hack(nodes): hack to get image urls in the invocation complete event

`set` is a python builtin

invokeai/app/api/dependencies.py

@@ -1,9 +1,11 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from logging import Logger
 import os
-
-import invokeai.backend.util.logging as logger
-from typing import types
+from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
+from invokeai.app.services.images import ImageService
+from invokeai.app.services.urls import LocalUrlService
+from invokeai.backend.util.logging import InvokeAILogger
 
 from ..services.default_graphs import create_system_graphs
 from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
@@ -11,7 +13,7 @@ from ...backend import Globals
 from ..services.model_manager_initializer import get_model_manager
 from ..services.restoration_services import RestorationServices
 from ..services.graph import GraphExecutionState, LibraryGraph
-from ..services.image_storage import DiskImageStorage
+from ..services.image_file_storage import DiskImageFileStorage
 from ..services.invocation_queue import MemoryInvocationQueue
 from ..services.invocation_services import InvocationServices
 from ..services.invoker import Invoker
@@ -37,13 +39,16 @@ def check_internet() -> bool:
         return False
 
 
+logger = InvokeAILogger.getLogger()
+
+
 class ApiDependencies:
     """Contains and initializes all dependencies for the API"""
 
     invoker: Invoker = None
 
     @staticmethod
-    def initialize(config, event_handler_id: int, logger: types.ModuleType=logger):
+    def initialize(config, event_handler_id: int, logger: Logger = logger):
         Globals.try_patchmatch = config.patchmatch
         Globals.always_use_cpu = config.always_use_cpu
         Globals.internet_available = config.internet_available and check_internet()
@@ -60,31 +65,47 @@ class ApiDependencies:
             os.path.join(os.path.dirname(__file__), "../../../../outputs")
         )
 
-        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents'))
+        latents = ForwardCacheLatentsStorage(
+            DiskLatentsStorage(f"{output_folder}/latents")
+        )
 
         metadata = PngMetadataService()
 
-        images = DiskImageStorage(f'{output_folder}/images', metadata_service=metadata)
+        urls = LocalUrlService()
+
+        image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
 
         # TODO: build a file/path manager?
         db_location = os.path.join(output_folder, "invokeai.db")
 
+        graph_execution_manager = SqliteItemStorage[GraphExecutionState](
+            filename=db_location, table_name="graph_executions"
+        )
+
+        image_record_storage = SqliteImageRecordStorage(db_location)
+
+        images_new = ImageService(
+            image_record_storage=image_record_storage,
+            image_file_storage=image_file_storage,
+            metadata=metadata,
+            url=urls,
+            logger=logger,
+        )
+
         services = InvocationServices(
-            model_manager=get_model_manager(config,logger),
+            model_manager=get_model_manager(config, logger),
             events=events,
             logger=logger,
             latents=latents,
-            images=images,
-            metadata=metadata,
+            images=image_file_storage,
+            images_new=images_new,
             queue=MemoryInvocationQueue(),
             graph_library=SqliteItemStorage[LibraryGraph](
                 filename=db_location, table_name="graphs"
             ),
-            graph_execution_manager=SqliteItemStorage[GraphExecutionState](
-                filename=db_location, table_name="graph_executions"
-            ),
+            graph_execution_manager=graph_execution_manager,
             processor=DefaultInvocationProcessor(),
-            restoration=RestorationServices(config,logger),
+            restoration=RestorationServices(config, logger),
         )
 
         create_system_graphs(services.graph_library)

invokeai/app/api/routers/image_files.py

@@ -0,0 +1,47 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
+from fastapi import HTTPException, Path
+from fastapi.responses import FileResponse
+from fastapi.routing import APIRouter
+from invokeai.app.models.image import ImageType
+
+from ..dependencies import ApiDependencies
+
+image_files_router = APIRouter(prefix="/v1/files/images", tags=["images", "files"])
+
+
+@image_files_router.get("/{image_type}/{image_name}", operation_id="get_image")
+async def get_image(
+    image_type: ImageType = Path(description="The type of the image to get"),
+    image_name: str = Path(description="The id of the image to get"),
+) -> FileResponse:
+    """Gets an image"""
+
+    try:
+        path = ApiDependencies.invoker.services.images_new.get_path(
+            image_type=image_type, image_name=image_name
+        )
+
+        return FileResponse(path)
+    except Exception as e:
+        raise HTTPException(status_code=404)
+
+
+@image_files_router.get(
+    "/{image_type}/{image_name}/thumbnail", operation_id="get_thumbnail"
+)
+async def get_thumbnail(
+    image_type: ImageType = Path(
+        description="The type of the image whose thumbnail to get"
+    ),
+    image_name: str = Path(description="The id of the image whose thumbnail to get"),
+) -> FileResponse:
+    """Gets a thumbnail"""
+
+    try:
+        path = ApiDependencies.invoker.services.images_new.get_path(
+            image_type=image_type, image_name=image_name, thumbnail=True
+        )
+
+        return FileResponse(path)
+    except Exception as e:
+        raise HTTPException(status_code=404)

invokeai/app/api/routers/image_records.py

@@ -0,0 +1,71 @@
+from fastapi import HTTPException, Path, Query
+from fastapi.routing import APIRouter
+from invokeai.app.models.image import (
+    ImageCategory,
+    ImageType,
+)
+from invokeai.app.services.item_storage import PaginatedResults
+from invokeai.app.services.models.image_record import ImageDTO
+
+from ..dependencies import ApiDependencies
+
+image_records_router = APIRouter(
+    prefix="/v1/images/records", tags=["images", "records"]
+)
+
+
+@image_records_router.get("/{image_type}/{image_name}", operation_id="get_image_record")
+async def get_image_record(
+    image_type: ImageType = Path(description="The type of the image record to get"),
+    image_name: str = Path(description="The id of the image record to get"),
+) -> ImageDTO:
+    """Gets an image record by id"""
+
+    try:
+        return ApiDependencies.invoker.services.images_new.get_dto(
+            image_type=image_type, image_name=image_name
+        )
+    except Exception as e:
+        raise HTTPException(status_code=404)
+
+
+@image_records_router.get(
+    "/",
+    operation_id="list_image_records",
+)
+async def list_image_records(
+    image_type: ImageType = Query(description="The type of image records to get"),
+    image_category: ImageCategory = Query(
+        description="The kind of image records to get"
+    ),
+    page: int = Query(default=0, description="The page of image records to get"),
+    per_page: int = Query(
+        default=10, description="The number of image records per page"
+    ),
+) -> PaginatedResults[ImageDTO]:
+    """Gets a list of image records by type and category"""
+
+    image_dtos = ApiDependencies.invoker.services.images_new.get_many(
+        image_type=image_type,
+        image_category=image_category,
+        page=page,
+        per_page=per_page,
+    )
+
+    return image_dtos
+
+
+@image_records_router.delete("/{image_type}/{image_name}", operation_id="delete_image")
+async def delete_image_record(
+    image_type: ImageType = Query(description="The type of image to delete"),
+    image_name: str = Path(description="The name of the image to delete"),
+) -> None:
+    """Deletes an image record"""
+
+    try:
+        ApiDependencies.invoker.services.images_new.delete(
+            image_type=image_type, image_name=image_name
+        )
+    except Exception as e:
+        # TODO: Does this need any exception handling at all?
+        pass

invokeai/app/api/routers/images.py

@@ -1,90 +1,39 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 import io
-from datetime import datetime, timezone
-import json
-import os
-from typing import Any
 import uuid
-
-from fastapi import Body, HTTPException, Path, Query, Request, UploadFile
-from fastapi.responses import FileResponse, Response
+from fastapi import HTTPException, Path, Query, Request, Response, UploadFile
 from fastapi.routing import APIRouter
 from PIL import Image
-from invokeai.app.api.models.images import (
-    ImageResponse,
-    ImageResponseMetadata,
+from invokeai.app.models.image import (
+    ImageCategory,
+    ImageType,
 )
+from invokeai.app.services.image_record_storage import ImageRecordStorageBase
+from invokeai.app.services.image_file_storage import ImageFileStorageBase
+from invokeai.app.services.models.image_record import ImageRecord
 from invokeai.app.services.item_storage import PaginatedResults
 
-from ...services.image_storage import ImageType
 from ..dependencies import ApiDependencies
 
 images_router = APIRouter(prefix="/v1/images", tags=["images"])
 
 
-@images_router.get("/{image_type}/{image_name}", operation_id="get_image")
-async def get_image(
-    image_type: ImageType = Path(description="The type of image to get"),
-    image_name: str = Path(description="The name of the image to get"),
-) -> FileResponse:
-    """Gets an image"""
-
-    path = ApiDependencies.invoker.services.images.get_path(
-        image_type=image_type, image_name=image_name
-    )
-
-    if ApiDependencies.invoker.services.images.validate_path(path):
-        return FileResponse(path)
-    else:
-        raise HTTPException(status_code=404)
-
-
-@images_router.delete("/{image_type}/{image_name}", operation_id="delete_image")
-async def delete_image(
-    image_type: ImageType = Path(description="The type of image to delete"),
-    image_name: str = Path(description="The name of the image to delete"),
-) -> None:
-    """Deletes an image and its thumbnail"""
-
-    ApiDependencies.invoker.services.images.delete(
-        image_type=image_type, image_name=image_name
-    )
-
-
-@images_router.get(
-    "/{thumbnail_type}/thumbnails/{thumbnail_name}", operation_id="get_thumbnail"
-)
-async def get_thumbnail(
-    thumbnail_type: ImageType = Path(description="The type of thumbnail to get"),
-    thumbnail_name: str = Path(description="The name of the thumbnail to get"),
-) -> FileResponse | Response:
-    """Gets a thumbnail"""
-
-    path = ApiDependencies.invoker.services.images.get_path(
-        image_type=thumbnail_type, image_name=thumbnail_name, is_thumbnail=True
-    )
-
-    if ApiDependencies.invoker.services.images.validate_path(path):
-        return FileResponse(path)
-    else:
-        raise HTTPException(status_code=404)
-
-
 @images_router.post(
-    "/uploads/",
+    "/",
     operation_id="upload_image",
     responses={
-        201: {
-            "description": "The image was uploaded successfully",
-            "model": ImageResponse,
-        },
+        201: {"description": "The image was uploaded successfully"},
         415: {"description": "Image upload failed"},
     },
     status_code=201,
 )
 async def upload_image(
-    file: UploadFile, image_type: ImageType, request: Request, response: Response
-) -> ImageResponse:
+    file: UploadFile,
+    image_type: ImageType,
+    request: Request,
+    response: Response,
+    image_category: ImageCategory = ImageCategory.IMAGE,
+) -> ImageRecord:
+    """Uploads an image"""
     if not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
 
@@ -96,53 +45,33 @@ async def upload_image(
         # Error opening the image
         raise HTTPException(status_code=415, detail="Failed to read image")
 
-    filename = f"{uuid.uuid4()}_{str(int(datetime.now(timezone.utc).timestamp()))}.png"
+    try:
+        image_record = ApiDependencies.invoker.services.images_new.create(
+            image=img,
+            image_type=image_type,
+            image_category=image_category,
+        )
 
-    saved_image = ApiDependencies.invoker.services.images.save(
-        image_type, filename, img
-    )
+        response.status_code = 201
+        response.headers["Location"] = image_record.image_url
 
-    invokeai_metadata = ApiDependencies.invoker.services.metadata.get_metadata(img)
-
-    image_url = ApiDependencies.invoker.services.images.get_uri(
-        image_type, saved_image.image_name
-    )
-
-    thumbnail_url = ApiDependencies.invoker.services.images.get_uri(
-        image_type, saved_image.image_name, True
-    )
-
-    res = ImageResponse(
-        image_type=image_type,
-        image_name=saved_image.image_name,
-        image_url=image_url,
-        thumbnail_url=thumbnail_url,
-        metadata=ImageResponseMetadata(
-            created=saved_image.created,
-            width=img.width,
-            height=img.height,
-            invokeai=invokeai_metadata,
-        ),
-    )
-
-    response.status_code = 201
-    response.headers["Location"] = image_url
-
-    return res
+        return image_record
+    except Exception as e:
+        raise HTTPException(status_code=500)
 
 
-@images_router.get(
-    "/",
-    operation_id="list_images",
-    responses={200: {"model": PaginatedResults[ImageResponse]}},
-)
-async def list_images(
-    image_type: ImageType = Query(
-        default=ImageType.RESULT, description="The type of images to get"
-    ),
-    page: int = Query(default=0, description="The page of images to get"),
-    per_page: int = Query(default=10, description="The number of images per page"),
-) -> PaginatedResults[ImageResponse]:
-    """Gets a list of images"""
-    result = ApiDependencies.invoker.services.images.list(image_type, page, per_page)
-    return result
+
+@images_router.delete("/{image_type}/{image_name}", operation_id="delete_image")
+async def delete_image_record(
+    image_type: ImageType = Query(description="The type of image to delete"),
+    image_name: str = Path(description="The name of the image to delete"),
+) -> None:
+    """Deletes an image record"""
+
+    try:
+        ApiDependencies.invoker.services.images_new.delete(
+            image_type=image_type, image_name=image_name
+        )
+    except Exception as e:
+        # TODO: Does this need any exception handling at all?
+        pass

invokeai/app/api/routers/results.py

@@ -0,0 +1,42 @@
+from fastapi import HTTPException, Path, Query
+from fastapi.routing import APIRouter
+from invokeai.app.services.results import ResultType, ResultWithSession
+from invokeai.app.services.item_storage import PaginatedResults
+
+from ..dependencies import ApiDependencies
+
+results_router = APIRouter(prefix="/v1/results", tags=["results"])
+
+
+@results_router.get("/{result_type}/{result_name}", operation_id="get_result")
+async def get_result(
+    result_type: ResultType = Path(description="The type of result to get"),
+    result_name: str = Path(description="The name of the result to get"),
+) -> ResultWithSession:
+    """Gets a result"""
+
+    result = ApiDependencies.invoker.services.results.get(
+        result_id=result_name, result_type=result_type
+    )
+
+    if result is not None:
+        return result
+    else:
+        raise HTTPException(status_code=404)
+
+
+@results_router.get(
+    "/",
+    operation_id="list_results",
+    responses={200: {"model": PaginatedResults[ResultWithSession]}},
+)
+async def list_results(
+    result_type: ResultType = Query(description="The type of results to get"),
+    page: int = Query(default=0, description="The page of results to get"),
+    per_page: int = Query(default=10, description="The number of results per page"),
+) -> PaginatedResults[ResultWithSession]:
+    """Gets a list of results"""
+    results = ApiDependencies.invoker.services.results.get_many(
+        result_type=result_type, page=page, per_page=per_page
+    )
+    return results

invokeai/app/api_app.py

@@ -3,6 +3,7 @@ import asyncio
 from inspect import signature
 
 import uvicorn
+from invokeai.app.models import resources
 import invokeai.backend.util.logging as logger
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
@@ -15,10 +16,11 @@ from pydantic.schema import schema
 
 from ..backend import Args
 from .api.dependencies import ApiDependencies
-from .api.routers import images, sessions, models
+from .api.routers import image_files, image_records, sessions, models
 from .api.sockets import SocketIO
 from .invocations.baseinvocation import BaseInvocation
 
+
 # Create the app
 # TODO: create this all in a method so configuration/etc. can be passed in?
 app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None)
@@ -74,10 +76,12 @@ async def shutdown_event():
 
 app.include_router(sessions.session_router, prefix="/api")
 
-app.include_router(images.images_router, prefix="/api")
+app.include_router(image_files.image_files_router, prefix="/api")
 
 app.include_router(models.models_router, prefix="/api")
 
