mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
feat(nodes): move fully* to new images service
* except i haven't rebuilt inpaint in latents
This commit is contained in:
psychedelicious
Kent Keirsey
parent
dd16f788ed
commit
d2c223de8f
@ -55,16 +55,6 @@ class ApiDependencies:
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os.path.join(os.path.dirname(__file__), "../../../../outputs")
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)
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latents = ForwardCacheLatentsStorage(
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DiskLatentsStorage(f"{output_folder}/latents")
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)
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metadata = CoreMetadataService()
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urls = LocalUrlService()
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image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
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# TODO: build a file/path manager?
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db_location = os.path.join(output_folder, "invokeai.db")
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@ -72,9 +62,16 @@ class ApiDependencies:
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filename=db_location, table_name="graph_executions"
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)
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urls = LocalUrlService()
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metadata = CoreMetadataService()
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image_record_storage = SqliteImageRecordStorage(db_location)
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image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
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images_new = ImageService(
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latents = ForwardCacheLatentsStorage(
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DiskLatentsStorage(f"{output_folder}/latents")
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)
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images = ImageService(
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image_record_storage=image_record_storage,
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image_file_storage=image_file_storage,
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metadata=metadata,
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@ -87,8 +84,7 @@ class ApiDependencies:
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model_manager=get_model_manager(config, logger),
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events=events,
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latents=latents,
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images=image_file_storage,
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images_new=images_new,
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images=images,
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queue=MemoryInvocationQueue(),
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graph_library=SqliteItemStorage[LibraryGraph](
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filename=db_location, table_name="graphs"
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@ -45,7 +45,7 @@ async def upload_image(
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raise HTTPException(status_code=415, detail="Failed to read image")
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try:
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image_dto = ApiDependencies.invoker.services.images_new.create(
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image_dto = ApiDependencies.invoker.services.images.create(
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pil_image,
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image_type,
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image_category,
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@ -67,7 +67,7 @@ async def delete_image(
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"""Deletes an image"""
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try:
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ApiDependencies.invoker.services.images_new.delete(image_type, image_name)
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ApiDependencies.invoker.services.images.delete(image_type, image_name)
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except Exception as e:
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# TODO: Does this need any exception handling at all?
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pass
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@ -85,7 +85,7 @@ async def get_image_metadata(
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"""Gets an image's metadata"""
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try:
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return ApiDependencies.invoker.services.images_new.get_dto(
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return ApiDependencies.invoker.services.images.get_dto(
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image_type, image_name
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)
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except Exception as e:
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@ -113,11 +113,11 @@ async def get_image_full(
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"""Gets a full-resolution image file"""
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try:
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path = ApiDependencies.invoker.services.images_new.get_path(
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path = ApiDependencies.invoker.services.images.get_path(
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image_type, image_name
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)
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if not ApiDependencies.invoker.services.images_new.validate_path(path):
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if not ApiDependencies.invoker.services.images.validate_path(path):
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raise HTTPException(status_code=404)
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return FileResponse(
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@ -149,10 +149,10 @@ async def get_image_thumbnail(
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"""Gets a thumbnail image file"""
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try:
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path = ApiDependencies.invoker.services.images_new.get_path(
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path = ApiDependencies.invoker.services.images.get_path(
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image_type, image_name, thumbnail=True
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)
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if not ApiDependencies.invoker.services.images_new.validate_path(path):
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if not ApiDependencies.invoker.services.images.validate_path(path):
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raise HTTPException(status_code=404)
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return FileResponse(
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@ -174,10 +174,10 @@ async def get_image_urls(
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"""Gets an image and thumbnail URL"""
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try:
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image_url = ApiDependencies.invoker.services.images_new.get_url(
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image_url = ApiDependencies.invoker.services.images.get_url(
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image_type, image_name
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)
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thumbnail_url = ApiDependencies.invoker.services.images_new.get_url(
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thumbnail_url = ApiDependencies.invoker.services.images.get_url(
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image_type, image_name, thumbnail=True
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)
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return ImageUrlsDTO(
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@ -205,7 +205,7 @@ async def list_images_with_metadata(
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) -> PaginatedResults[ImageDTO]:
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"""Gets a list of images with metadata"""
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image_dtos = ApiDependencies.invoker.services.images_new.get_many(
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image_dtos = ApiDependencies.invoker.services.images.get_many(
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image_type,
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image_category,
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page,
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@ -7,9 +7,9 @@ import numpy
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from PIL import Image, ImageOps
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from pydantic import BaseModel, Field
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from invokeai.app.models.image import ImageField, ImageType
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from invokeai.app.models.image import ImageCategory, ImageField, ImageType
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from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
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from .image import ImageOutput, build_image_output
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from .image import ImageOutput
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class CvInvocationConfig(BaseModel):
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@ -26,24 +26,27 @@ class CvInvocationConfig(BaseModel):
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class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
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"""Simple inpaint using opencv."""
