mirror of
https://github.com/invoke-ai/InvokeAI
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34402cc46a
- 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
47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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from datetime import datetime, timezone
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from typing import Literal, Union
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from pydantic import Field
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from invokeai.app.models.image import ImageField, ImageType
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from ..services.invocation_services import InvocationServices
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from .baseinvocation import BaseInvocation, InvocationContext
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from .image import ImageOutput
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class UpscaleInvocation(BaseInvocation):
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"""Upscales an image."""
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#fmt: off
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type: Literal["upscale"] = "upscale"
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# Inputs
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image: Union[ImageField, None] = Field(description="The input image", default=None)
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strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
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level: Literal[2, 4] = Field(default=2, description="The upscale level")
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#fmt: on
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get(
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self.image.image_type, self.image.image_name
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)
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results = context.services.restoration.upscale_and_reconstruct(
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image_list=[[image, 0]],
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upscale=(self.level, self.strength),
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strength=0.0, # GFPGAN strength
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save_original=False,
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image_callback=None,
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)
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# Results are image and seed, unwrap for now
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# TODO: can this return multiple results?
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image_type = ImageType.RESULT
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image_name = context.services.images.create_name(
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context.graph_execution_state_id, self.id
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)
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context.services.images.save(image_type, image_name, results[0][0])
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return ImageOutput(
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image=ImageField(image_type=image_type, image_name=image_name)
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)
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