+app.include_router(image_records.image_records_router, prefix="/api")
+
 
 # Build a custom OpenAPI to include all outputs
 # TODO: can outputs be included on metadata of invocation schemas somehow?
@@ -126,6 +130,7 @@ app.openapi = custom_openapi
 # Override API doc favicons
 app.mount("/static", StaticFiles(directory="static/dream_web"), name="static")
 
+
 @app.get("/docs", include_in_schema=False)
 def overridden_swagger():
     return get_swagger_ui_html(
@@ -143,8 +148,12 @@ def overridden_redoc():
         redoc_favicon_url="/static/favicon.ico",
     )
 
+
 # Must mount *after* the other routes else it borks em
-app.mount("/", StaticFiles(directory="invokeai/frontend/web/dist", html=True), name="ui")
+app.mount(
+    "/", StaticFiles(directory="invokeai/frontend/web/dist", html=True), name="ui"
+)
+
 
 def invoke_api():
     # Start our own event loop for eventing usage

invokeai/app/cli_app.py

@@ -28,7 +28,7 @@ from .services.model_manager_initializer import get_model_manager
 from .services.restoration_services import RestorationServices
 from .services.graph import Edge, EdgeConnection, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible
 from .services.default_graphs import default_text_to_image_graph_id
-from .services.image_storage import DiskImageStorage
+from .services.image_file_storage import DiskImageFileStorage
 from .services.invocation_queue import MemoryInvocationQueue
 from .services.invocation_services import InvocationServices
 from .services.invoker import Invoker
@@ -214,7 +214,7 @@ def invoke_cli():
         model_manager=model_manager,
         events=events,
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents')),
-        images=DiskImageStorage(f'{output_folder}/images', metadata_service=metadata),
+        images=DiskImageFileStorage(f'{output_folder}/images', metadata_service=metadata),
         metadata=metadata,
         queue=MemoryInvocationQueue(),
         graph_library=SqliteItemStorage[LibraryGraph](

invokeai/app/invocations/baseinvocation.py

@@ -1,12 +1,15 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 from inspect import signature
-from typing import get_args, get_type_hints, Dict, List, Literal, TypedDict
+from typing import get_args, get_type_hints, Dict, List, Literal, TypedDict, TYPE_CHECKING
 
 from pydantic import BaseModel, Field
 
-from ..services.invocation_services import InvocationServices
+if TYPE_CHECKING:
+    from ..services.invocation_services import InvocationServices
 
 
 class InvocationContext:

invokeai/app/invocations/compel.py

@@ -120,7 +120,7 @@ class CompelInvocation(BaseInvocation):
         conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
 
         # TODO: hacky but works ;D maybe rename latents somehow?
-        context.services.latents.set(conditioning_name, (c, ec))
+        context.services.latents.save(conditioning_name, (c, ec))
 
         return CompelOutput(
             conditioning=ConditioningField(

invokeai/app/invocations/generate.py

@@ -10,6 +10,7 @@ from pydantic import BaseModel, Field
 
 from invokeai.app.models.image import ColorField, ImageField, ImageType
 from invokeai.app.invocations.util.choose_model import choose_model
+from invokeai.app.models.image import ImageCategory, ImageType
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from invokeai.backend.generator.inpaint import infill_methods
 from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
@@ -91,24 +92,42 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
         # each time it is called. We only need the first one.
         generate_output = next(outputs)
 
+        image_dto = context.services.images_new.create(
+            image=generate_output.image,
+            image_type=ImageType.RESULT,
+            image_category=ImageCategory.IMAGE,
+            session_id=context.graph_execution_state_id,
+            node_id=self.id,
+        )
+
         # Results are image and seed, unwrap for now and ignore the seed
         # TODO: pre-seed?
         # TODO: can this return multiple results? Should it?
-        image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
+        # image_type = ImageType.RESULT
+        # image_name = context.services.images.create_name(
+        #     context.graph_execution_state_id, self.id
+        # )
 
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
+        # metadata = context.services.metadata.build_metadata(
+        #     session_id=context.graph_execution_state_id, node=self
+        # )
+
+        # context.services.images.save(
+        #     image_type, image_name, generate_output.image, metadata
+        # )
+
+        # context.services.images_db.set(
+        #     id=image_name,
+        #     image_type=ImageType.RESULT,
+        #     image_category=ImageCategory.IMAGE,
+        #     session_id=context.graph_execution_state_id,
+        #     node_id=self.id,
+        #     metadata=GeneratedImageOrLatentsMetadata(),
+        # )
 
-        context.services.images.save(
-            image_type, image_name, generate_output.image, metadata
-        )
         return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
+            image_type=image_dto.image_type,
+            image_name=image_dto.image_name,
             image=generate_output.image,
         )
 

invokeai/app/invocations/image.py

@@ -31,7 +31,7 @@ class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
 
     # fmt: off
-    type: Literal["image"] = "image"
+    type: Literal["image_output"] = "image_output"
     image:      ImageField = Field(default=None, description="The output image")
     width:             int = Field(description="The width of the image in pixels")
     height:            int = Field(description="The height of the image in pixels")

invokeai/app/invocations/latent.py

@@ -20,7 +20,7 @@ from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, Sta
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 import numpy as np
-from ..services.image_storage import ImageType
+from ..services.image_file_storage import ImageType
 from .baseinvocation import BaseInvocation, InvocationContext
 from .image import ImageField, ImageOutput, build_image_output
 from .compel import ConditioningField
@@ -144,7 +144,7 @@ class NoiseInvocation(BaseInvocation):
         noise = get_noise(self.width, self.height, device, self.seed)
 
         name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, noise)
+        context.services.latents.save(name, noise)
         return build_noise_output(latents_name=name, latents=noise)
 
 
@@ -260,7 +260,7 @@ class TextToLatentsInvocation(BaseInvocation):
         torch.cuda.empty_cache()
 
         name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, result_latents)
+        context.services.latents.save(name, result_latents)
         return build_latents_output(latents_name=name, latents=result_latents)
 
 
@@ -319,7 +319,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
         torch.cuda.empty_cache()
 
         name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, result_latents)
+        context.services.latents.save(name, result_latents)
         return build_latents_output(latents_name=name, latents=result_latents)
 
 
@@ -404,7 +404,7 @@ class ResizeLatentsInvocation(BaseInvocation):
         torch.cuda.empty_cache()
 
         name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.set(name, resized_latents)
+        context.services.latents.save(name, resized_latents)
         return build_latents_output(latents_name=name, latents=resized_latents)
 
 
@@ -434,7 +434,7 @@ class ScaleLatentsInvocation(BaseInvocation):
         torch.cuda.empty_cache()
 
         name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.set(name, resized_latents)
+        context.services.latents.save(name, resized_latents)
         return build_latents_output(latents_name=name, latents=resized_latents)
 
 
@@ -478,5 +478,5 @@ class ImageToLatentsInvocation(BaseInvocation):
         )
 
         name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.set(name, latents)
+        context.services.latents.save(name, latents)
         return build_latents_output(latents_name=name, latents=latents)

invokeai/app/models/image.py

@@ -2,11 +2,23 @@ from enum import Enum
 from typing import Optional, Tuple
 from pydantic import BaseModel, Field
 
+from invokeai.app.util.enum import MetaEnum
+
+
+class ImageType(str, Enum, metaclass=MetaEnum):
+    """The type of an image."""
 
-class ImageType(str, Enum):
     RESULT = "results"
-    INTERMEDIATE = "intermediates"
     UPLOAD = "uploads"
+    INTERMEDIATE = "intermediates"
+
+
+class ImageCategory(str, Enum, metaclass=MetaEnum):
+    """The category of an image. Use ImageCategory.OTHER for non-default categories."""
+
+    IMAGE = "image"
+    CONTROL_IMAGE = "control_image"
+    OTHER = "other"
 
 
 def is_image_type(obj):

invokeai/app/models/metadata.py

@@ -0,0 +1,59 @@
+from typing import Optional
+from pydantic import BaseModel, Field, StrictFloat, StrictInt, StrictStr
+
+
+class ImageMetadata(BaseModel):
+    """
+    Core generation metadata for an image/tensor generated in InvokeAI.
+
+    Also includes any metadata from the image's PNG tEXt chunks.
+
+    Generated by traversing the execution graph, collecting the parameters of the nearest ancestors of a given node.
+
+    Full metadata may be accessed by querying for the session in the `graph_executions` table.
+    """
+
+    positive_conditioning: Optional[StrictStr] = Field(
+        default=None, description="The positive conditioning."
+    )
+    negative_conditioning: Optional[StrictStr] = Field(
+        default=None, description="The negative conditioning."
+    )
+    width: Optional[StrictInt] = Field(
+        default=None, description="Width of the image/tensor in pixels."
+    )
+    height: Optional[StrictInt] = Field(
+        default=None, description="Height of the image/tensor in pixels."
+    )
+    seed: Optional[StrictInt] = Field(
+        default=None, description="The seed used for noise generation."
+    )
+    cfg_scale: Optional[StrictFloat] = Field(
+        default=None, description="The classifier-free guidance scale."
+    )
+    steps: Optional[StrictInt] = Field(
+        default=None, description="The number of steps used for inference."
+    )
+    scheduler: Optional[StrictStr] = Field(
+        default=None, description="The scheduler used for inference."
+    )
+    model: Optional[StrictStr] = Field(
+        default=None, description="The model used for inference."
+    )
+    strength: Optional[StrictFloat] = Field(
+        default=None,
+        description="The strength used for image-to-image/tensor-to-tensor.",
+    )
+    image: Optional[StrictStr] = Field(
+        default=None, description="The ID of the initial image."
+    )
+    tensor: Optional[StrictStr] = Field(
+        default=None, description="The ID of the initial tensor."
+    )
+    # Pending model refactor:
+    # vae: Optional[str] = Field(default=None,description="The VAE used for decoding.")
+    # unet: Optional[str] = Field(default=None,description="The UNet used dor inference.")
+    # clip: Optional[str] = Field(default=None,description="The CLIP Encoder used for conditioning.")
+    extra: Optional[StrictStr] = Field(
+        default=None, description="Extra metadata, extracted from the PNG tEXt chunk."
+    )

invokeai/app/models/resources.py

@@ -0,0 +1,28 @@
+# TODO: Make a new model for this
+from enum import Enum
+
+from invokeai.app.util.enum import MetaEnum
+
+
+class ResourceType(str, Enum, metaclass=MetaEnum):
+    """The type of a resource."""
+
+    IMAGES = "images"
+    TENSORS = "tensors"
+
+
+# class ResourceOrigin(str, Enum, metaclass=MetaEnum):
+#     """The origin of a resource (eg image or tensor)."""
+
+#     RESULTS = "results"
+#     UPLOADS = "uploads"
+#     INTERMEDIATES = "intermediates"
+
+
+
+class TensorKind(str, Enum, metaclass=MetaEnum):
+    """The kind of a tensor. Use TensorKind.OTHER for non-default kinds."""
+
+    IMAGE_LATENTS = "image_latents"
+    CONDITIONING = "conditioning"
+    OTHER = "other"