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#fmt: off
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# fmt: off
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type: Literal["cv_inpaint"] = "cv_inpaint"
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# Inputs
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image: ImageField = Field(default=None, description="The image to inpaint")
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mask: ImageField = Field(default=None, description="The mask to use when inpainting")
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#fmt: on
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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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image = context.services.images.get_pil_image(
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self.image.image_type, self.image.image_name
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)
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mask = context.services.images.get(self.mask.image_type, self.mask.image_name)
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mask = context.services.images.get_pil_image(
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self.mask.image_type, self.mask.image_name
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)
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# Convert to cv image/mask
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# TODO: consider making these utility functions
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cv_image = cv.cvtColor(numpy.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
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cv_mask = numpy.array(ImageOps.invert(mask))
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cv_mask = numpy.array(ImageOps.invert(mask.convert("L")))
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# Inpaint
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cv_inpainted = cv.inpaint(cv_image, cv_mask, 3, cv.INPAINT_TELEA)
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@ -52,18 +55,19 @@ class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
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# TODO: consider making a utility function
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image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
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image_type = ImageType.INTERMEDIATE
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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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image_dto = context.services.images.create(
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image=image_inpainted,
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image_type=ImageType.INTERMEDIATE,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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session_id=context.graph_execution_state_id,
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)
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metadata = context.services.metadata.build_metadata(
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session_id=context.graph_execution_state_id, node=self
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return ImageOutput(
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image=ImageField(
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image_name=image_dto.image_name,
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image_type=image_dto.image_type,
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),
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width=image_dto.width,
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height=image_dto.height,
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)
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context.services.images.save(image_type, image_name, image_inpainted, metadata)
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return build_image_output(
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image_type=image_type,
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image_name=image_name,
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image=image_inpainted,
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)
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@ -1,13 +1,13 @@
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# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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import io
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from typing import Literal, Optional
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from typing import Literal, Optional, Union
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import numpy
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from PIL import Image, ImageFilter, ImageOps
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from pydantic import BaseModel, Field
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from ..models.image import ImageField, ImageType
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from ..models.image import ImageCategory, ImageField, ImageType
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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@ -41,27 +41,14 @@ class ImageOutput(BaseInvocationOutput):
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schema_extra = {"required": ["type", "image", "width", "height"]}
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def build_image_output(
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image_type: ImageType, image_name: str, image: Image.Image
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) -> ImageOutput:
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"""Builds an ImageOutput and its ImageField"""
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image_field = ImageField(
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image_name=image_name,
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image_type=image_type,
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)
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return ImageOutput(
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image=image_field,
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width=image.width,
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height=image.height,
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)
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class MaskOutput(BaseInvocationOutput):
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"""Base class for invocations that output a mask"""
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# fmt: off
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type: Literal["mask"] = "mask"
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mask: ImageField = Field(default=None, description="The output mask")
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width: int = Field(description="The width of the mask in pixels")
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height: int = Field(description="The height of the mask in pixels")
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# fmt: on
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class Config:
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@ -84,12 +71,15 @@ class LoadImageInvocation(BaseInvocation):
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image_name: str = Field(description="The name of the image")
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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(self.image_type, self.image_name)
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image = context.services.images.get_pil_image(self.image_type, self.image_name)
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return build_image_output(
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image_type=self.image_type,
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image_name=self.image_name,
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image=image,
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return ImageOutput(
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image=ImageField(
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image_name=self.image_name,
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image_type=self.image_type,
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),
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width=image.width,
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height=image.height,
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)
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@ -99,10 +89,12 @@ class ShowImageInvocation(BaseInvocation):
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type: Literal["show_image"] = "show_image"
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# Inputs
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image: ImageField = Field(default=None, description="The image to show")
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image: Union[ImageField, None] = Field(
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default=None, description="The image to show"
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)
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get(
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image = context.services.images.get_pil_image(
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self.image.image_type, self.image.image_name
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)
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if image:
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@ -110,10 +102,13 @@ class ShowImageInvocation(BaseInvocation):
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# TODO: how to handle failure?
