mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
044d4c107a
All invocation metadata (type, title, tags and category) are now defined in decorators.
The decorators add the `type: Literal["invocation_type"]: "invocation_type"` field to the invocation.
Category is a new invocation metadata, but it is not used by the frontend just yet.
- `@invocation()` decorator for invocations
```py
@invocation(
"sdxl_compel_prompt",
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
...
```
- `@invocation_output()` decorator for invocation outputs
```py
@invocation_output("clip_skip_output")
class ClipSkipInvocationOutput(BaseInvocationOutput):
...
```
- update invocation docs
- add category to decorator
- regen frontend types
180 lines
7.6 KiB
Python
180 lines
7.6 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.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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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 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: int = Field(
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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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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_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: 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("metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata")
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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: int = InputField(
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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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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_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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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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