invokeai/app/services/db.ipynb

@@ -0,0 +1,578 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from abc import ABC, abstractmethod\n",
+    "from enum import Enum\n",
+    "import enum\n",
+    "import sqlite3\n",
+    "import threading\n",
+    "from typing import Optional, Type, TypeVar, Union\n",
+    "from PIL.Image import Image as PILImage\n",
+    "from pydantic import BaseModel, Field\n",
+    "from torch import Tensor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "class ResourceOrigin(str, Enum):\n",
+    "    \"\"\"The origin of a resource (eg image or tensor).\"\"\"\n",
+    "\n",
+    "    RESULTS = \"results\"\n",
+    "    UPLOADS = \"uploads\"\n",
+    "    INTERMEDIATES = \"intermediates\"\n",
+    "\n",
+    "\n",
+    "class ImageKind(str, Enum):\n",
+    "    \"\"\"The kind of an image. Use ImageKind.OTHER for non-default kinds.\"\"\"\n",
+    "\n",
+    "    IMAGE = \"image\"\n",
+    "    CONTROL_IMAGE = \"control_image\"\n",
+    "    OTHER = \"other\"\n",
+    "\n",
+    "\n",
+    "class TensorKind(str, Enum):\n",
+    "    \"\"\"The kind of a tensor. Use TensorKind.OTHER for non-default kinds.\"\"\"\n",
+    "\n",
+    "    IMAGE_LATENTS = \"image_latents\"\n",
+    "    CONDITIONING = \"conditioning\"\n",
+    "    OTHER = \"other\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def create_sql_values_string_from_string_enum(enum: Type[Enum]):\n",
+    "    \"\"\"\n",
+    "    Creates a string of the form \"('value1'), ('value2'), ..., ('valueN')\" from a StrEnum.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    delimiter = \", \"\n",
+    "    values = [f\"('{e.value}')\" for e in enum]\n",
+    "    return delimiter.join(values)\n",
+    "\n",
+    "\n",
+    "def create_sql_table_from_enum(\n",
+    "    enum: Type[Enum],\n",
+    "    table_name: str,\n",
+    "    primary_key_name: str,\n",
+    "    conn: sqlite3.Connection,\n",
+    "    cursor: sqlite3.Cursor,\n",
+    "    lock: threading.Lock,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Creates and populates a table to be used as a functional enum.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    try:\n",
+    "        lock.acquire()\n",
+    "\n",
+    "        values_string = create_sql_values_string_from_string_enum(enum)\n",
+    "\n",
+    "        cursor.execute(\n",
+    "            f\"\"\"--sql\n",
+    "            CREATE TABLE IF NOT EXISTS {table_name} (\n",
+    "                {primary_key_name} TEXT PRIMARY KEY\n",
+    "            );\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            f\"\"\"--sql\n",
+    "            INSERT OR IGNORE INTO {table_name} ({primary_key_name}) VALUES {values_string};\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        conn.commit()\n",
+    "    finally:\n",
+    "        lock.release()\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "`resource_origins` functions as an enum for the ResourceOrigin model.\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "# def create_resource_origins_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "#     create_sql_table_from_enum(\n",
+    "#         enum=ResourceOrigin,\n",
+    "#         table_name=\"resource_origins\",\n",
+    "#         primary_key_name=\"origin_name\",\n",
+    "#         conn=conn,\n",
+    "#         cursor=cursor,\n",
+    "#         lock=lock,\n",
+    "#     )\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "`image_kinds` functions as an enum for the ImageType model.\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "# def create_image_kinds_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "    # create_sql_table_from_enum(\n",
+    "    #     enum=ImageKind,\n",
+    "    #     table_name=\"image_kinds\",\n",
+    "    #     primary_key_name=\"kind_name\",\n",
+    "    #     conn=conn,\n",
+    "    #     cursor=cursor,\n",
+    "    #     lock=lock,\n",
+    "    # )\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "`tensor_kinds` functions as an enum for the TensorType model.\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "# def create_tensor_kinds_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "    # create_sql_table_from_enum(\n",
+    "    #     enum=TensorKind,\n",
+    "    #     table_name=\"tensor_kinds\",\n",
+    "    #     primary_key_name=\"kind_name\",\n",
+    "    #     conn=conn,\n",
+    "    #     cursor=cursor,\n",
+    "    #     lock=lock,\n",
+    "    # )\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "`images` stores all images, regardless of type\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "def create_images_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "    try:\n",
+    "        lock.acquire()\n",
+    "\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE TABLE IF NOT EXISTS images (\n",
+    "                id TEXT PRIMARY KEY,\n",
+    "                origin TEXT,\n",
+    "                image_kind TEXT,\n",
+    "                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n",
+    "                FOREIGN KEY(origin) REFERENCES resource_origins(origin_name),\n",
+    "                FOREIGN KEY(image_kind) REFERENCES image_kinds(kind_name)\n",
+    "            );\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE UNIQUE INDEX IF NOT EXISTS idx_images_id ON images(id);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE INDEX IF NOT EXISTS idx_images_origin ON images(origin);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE INDEX IF NOT EXISTS idx_images_image_kind ON images(image_kind);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        conn.commit()\n",
+    "    finally:\n",
+    "        lock.release()\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "`images_results` stores additional data specific to `results` images.\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "def create_images_results_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "    try:\n",
+    "        lock.acquire()\n",
+    "\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE TABLE IF NOT EXISTS images_results (\n",
+    "                images_id TEXT PRIMARY KEY,\n",
+    "                session_id TEXT NOT NULL,\n",
+    "                node_id TEXT NOT NULL,\n",
+    "                FOREIGN KEY(images_id) REFERENCES images(id) ON DELETE CASCADE\n",
+    "            );\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE UNIQUE INDEX IF NOT EXISTS idx_images_results_images_id ON images_results(images_id);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        conn.commit()\n",
+    "    finally:\n",
+    "        lock.release()\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "`images_intermediates` stores additional data specific to `intermediates` images\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "def create_images_intermediates_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "    try:\n",
+    "        lock.acquire()\n",
+    "\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE TABLE IF NOT EXISTS images_intermediates (\n",
+    "                images_id TEXT PRIMARY KEY,\n",
+    "                session_id TEXT NOT NULL,\n",
+    "                node_id TEXT NOT NULL,\n",
+    "                FOREIGN KEY(images_id) REFERENCES images(id) ON DELETE CASCADE\n",
+    "            );\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE UNIQUE INDEX IF NOT EXISTS idx_images_intermediates_images_id ON images_intermediates(images_id);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        conn.commit()\n",
+    "    finally:\n",
+    "        lock.release()\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "`images_metadata` stores basic metadata for any image type\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "def create_images_metadata_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "    try:\n",
+    "        lock.acquire()\n",
+    "\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE TABLE IF NOT EXISTS images_metadata (\n",
+    "                images_id TEXT PRIMARY KEY,\n",
+    "                metadata TEXT,\n",
+    "                FOREIGN KEY(images_id) REFERENCES images(id) ON DELETE CASCADE\n",
+    "            );\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE UNIQUE INDEX IF NOT EXISTS idx_images_metadata_images_id ON images_metadata(images_id);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        conn.commit()\n",
+    "    finally:\n",
+    "        lock.release()\n",
+    "\n",
+    "\n",
+    "# `tensors` table: stores references to tensor\n",
+    "\n",
+    "\n",
+    "def create_tensors_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "    try:\n",
+    "        lock.acquire()\n",
+    "\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE TABLE IF NOT EXISTS tensors (\n",
+    "                id TEXT PRIMARY KEY,\n",
+    "                origin TEXT,\n",
+    "                tensor_kind TEXT,\n",
+    "                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n",
+    "                FOREIGN KEY(origin) REFERENCES resource_origins(origin_name),\n",
+    "                FOREIGN KEY(tensor_kind) REFERENCES tensor_kinds(kind_name)\n",
+    "            );\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE UNIQUE INDEX IF NOT EXISTS idx_tensors_id ON tensors(id);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE INDEX IF NOT EXISTS idx_tensors_origin ON tensors(origin);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE INDEX IF NOT EXISTS idx_tensors_tensor_kind ON tensors(tensor_kind);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        conn.commit()\n",
+    "    finally:\n",
+    "        lock.release()\n",
+    "\n",
+    "\n",
+    "# `tensors_results` stores additional data specific to `result` tensor\n",
+    "\n",
+    "\n",
+    "def create_tensors_results_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "    try:\n",
+    "        lock.acquire()\n",
+    "\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE TABLE IF NOT EXISTS tensors_results (\n",
+    "                tensors_id TEXT PRIMARY KEY,\n",
+    "                session_id TEXT NOT NULL,\n",
+    "                node_id TEXT NOT NULL,\n",
+    "                FOREIGN KEY(tensors_id) REFERENCES tensors(id) ON DELETE CASCADE\n",
+    "            );\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE UNIQUE INDEX IF NOT EXISTS idx_tensors_results_tensors_id ON tensors_results(tensors_id);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        conn.commit()\n",
+    "    finally:\n",
+    "        lock.release()\n",
+    "\n",
+    "\n",
+    "# `tensors_intermediates` stores additional data specific to `intermediate` tensor\n",
+    "\n",
+    "\n",
+    "def create_tensors_intermediates_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "    try:\n",
+    "        lock.acquire()\n",
+    "\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE TABLE IF NOT EXISTS tensors_intermediates (\n",
+    "                tensors_id TEXT PRIMARY KEY,\n",
+    "                session_id TEXT NOT NULL,\n",
+    "                node_id TEXT NOT NULL,\n",
+    "                FOREIGN KEY(tensors_id) REFERENCES tensors(id) ON DELETE CASCADE\n",
+    "            );\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE UNIQUE INDEX IF NOT EXISTS idx_tensors_intermediates_tensors_id ON tensors_intermediates(tensors_id);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        conn.commit()\n",
+    "    finally:\n",
+    "        lock.release()\n",
+    "\n",
+    "\n",
+    "# `tensors_metadata` table: stores generated/transformed metadata for tensor\n",
+    "\n",
+    "\n",
+    "def create_tensors_metadata_table(conn: sqlite3.Connection, cursor: sqlite3.Cursor, lock: threading.Lock):\n",
+    "    try:\n",
+    "        lock.acquire()\n",
+    "\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE TABLE IF NOT EXISTS tensors_metadata (\n",
+    "                tensors_id TEXT PRIMARY KEY,\n",
+    "                metadata TEXT,\n",
+    "                FOREIGN KEY(tensors_id) REFERENCES tensors(id) ON DELETE CASCADE\n",
+    "            );\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        cursor.execute(\n",
+    "            \"\"\"--sql\n",
+    "            CREATE UNIQUE INDEX IF NOT EXISTS idx_tensors_metadata_tensors_id ON tensors_metadata(tensors_id);\n",
+    "            \"\"\"\n",
+    "        )\n",
+    "        conn.commit()\n",
+    "    finally:\n",
+    "        lock.release()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "db_path = '/home/bat/Documents/Code/outputs/test.db'\n",
+    "if (os.path.exists(db_path)):\n",
+    "    os.remove(db_path)\n",
+    "\n",
+    "conn = sqlite3.connect(\n",
+    "    db_path, check_same_thread=False\n",
+    ")\n",
+    "cursor = conn.cursor()\n",
+    "lock = threading.Lock()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "create_sql_table_from_enum(\n",
+    "    enum=ResourceOrigin,\n",
+    "    table_name=\"resource_origins\",\n",
+    "    primary_key_name=\"origin_name\",\n",
+    "    conn=conn,\n",
+    "    cursor=cursor,\n",
+    "    lock=lock,\n",
+    ")\n",
+    "\n",
+    "create_sql_table_from_enum(\n",
+    "    enum=ImageKind,\n",
+    "    table_name=\"image_kinds\",\n",
+    "    primary_key_name=\"kind_name\",\n",
+    "    conn=conn,\n",
+    "    cursor=cursor,\n",
+    "    lock=lock,\n",
+    ")\n",
+    "\n",
+    "create_sql_table_from_enum(\n",
+    "    enum=TensorKind,\n",
+    "    table_name=\"tensor_kinds\",\n",
+    "    primary_key_name=\"kind_name\",\n",
+    "    conn=conn,\n",
+    "    cursor=cursor,\n",
+    "    lock=lock,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "create_images_table(conn, cursor, lock)\n",
+    "create_images_results_table(conn, cursor, lock)\n",
+    "create_images_intermediates_table(conn, cursor, lock)\n",
+    "create_images_metadata_table(conn, cursor, lock)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "create_tensors_table(conn, cursor, lock)\n",
+    "create_tensors_results_table(conn, cursor, lock)\n",
+    "create_tensors_intermediates_table(conn, cursor, lock)\n",
+    "create_tensors_metadata_table(conn, cursor, lock)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from pydantic import StrictStr\n",
+    "\n",
+    "\n",
+    "class GeneratedImageOrLatentsMetadata(BaseModel):\n",
+    "    \"\"\"Core generation metadata for an image/tensor generated in InvokeAI.\n",
+    "\n",
+    "    Generated by traversing the execution graph, collecting the parameters of the nearest ancestors of a given node.\n",
+    "\n",
+    "    Full metadata may be accessed by querying for the session in the `graph_executions` table.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    positive_conditioning: Optional[StrictStr] = Field(\n",
+    "        default=None, description=\"The positive conditioning.\"\n",
+    "    )\n",
+    "    negative_conditioning: Optional[str] = Field(\n",
+    "        default=None, description=\"The negative conditioning.\"\n",
+    "    )\n",
+    "    width: Optional[int] = Field(\n",
+    "        default=None, description=\"Width of the image/tensor in pixels.\"\n",
+    "    )\n",
+    "    height: Optional[int] = Field(\n",
+    "        default=None, description=\"Height of the image/tensor in pixels.\"\n",
+    "    )\n",
+    "    seed: Optional[int] = Field(\n",
+    "        default=None, description=\"The seed used for noise generation.\"\n",
+    "    )\n",
+    "    cfg_scale: Optional[float] = Field(\n",
+    "        default=None, description=\"The classifier-free guidance scale.\"\n",
+    "    )\n",
+    "    steps: Optional[int] = Field(\n",
+    "        default=None, description=\"The number of steps used for inference.\"\n",
+    "    )\n",
+    "    scheduler: Optional[str] = Field(\n",
+    "        default=None, description=\"The scheduler used for inference.\"\n",
+    "    )\n",
+    "    model: Optional[str] = Field(\n",
+    "        default=None, description=\"The model used for inference.\"\n",
+    "    )\n",
+    "    strength: Optional[float] = Field(\n",
+    "        default=None,\n",
+    "        description=\"The strength used for image-to-image/tensor-to-tensor.\",\n",
+    "    )\n",
+    "    image: Optional[str] = Field(\n",
+    "        default=None, description=\"The ID of the initial image.\"\n",
+    "    )\n",
+    "    tensor: Optional[str] = Field(\n",
+    "        default=None, description=\"The ID of the initial tensor.\"\n",
+    "    )\n",
+    "    # Pending model refactor:\n",
+    "    # vae: Optional[str] = Field(default=None,description=\"The VAE used for decoding.\")\n",
+    "    # unet: Optional[str] = Field(default=None,description=\"The UNet used dor inference.\")\n",
+    "    # clip: Optional[str] = Field(default=None,description=\"The CLIP Encoder used for conditioning.\")\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GeneratedImageOrLatentsMetadata(positive_conditioning='123', negative_conditioning=None, width=None, height=None, seed=None, cfg_scale=None, steps=None, scheduler=None, model=None, strength=None, image=None, tensor=None)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "GeneratedImageOrLatentsMetadata(positive_conditioning='123')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

invokeai/app/services/events.py

@@ -1,6 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Any
+from typing import Any, Optional
 from invokeai.app.api.models.images import ProgressImage
 from invokeai.app.util.misc import get_timestamp
 
@@ -50,6 +50,8 @@ class EventServiceBase:
         result: dict,
         node: dict,
         source_node_id: str,
+        image_url: Optional[str] = None,
+        thumbnail_url: Optional[str] = None,
     ) -> None:
         """Emitted when an invocation has completed"""
         self.__emit_session_event(
@@ -59,6 +61,8 @@ class EventServiceBase:
                 node=node,
                 source_node_id=source_node_id,
                 result=result,
+                image_url=image_url,
+                thumbnail_url=thumbnail_url
             ),
         )
 

invokeai/app/services/image_file_storage.py

@@ -0,0 +1,180 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
+import os
+from abc import ABC, abstractmethod
+from pathlib import Path
+from queue import Queue
+from typing import Dict, Optional
+
+from PIL.Image import Image as PILImageType
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+from send2trash import send2trash
+
+from invokeai.app.models.image import ImageType
+from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
+
+
+class ImageFileStorageBase(ABC):
+    """Low-level service responsible for storing and retrieving image files."""
+
+    class ImageFileNotFoundException(Exception):
+        """Raised when an image file is not found in storage."""
+
+        def __init__(self, message="Image file not found"):
+            super().__init__(message)
+
+    class ImageFileSaveException(Exception):
+        """Raised when an image cannot be saved."""
+
+        def __init__(self, message="Image file not saved"):
+            super().__init__(message)
+
+    class ImageFileDeleteException(Exception):
+        """Raised when an image cannot be deleted."""
+
+        def __init__(self, message="Image file not deleted"):
+            super().__init__(message)
+
+    @abstractmethod
+    def get(self, image_type: ImageType, image_name: str) -> PILImageType:
+        """Retrieves an image as PIL Image."""
+        pass
+
+    # # TODO: make this a bit more flexible for e.g. cloud storage
+    @abstractmethod
+    def get_path(
+        self, image_type: ImageType, image_name: str, thumbnail: bool = False
+    ) -> str:
+        """Gets the internal path to an image or thumbnail."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+        image: PILImageType,
+        image_type: ImageType,
+        image_name: str,
+        pnginfo: Optional[PngInfo] = None,
+        thumbnail_size: int = 256,
+    ) -> None:
+        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
+        pass
+
+    @abstractmethod
+    def delete(self, image_type: ImageType, image_name: str) -> None:
+        """Deletes an image and its thumbnail (if one exists)."""
+        pass
+
+
+class DiskImageFileStorage(ImageFileStorageBase):
+    """Stores images on disk"""
+
+    __output_folder: str
+    __cache_ids: Queue  # TODO: this is an incredibly naive cache
+    __cache: Dict[str, PILImageType]
+    __max_cache_size: int
+
+    def __init__(self, output_folder: str):
+        self.__output_folder = output_folder
+        self.__cache = dict()
+        self.__cache_ids = Queue()
+        self.__max_cache_size = 10  # TODO: get this from config
+
+        Path(output_folder).mkdir(parents=True, exist_ok=True)
+
+        # TODO: don't hard-code. get/save/delete should maybe take subpath?
+        for image_type in ImageType:
+            Path(os.path.join(output_folder, image_type)).mkdir(
+                parents=True, exist_ok=True
+            )
+            Path(os.path.join(output_folder, image_type, "thumbnails")).mkdir(
+                parents=True, exist_ok=True
+            )
+
+    def get(self, image_type: ImageType, image_name: str) -> PILImageType:
+        try:
+            image_path = self.get_path(image_type, image_name)
+            cache_item = self.__get_cache(image_path)
+            if cache_item:
+                return cache_item
+
+            image = Image.open(image_path)
+            self.__set_cache(image_path, image)
+            return image
+        except FileNotFoundError as e:
+            raise ImageFileStorageBase.ImageFileNotFoundException from e
+
+    def save(
+        self,
+        image: PILImageType,
+        image_type: ImageType,
+        image_name: str,
+        pnginfo: Optional[PngInfo] = None,
+        thumbnail_size: int = 256,
+    ) -> None:
+        try:
+            image_path = self.get_path(image_type, image_name)
+            image.save(image_path, "PNG", pnginfo=pnginfo)
+
+            thumbnail_name = get_thumbnail_name(image_name)
+            thumbnail_path = self.get_path(image_type, thumbnail_name, thumbnail=True)
+            thumbnail_image = make_thumbnail(image, thumbnail_size)
+            thumbnail_image.save(thumbnail_path)
+
+            self.__set_cache(image_path, image)
+            self.__set_cache(thumbnail_path, thumbnail_image)
+        except Exception as e:
+            raise ImageFileStorageBase.ImageFileSaveException from e
+
+    def delete(self, image_type: ImageType, image_name: str) -> None:
+        try:
+            basename = os.path.basename(image_name)
+            image_path = self.get_path(image_type, basename)
+
+            if os.path.exists(image_path):
+                send2trash(image_path)
+            if image_path in self.__cache:
+                del self.__cache[image_path]
+
+            thumbnail_name = get_thumbnail_name(image_name)
+            thumbnail_path = self.get_path(image_type, thumbnail_name, True)
+
+            if os.path.exists(thumbnail_path):
+                send2trash(thumbnail_path)
+            if thumbnail_path in self.__cache:
+                del self.__cache[thumbnail_path]
+        except Exception as e:
+            raise ImageFileStorageBase.ImageFileDeleteException from e
+
+    # TODO: make this a bit more flexible for e.g. cloud storage
+    def get_path(
+        self, image_type: ImageType, image_name: str, thumbnail: bool = False
+    ) -> str:
+        # strip out any relative path shenanigans
+        basename = os.path.basename(image_name)
+
+        if thumbnail:
+            thumbnail_name = get_thumbnail_name(basename)
+            path = os.path.join(
+                self.__output_folder, image_type, "thumbnails", thumbnail_name
+            )
+        else:
+            path = os.path.join(self.__output_folder, image_type, basename)
+
+        abspath = os.path.abspath(path)
+
+        return abspath
+
+    def __get_cache(self, image_name: str) -> PILImageType | None:
+        return None if image_name not in self.__cache else self.__cache[image_name]
+
+    def __set_cache(self, image_name: str, image: PILImageType):
+        if not image_name in self.__cache:
+            self.__cache[image_name] = image
+            self.__cache_ids.put(
+                image_name
+            )  # TODO: this should refresh position for LRU cache
+            if len(self.__cache) > self.__max_cache_size:
+                cache_id = self.__cache_ids.get()
+                if cache_id in self.__cache:
+                    del self.__cache[cache_id]