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return build_image_output(
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image_type=self.image.image_type,
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image_name=self.image.image_name,
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image=image,
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return ImageOutput(
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image=ImageField(
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image_name=self.image.image_name,
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image_type=self.image.image_type,
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),
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width=image.width,
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height=image.height,
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)
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@ -124,7 +119,7 @@ class CropImageInvocation(BaseInvocation, PILInvocationConfig):
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type: Literal["crop"] = "crop"
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# Inputs
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image: ImageField = Field(default=None, description="The image to crop")
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image: Union[ImageField, None] = Field(default=None, description="The image to crop")
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x: int = Field(default=0, description="The left x coordinate of the crop rectangle")
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y: int = Field(default=0, description="The top y coordinate of the crop rectangle")
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width: int = Field(default=512, gt=0, description="The width of the crop rectangle")
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@ -132,7 +127,7 @@ class CropImageInvocation(BaseInvocation, PILInvocationConfig):
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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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image = context.services.images.get_pil_image(
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self.image.image_type, self.image.image_name
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)
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@ -141,20 +136,21 @@ class CropImageInvocation(BaseInvocation, PILInvocationConfig):
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)
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image_crop.paste(image, (-self.x, -self.y))
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image_type = ImageType.INTERMEDIATE
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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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metadata = context.services.metadata.build_metadata(
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session_id=context.graph_execution_state_id, node=self
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)
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context.services.images.save(image_type, image_name, image_crop, metadata)
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return build_image_output(
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image_type=image_type,
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image_name=image_name,
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image_dto = context.services.images.create(
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image=image_crop,
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image_type=ImageType.INTERMEDIATE,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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session_id=context.graph_execution_state_id,
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)
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return ImageOutput(
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image=ImageField(
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image_name=image_dto.image_name,
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image_type=image_dto.image_type,
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),
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width=image_dto.width,
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height=image_dto.height,
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)
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@ -165,25 +161,27 @@ class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
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type: Literal["paste"] = "paste"
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# Inputs
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base_image: ImageField = Field(default=None, description="The base image")
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image: ImageField = Field(default=None, description="The image to paste")
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base_image: Union[ImageField, None] = Field(default=None, description="The base image")
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image: Union[ImageField, None] = Field(default=None, description="The image to paste")
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mask: Optional[ImageField] = Field(default=None, description="The mask to use when pasting")
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x: int = Field(default=0, description="The left x coordinate at which to paste the image")
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y: int = Field(default=0, description="The top y coordinate at which to paste the image")
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# fmt: on
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def invoke(self, context: InvocationContext) -> ImageOutput:
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base_image = context.services.images.get(
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base_image = context.services.images.get_pil_image(
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self.base_image.image_type, self.base_image.image_name
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)
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image = context.services.images.get(
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image = context.services.images.get_pil_image(
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self.image.image_type, self.image.image_name
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)
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mask = (
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None
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if self.mask is None
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else ImageOps.invert(
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context.services.images.get(self.mask.image_type, self.mask.image_name)
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context.services.images.get_pil_image(
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self.mask.image_type, self.mask.image_name
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)
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)
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)
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# TODO: probably shouldn't invert mask here... should user be required to do it?
