InvokeAI/invokeai/app/invocations/reconstruct.py
psychedelicious 34402cc46a feat(nodes): add list_images endpoint
- add `list_images` endpoint at `GET api/v1/images`
- extend `ImageStorageBase` with `list()` method, implemented it for `DiskImageStorage`
- add `ImageReponse` class to for image responses, which includes urls, metadata
- add `ImageMetadata` class (basically a stub at the moment)
- uploaded images now named `"{uuid}_{timestamp}.png"`
- add `models` modules. besides separating concerns more clearly, this helps to mitigate circular dependencies
- improve thumbnail handling
2023-04-09 13:48:44 +10:00

43 lines
1.6 KiB
Python

from datetime import datetime, timezone
from typing import Literal, Union
from pydantic import Field
from invokeai.app.models.image import ImageField, ImageType
from ..services.invocation_services import InvocationServices
from .baseinvocation import BaseInvocation, InvocationContext
from .image import ImageOutput
class RestoreFaceInvocation(BaseInvocation):
"""Restores faces in an image."""
#fmt: off
type: Literal["restore_face"] = "restore_face"
# Inputs
image: Union[ImageField, None] = Field(description="The input image")
strength: float = Field(default=0.75, gt=0, le=1, description="The strength of the restoration" )
#fmt: on
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get(
self.image.image_type, self.image.image_name
)
results = context.services.restoration.upscale_and_reconstruct(
image_list=[[image, 0]],
upscale=None,
strength=self.strength, # GFPGAN strength
save_original=False,
image_callback=None,
)
# Results are image and seed, unwrap for now
# TODO: can this return multiple results?
image_type = ImageType.RESULT
image_name = context.services.images.create_name(
context.graph_execution_state_id, self.id
)
context.services.images.save(image_type, image_name, results[0][0])
return ImageOutput(
image=ImageField(image_type=image_type, image_name=image_name)
)