invokeai/app/services/image_record_storage.py

@@ -0,0 +1,318 @@
+from abc import ABC, abstractmethod
+import datetime
+from typing import Optional
+import sqlite3
+import threading
+from typing import Optional, Union
+
+from invokeai.app.models.metadata import ImageMetadata
+from invokeai.app.models.image import (
+    ImageCategory,
+    ImageType,
+)
+from invokeai.app.services.util.create_enum_table import create_enum_table
+from invokeai.app.services.models.image_record import (
+    ImageRecord,
+    deserialize_image_record,
+)
+
+from invokeai.app.services.item_storage import PaginatedResults
+
+
+class ImageRecordStorageBase(ABC):
+    """Low-level service responsible for interfacing with the image record store."""
+
+    class ImageRecordNotFoundException(Exception):
+        """Raised when an image record is not found."""
+
+        def __init__(self, message="Image record not found"):
+            super().__init__(message)
+
+    class ImageRecordSaveException(Exception):
+        """Raised when an image record cannot be saved."""
+
+        def __init__(self, message="Image record not saved"):
+            super().__init__(message)
+
+    class ImageRecordDeleteException(Exception):
+        """Raised when an image record cannot be deleted."""
+
+        def __init__(self, message="Image record not deleted"):
+            super().__init__(message)
+
+    @abstractmethod
+    def get(self, image_type: ImageType, image_name: str) -> ImageRecord:
+        """Gets an image record."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        image_type: ImageType,
+        image_category: ImageCategory,
+        page: int = 0,
+        per_page: int = 10,
+    ) -> PaginatedResults[ImageRecord]:
+        """Gets a page of image records."""
+        pass
+
+    @abstractmethod
+    def delete(self, image_type: ImageType, image_name: str) -> None:
+        """Deletes an image record."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+        image_name: str,
+        image_type: ImageType,
+        image_category: ImageCategory,
+        session_id: Optional[str],
+        node_id: Optional[str],
+        metadata: Optional[ImageMetadata],
+        created_at: str = datetime.datetime.utcnow().isoformat(),
+    ) -> None:
+        """Saves an image record."""
+        pass
+
+
+class SqliteImageRecordStorage(ImageRecordStorageBase):
+    _filename: str
+    _conn: sqlite3.Connection
+    _cursor: sqlite3.Cursor
+    _lock: threading.Lock
+
+    def __init__(self, filename: str) -> None:
+        super().__init__()
+
+        self._filename = filename
+        self._conn = sqlite3.connect(filename, check_same_thread=False)
+        # Enable row factory to get rows as dictionaries (must be done before making the cursor!)
+        self._conn.row_factory = sqlite3.Row
+        self._cursor = self._conn.cursor()
+        self._lock = threading.Lock()
+
+        try:
+            self._lock.acquire()
+            # Enable foreign keys
+            self._conn.execute("PRAGMA foreign_keys = ON;")
+            self._create_tables()
+            self._conn.commit()
+        finally:
+            self._lock.release()
+
+    def _create_tables(self) -> None:
+        """Creates the tables for the `images` database."""
+
+        # Create the `images` table.
+        self._cursor.execute(
+            f"""--sql
+            CREATE TABLE IF NOT EXISTS images (
+                id TEXT PRIMARY KEY,
+                image_type TEXT, -- non-nullable via foreign key constraint
+                image_category TEXT, -- non-nullable via foreign key constraint
+                session_id TEXT, -- nullable
+                node_id TEXT, -- nullable
+                metadata TEXT, -- nullable
+                created_at TEXT NOT NULL,
+                FOREIGN KEY(image_type) REFERENCES image_types(type_name),
+                FOREIGN KEY(image_category) REFERENCES image_categories(category_name)
+            );
+            """
+        )
+
+        # Create the `images` table indices.
+        self._cursor.execute(
+            """--sql
+            CREATE UNIQUE INDEX IF NOT EXISTS idx_images_id ON images(id);
+            """
+        )
+        self._cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_images_image_type ON images(image_type);
+            """
+        )
+        self._cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_images_image_category ON images(image_category);
+            """
+        )
+
+        # Create the tables for image-related enums
+        create_enum_table(
+            enum=ImageType,
+            table_name="image_types",
+            primary_key_name="type_name",
+            cursor=self._cursor,
+        )
+
+        create_enum_table(
+            enum=ImageCategory,
+            table_name="image_categories",
+            primary_key_name="category_name",
+            cursor=self._cursor,
+        )
+
+        # Create the `tags` table. TODO: do this elsewhere, shouldn't be in images db service
+        self._cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS tags (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                tag_name TEXT UNIQUE NOT NULL
+            );
+            """
+        )
+
+        # Create the `images_tags` junction table.
+        self._cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS images_tags (
+                image_id TEXT,
+                tag_id INTEGER,
+                PRIMARY KEY (image_id, tag_id),
+                FOREIGN KEY(image_id) REFERENCES images(id) ON DELETE CASCADE,
+                FOREIGN KEY(tag_id) REFERENCES tags(id) ON DELETE CASCADE
+            );
+            """
+        )
+
+        # Create the `images_favorites` table.
+        self._cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS images_favorites (
+                image_id TEXT PRIMARY KEY,
+                favorited_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                FOREIGN KEY(image_id) REFERENCES images(id) ON DELETE CASCADE
+            );
+            """
+        )
+
+    def get(self, image_type: ImageType, image_name: str) -> Union[ImageRecord, None]:
+        try:
+            self._lock.acquire()
+
+            self._cursor.execute(
+                f"""--sql
+                SELECT * FROM images
+                WHERE id = ?;
+                """,
+                (image_name,),
+            )
+
+            result = self._cursor.fetchone()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise self.ImageRecordNotFoundException from e
+        finally:
+            self._lock.release()
+
+        if not result:
+            raise self.ImageRecordNotFoundException
+
+        return deserialize_image_record(result)
+
+    def get_many(
+        self,
+        image_type: ImageType,
+        image_category: ImageCategory,
+        page: int = 0,
+        per_page: int = 10,
+    ) -> PaginatedResults[ImageRecord]:
+        try:
+            self._lock.acquire()
+
+            self._cursor.execute(
+                f"""--sql
+                SELECT * FROM images
+                WHERE image_type = ? AND image_category = ?
+                LIMIT ? OFFSET ?;
+                """,
+                (image_type.value, image_category.value, per_page, page * per_page),
+            )
+
+            result = self._cursor.fetchall()
+
+            images = list(map(lambda r: deserialize_image_record(r), result))
+
+            self._cursor.execute(
+                """--sql
+                SELECT count(*) FROM images
+                WHERE image_type = ? AND image_category = ?
+                """,
+                (image_type.value, image_category.value),
+            )
+
+            count = self._cursor.fetchone()[0]
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise e
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedResults(
+            items=images, page=page, pages=pageCount, per_page=per_page, total=count
+        )
+
+    def delete(self, image_type: ImageType, image_name: str) -> None:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """--sql
+                DELETE FROM images
+                WHERE id = ?;
+                """,
+                (image_name,),
+            )
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise ImageRecordStorageBase.ImageRecordDeleteException from e
+        finally:
+            self._lock.release()
+
+    def save(
+        self,
+        image_name: str,
+        image_type: ImageType,
+        image_category: ImageCategory,
+        session_id: Optional[str],
+        node_id: Optional[str],
+        metadata: Optional[ImageMetadata],
+        created_at: str,
+    ) -> None:
+        try:
+            metadata_json = (
+                None if metadata is None else metadata.json(exclude_none=True)
+            )
+            self._lock.acquire()
+            self._cursor.execute(
+                """--sql
+                INSERT OR IGNORE INTO images (
+                    id,
+                    image_type,
+                    image_category,
+                    node_id,
+                    session_id,
+                    metadata,
+                    created_at
+                    )
+                VALUES (?, ?, ?, ?, ?, ?, ?);
+                """,
+                (
+                    image_name,
+                    image_type.value,
+                    image_category.value,
+                    node_id,
+                    session_id,
+                    metadata_json,
+                    created_at,
+                ),
+            )
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise ImageRecordStorageBase.ImageRecordNotFoundException from e
+        finally:
+            self._lock.release()

invokeai/app/services/image_storage.py

@@ -1,274 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-import os
-from glob import glob
-from abc import ABC, abstractmethod
-from pathlib import Path
-from queue import Queue
-from typing import Dict, List
-
-from PIL.Image import Image
-import PIL.Image as PILImage
-from send2trash import send2trash
-from invokeai.app.api.models.images import (
-    ImageResponse,
-    ImageResponseMetadata,
-    SavedImage,
-)
-from invokeai.app.models.image import ImageType
-from invokeai.app.services.metadata import (
-    InvokeAIMetadata,
-    MetadataServiceBase,
-    build_invokeai_metadata_pnginfo,
-)
-from invokeai.app.services.item_storage import PaginatedResults
-from invokeai.app.util.misc import get_timestamp
-from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
-
-
-class ImageStorageBase(ABC):
-    """Responsible for storing and retrieving images."""
-
-    @abstractmethod
-    def get(self, image_type: ImageType, image_name: str) -> Image:
-        """Retrieves an image as PIL Image."""
-        pass
-
-    @abstractmethod
-    def list(
-        self, image_type: ImageType, page: int = 0, per_page: int = 10
-    ) -> PaginatedResults[ImageResponse]:
-        """Gets a paginated list of images."""
-        pass
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    @abstractmethod
-    def get_path(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        """Gets the internal path to an image or its thumbnail."""
-        pass
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    @abstractmethod
-    def get_uri(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        """Gets the external URI to an image or its thumbnail."""
-        pass
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates an image path."""
-        pass
-
-    @abstractmethod
-    def save(
-        self,
-        image_type: ImageType,
-        image_name: str,
-        image: Image,
-        metadata: InvokeAIMetadata | None = None,
-    ) -> SavedImage:
-        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
-        pass
-
-    @abstractmethod
-    def delete(self, image_type: ImageType, image_name: str) -> None:
-        """Deletes an image and its thumbnail (if one exists)."""
-        pass
-
-    def create_name(self, context_id: str, node_id: str) -> str:
-        """Creates a unique contextual image filename."""
-        return f"{context_id}_{node_id}_{str(get_timestamp())}.png"
-
-
-class DiskImageStorage(ImageStorageBase):
-    """Stores images on disk"""
-
-    __output_folder: str
-    __cache_ids: Queue  # TODO: this is an incredibly naive cache
-    __cache: Dict[str, Image]
-    __max_cache_size: int
-    __metadata_service: MetadataServiceBase
-
-    def __init__(self, output_folder: str, metadata_service: MetadataServiceBase):
-        self.__output_folder = output_folder
-        self.__cache = dict()
-        self.__cache_ids = Queue()
-        self.__max_cache_size = 10  # TODO: get this from config
-        self.__metadata_service = metadata_service
-
-        Path(output_folder).mkdir(parents=True, exist_ok=True)
-
-        # TODO: don't hard-code. get/save/delete should maybe take subpath?
-        for image_type in ImageType:
-            Path(os.path.join(output_folder, image_type)).mkdir(
-                parents=True, exist_ok=True
-            )
-            Path(os.path.join(output_folder, image_type, "thumbnails")).mkdir(
-                parents=True, exist_ok=True
-            )
-
-    def list(
-        self, image_type: ImageType, page: int = 0, per_page: int = 10
-    ) -> PaginatedResults[ImageResponse]:
-        dir_path = os.path.join(self.__output_folder, image_type)
-        image_paths = glob(f"{dir_path}/*.png")
-        count = len(image_paths)
-
-        sorted_image_paths = sorted(
-            glob(f"{dir_path}/*.png"), key=os.path.getctime, reverse=True
-        )
-
-        page_of_image_paths = sorted_image_paths[
-            page * per_page : (page + 1) * per_page
-        ]
-
-        page_of_images: List[ImageResponse] = []
-
-        for path in page_of_image_paths:
-            filename = os.path.basename(path)
-            img = PILImage.open(path)
-
-            invokeai_metadata = self.__metadata_service.get_metadata(img)
-
-            page_of_images.append(
-                ImageResponse(
-                    image_type=image_type.value,
-                    image_name=filename,
-                    # TODO: DiskImageStorage should not be building URLs...?
-                    image_url=self.get_uri(image_type, filename),
-                    thumbnail_url=self.get_uri(image_type, filename, True),
-                    # TODO: Creation of this object should happen elsewhere (?), just making it fit here so it works
-                    metadata=ImageResponseMetadata(
-                        created=int(os.path.getctime(path)),
-                        width=img.width,
-                        height=img.height,
-                        invokeai=invokeai_metadata,
-                    ),
-                )
-            )
-
-        page_count_trunc = int(count / per_page)
-        page_count_mod = count % per_page
-        page_count = page_count_trunc if page_count_mod == 0 else page_count_trunc + 1
-
-        return PaginatedResults[ImageResponse](
-            items=page_of_images,
-            page=page,
-            pages=page_count,
-            per_page=per_page,
-            total=count,
-        )
-
-    def get(self, image_type: ImageType, image_name: str) -> Image:
-        image_path = self.get_path(image_type, image_name)
-        cache_item = self.__get_cache(image_path)
-        if cache_item:
-            return cache_item
-
-        image = PILImage.open(image_path)
-        self.__set_cache(image_path, image)
-        return image
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    def get_path(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        # strip out any relative path shenanigans
-        basename = os.path.basename(image_name)
-
-        if is_thumbnail:
-            path = os.path.join(
-                self.__output_folder, image_type, "thumbnails", basename
-            )
-        else:
-            path = os.path.join(self.__output_folder, image_type, basename)
-
-        abspath = os.path.abspath(path)
-
-        return abspath
-
-    def get_uri(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        # strip out any relative path shenanigans
-        basename = os.path.basename(image_name)
-
-        if is_thumbnail:
-            thumbnail_basename = get_thumbnail_name(basename)
-            uri = f"api/v1/images/{image_type.value}/thumbnails/{thumbnail_basename}"
-        else:
-            uri = f"api/v1/images/{image_type.value}/{basename}"
-
-        return uri
-
-    def validate_path(self, path: str) -> bool:
-        try:
-            os.stat(path)
-            return True
-        except Exception:
-            return False
-
-    def save(
-        self,
-        image_type: ImageType,
-        image_name: str,
-        image: Image,
-        metadata: InvokeAIMetadata | None = None,
-    ) -> SavedImage:
-        image_path = self.get_path(image_type, image_name)
-
-        # TODO: Reading the image and then saving it strips the metadata...
-        if metadata:
-            pnginfo = build_invokeai_metadata_pnginfo(metadata=metadata)
-            image.save(image_path, "PNG", pnginfo=pnginfo)
-        else:
-            image.save(image_path)  # this saved image has an empty info
-
-        thumbnail_name = get_thumbnail_name(image_name)
-        thumbnail_path = self.get_path(image_type, thumbnail_name, is_thumbnail=True)
-        thumbnail_image = make_thumbnail(image)
-        thumbnail_image.save(thumbnail_path)
-
-        self.__set_cache(image_path, image)
-        self.__set_cache(thumbnail_path, thumbnail_image)
-
-        return SavedImage(
-            image_name=image_name,
-            thumbnail_name=thumbnail_name,
-            created=int(os.path.getctime(image_path)),
-        )
-
-    def delete(self, image_type: ImageType, image_name: str) -> None:
-        basename = os.path.basename(image_name)
-        image_path = self.get_path(image_type, basename)
-
-        if os.path.exists(image_path):
-            send2trash(image_path)
-        if image_path in self.__cache:
-            del self.__cache[image_path]
-
-        thumbnail_name = get_thumbnail_name(image_name)
-        thumbnail_path = self.get_path(image_type, thumbnail_name, True)
-
-        if os.path.exists(thumbnail_path):
-            send2trash(thumbnail_path)
-        if thumbnail_path in self.__cache:
-            del self.__cache[thumbnail_path]
-
-    def __get_cache(self, image_name: str) -> Image | None:
-        return None if image_name not in self.__cache else self.__cache[image_name]
-
-    def __set_cache(self, image_name: str, image: Image):
-        if not image_name in self.__cache:
-            self.__cache[image_name] = image
-            self.__cache_ids.put(
-                image_name
-            )  # TODO: this should refresh position for LRU cache
-            if len(self.__cache) > self.__max_cache_size:
-                cache_id = self.__cache_ids.get()
-                if cache_id in self.__cache:
-                    del self.__cache[cache_id]