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@ -199,20 +197,21 @@ class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
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new_image.paste(base_image, (abs(min_x), abs(min_y)))
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new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
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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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metadata = context.services.metadata.build_metadata(
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session_id=context.graph_execution_state_id, node=self
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)
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context.services.images.save(image_type, image_name, new_image, metadata)
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return build_image_output(
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image_type=image_type,
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image_name=image_name,
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image_dto = context.services.images.create(
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image=new_image,
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image_type=ImageType.RESULT,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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session_id=context.graph_execution_state_id,
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)
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return ImageOutput(
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image=ImageField(
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image_name=image_dto.image_name,
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image_type=image_dto.image_type,
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),
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width=image_dto.width,
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height=image_dto.height,
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)
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@ -223,12 +222,12 @@ class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
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type: Literal["tomask"] = "tomask"
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# Inputs
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image: ImageField = Field(default=None, description="The image to create the mask from")
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image: Union[ImageField, None] = Field(default=None, description="The image to create the mask from")
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invert: bool = Field(default=False, description="Whether or not to invert the mask")
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# fmt: on
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def invoke(self, context: InvocationContext) -> MaskOutput:
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image = context.services.images.get(
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image = context.services.images.get_pil_image(
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self.image.image_type, self.image.image_name
|
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)
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@ -236,18 +235,22 @@ class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
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if self.invert:
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image_mask = ImageOps.invert(image_mask)
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image_type = ImageType.INTERMEDIATE
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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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image_dto = context.services.images.create(
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image=image_mask,
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image_type=ImageType.INTERMEDIATE,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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session_id=context.graph_execution_state_id,
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)
|
||||
|
||||
metadata = context.services.metadata.build_metadata(
|
||||
session_id=context.graph_execution_state_id, node=self
|
||||
return MaskOutput(
|
||||
mask=ImageField(
|
||||
image_type=image_dto.image_type, image_name=image_dto.image_name
|
||||
),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
context.services.images.save(image_type, image_name, image_mask, metadata)
|
||||
return MaskOutput(mask=ImageField(image_type=image_type, image_name=image_name))
|
||||
|
||||
|
||||
class BlurInvocation(BaseInvocation, PILInvocationConfig):
|
||||
"""Blurs an image"""
|
||||
@ -256,13 +259,13 @@ class BlurInvocation(BaseInvocation, PILInvocationConfig):
|
||||
type: Literal["blur"] = "blur"
|
||||
|
||||
# Inputs
|
||||
image: ImageField = Field(default=None, description="The image to blur")
|
||||
image: Union[ImageField, None] = Field(default=None, description="The image to blur")
|
||||
