mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
feat: node editor
squashed rebase on main after backendd refactor
This commit is contained in:
psychedelicious
@ -1,18 +1,21 @@
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from typing import Literal, Optional, Union
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from typing import Literal, Optional
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from pydantic import Field
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from ...version import __version__
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from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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InvocationConfig,
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InputField,
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InvocationContext,
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tags,
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title,
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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.model import LoRAModelField, MainModelField, VAEModelField
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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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@ -43,37 +46,37 @@ class CoreMetadata(BaseModelExcludeNull):
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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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loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
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vae: Union[VAEModelField, None] = Field(
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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: Union[float, None] = Field(
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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: Union[str, None] = Field(default=None, description="The name of the initial image")
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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: Union[str, None] = Field(default=None, description="The positive style prompt parameter")
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negative_style_prompt: Union[str, None] = Field(default=None, description="The negative style prompt parameter")
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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: Union[MainModelField, None] = Field(default=None, description="The SDXL Refiner model used")
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refiner_cfg_scale: Union[float, None] = Field(
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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: Union[int, None] = Field(default=None, description="The number of steps used for the refiner")
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refiner_scheduler: Union[str, None] = Field(default=None, description="The scheduler used for the refiner")
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refiner_positive_aesthetic_store: Union[float, None] = Field(
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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_store: 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_store: Union[float, None] = Field(
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refiner_negative_aesthetic_store: 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: Union[float, None] = Field(default=None, description="The start value used for refiner denoising")
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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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@ -94,66 +97,83 @@ class MetadataAccumulatorOutput(BaseInvocationOutput):
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metadata: CoreMetadata = Field(description="The core metadata for the image")
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@title("Metadata Accumulator")
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@tags("metadata")
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class MetadataAccumulatorInvocation(BaseInvocation):
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"""Outputs a Core Metadata Object"""
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type: Literal["metadata_accumulator"] = "metadata_accumulator"
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generation_mode: str = Field(
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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 = 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: int = Field(
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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: int = InputField(
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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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loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
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strength: Union[float, None] = Field(
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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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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: Union[str, None] = Field(default=None, description="The name of the initial image")
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vae: Union[VAEModelField, None] = Field(
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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: Union[str, None] = Field(default=None, description="The positive style prompt parameter")
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negative_style_prompt: Union[str, None] = Field(default=None, description="The negative style prompt parameter")
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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: Union[MainModelField, None] = Field(default=None, description="The SDXL Refiner model used")
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refiner_cfg_scale: Union[float, None] = Field(
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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: Union[int, None] = Field(default=None, description="The number of steps used for the refiner")
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refiner_scheduler: Union[str, None] = Field(default=None, description="The scheduler used for the refiner")
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refiner_positive_aesthetic_score: Union[float, None] = Field(
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default=None, description="The aesthetic score used for the refiner"
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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_negative_aesthetic_score: Union[float, None] = Field(
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default=None, description="The aesthetic score used for the refiner"
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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_store: 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_store: 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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refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising")
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Metadata Accumulator",
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"tags": ["image", "metadata", "generation"],
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},
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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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