invokeai/app/services/images.py

@@ -0,0 +1,355 @@
+from abc import ABC, abstractmethod
+import json
+from logging import Logger
+from typing import Optional, Union
+import uuid
+from PIL.Image import Image as PILImageType
+from PIL import PngImagePlugin
+
+from invokeai.app.models.image import ImageCategory, ImageType
+from invokeai.app.models.metadata import ImageMetadata
+from invokeai.app.services.image_record_storage import (
+    ImageRecordStorageBase,
+)
+from invokeai.app.services.models.image_record import (
+    ImageRecord,
+    ImageDTO,
+    image_record_to_dto,
+)
+from invokeai.app.services.image_file_storage import ImageFileStorageBase
+from invokeai.app.services.item_storage import PaginatedResults
+from invokeai.app.services.metadata import MetadataServiceBase
+from invokeai.app.services.urls import UrlServiceBase
+from invokeai.app.util.misc import get_iso_timestamp
+
+
+class ImageServiceABC(ABC):
+    """
+    High-level service for image management.
+
+    Provides methods for creating, retrieving, and deleting images.
+    """
+
+    @abstractmethod
+    def create(
+        self,
+        image: PILImageType,
+        image_type: ImageType,
+        image_category: ImageCategory,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+        metadata: Optional[ImageMetadata] = None,
+    ) -> ImageDTO:
+        """Creates an image, storing the file and its metadata."""
+        pass
+
+    @abstractmethod
+    def get_pil_image(self, image_type: ImageType, image_name: str) -> PILImageType:
+        """Gets an image as a PIL image."""
+        pass
+
+    @abstractmethod
+    def get_record(self, image_type: ImageType, image_name: str) -> ImageRecord:
+        """Gets an image record."""
+        pass
+
+    @abstractmethod
+    def get_path(self, image_type: ImageType, image_name: str) -> str:
+        """Gets an image's path"""
+        pass
+
+    @abstractmethod
+    def get_url(self, image_type: ImageType, image_name: str, thumbnail: bool = False) -> str:
+        """Gets an image's or thumbnail's URL"""
+        pass
+
+    @abstractmethod
+    def get_dto(self, image_type: ImageType, image_name: str) -> ImageDTO:
+        """Gets an image DTO."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        image_type: ImageType,
+        image_category: ImageCategory,
+        page: int = 0,
+        per_page: int = 10,
+    ) -> PaginatedResults[ImageDTO]:
+        """Gets a paginated list of image DTOs."""
+        pass
+
+    @abstractmethod
+    def delete(self, image_type: ImageType, image_name: str):
+        """Deletes an image."""
+        pass
+
+    @abstractmethod
+    def add_tag(self, image_type: ImageType, image_id: str, tag: str) -> None:
+        """Adds a tag to an image."""
+        pass
+
+    @abstractmethod
+    def remove_tag(self, image_type: ImageType, image_id: str, tag: str) -> None:
+        """Removes a tag from an image."""
+        pass
+
+    @abstractmethod
+    def favorite(self, image_type: ImageType, image_id: str) -> None:
+        """Favorites an image."""
+        pass
+
+    @abstractmethod
+    def unfavorite(self, image_type: ImageType, image_id: str) -> None:
+        """Unfavorites an image."""
+        pass
+
+
+class ImageServiceDependencies:
+    """Service dependencies for the ImageService."""
+
+    records: ImageRecordStorageBase
+    files: ImageFileStorageBase
+    metadata: MetadataServiceBase
+    urls: UrlServiceBase
+    logger: Logger
+
+    def __init__(
+        self,
+        image_record_storage: ImageRecordStorageBase,
+        image_file_storage: ImageFileStorageBase,
+        metadata: MetadataServiceBase,
+        url: UrlServiceBase,
+        logger: Logger,
+    ):
+        self.records = image_record_storage
+        self.files = image_file_storage
+        self.metadata = metadata
+        self.urls = url
+        self.logger = logger
+
+
+class ImageService(ImageServiceABC):
+    _services: ImageServiceDependencies
+
+    def __init__(
+        self,
+        image_record_storage: ImageRecordStorageBase,
+        image_file_storage: ImageFileStorageBase,
+        metadata: MetadataServiceBase,
+        url: UrlServiceBase,
+        logger: Logger,
+    ):
+        self._services = ImageServiceDependencies(
+            image_record_storage=image_record_storage,
+            image_file_storage=image_file_storage,
+            metadata=metadata,
+            url=url,
+            logger=logger,
+        )
+
+    def create(
+        self,
+        image: PILImageType,
+        image_type: ImageType,
+        image_category: ImageCategory,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+        metadata: Optional[ImageMetadata] = None,
+    ) -> ImageDTO:
+        image_name = self._create_image_name(
+            image_type=image_type,
+            image_category=image_category,
+            node_id=node_id,
+            session_id=session_id,
+        )
+
+        timestamp = get_iso_timestamp()
+
+        if metadata is not None:
+            pnginfo = PngImagePlugin.PngInfo()
+            pnginfo.add_text("invokeai", json.dumps(metadata))
+        else:
+            pnginfo = None
+
+        try:
+            # TODO: Consider using a transaction here to ensure consistency between storage and database
+            self._services.files.save(
+                image_type=image_type,
+                image_name=image_name,
+                image=image,
+                pnginfo=pnginfo,
+            )
+
+            self._services.records.save(
+                image_name=image_name,
+                image_type=image_type,
+                image_category=image_category,
+                node_id=node_id,
+                session_id=session_id,
+                metadata=metadata,
+                created_at=timestamp,
+            )
+
+            image_url = self._services.urls.get_image_url(image_type, image_name)
+            thumbnail_url = self._services.urls.get_image_url(
+                image_type, image_name, True
+            )
+
+            return ImageDTO(
+                image_name=image_name,
+                image_type=image_type,
+                image_category=image_category,
+                node_id=node_id,
+                session_id=session_id,
+                metadata=metadata,
+                created_at=timestamp,
+                image_url=image_url,
+                thumbnail_url=thumbnail_url,
+            )
+        except ImageRecordStorageBase.ImageRecordSaveException:
+            self._services.logger.error("Failed to save image record")
+            raise
+        except ImageFileStorageBase.ImageFileSaveException:
+            self._services.logger.error("Failed to save image file")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem saving image record and file")
+            raise e
+
+    def get_pil_image(self, image_type: ImageType, image_name: str) -> PILImageType:
+        try:
+            return self._services.files.get(image_type, image_name)
+        except ImageFileStorageBase.ImageFileNotFoundException:
+            self._services.logger.error("Failed to get image file")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem getting image file")
+            raise e
+
+    def get_record(self, image_type: ImageType, image_name: str) -> ImageRecord:
+        try:
+            return self._services.records.get(image_type, image_name)
+        except ImageRecordStorageBase.ImageRecordNotFoundException:
+            self._services.logger.error("Image record not found")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem getting image record")
+            raise e
+
+    def get_path(
+        self, image_type: ImageType, image_name: str, thumbnail: bool = False
+    ) -> str:
+        try:
+            return self._services.files.get_path(image_type, image_name, thumbnail)
+        except Exception as e:
+            self._services.logger.error("Problem getting image path")
+            raise e
+
+    def get_url(
+        self, image_type: ImageType, image_name: str, thumbnail: bool = False
+    ) -> str:
+        try:
+            return self._services.urls.get_image_url(image_type, image_name, thumbnail)
+        except Exception as e:
+            self._services.logger.error("Problem getting image path")
+            raise e
+
+    def get_dto(self, image_type: ImageType, image_name: str) -> ImageDTO:
+        try:
+            image_record = self._services.records.get(image_type, image_name)
+
+            image_dto = image_record_to_dto(
+                image_record,
+                self._services.urls.get_image_url(image_type, image_name),
+                self._services.urls.get_image_url(image_type, image_name, True),
+            )
+
+            return image_dto
+        except ImageRecordStorageBase.ImageRecordNotFoundException:
+            self._services.logger.error("Image record not found")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem getting image DTO")
+            raise e
+
+    def get_many(
+        self,
+        image_type: ImageType,
+        image_category: ImageCategory,
+        page: int = 0,
+        per_page: int = 10,
+    ) -> PaginatedResults[ImageDTO]:
+        try:
+            results = self._services.records.get_many(
+                image_type,
+                image_category,
+                page,
+                per_page,
+            )
+
+            image_dtos = list(
+                map(
+                    lambda r: image_record_to_dto(
+                        r,
+                        self._services.urls.get_image_url(image_type, r.image_name),
+                        self._services.urls.get_image_url(
+                            image_type, r.image_name, True
+                        ),
+                    ),
+                    results.items,
+                )
+            )
+
+            return PaginatedResults[ImageDTO](
+                items=image_dtos,
+                page=results.page,
+                pages=results.pages,
+                per_page=results.per_page,
+                total=results.total,
+            )
+        except Exception as e:
+            self._services.logger.error("Problem getting paginated image DTOs")
+            raise e
+
+    def delete(self, image_type: ImageType, image_name: str):
+        # TODO: Consider using a transaction here to ensure consistency between storage and database
+        try:
+            self._services.files.delete(image_type, image_name)
+            self._services.records.delete(image_type, image_name)
+        except ImageRecordStorageBase.ImageRecordDeleteException:
+            self._services.logger.error(f"Failed to delete image record")
+            raise
+        except ImageFileStorageBase.ImageFileDeleteException:
+            self._services.logger.error(f"Failed to delete image file")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem deleting image record and file")
+            raise e
+
+    def add_tag(self, image_type: ImageType, image_id: str, tag: str) -> None:
+        raise NotImplementedError("The 'add_tag' method is not implemented yet.")
+
+    def remove_tag(self, image_type: ImageType, image_id: str, tag: str) -> None:
+        raise NotImplementedError("The 'remove_tag' method is not implemented yet.")
+
+    def favorite(self, image_type: ImageType, image_id: str) -> None:
+        raise NotImplementedError("The 'favorite' method is not implemented yet.")
+
+    def unfavorite(self, image_type: ImageType, image_id: str) -> None:
+        raise NotImplementedError("The 'unfavorite' method is not implemented yet.")
+
+    def _create_image_name(
+        self,
+        image_type: ImageType,
+        image_category: ImageCategory,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+    ) -> str:
+        """Create a unique image name."""
+        uuid_str = str(uuid.uuid4())
+
+        if node_id is not None and session_id is not None:
+            return f"{image_type.value}_{image_category.value}_{session_id}_{node_id}_{uuid_str}.png"
+
+        return f"{image_type.value}_{image_category.value}_{uuid_str}.png"

invokeai/app/services/invocation_services.py

@@ -1,26 +1,32 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
+from typing import TYPE_CHECKING
+from logging import Logger
 
-from typing import types
-from invokeai.app.services.metadata import MetadataServiceBase
+from invokeai.app.services.images import ImageService
 from invokeai.backend import ModelManager
-
 from .events import EventServiceBase
 from .latent_storage import LatentsStorageBase
-from .image_storage import ImageStorageBase
+from .image_file_storage import ImageFileStorageBase
 from .restoration_services import RestorationServices
 from .invocation_queue import InvocationQueueABC
 from .item_storage import ItemStorageABC
 
+
+if TYPE_CHECKING:
+    from invokeai.app.services.graph import GraphExecutionState, LibraryGraph
+    from invokeai.app.services.invoker import InvocationProcessorABC
+
+
 class InvocationServices:
     """Services that can be used by invocations"""
 
     events: EventServiceBase
     latents: LatentsStorageBase
-    images: ImageStorageBase
-    metadata: MetadataServiceBase
+    images: ImageFileStorageBase
     queue: InvocationQueueABC
     model_manager: ModelManager
     restoration: RestorationServices
+    images_new: ImageService
 