radius: float = Field(default=8.0, ge=0, description="The blur radius")
|
||||
blur_type: Literal["gaussian", "box"] = Field(default="gaussian", description="The type of blur")
|
||||
# fmt: on
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get(
|
||||
image = context.services.images.get_pil_image(
|
||||
self.image.image_type, self.image.image_name
|
||||
)
|
||||
|
||||
@ -273,18 +276,21 @@ class BlurInvocation(BaseInvocation, PILInvocationConfig):
|
||||
)
|
||||
blur_image = image.filter(blur)
|
||||
|
||||
image_type = ImageType.INTERMEDIATE
|
||||
image_name = context.services.images.create_name(
|
||||
context.graph_execution_state_id, self.id
|
||||
image_dto = context.services.images.create(
|
||||
image=blur_image,
|
||||
image_type=ImageType.INTERMEDIATE,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
)
|
||||
|
||||
metadata = context.services.metadata.build_metadata(
|
||||
session_id=context.graph_execution_state_id, node=self
|
||||
)
|
||||
|
||||
context.services.images.save(image_type, image_name, blur_image, metadata)
|
||||
return build_image_output(
|
||||
image_type=image_type, image_name=image_name, image=blur_image
|
||||
return ImageOutput(
|
||||
image=ImageField(
|
||||
image_name=image_dto.image_name,
|
||||
image_type=image_dto.image_type,
|
||||
),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@ -295,13 +301,13 @@ class LerpInvocation(BaseInvocation, PILInvocationConfig):
|
||||
type: Literal["lerp"] = "lerp"
|
||||
|
||||
# Inputs
|
||||
image: ImageField = Field(default=None, description="The image to lerp")
|
||||
image: Union[ImageField, None] = Field(default=None, description="The image to lerp")
|
||||
min: int = Field(default=0, ge=0, le=255, description="The minimum output value")
|
||||
max: int = Field(default=255, ge=0, le=255, description="The maximum output value")
|
||||
# fmt: on
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get(
|
||||
image = context.services.images.get_pil_image(
|
||||
self.image.image_type, self.image.image_name
|
||||
)
|
||||
|
||||
@ -310,18 +316,21 @@ class LerpInvocation(BaseInvocation, PILInvocationConfig):
|
||||
|
||||
lerp_image = Image.fromarray(numpy.uint8(image_arr))
|
||||
|
||||
image_type = ImageType.INTERMEDIATE
|
||||
image_name = context.services.images.create_name(
|
||||
context.graph_execution_state_id, self.id
|
||||
image_dto = context.services.images.create(
|
||||
image=lerp_image,
|
||||
image_type=ImageType.INTERMEDIATE,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
)
|
||||
|
||||
metadata = context.services.metadata.build_metadata(
|
||||
session_id=context.graph_execution_state_id, node=self
|
||||
)
|
||||
|
||||
context.services.images.save(image_type, image_name, lerp_image, metadata)
|
||||
return build_image_output(
|
||||
image_type=image_type, image_name=image_name, image=lerp_image
|
||||
return ImageOutput(
|
||||
image=ImageField(
|
||||
image_name=image_dto.image_name,
|
||||
image_type=image_dto.image_type,
|
||||
),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@ -332,13 +341,13 @@ class InverseLerpInvocation(BaseInvocation, PILInvocationConfig):
|
||||
type: Literal["ilerp"] = "ilerp"
|
||||
|
||||
# Inputs
|
||||
image: ImageField = Field(default=None, description="The image to lerp")
|
||||
image: Union[ImageField, None] = Field(default=None, description="The image to lerp")
|
||||
min: int = Field(default=0, ge=0, le=255, description="The minimum input value")
|
||||
max: int = Field(default=255, ge=0, le=255, description="The maximum input value")
|
||||
# fmt: on
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get(
|
||||
image = context.services.images.get_pil_image(
|
||||
self.image.image_type, self.image.image_name
|
||||
)
|
||||
|
||||
@ -352,16 +361,19 @@ class InverseLerpInvocation(BaseInvocation, PILInvocationConfig):
|
||||
|
||||
ilerp_image = Image.fromarray(numpy.uint8(image_arr))
|
||||
|
||||
image_type = ImageType.INTERMEDIATE
|
||||
image_name = context.services.images.create_name(
|
||||
context.graph_execution_state_id, self.id
|
||||
image_dto = context.services.images.create(
|
||||
image=ilerp_image,
|
||||
image_type=ImageType.INTERMEDIATE,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
)
|
||||
|
||||
metadata = context.services.metadata.build_metadata(
|
||||
session_id=context.graph_execution_state_id, node=self
|
||||
)
|
||||
|
||||
context.services.images.save(image_type, image_name, ilerp_image, metadata)
|
||||
return build_image_output(
|
||||
image_type=image_type, image_name=image_name, image=ilerp_image
|
||||
return ImageOutput(
|
||||
image=ImageField(
|
||||
image_name=image_dto.image_name,
|
||||
image_type=image_dto.image_type,
|
||||
),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
@ -1,17 +1,17 @@
|
||||
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
|
||||
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
|
||||
|
||||
from typing import Literal, Optional, Union, get_args
|
||||
from typing import Literal, Union, get_args
|
||||
|
||||
import numpy as np
|
||||
import math
|
||||
from PIL import Image, ImageOps
|
||||
from pydantic import Field
|
||||
|
||||
from invokeai.app.invocations.image import ImageOutput, build_image_output
|
||||
