfg_scale: Optional[float] = Field(
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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] = Field(default=None, description="The number of steps used for the refiner")
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refiner_scheduler: Optional[str] = Field(default=None, description="The scheduler used for the refiner")
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refiner_positive_aesthetic_score: Optional[float] = Field(
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default=None, description="The aesthetic score used for the refiner"
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)
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refiner_negative_aesthetic_score: Optional[float] = Field(
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default=None, description="The aesthetic score used for the refiner"
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)
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refiner_start: Optional[float] = Field(default=None, description="The start value used for refiner denoising")
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class ImageMetadata(BaseModelExcludeNull):
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"""An image's generation metadata"""
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metadata: Optional[dict] = Field(
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default=None,
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description="The image's core metadata, if it was created in the Linear or Canvas UI",
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)
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graph: Optional[dict] = Field(default=None, description="The graph that created the image")
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@invocation_output("metadata_accumulator_output")
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class MetadataAccumulatorOutput(BaseInvocationOutput):
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"""The output of the MetadataAccumulator node"""
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metadata: CoreMetadata = OutputField(description="The core metadata for the image")
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@invocation(
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"metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0"
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)
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class MetadataAccumulatorInvocation(BaseInvocation):
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"""Outputs a Core Metadata Object"""
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generation_mode: str = InputField(
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description="The generation mode that output this image",
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)
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positive_prompt: str = InputField(description="The positive prompt parameter")
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negative_prompt: str = InputField(description="The negative prompt parameter")
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width: int = InputField(description="The width parameter")
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height: int = InputField(description="The height parameter")
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seed: int = InputField(description="The seed used for noise generation")
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rand_device: str = InputField(description="The device used for random number generation")
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cfg_scale: float = InputField(description="The classifier-free guidance scale parameter")
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steps: int = InputField(description="The number of steps used for inference")
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scheduler: str = InputField(description="The scheduler used for inference")
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clip_skip: Optional[int] = Field(
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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: MainModelField = InputField(description="The main model used for inference")
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controlnets: list[ControlField] = InputField(description="The ControlNets used for inference")
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ipAdapters: list[IPAdapterMetadataField] = InputField(description="The IP Adapters used for inference")
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loras: list[LoRAMetadataField] = InputField(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[VAEModelField] = 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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# 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[MainModelField] = 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) -> MetadataAccumulatorOutput:
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"""Collects and outputs a CoreMetadata object"""
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return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict()))
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