     # NOTE: we must forward-declare any types that include invocations, since invocations can use services
     graph_library: ItemStorageABC["LibraryGraph"]
@@ -28,26 +34,26 @@ class InvocationServices:
     processor: "InvocationProcessorABC"
 
     def __init__(
-            self,
-            model_manager: ModelManager,
-            events: EventServiceBase,
-            logger: types.ModuleType,
-            latents: LatentsStorageBase,
-            images: ImageStorageBase,
-            metadata: MetadataServiceBase,
-            queue: InvocationQueueABC,
-            graph_library: ItemStorageABC["LibraryGraph"],
-            graph_execution_manager: ItemStorageABC["GraphExecutionState"],
-            processor: "InvocationProcessorABC",
-            restoration: RestorationServices,
+        self,
+        model_manager: ModelManager,
+        events: EventServiceBase,
+        logger: Logger,
+        latents: LatentsStorageBase,
+        images: ImageFileStorageBase,
+        queue: InvocationQueueABC,
+        images_new: ImageService,
+        graph_library: ItemStorageABC["LibraryGraph"],
+        graph_execution_manager: ItemStorageABC["GraphExecutionState"],
+        processor: "InvocationProcessorABC",
+        restoration: RestorationServices,
     ):
         self.model_manager = model_manager
         self.events = events
         self.logger = logger
         self.latents = latents
         self.images = images
-        self.metadata = metadata
         self.queue = queue
+        self.images_new = images_new
         self.graph_library = graph_library
         self.graph_execution_manager = graph_execution_manager
         self.processor = processor

invokeai/app/services/latent_storage.py

@@ -16,7 +16,7 @@ class LatentsStorageBase(ABC):
         pass
 
     @abstractmethod
-    def set(self, name: str, data: torch.Tensor) -> None:
+    def save(self, name: str, data: torch.Tensor) -> None:
         pass
 
     @abstractmethod
@@ -47,8 +47,8 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
         self.__set_cache(name, latent)
         return latent
 
-    def set(self, name: str, data: torch.Tensor) -> None:
-        self.__underlying_storage.set(name, data)
+    def save(self, name: str, data: torch.Tensor) -> None:
+        self.__underlying_storage.save(name, data)
         self.__set_cache(name, data)
 
     def delete(self, name: str) -> None:
@@ -80,7 +80,7 @@ class DiskLatentsStorage(LatentsStorageBase):
         latent_path = self.get_path(name)
         return torch.load(latent_path)
 
-    def set(self, name: str, data: torch.Tensor) -> None:
+    def save(self, name: str, data: torch.Tensor) -> None:
         latent_path = self.get_path(name)
         torch.save(data, latent_path)
 

invokeai/app/services/metadata.py

@@ -22,16 +22,24 @@ class MetadataLatentsField(TypedDict):
 
 class MetadataColorField(TypedDict):
     """Pydantic-less ColorField, used for metadata parsing"""
+
     r: int
     g: int
     b: int
     a: int
 
 
-
 # TODO: This is a placeholder for `InvocationsUnion` pending resolution of circular imports
 NodeMetadata = Dict[
-    str, None | str | int | float | bool | MetadataImageField | MetadataLatentsField | MetadataColorField
+    str,
+    None
+    | str
+    | int
+    | float
+    | bool
+    | MetadataImageField
+    | MetadataLatentsField
+    | MetadataColorField,
 ]
 
 
@@ -67,6 +75,11 @@ class MetadataServiceBase(ABC):
         """Builds an InvokeAIMetadata object"""
         pass
 
+    # @abstractmethod
+    # def create_metadata(self, session_id: str, node_id: str) -> dict:
+    #     """Creates metadata for a result"""
+    #     pass
+
 
 class PngMetadataService(MetadataServiceBase):
     """Handles loading and building metadata for images."""

invokeai/app/services/models/image_record.py

@@ -0,0 +1,71 @@
+import datetime
+import sqlite3
+from typing import Optional, Union
+from pydantic import BaseModel, Field
+from invokeai.app.models.image import ImageCategory, ImageType
+from invokeai.app.models.metadata import ImageMetadata
+from invokeai.app.util.misc import get_iso_timestamp
+
+
+class ImageRecord(BaseModel):
+    """Deserialized image record."""
+
+    image_name: str = Field(description="The name of the image.")
+    image_type: ImageType = Field(description="The type of the image.")
+    image_category: ImageCategory = Field(description="The category of the image.")
+    created_at: Union[datetime.datetime, str] = Field(
+        description="The created timestamp of the image."
+    )
+    session_id: Optional[str] = Field(default=None, description="The session ID.")
+    node_id: Optional[str] = Field(default=None, description="The node ID.")
+    metadata: Optional[ImageMetadata] = Field(
+        default=None, description="The image's metadata."
+    )
+
+
+class ImageDTO(ImageRecord):
+    """Deserialized image record with URLs."""
+
+    image_url: str = Field(description="The URL of the image.")
+    thumbnail_url: str = Field(description="The thumbnail URL of the image.")
+
+
+def image_record_to_dto(
+    image_record: ImageRecord, image_url: str, thumbnail_url: str
+) -> ImageDTO:
+    """Converts an image record to an image DTO."""
+    return ImageDTO(
+        image_name=image_record.image_name,
+        image_type=image_record.image_type,
+        image_category=image_record.image_category,
+        created_at=image_record.created_at,
+        session_id=image_record.session_id,
+        node_id=image_record.node_id,
+        metadata=image_record.metadata,
+        image_url=image_url,
+        thumbnail_url=thumbnail_url,
+    )
+
+
+def deserialize_image_record(image_row: sqlite3.Row) -> ImageRecord:
+    """Deserializes an image record."""
+
+    image_dict = dict(image_row)
+
+    image_type = ImageType(image_dict.get("image_type", ImageType.RESULT.value))
+
+    raw_metadata = image_dict.get("metadata", "{}")
+
+    metadata = ImageMetadata.parse_raw(raw_metadata)
+
+    return ImageRecord(
+        image_name=image_dict.get("id", "unknown"),
+        session_id=image_dict.get("session_id", None),
+        node_id=image_dict.get("node_id", None),
+        metadata=metadata,
+        image_type=image_type,
+        image_category=ImageCategory(
+            image_dict.get("image_category", ImageCategory.IMAGE.value)
+        ),
+        created_at=image_dict.get("created_at", get_iso_timestamp()),
+    )

invokeai/app/services/processor.py

@@ -1,7 +1,10 @@
 import time
 import traceback
 from threading import Event, Thread, BoundedSemaphore
+from typing import Any, TypeGuard
 
+from invokeai.app.invocations.image import ImageOutput
+from invokeai.app.models.image import ImageType
 from ..invocations.baseinvocation import InvocationContext
 from .invocation_queue import InvocationQueueItem
 from .invoker import InvocationProcessorABC, Invoker
@@ -88,12 +91,30 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         graph_execution_state
                     )
 
+                    def is_image_output(obj: Any) -> TypeGuard[ImageOutput]:
+                        return obj.__class__ == ImageOutput
+                    
+                    outputs_dict = outputs.dict()
+
+                    if is_image_output(outputs):
+                        image_url = self.__invoker.services.images_new.get_url(
+                            ImageType.RESULT, outputs.image.image_name
+                        )
+                        thumbnail_url = self.__invoker.services.images_new.get_url(
+                            ImageType.RESULT, outputs.image.image_name, True
+                        )
+                    else:
+                        image_url = None
+                        thumbnail_url = None
+
                     # Send complete event
                     self.__invoker.services.events.emit_invocation_complete(
                         graph_execution_state_id=graph_execution_state.id,
                         node=invocation.dict(),
                         source_node_id=source_node_id,
-                        result=outputs.dict(),
+                        result=outputs_dict,
+                        image_url=image_url,
+                        thumbnail_url=thumbnail_url,
                     )
 
                 except KeyboardInterrupt:

