mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
bf94412d14
multi-select actions include:
- drag to board to move all to that board
- right click to add all to board or delete all
backend changes:
- add routes for changing board for list of image names, deleting list of images
- change image-specific routes to `images/i/{image_name}` to not clobber other routes (like `images/upload`, `images/delete`)
- subclass pydantic `BaseModel` as `BaseModelExcludeNull`, which excludes null values when calling `dict()` on the model. this fixes inconsistent types related to JSON parsing null values into `null` instead of `undefined`
- remove `board_id` from `remove_image_from_board`
frontend changes:
- multi-selection stuff uses `ImageDTO[]` as payloads, for dnd and other mutations. this gives us access to image `board_id`s when hitting routes, and enables efficient cache updates.
- consolidate change board and delete image modals to handle single and multiples
- board totals are now re-fetched on mutation and not kept in sync manually - was way too tedious to do this
- fixed warning about nested `<p>` elements
- closes #4088 , need to handle case when `autoAddBoardId` is `"none"`
- add option to show gallery image delete button on every gallery image
frontend refactors/organisation:
- make typegen script js instead of ts
- enable `noUncheckedIndexedAccess` to help avoid bugs when indexing into arrays, many small changes needed to satisfy TS after this
- move all image-related endpoints into `endpoints/images.ts`, its a big file now, but this fixes a number of circular dependency issues that were otherwise felt impossible to resolve
154 lines
7.0 KiB
Python
154 lines
7.0 KiB
Python
from typing import Literal, Optional, Union
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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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InvocationConfig,
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InvocationContext,
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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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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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generation_mode: str = Field(
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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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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: Union[VAEModelField, None] = 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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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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# 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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# 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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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_aesthetic_store: Union[float, None] = 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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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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class MetadataAccumulatorOutput(BaseInvocationOutput):
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"""The output of the MetadataAccumulator node"""
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type: Literal["metadata_accumulator_output"] = "metadata_accumulator_output"
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metadata: CoreMetadata = Field(description="The core metadata for the image")
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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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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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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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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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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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# 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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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_aesthetic_store: Union[float, None] = 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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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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return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict()))
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