from invokeai.app.invocations.image import ImageOutput
|
||||
from invokeai.app.util.misc import SEED_MAX, get_random_seed
|
||||
from invokeai.backend.image_util.patchmatch import PatchMatch
|
||||
|
||||
from ..models.image import ColorField, ImageField, ImageType
|
||||
from ..models.image import ColorField, ImageCategory, ImageField, ImageType
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
InvocationContext,
|
||||
@ -125,36 +125,39 @@ class InfillColorInvocation(BaseInvocation):
|
||||
"""Infills transparent areas of an image with a solid color"""
|
||||
|
||||
type: Literal["infill_rgba"] = "infill_rgba"
|
||||
image: Optional[ImageField] = Field(default=None, description="The image to infill")
|
||||
color: Optional[ColorField] = Field(
|
||||
image: Union[ImageField, None] = Field(
|
||||
default=None, description="The image to infill"
|
||||
)
|
||||
color: ColorField = Field(
|
||||
default=ColorField(r=127, g=127, b=127, a=255),
|
||||
description="The color to use to infill",
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get(
|
||||
image = context.services.images.get_pil_image(
|
||||
self.image.image_type, self.image.image_name
|
||||
)
|
||||
|
||||
solid_bg = Image.new("RGBA", image.size, self.color.tuple())
|
||||
infilled = Image.alpha_composite(solid_bg, image)
|
||||
infilled = Image.alpha_composite(solid_bg, image.convert("RGBA"))
|
||||
|
||||
infilled.paste(image, (0, 0), image.split()[-1])
|
||||
|
||||
image_type = ImageType.RESULT
|
||||
image_name = context.services.images.create_name(
|
||||
context.graph_execution_state_id, self.id
|
||||
image_dto = context.services.images.create(
|
||||
image=infilled,
|
||||
image_type=ImageType.RESULT,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
)
|
||||
|
||||
metadata = context.services.metadata.build_metadata(
|
||||
session_id=context.graph_execution_state_id, node=self
|
||||
)
|
||||
|
||||
context.services.images.save(image_type, image_name, infilled, metadata)
|
||||
return build_image_output(
|
||||
image_type=image_type,
|
||||
image_name=image_name,
|
||||
image=image,
|
||||
return ImageOutput(
|
||||
image=ImageField(
|
||||
image_name=image_dto.image_name,
|
||||
image_type=image_dto.image_type,
|
||||
),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@ -163,7 +166,9 @@ class InfillTileInvocation(BaseInvocation):
|
||||
|
||||
type: Literal["infill_tile"] = "infill_tile"
|
||||
|
||||
image: Optional[ImageField] = Field(default=None, description="The image to infill")
|
||||
image: Union[ImageField, None] = Field(
|
||||
default=None, description="The image to infill"
|
||||
)
|
||||
tile_size: int = Field(default=32, ge=1, description="The tile size (px)")
|
||||
seed: int = Field(
|
||||
ge=0,
|
||||
@ -173,7 +178,7 @@ class InfillTileInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get(
|
||||
image = context.services.images.get_pil_image(
|
||||
self.image.image_type, self.image.image_name
|
||||
)
|
||||
|
||||
@ -182,20 +187,21 @@ class InfillTileInvocation(BaseInvocation):
|
||||
)
|
||||
infilled.paste(image, (0, 0), image.split()[-1])
|
||||
|
||||
image_type = ImageType.RESULT
|
||||
image_name = context.services.images.create_name(
|
||||
context.graph_execution_state_id, self.id
|
||||
image_dto = context.services.images.create(
|
||||
image=infilled,
|
||||
image_type=ImageType.RESULT,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
)
|
||||
|
||||
metadata = context.services.metadata.build_metadata(
|
||||
session_id=context.graph_execution_state_id, node=self
|
||||
)
|
||||
|
||||
context.services.images.save(image_type, image_name, infilled, metadata)
|
||||
return build_image_output(
|
||||
image_type=image_type,
|
||||
image_name=image_name,
|
||||
image=image,
|
||||
return ImageOutput(
|
||||
image=ImageField(
|
||||
image_name=image_dto.image_name,
|
||||
image_type=image_dto.image_type,
|
||||
),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@ -204,10 +210,12 @@ class InfillPatchMatchInvocation(BaseInvocation):
|
||||
|
||||
type: Literal["infill_patchmatch"] = "infill_patchmatch"
|
||||
|
||||
image: Optional[ImageField] = Field(default=None, description="The image to infill")
|
||||
image: Union[ImageField, None] = Field(
|
||||
default=None, description="The image to infill"
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get(
|
||||
image = context.services.images.get_pil_image(
|
||||
self.image.image_type, self.image.image_name
|
||||
)
|
||||
|
||||
@ -216,18 +224,19 @@ class InfillPatchMatchInvocation(BaseInvocation):
|
||||
else:
|
||||
raise ValueError("PatchMatch is not available on this system")
|
||||
|
||||
image_type = ImageType.RESULT
|
||||
image_name = context.services.images.create_name(
|
||||
context.graph_execution_state_id, self.id
|
||||
image_dto = context.services.images.create(
|
||||
image=infilled,
|
||||
image_type=ImageType.RESULT,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
)
|
||||
|
||||
metadata = context.services.metadata.build_metadata(
|
||||
session_id=context.graph_execution_state_id, node=self
|
||||
)
|
||||
|
||||
context.services.images.save(image_type, image_name, infilled, metadata)
|
||||
return build_image_output(
|
||||
image_type=image_type,
|
||||
image_name=image_name,