invokeai/app/services/proposeddesign.py

@@ -0,0 +1,657 @@
+from abc import ABC, abstractmethod
+from enum import Enum
+import enum
+import sqlite3
+import threading
+from typing import Optional, Type, TypeVar, Union
+from PIL.Image import Image as PILImage
+from pydantic import BaseModel, Field
+from torch import Tensor
+
+from invokeai.app.services.item_storage import PaginatedResults
+
+
+"""
+Substantial proposed changes to the management of images and tensor.
+
+tl;dr:
+With the upcoming move to latents-only nodes, we need to handle metadata differently. After struggling with this unsuccessfully - trying to smoosh it in to the existing setup - I believe we need to expand the scope of the refactor to include the management of images and latents - and make `latents` a special case of `tensor`. 
+
+full story:
+The consensus for tensor-only nodes' metadata was to traverse the execution graph and grab the core parameters to write to the image. This was straightforward, and I've written functions to find the nearest t2l/l2l, noise, and compel nodes and build the metadata from those.
+
+But struggling to integrate this and the associated edge cases this brought up a number of issues deeper in the system (some of which I had previously implemented). The ImageStorageService is doing way too much, and we have a need to be able to retrieve sessions the session given image/latents id, which is not currently feasible due to SQLite's JSON parsing performance.
+
+I made a new ResultsService and `results` table in the db to facilitate this. This first attempt failed because it doesn't handle uploads and leaves the codebase messy.
+
+So I've spent the day trying to figure out to handle this in a sane way and think I've got something decent. I've described some changes to service bases and the database below.
+
+The gist of it is to store the core parameters for an image in its metadata when the image is saved, but never to read from it. Instead, the same metadata is stored in the database, which will be set up for efficient access. So when a page of images is requested, the metadata comes from the db instead of a filesystem operation.
+
+The URL generation responsibilities have been split off the image storage service in to a URL service. New database services/tables for images and tensor are added. These services will provide paginated images/tensors for the API to serve. This also paves the way for handling tensors as first-class outputs.
+"""
+
+
+# TODO: Make a new model for this
+class ResourceOrigin(str, Enum):
+    """The origin of a resource (eg image or tensor)."""
+
+    RESULTS = "results"
+    UPLOADS = "uploads"
+    INTERMEDIATES = "intermediates"
+
+
+class ImageKind(str, Enum):
+    """The kind of an image."""
+
+    IMAGE = "image"
+    CONTROL_IMAGE = "control_image"
+
+
+class TensorKind(str, Enum):
+    """The kind of a tensor."""
+
+    IMAGE_TENSOR = "tensor"
+    CONDITIONING = "conditioning"
+
+
+"""
+Core Generation Metadata Pydantic Model
+
+I've already implemented the code to traverse a session to build this object.
+"""
+
+
+class CoreGenerationMetadata(BaseModel):
+    """Core generation metadata for an image/tensor generated in InvokeAI.
+
+    Generated by traversing the execution graph, collecting the parameters of the nearest ancestors of a given node.
+
+    Full metadata may be accessed by querying for the session in the `graph_executions` table.
+    """
+
+    positive_conditioning: Optional[str] = Field(
+        description="The positive conditioning."
+    )
+    negative_conditioning: Optional[str] = Field(
+        description="The negative conditioning."
+    )
+    width: Optional[int] = Field(description="Width of the image/tensor in pixels.")
+    height: Optional[int] = Field(description="Height of the image/tensor in pixels.")
+    seed: Optional[int] = Field(description="The seed used for noise generation.")
+    cfg_scale: Optional[float] = Field(
+        description="The classifier-free guidance scale."
+    )
+    steps: Optional[int] = Field(description="The number of steps used for inference.")
+    scheduler: Optional[str] = Field(description="The scheduler used for inference.")
+    model: Optional[str] = Field(description="The model used for inference.")
+    strength: Optional[float] = Field(
+        description="The strength used for image-to-image/tensor-to-tensor."
+    )
+    image: Optional[str] = Field(description="The ID of the initial image.")
+    tensor: Optional[str] = Field(description="The ID of the initial tensor.")
+    # Pending model refactor:
+    # vae: Optional[str] = Field(description="The VAE used for decoding.")
+    # unet: Optional[str] = Field(description="The UNet used dor inference.")
+    # clip: Optional[str] = Field(description="The CLIP Encoder used for conditioning.")
+
+
+"""
+Minimal Uploads Metadata Model
+"""
+
+
+class UploadsMetadata(BaseModel):
+    """Limited metadata for an uploaded image/tensor."""
+
+    width: Optional[int] = Field(description="Width of the image/tensor in pixels.")
+    height: Optional[int] = Field(description="Height of the image/tensor in pixels.")
+    # The extra field will be the contents of the PNG file's tEXt chunk. It may have come
+    # from another SD application or InvokeAI, so we need to make it very flexible. I think it's
+    # best to just store it as a string and let the frontend parse it.
+    # If the upload is a tensor type, this will be omitted.
+    extra: Optional[str] = Field(
+        description="Extra metadata, extracted from the PNG tEXt chunk."
+    )
+
+
+"""
+Slimmed-down Image Storage Service Base
+  - No longer lists images or generates URLs - only stores and retrieves images.
+  - OSS implementation for disk storage
+"""
+
+
+class ImageStorageBase(ABC):
+    """Responsible for storing and retrieving images."""
+
+    @abstractmethod
+    def save(
+        self,
+        image: PILImage,
+        image_kind: ImageKind,
+        origin: ResourceOrigin,
+        context_id: str,
+        node_id: str,
+        metadata: CoreGenerationMetadata,
+    ) -> str:
+        """Saves an image and its thumbnail, returning its unique identifier."""
+        pass
+
+    @abstractmethod
+    def get(self, id: str, thumbnail: bool = False) -> Union[PILImage, None]:
+        """Retrieves an image as a PIL Image."""
+        pass
+
+    @abstractmethod
+    def delete(self, id: str) -> None:
+        """Deletes an image."""
+        pass
+
+
+class TensorStorageBase(ABC):
+    """Responsible for storing and retrieving tensors."""
+
+    @abstractmethod
+    def save(
+        self,
+        tensor: Tensor,
+        tensor_kind: TensorKind,
+        origin: ResourceOrigin,
+        context_id: str,
+        node_id: str,
+        metadata: CoreGenerationMetadata,
+    ) -> str:
+        """Saves a tensor, returning its unique identifier."""
+        pass
+
+    @abstractmethod
+    def get(self, id: str, thumbnail: bool = False) -> Union[Tensor, None]:
+        """Retrieves a tensor as a torch Tensor."""
+        pass
+
+    @abstractmethod
+    def delete(self, id: str) -> None:
+        """Deletes a tensor."""
+        pass
+
+
+"""
+New Url Service Base
+  - Abstracts the logic for generating URLs out of the storage service
+  - OSS implementation for locally-hosted URLs
+  - Also provides a method to get the internal path to a resource (for OSS, the FS path)
+"""
+
+
+class ResourceLocationServiceBase(ABC):
+    """Responsible for locating resources (eg images or tensors)."""
+
+    @abstractmethod
+    def get_url(self, id: str) -> str:
+        """Gets the URL for a resource."""
+        pass
+
+    @abstractmethod
+    def get_path(self, id: str) -> str:
+        """Gets the path for a resource."""
+        pass
+
+
+"""
+New Images Database Service Base
+
+This is a new service that will be responsible for the new `images` table(s):
+  - Storing images in the table
+  - Retrieving individual images and pages of images
+  - Deleting individual images
+
+Operations will typically use joins with the various `images` tables.
+"""
+
+
+class ImagesDbServiceBase(ABC):
+    """Responsible for interfacing with `images` table."""
+
+    class GeneratedImageEntity(BaseModel):
+        id: str = Field(description="The unique identifier for the image.")
+        session_id: str = Field(description="The session ID.")
+        node_id: str = Field(description="The node ID.")
+        metadata: CoreGenerationMetadata = Field(
+            description="The metadata for the image."
+        )
+
+    class UploadedImageEntity(BaseModel):
+        id: str = Field(description="The unique identifier for the image.")
+        metadata: UploadsMetadata = Field(description="The metadata for the image.")
+
+    @abstractmethod
+    def get(self, id: str) -> Union[GeneratedImageEntity, UploadedImageEntity, None]:
+        """Gets an image from the `images` table."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self, image_kind: ImageKind, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[Union[GeneratedImageEntity, UploadedImageEntity]]:
+        """Gets a page of images from the `images` table."""
+        pass
+
+    @abstractmethod
+    def delete(self, id: str) -> None:
+        """Deletes an image from the `images` table."""
+        pass
+
+    @abstractmethod
+    def set(
+        self,
+        id: str,
+        image_kind: ImageKind,
+        session_id: Optional[str],
+        node_id: Optional[str],
+        metadata: CoreGenerationMetadata | UploadsMetadata,
+    ) -> None:
+        """Sets an image in the `images` table."""
+        pass
+
+
+"""
+New Tensor Database Service Base
+
+This is a new service that will be responsible for the new `tensor` table:
+  - Storing tensor in the table
+  - Retrieving individual tensor and pages of tensor
+  - Deleting individual tensor
+
+Operations will always use joins with the `tensor_metadata` table.
+"""
+
+
+class TensorDbServiceBase(ABC):
+    """Responsible for interfacing with `tensor` table."""
+
+    class GeneratedTensorEntity(BaseModel):
+        id: str = Field(description="The unique identifier for the tensor.")
+        session_id: str = Field(description="The session ID.")
+        node_id: str = Field(description="The node ID.")
+        metadata: CoreGenerationMetadata = Field(
+            description="The metadata for the tensor."
+        )
+
+    class UploadedTensorEntity(BaseModel):
+        id: str = Field(description="The unique identifier for the tensor.")
+        metadata: UploadsMetadata = Field(description="The metadata for the tensor.")
+
+    @abstractmethod
+    def get(self, id: str) -> Union[GeneratedTensorEntity, UploadedTensorEntity, None]:
+        """Gets a tensor from the `tensor` table."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self, tensor_kind: TensorKind, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[Union[GeneratedTensorEntity, UploadedTensorEntity]]:
+        """Gets a page of tensor from the `tensor` table."""
+        pass
+
+    @abstractmethod
+    def delete(self, id: str) -> None:
+        """Deletes a tensor from the `tensor` table."""
+        pass
+
+    @abstractmethod
+    def set(
+        self,
+        id: str,
+        tensor_kind: TensorKind,
+        session_id: Optional[str],
+        node_id: Optional[str],
+        metadata: CoreGenerationMetadata | UploadsMetadata,
+    ) -> None:
+        """Sets a tensor in the `tensor` table."""
+        pass
+
+
+"""
+Database Changes
+
+The existing tables will remain as-is, new tables will be added. 
+
+Tensor now also have the same types as images - `results`, `intermediates`, `uploads`. Storage, retrieval, and operations may diverge from images in the future, so they are managed separately.
+
+A few `images` tables are created to store all images:
+  - `results` and `intermediates` images have additional data: `session_id` and `node_id`, and may be further differentiated in the future. For this reason, they each get their own table.
+  - `uploads` do not get their own table, as they are never going to have more than an `id`, `image_kind` and `timestamp`.
+  - `images_metadata` holds the same image metadata that is written to the image. This table, along with the URL service, allow us to more efficiently serve images without having to read the image from storage.
+
+The same tables are made for `tensor` and for the moment, implementation is expected to be identical.
+
+Schemas for each table below. 
+
+Insertions and updates of ancillary tables (e.g. `results_images`, `images_metadata`, etc) will need to be done manually in the services, but should be straightforward. Deletion via cascading will be handled by the database.
+"""
+
+
+def create_sql_values_string_from_string_enum(enum: Type[Enum]):
+    """
+    Creates a string of the form "('value1'), ('value2'), ..., ('valueN')" from a StrEnum.
+    """
+
+    delimiter = ", "
+    values = [f"('{e.value}')" for e in enum]
+    return delimiter.join(values)
+
+
+def create_sql_table_from_enum(
+    enum: Type[Enum],
+    table_name: str,
+    primary_key_name: str,
+    cursor: sqlite3.Cursor,
+    lock: threading.Lock,
+):
+    """
+    Creates and populates a table to be used as a functional enum.
+    """
+
+    try:
+        lock.acquire()
+
+        values_string = create_sql_values_string_from_string_enum(enum)
+
+        cursor.execute(
+            f"""--sql
+            CREATE TABLE IF NOT EXISTS {table_name} (
+                {primary_key_name} TEXT PRIMARY KEY
+            );
+            """
+        )
+        cursor.execute(
+            f"""--sql
+            INSERT OR IGNORE INTO {table_name} ({primary_key_name}) VALUES {values_string};
+            """
+        )
+    finally:
+        lock.release()
+
+
+"""
+`resource_origins` functions as an enum for the ResourceOrigin model.
+"""
+
+
+def create_resource_origins_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    create_sql_table_from_enum(
+        enum=ResourceOrigin,
+        table_name="resource_origins",
+        primary_key_name="origin_name",
+        cursor=cursor,
+        lock=lock,
+    )
+
+
+"""
+`image_kinds` functions as an enum for the ImageType model.
+"""
+
+
+def create_image_kinds_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    create_sql_table_from_enum(
+        enum=ImageKind,
+        table_name="image_kinds",
+        primary_key_name="kind_name",
+        cursor=cursor,
+        lock=lock,
+    )
+
+
+"""
+`tensor_kinds` functions as an enum for the TensorType model.
+"""
+
+
+def create_tensor_kinds_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    create_sql_table_from_enum(
+        enum=TensorKind,
+        table_name="tensor_kinds",
+        primary_key_name="kind_name",
+        cursor=cursor,
+        lock=lock,
+    )
+
+
+"""
+`images` stores all images, regardless of type
+"""
+
+
+def create_images_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    try:
+        lock.acquire()
+
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS images (
+                id TEXT PRIMARY KEY,
+                origin TEXT,
+                image_kind TEXT,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                FOREIGN KEY(origin) REFERENCES resource_origins(origin_name),
+                FOREIGN KEY(image_kind) REFERENCES image_kinds(kind_name)
+            );
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE UNIQUE INDEX IF NOT EXISTS idx_images_id ON images(id);
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_images_origin ON images(origin);
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_images_image_kind ON images(image_kind);
+            """
+        )
+    finally:
+        lock.release()
+
+
+"""
+`image_results` stores additional data specific to `results` images.
+"""
+
+
+def create_image_results_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    try:
+        lock.acquire()
+
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS image_results (
+                images_id TEXT PRIMARY KEY,
+                session_id TEXT NOT NULL,
+                node_id TEXT NOT NULL,
+                FOREIGN KEY(images_id) REFERENCES images(id) ON DELETE CASCADE
+            );
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE UNIQUE INDEX IF NOT EXISTS idx_image_results_images_id ON image_results(id);
+            """
+        )
+    finally:
+        lock.release()
+
+
+"""
+`image_intermediates` stores additional data specific to `intermediates` images
+"""
+
+
+def create_image_intermediates_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    try:
+        lock.acquire()
+
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS image_intermediates (
+                images_id TEXT PRIMARY KEY,
+                session_id TEXT NOT NULL,
+                node_id TEXT NOT NULL,
+                FOREIGN KEY(images_id) REFERENCES images(id) ON DELETE CASCADE
+            );
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE UNIQUE INDEX IF NOT EXISTS idx_image_intermediates_images_id ON image_intermediates(id);
+            """
+        )
+    finally:
+        lock.release()
+
+
+"""
+`images_metadata` stores basic metadata for any image type
+"""
+
+
+def create_images_metadata_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    try:
+        lock.acquire()
+
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS images_metadata (
+                images_id TEXT PRIMARY KEY,
+                metadata TEXT,
+                FOREIGN KEY(images_id) REFERENCES images(id) ON DELETE CASCADE
+            );
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE UNIQUE INDEX IF NOT EXISTS idx_images_metadata_images_id ON images_metadata(images_id);
+            """
+        )
+    finally:
+        lock.release()
+
+
+# `tensor` table: stores references to tensor
+
+
+def create_tensors_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    try:
+        lock.acquire()
+
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS tensors (
+                id TEXT PRIMARY KEY,
+                origin TEXT,
+                tensor_kind TEXT,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                FOREIGN KEY(origin) REFERENCES resource_origins(origin_name),
+                FOREIGN KEY(tensor_kind) REFERENCES tensor_kinds(kind_name),
+            );
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE UNIQUE INDEX IF NOT EXISTS idx_tensors_id ON tensors(id);
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_tensors_origin ON tensors(origin);
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE INDEX IF NOT EXISTS idx_tensors_tensor_kind ON tensors(tensor_kind);
+            """
+        )
+    finally:
+        lock.release()
+
+
+# `results_tensor` stores additional data specific to `result` tensor
+
+
+def create_tensor_results_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    try:
+        lock.acquire()
+
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS tensor_results (
+                tensor_id TEXT PRIMARY KEY,
+                session_id TEXT NOT NULL,
+                node_id TEXT NOT NULL,
+                FOREIGN KEY(tensor_id) REFERENCES tensors(id) ON DELETE CASCADE
+            );
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE UNIQUE INDEX IF NOT EXISTS idx_tensor_results_tensor_id ON tensor_results(tensor_id);
+            """
+        )
+    finally:
+        lock.release()
+
+
+# `tensor_intermediates` stores additional data specific to `intermediate` tensor
+
+
+def create_tensor_intermediates_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    try:
+        lock.acquire()
+
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS tensor_intermediates (
+                tensor_id TEXT PRIMARY KEY,
+                session_id TEXT NOT NULL,
+                node_id TEXT NOT NULL,
+                FOREIGN KEY(tensor_id) REFERENCES tensors(id) ON DELETE CASCADE
+            );
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE UNIQUE INDEX IF NOT EXISTS idx_tensor_intermediates_tensor_id ON tensor_intermediates(tensor_id);
+            """
+        )
+    finally:
+        lock.release()
+
+
+# `tensors_metadata` table: stores generated/transformed metadata for tensor
+
+
+def create_tensors_metadata_table(cursor: sqlite3.Cursor, lock: threading.Lock):
+    try:
+        lock.acquire()
+
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS tensors_metadata (
+                tensor_id TEXT PRIMARY KEY,
+                metadata TEXT,
+                FOREIGN KEY(tensor_id) REFERENCES tensors(id) ON DELETE CASCADE
+            );
+            """
+        )
+        cursor.execute(
+            """--sql
+            CREATE UNIQUE INDEX IF NOT EXISTS idx_tensors_metadata_tensor_id ON tensors_metadata(tensor_id);
+            """
+        )
+    finally:
+        lock.release()