|
||||
image=image,
|
||||
return ImageOutput(
|
||||
image=ImageField(
|
||||
image_name=image_dto.image_name,
|
||||
image_type=image_dto.image_type,
|
||||
),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
import random
|
||||
from typing import Literal, Optional, Union
|
||||
import einops
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, Field, validator
|
||||
import torch
|
||||
|
||||
from invokeai.app.invocations.util.choose_model import choose_model
|
||||
@ -23,7 +23,7 @@ from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationCont
|
||||
import numpy as np
|
||||
from ..services.image_file_storage import ImageType
|
||||
from .baseinvocation import BaseInvocation, InvocationContext
|
||||
from .image import ImageField, ImageOutput, build_image_output
|
||||
from .image import ImageField, ImageOutput
|
||||
from .compel import ConditioningField
|
||||
from ...backend.stable_diffusion import PipelineIntermediateState
|
||||
from diffusers.schedulers import SchedulerMixin as Scheduler
|
||||
@ -362,19 +362,9 @@ class LatentsToImageInvocation(BaseInvocation):
|
||||
np_image = model.decode_latents(latents)
|
||||
image = model.numpy_to_pil(np_image)[0]
|
||||
|
||||
# image_type = ImageType.RESULT
|
||||
# image_name = context.services.images.create_name(
|
||||
# context.graph_execution_state_id, self.id
|
||||
# )
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# metadata = context.services.metadata.build_metadata(
|
||||
# session_id=context.graph_execution_state_id, node=self
|
||||
# )
|
||||
|
||||
# torch.cuda.empty_cache()
|
||||
|
||||
# context.services.images.save(image_type, image_name, image, metadata)
|
||||
image_dto = context.services.images_new.create(
|
||||
image_dto = context.services.images.create(
|
||||
image=image,
|
||||
image_type=ImageType.RESULT,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
@ -382,10 +372,13 @@ class LatentsToImageInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
)
|
||||
|
||||
return build_image_output(
|
||||
image_type=image_dto.image_type,
|
||||
image_name=image_dto.image_name,
|
||||
image=image,
|
||||
return ImageOutput(
|
||||
image=ImageField(
|
||||
image_name=image_dto.image_name,
|
||||
image_type=image_dto.image_type,
|
||||
),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@ -474,7 +467,7 @@ class ImageToLatentsInvocation(BaseInvocation):
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> LatentsOutput:
|
||||
image = context.services.images.get(
|
||||
image = context.services.images.get_pil_image(
|
||||
self.image.image_type, self.image.image_name
|
||||
)
|
||||
|
||||
@ -496,3 +489,4 @@ class ImageToLatentsInvocation(BaseInvocation):
|
||||
name = f"{context.graph_execution_state_id}__{self.id}"
|
||||
context.services.latents.save(name, latents)
|
||||
return build_latents_output(latents_name=name, latents=latents)
|
||||
|
||||
|
||||
@ -2,21 +2,23 @@ from typing import Literal, Union
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from invokeai.app.models.image import ImageField, ImageType
|
||||
from invokeai.app.models.image import ImageCategory, ImageField, ImageType
|
||||
|
||||
from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
|
||||
from .image import ImageOutput, build_image_output
|
||||
from .image import ImageOutput
|
||||
|
||||
|
||||
class RestoreFaceInvocation(BaseInvocation):
|
||||
"""Restores faces in an image."""
|
||||
#fmt: off
|
||||
|
||||
# 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
|
||||
|
||||
# fmt: on
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
@ -26,7 +28,7 @@ class RestoreFaceInvocation(BaseInvocation):
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get(
|
||||
image = context.services.images.get_pil_image(
|
||||
self.image.image_type, self.image.image_name
|
||||
)
|
||||
results = context.services.restoration.upscale_and_reconstruct(
|
||||
@ -39,18 +41,19 @@ class RestoreFaceInvocation(BaseInvocation):
|
||||
|
||||
# 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
|
||||
image_dto = context.services.images.create(
|
||||
image=results[0][0],
|
||||
image_type=ImageType.INTERMEDIATE,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
)
|
||||
|
||||
metadata = context.services.metadata.build_metadata(
|
||||
session_id=context.graph_execution_state_id, node=self
|
||||
return ImageOutput(
|
||||
image=ImageField(
|
||||
image_name=image_dto.image_name,
|
||||
image_type=image_dto.image_type,
|
||||
),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
context.services.images.save(image_type, image_name, results[0][0], metadata)
|
||||
return build_image_output(
|
||||
image_type=image_type,
|
||||
image_name=image_name,
|
||||
image=results[0][0]
|
||||
)
|
||||
@ -4,22 +4,22 @@ from typing import Literal, Union
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from invokeai.app.models.image import ImageField, ImageType
|
||||
from invokeai.app.models.image import ImageCategory, ImageField, ImageType
|
||||
from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
|
||||
from .image import ImageOutput, build_image_output
|
||||
from .image import ImageOutput
|
||||
|
||||
|
||||
class UpscaleInvocation(BaseInvocation):
|
||||
"""Upscales an image."""
|
||||
#fmt: off
|
||||
|
||||
# fmt: off
|
||||
type: Literal["upscale"] = "upscale"
|
||||
|
||||
# Inputs
|
||||
image: Union[ImageField, None] = Field(description="The input image", default=None)
|
||||
strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
|
||||
level: Literal[2, 4] = Field(default=2, description="The upscale level")
|
||||
#fmt: on
|
||||
|
||||
# fmt: on
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
@ -30,7 +30,7 @@ class UpscaleInvocation(BaseInvocation):
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get(
|
||||
image = context.services.images.get_pil_image(
|
||||
self.image.image_type, self.image.image_name
|
||||
)
|
||||
results = context.services.restoration.upscale_and_reconstruct(
|
||||
@ -43,18 +43,19 @@ class UpscaleInvocation(BaseInvocation):
|
||||
|
||||
# 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
|
||||
image_dto = context.services.images.create(
|
||||
image=results[0][0],
|
||||
image_type=ImageType.RESULT,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
)
|
||||
|
||||
metadata = context.services.metadata.build_metadata(
|
||||
session_id=context.graph_execution_state_id, node=self
|
||||
return ImageOutput(
|
||||
image=ImageField(
|
||||
image_name=image_dto.image_name,
|
||||
image_type=image_dto.image_type,
|
||||
),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
context.services.images.save(image_type, image_name, results[0][0], metadata)
|
||||
return build_image_output(
|
||||
image_type=image_type,
|
||||
image_name=image_name,
|
||||
image=results[0][0]
|
||||
)
|
||||
@ -6,7 +6,6 @@ from invokeai.app.services.images import ImageService
|
||||
from invokeai.backend import ModelManager
|
||||
from .events import EventServiceBase
|
||||
from .latent_storage import LatentsStorageBase
|
||||
from .image_file_storage import ImageFileStorageBase
|
||||
from .restoration_services import RestorationServices
|
||||
from .invocation_queue import InvocationQueueABC
|
||||
from .item_storage import ItemStorageABC
|
||||
@ -23,12 +22,11 @@ class InvocationServices:
|
||||
|
||||
events: EventServiceBase
|
||||
latents: LatentsStorageBase
|
||||
images: ImageFileStorageBase
|
||||
queue: InvocationQueueABC
|
||||
model_manager: ModelManager
|
||||
restoration: RestorationServices
|
||||
configuration: InvokeAISettings
|
||||
images_new: ImageService
|
||||
images: ImageService
|
||||
|
||||
# NOTE: we must forward-declare any types that include invocations, since invocations can use services
|
||||
graph_library: ItemStorageABC["LibraryGraph"]
|
||||
@ -41,9 +39,8 @@ class InvocationServices:
|
||||
events: EventServiceBase,
|
||||
logger: Logger,
|
||||
latents: LatentsStorageBase,
|
||||
images: ImageFileStorageBase,
|
||||
images: ImageService,
|
||||
queue: InvocationQueueABC,
|
||||
images_new: ImageService,
|
||||
graph_library: ItemStorageABC["LibraryGraph"],
|
||||
graph_execution_manager: ItemStorageABC["GraphExecutionState"],
|
||||
processor: "InvocationProcessorABC",
|
||||
@ -56,7 +53,6 @@ class InvocationServices:
|
||||
self.latents = latents
|
||||
self.images = images
|
||||
self.queue = queue
|
||||
self.images_new = images_new
|
||||
self.graph_library = graph_library
|
||||
self.graph_execution_manager = graph_execution_manager
|
||||
self.processor = processor
|
||||
|
||||
Loading…
Reference in New Issue
Block a user