invokeai/app/services/results.py

@@ -0,0 +1,466 @@
+from enum import Enum
+
+from abc import ABC, abstractmethod
+import json
+import sqlite3
+from threading import Lock
+from typing import Any, Union
+
+import networkx as nx
+
+from pydantic import BaseModel, Field, parse_obj_as, parse_raw_as
+from invokeai.app.invocations.image import ImageOutput
+from invokeai.app.services.graph import Edge, GraphExecutionState
+from invokeai.app.invocations.latent import LatentsOutput
+from invokeai.app.services.item_storage import PaginatedResults
+from invokeai.app.util.misc import get_timestamp
+
+
+class ResultType(str, Enum):
+    image_output = "image_output"
+    latents_output = "latents_output"
+
+
+class Result(BaseModel):
+    """A session result"""
+
+    id: str = Field(description="Result ID")
+    session_id: str = Field(description="Session ID")
+    node_id: str = Field(description="Node ID")
+    data: Union[LatentsOutput, ImageOutput] = Field(description="The result data")
+
+
+class ResultWithSession(BaseModel):
+    """A result with its session"""
+
+    result: Result = Field(description="The result")
+    session: GraphExecutionState = Field(description="The session")
+
+
+# Create a directed graph
+from typing import Any, TypedDict, Union
+from networkx import DiGraph
+import networkx as nx
+import json
+
+
+# We need to use a loose class for nodes to allow for graceful parsing - we cannot use the stricter
+# model used by the system, because we may be a graph in an old format. We can, however, use the 
+# Edge model, because the edge format does not change.
+class LooseGraph(BaseModel):
+    id: str
+    nodes: dict[str, dict[str, Any]]
+    edges: list[Edge]
+
+
+# An intermediate type used during parsing
+class NearestAncestor(TypedDict):
+    node_id: str
+    metadata: dict[str, Any]
+
+
+# The ancestor types that contain the core metadata
+ANCESTOR_TYPES = ['t2l', 'l2l']
+
+# The core metadata parameters in the ancestor types
+ANCESTOR_PARAMS = ['steps', 'model', 'cfg_scale', 'scheduler', 'strength']
+
+# The core metadata parameters in the noise node
+NOISE_FIELDS = ['seed', 'width', 'height']
+
+# Find nearest t2l or l2l ancestor from a given l2i node
+def find_nearest_ancestor(G: DiGraph, node_id: str) -> Union[NearestAncestor, None]:
+    """Returns metadata for the nearest ancestor of a given node.
+
+    Parameters:
+    G (DiGraph): A directed graph.
+    node_id (str): The ID of the starting node.
+
+    Returns:
+    NearestAncestor | None: An object with the ID and metadata of the nearest ancestor.
+    """
+
+    # Retrieve the node from the graph
+    node = G.nodes[node_id]
+
+    # If the node type is one of the core metadata node types, gather necessary metadata and return
+    if node.get('type') in ANCESTOR_TYPES:
+        parsed_metadata = {param: val for param, val in node.items() if param in ANCESTOR_PARAMS}
+        return NearestAncestor(node_id=node_id, metadata=parsed_metadata)
+        
+
+    # Else, look for the ancestor in the predecessor nodes
+    for predecessor in G.predecessors(node_id):
+        result = find_nearest_ancestor(G, predecessor)
+        if result:
+            return result
+        
+    # If there are no valid ancestors, return None
+    return None
+
+
+def get_additional_metadata(graph: LooseGraph, node_id: str) -> Union[dict[str, Any], None]:
+    """Collects additional metadata from nodes connected to a given node.
+
+    Parameters:
+    graph (LooseGraph): The graph.
+    node_id (str): The ID of the node.
+
+    Returns:
+    dict | None: A dictionary containing additional metadata.
+    """
+
+    metadata = {}
+
+    # Iterate over all edges in the graph
+    for edge in graph.edges:
+        dest_node_id = edge.destination.node_id
+        dest_field =  edge.destination.field
+        source_node = graph.nodes[edge.source.node_id]
+        
+        # If the destination node ID matches the given node ID, gather necessary metadata
+        if dest_node_id == node_id:
+            # If the destination field is 'positive_conditioning', add the 'prompt' from the source node
+            if dest_field == 'positive_conditioning':
+                metadata['positive_conditioning'] = source_node.get('prompt')
+            # If the destination field is 'negative_conditioning', add the 'prompt' from the source node
+            if dest_field == 'negative_conditioning':
+                metadata['negative_conditioning'] = source_node.get('prompt')
+            # If the destination field is 'noise', add the core noise fields from the source node
+            if dest_field == 'noise':
+                for field in NOISE_FIELDS:
+                    metadata[field] = source_node.get(field)
+    return metadata
+
+def build_core_metadata(graph_raw: str, node_id: str) -> Union[dict, None]:
+    """Builds the core metadata for a given node.
+
+    Parameters:
+    graph_raw (str): The graph structure as a raw string.
+    node_id (str): The ID of the node.
+
+    Returns:
+    dict | None: A dictionary containing core metadata.
+    """
+
+    # Create a directed graph to facilitate traversal
+    G = nx.DiGraph()
+
+    # Convert the raw graph string into a JSON object
+    graph = parse_obj_as(LooseGraph, graph_raw)
+
+    # Add nodes and edges to the graph
+    for node_id, node_data in graph.nodes.items():
+        G.add_node(node_id, **node_data)
+    for edge in graph.edges:
+        G.add_edge(edge.source.node_id, edge.destination.node_id)
+
+    # Find the nearest ancestor of the given node
+    ancestor = find_nearest_ancestor(G, node_id)
+
+    # If no ancestor was found, return None
+    if ancestor is None:
+        return None
+    
+    metadata = ancestor['metadata']
+    ancestor_id = ancestor['node_id']
+
+    # Get additional metadata related to the ancestor
+    addl_metadata = get_additional_metadata(graph, ancestor_id)
+
+    # If additional metadata was found, add it to the main metadata
+    if addl_metadata is not None:
+        metadata.update(addl_metadata)
+
+    return metadata
+
+
+
+class ResultsServiceABC(ABC):
+    """The Results service is responsible for retrieving results."""
+
+    @abstractmethod
+    def get(
+        self, result_id: str, result_type: ResultType
+    ) -> Union[ResultWithSession, None]:
+        pass
+
+    @abstractmethod
+    def get_many(
+        self, result_type: ResultType, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[ResultWithSession]:
+        pass
+
+    @abstractmethod
+    def search(
+        self, query: str, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[ResultWithSession]:
+        pass
+
+    @abstractmethod
+    def handle_graph_execution_state_change(self, session: GraphExecutionState) -> None:
+        pass
+
+
+class SqliteResultsService(ResultsServiceABC):
+    """SQLite implementation of the Results service."""
+
+    _filename: str
+    _conn: sqlite3.Connection
+    _cursor: sqlite3.Cursor
+    _lock: Lock
+
+    def __init__(self, filename: str):
+        super().__init__()
+
+        self._filename = filename
+        self._lock = Lock()
+
+        self._conn = sqlite3.connect(
+            self._filename, check_same_thread=False
+        )  # TODO: figure out a better threading solution
+        self._cursor = self._conn.cursor()
+
+        self._create_table()
+
+    def _create_table(self):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """--sql
+                CREATE TABLE IF NOT EXISTS results (
+                  id TEXT PRIMARY KEY, -- the result's name
+                  result_type TEXT, -- `image_output` | `latents_output`
+                  node_id TEXT, -- the node that produced this result
+                  session_id TEXT, -- the session that produced this result
+                  created_at INTEGER, -- the time at which this result was created
+                  data TEXT -- the result itself
+                );
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE UNIQUE INDEX IF NOT EXISTS idx_result_id ON results(id);
+                """
+            )
+        finally:
+            self._lock.release()
+
+    def _parse_joined_result(self, result_row: Any, column_names: list[str]):
+        result_raw = {}
+        session_raw = {}
+
+        for idx, name in enumerate(column_names):
+            if name == "session":
+                session_raw = json.loads(result_row[idx])
+            elif name == "data":
+                result_raw[name] = json.loads(result_row[idx])
+            else:
+                result_raw[name] = result_row[idx]
+
+        graph_raw = session_raw['execution_graph']
+
+        result = parse_obj_as(Result, result_raw)
+        session = parse_obj_as(GraphExecutionState, session_raw)
+
+        m = build_core_metadata(graph_raw, result.node_id)
+        print(m)
+
+        # g = session.execution_graph.nx_graph()
+        # ancestors = nx.dag.ancestors(g, result.node_id)
+
+        # nodes = [session.execution_graph.get_node(result.node_id)]
+        # for ancestor in ancestors:
+        #     nodes.append(session.execution_graph.get_node(ancestor))
+
+        # filtered_nodes = filter(lambda n: n.type in NODE_TYPE_ALLOWLIST, nodes)
+        # print(list(map(lambda n: n.dict(), filtered_nodes)))
+        # metadata = {}
+        # for node in nodes:
+        #     if (node.type in ['txt2img', 'img2img',])
+        #     for field, value in node.dict().items():
+        #         if field not in ['type', 'id']:
+        #             if field not in metadata:
+        #                 metadata[field] = value
+
+        # print(ancestors)
+        # print(nodes)
+        # print(metadata)
+
+        # for node in nodes:
+        #     print(node.dict())
+
+        # print(nodes)
+
+        return ResultWithSession(
+            result=result,
+            session=session,
+        )
+
+    def get(
+        self, result_id: str, result_type: ResultType
+    ) -> Union[ResultWithSession, None]:
+        """Retrieves a result by ID and type."""
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """--sql
+                SELECT
+                    results.id AS id,
+                    results.result_type AS result_type,
+                    results.node_id AS node_id,
+                    results.session_id AS session_id,
+                    results.data AS data,
+                    graph_executions.item AS session
+                FROM results
+                JOIN graph_executions ON results.session_id = graph_executions.id
+                WHERE results.id = ? AND results.result_type = ?
+                """,
+                (result_id, result_type),
+            )
+
+            result_row = self._cursor.fetchone()
+
+            if result_row is None:
+                return None
+
+            column_names = list(map(lambda x: x[0], self._cursor.description))
+            result_parsed = self._parse_joined_result(result_row, column_names)
+        finally:
+            self._lock.release()
+
+        if not result_parsed:
+            return None
+
+        return result_parsed
+
+    def get_many(
+        self,
+        result_type: ResultType,
+        page: int = 0,
+        per_page: int = 10,
+    ) -> PaginatedResults[ResultWithSession]:
+        """Lists results of a given type."""
+        try:
+            self._lock.acquire()
+
+            self._cursor.execute(
+                f"""--sql
+                SELECT
+                    results.id AS id,
+                    results.result_type AS result_type,
+                    results.node_id AS node_id,
+                    results.session_id AS session_id,
+                    results.data AS data,
+                    graph_executions.item AS session
+                FROM results
+                JOIN graph_executions ON results.session_id = graph_executions.id
+                WHERE results.result_type = ?
+                LIMIT ? OFFSET ?;
+                """,
+                (result_type.value, per_page, page * per_page),
+            )
+
+            result_rows = self._cursor.fetchall()
+            column_names = list(map(lambda c: c[0], self._cursor.description))
+
+            result_parsed = []
+
+            for result_row in result_rows:
+                result_parsed.append(
+                    self._parse_joined_result(result_row, column_names)
+                )
+
+            self._cursor.execute("""SELECT count(*) FROM results;""")
+            count = self._cursor.fetchone()[0]
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedResults[ResultWithSession](
+            items=result_parsed,
+            page=page,
+            pages=pageCount,
+            per_page=per_page,
+            total=count,
+        )
+
+    def search(
+        self,
+        query: str,
+        page: int = 0,
+        per_page: int = 10,
+    ) -> PaginatedResults[ResultWithSession]:
+        """Finds results by query."""
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """--sql
+                SELECT results.data, graph_executions.item
+                FROM results
+                JOIN graph_executions ON results.session_id = graph_executions.id
+                WHERE item LIKE ?
+                LIMIT ? OFFSET ?;
+                """,
+                (f"%{query}%", per_page, page * per_page),
+            )
+
+            result_rows = self._cursor.fetchall()
+
+            items = list(
+                map(
+                    lambda r: ResultWithSession(
+                        result=parse_raw_as(Result, r[0]),
+                        session=parse_raw_as(GraphExecutionState, r[1]),
+                    ),
+                    result_rows,
+                )
+            )
+            self._cursor.execute(
+                """--sql
+                SELECT count(*) FROM results WHERE item LIKE ?;
+                """,
+                (f"%{query}%",),
+            )
+            count = self._cursor.fetchone()[0]
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedResults[ResultWithSession](
+            items=items, page=page, pages=pageCount, per_page=per_page, total=count
+        )
+
+    def handle_graph_execution_state_change(self, session: GraphExecutionState) -> None:
+        """Updates the results table with the results from the session."""
+        with self._conn as conn:
+            for node_id, result in session.results.items():
+                # We'll only process 'image_output' or 'latents_output'
+                if result.type not in ["image_output", "latents_output"]:
+                    continue
+
+                # The id depends on the result type
+                if result.type == "image_output":
+                    id = result.image.image_name
+                    result_type = "image_output"
+                else:
+                    id = result.latents.latents_name
+                    result_type = "latents_output"
+
+                # Insert the result into the results table, ignoring if it already exists
+                conn.execute(
+                    """--sql
+                    INSERT OR IGNORE INTO results (id, result_type, node_id, session_id, created_at, data)
+                    VALUES (?, ?, ?, ?, ?, ?)
+                    """,
+                    (
+                        id,
+                        result_type,
+                        node_id,
+                        session.id,
+                        get_timestamp(),
+                        result.json(),
+                    ),
+                )

invokeai/app/services/urls.py

@@ -0,0 +1,30 @@
+import os
+from abc import ABC, abstractmethod
+
+from invokeai.app.models.image import ImageType
+from invokeai.app.util.thumbnails import get_thumbnail_name
+
+
+class UrlServiceBase(ABC):
+    """Responsible for building URLs for resources."""
+
+    @abstractmethod
+    def get_image_url(
+        self, image_type: ImageType, image_name: str, thumbnail: bool = False
+    ) -> str:
+        """Gets the URL for an image or thumbnail."""
+        pass
+
+
+class LocalUrlService(UrlServiceBase):
+    def __init__(self, base_url: str = "api/v1"):
+        self._base_url = base_url
+
+    def get_image_url(
+        self, image_type: ImageType, image_name: str, thumbnail: bool = False
+    ) -> str:
+        image_basename = os.path.basename(image_name)
+        if thumbnail:
+            return f"{self._base_url}/files/images/{image_type.value}/{image_basename}/thumbnail"
+
+        return f"{self._base_url}/files/images/{image_type.value}/{image_basename}"

invokeai/app/services/util/create_enum_table.py

@@ -0,0 +1,39 @@
+from enum import Enum
+import sqlite3
+from typing import Type
+
+
+def create_sql_values_string_from_string_enum(enum: Type[Enum]):
+    """
+    Creates a string of the form "('value1'), ('value2'), ..., ('valueN')" from a StrEnum.
+    """
+
+    delimiter = ", "
+    values = [f"('{e.value}')" for e in enum]
+    return delimiter.join(values)
+
+
+def create_enum_table(
+    enum: Type[Enum],
+    table_name: str,
+    primary_key_name: str,
+    cursor: sqlite3.Cursor,
+):
+    """
+    Creates and populates a table to be used as a functional enum.
+    """
+
+    values_string = create_sql_values_string_from_string_enum(enum)
+
+    cursor.execute(
+        f"""--sql
+        CREATE TABLE IF NOT EXISTS {table_name} (
+            {primary_key_name} TEXT PRIMARY KEY
+        );
+        """
+    )
+    cursor.execute(
+        f"""--sql
+        INSERT OR IGNORE INTO {table_name} ({primary_key_name}) VALUES {values_string};
+        """
+    )

invokeai/app/util/enum.py

@@ -0,0 +1,12 @@
+from enum import EnumMeta
+
+
+class MetaEnum(EnumMeta):
+    """Metaclass to support `in` syntax value checking in String Enums"""
+
+    def __contains__(cls, item):
+        try:
+            cls(item)
+        except ValueError:
+            return False
+        return True

invokeai/app/util/misc.py

@@ -6,6 +6,14 @@ def get_timestamp():
     return int(datetime.datetime.now(datetime.timezone.utc).timestamp())
 
 
+def get_iso_timestamp() -> str:
+    return datetime.datetime.utcnow().isoformat()
+
+
+def get_datetime_from_iso_timestamp(iso_timestamp: str) -> datetime.datetime:
+    return datetime.datetime.fromisoformat(iso_timestamp)
+
+
 SEED_MAX = np.iinfo(np.int32).max
 
 

invokeai/backend/util/logging.py

@@ -76,16 +76,16 @@ class InvokeAILogFormatter(logging.Formatter):
     reset = "\x1b[0m"
 
     # Log Format
-    format = "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
+    log_format = "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
     ## More Formatting Options: %(pathname)s, %(filename)s, %(module)s, %(lineno)d
 
     # Format Map
     FORMATS = {
-        logging.DEBUG: cyan + format + reset,
-        logging.INFO: grey + format + reset,
-        logging.WARNING: yellow + format + reset,
-        logging.ERROR: red + format + reset,
-        logging.CRITICAL: bold_red + format + reset
+        logging.DEBUG: cyan + log_format + reset,
+        logging.INFO: grey + log_format + reset,
+        logging.WARNING: yellow + log_format + reset,
+        logging.ERROR: red + log_format + reset,
+        logging.CRITICAL: bold_red + log_format + reset
     }
 
     def format(self, record):
@@ -98,13 +98,13 @@ class InvokeAILogger(object):
     loggers = dict()
 
     @classmethod
-    def getLogger(self, name: str = 'InvokeAI') -> logging.Logger:
-        if name not in self.loggers:
+    def getLogger(cls, name: str = 'InvokeAI') -> logging.Logger:
+        if name not in cls.loggers:
             logger = logging.getLogger(name)
             logger.setLevel(logging.DEBUG)
             ch = logging.StreamHandler()
             fmt = InvokeAILogFormatter()
             ch.setFormatter(fmt)
             logger.addHandler(ch)
-            self.loggers[name] = logger
-        return self.loggers[name]
+            cls.loggers[name] = logger
+        return cls.loggers[name]

invokeai/frontend/web/src/features/gallery/components/CurrentImageButtons.tsx

@@ -61,6 +61,7 @@ import UpscaleSettings from 'features/parameters/components/Parameters/Upscale/U
 import { allParametersSet } from 'features/parameters/store/generationSlice';
 import DeleteImageButton from './ImageActionButtons/DeleteImageButton';
 import { useAppToaster } from 'app/components/Toaster';
+import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
 
 const currentImageButtonsSelector = createSelector(
   [
@@ -329,7 +330,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
     if (!image) return;
     if (isLightboxOpen) dispatch(setIsLightboxOpen(false));
 
-    // dispatch(setInitialCanvasImage(selectedImage));
+    dispatch(setInitialCanvasImage(image));
     dispatch(requestCanvasRescale());
 
     if (activeTabName !== 'unifiedCanvas') {

invokeai/frontend/web/src/services/api/models/ImageOutput.ts

@@ -8,7 +8,7 @@ import type { ImageField } from './ImageField';
  * Base class for invocations that output an image
  */
 export type ImageOutput = {
-  type: 'image';
+  type: 'image_output';
   /**
    * The output image
    */

invokeai/frontend/web/src/services/api/models/RandomIntInvocation.ts

@@ -11,5 +11,13 @@ export type RandomIntInvocation = {
    */
   id: string;
   type?: 'rand_int';
+  /**
+   * The inclusive low value
+   */
+  low?: number;
+  /**
+   * The exclusive high value
+   */
+  high?: number;
 };
 

invokeai/frontend/web/src/services/api/schemas/$RandomIntInvocation.ts

@@ -12,5 +12,13 @@ export const $RandomIntInvocation = {
     type: {
       type: 'Enum',
     },
+    low: {
+      type: 'number',
+      description: `The inclusive low value`,
+    },
+    high: {
+      type: 'number',
+      description: `The exclusive high value`,
+    },
   },
 } as const;

invokeai/frontend/web/src/services/types/guards.ts

@@ -10,11 +10,16 @@ import {
   CollectInvocationOutput,
   ImageType,
   ImageField,
+  LatentsOutput,
 } from 'services/api';
 
 export const isImageOutput = (
   output: GraphExecutionState['results'][string]
-): output is ImageOutput => output.type === 'image';
+): output is ImageOutput => output.type === 'image_output';
+
+export const isLatentsOutput = (
+  output: GraphExecutionState['results'][string]
+): output is LatentsOutput => output.type === 'latents_output';
 
 export const isMaskOutput = (
   output: GraphExecutionState['results'][string]