Final phase of source tree restructure (#2833)

# All python code has been moved under `invokeai`. All vestiges of `ldm`
and `ldm.invoke` are now gone.

***You will need to run `pip install -e .` before the code will work
again!***

Everything seems to be functional, but extensive testing is advised.

A guide to where the files have gone is forthcoming.

.github/CODEOWNERS

@@ -1,51 +1,34 @@
 # continuous integration
-/.github/workflows/ @mauwii @lstein @blessedcoolant
+/.github/workflows/ @mauwii @lstein
 
 # documentation
-/docs/ @lstein @mauwii @tildebyte @blessedcoolant
-mkdocs.yml @lstein @mauwii @blessedcoolant
+/docs/ @lstein @mauwii @tildebyte
+/mkdocs.yml @lstein @mauwii
+
+# nodes
+/invokeai/app/ @Kyle0654 @blessedcoolant
 
 # installation and configuration
-/pyproject.toml @mauwii @lstein @ebr @blessedcoolant
-/docker/ @mauwii @lstein @blessedcoolant
-/scripts/ @ebr @lstein @blessedcoolant
-/installer/ @ebr @lstein @tildebyte  @blessedcoolant
-ldm/invoke/config @lstein @ebr  @blessedcoolant
-invokeai/assets @lstein @ebr  @blessedcoolant
-invokeai/configs @lstein @ebr  @blessedcoolant
-/ldm/invoke/_version.py @lstein @blessedcoolant
+/pyproject.toml @mauwii @lstein @blessedcoolant
+/docker/ @mauwii @lstein
+/scripts/ @ebr @lstein
+/installer/ @lstein @ebr
+/invokeai/assets @lstein @ebr
+/invokeai/configs @lstein
+/invokeai/version @lstein @blessedcoolant
 
 # web ui
 /invokeai/frontend @blessedcoolant @psychedelicious @lstein
 /invokeai/backend @blessedcoolant @psychedelicious @lstein
 
-# generation and model management
-/ldm/*.py @lstein @blessedcoolant
-/ldm/generate.py @lstein @keturn @blessedcoolant
-/ldm/invoke/args.py @lstein @blessedcoolant
-/ldm/invoke/ckpt* @lstein @blessedcoolant
-/ldm/invoke/ckpt_generator @lstein @blessedcoolant
-/ldm/invoke/CLI.py @lstein @blessedcoolant
-/ldm/invoke/config @lstein @ebr @mauwii @blessedcoolant
-/ldm/invoke/generator @keturn @damian0815 @blessedcoolant
-/ldm/invoke/globals.py @lstein @blessedcoolant 
-/ldm/invoke/merge_diffusers.py @lstein @blessedcoolant
-/ldm/invoke/model_manager.py @lstein @blessedcoolant
-/ldm/invoke/txt2mask.py @lstein @blessedcoolant
-/ldm/invoke/patchmatch.py @Kyle0654 @blessedcoolant @lstein
-/ldm/invoke/restoration @lstein @blessedcoolant
+# generation, model management, postprocessing
+/invokeai/backend @keturn @damian0815 @lstein @blessedcoolant @jpphoto
 
-# attention, textual inversion, model configuration
-/ldm/models @damian0815 @keturn @lstein @blessedcoolant
-/ldm/modules @damian0815 @keturn @lstein @blessedcoolant
+# front ends
+/invokeai/frontend/CLI @lstein
+/invokeai/frontend/install @lstein @ebr @mauwii 
+/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/web @psychedelicious @blessedcoolant
 
-# Nodes
-apps/ @Kyle0654 @lstein @blessedcoolant
-
-# legacy REST API
-# is CapableWeb still engaged?
-/ldm/invoke/pngwriter.py @CapableWeb @lstein @blessedcoolant
-/ldm/invoke/server_legacy.py @CapableWeb @lstein @blessedcoolant
-/scripts/legacy_api.py @CapableWeb @lstein @blessedcoolant
-/tests/legacy_tests.sh @CapableWeb @lstein @blessedcoolant
 

.github/workflows/build-container.yml

@@ -9,7 +9,7 @@ on:
       - 'dev/docker/*'
     paths:
       - 'pyproject.toml'
-      - 'ldm/**'
+      - 'invokeai/**'
       - 'invokeai/backend/**'
       - 'invokeai/configs/**'
       - 'invokeai/frontend/dist/**'

.github/workflows/lint-frontend.yml

@@ -3,14 +3,14 @@ name: Lint frontend
 on:
   pull_request:
     paths:
-      - 'invokeai/frontend/**'
+      - 'invokeai/frontend/web/**'
   push:
     paths:
-      - 'invokeai/frontend/**'
+      - 'invokeai/frontend/web/**'
 
 defaults:
   run:
-    working-directory: invokeai/frontend
+    working-directory: invokeai/frontend/web
 
 jobs:
   lint-frontend:

.github/workflows/pypi-release.yml

@@ -3,7 +3,7 @@ name: PyPI Release
 on:
   push:
     paths:
-      - 'ldm/invoke/_version.py'
+      - 'invokeai/version/invokeai_version.py'
   workflow_dispatch:
 
 jobs:

.github/workflows/test-invoke-pip-skip.yml

@@ -3,7 +3,7 @@ on:
   pull_request:
     paths-ignore:
       - 'pyproject.toml'
-      - 'ldm/**'
+      - 'invokeai/**'
       - 'invokeai/backend/**'
       - 'invokeai/configs/**'
       - 'invokeai/frontend/dist/**'

.github/workflows/test-invoke-pip.yml

@@ -5,14 +5,14 @@ on:
       - 'main'
     paths:
       - 'pyproject.toml'
-      - 'ldm/**'
+      - 'invokeai/**'
       - 'invokeai/backend/**'
       - 'invokeai/configs/**'
       - 'invokeai/frontend/dist/**'
   pull_request:
     paths:
       - 'pyproject.toml'
-      - 'ldm/**'
+      - 'invokeai/**'
       - 'invokeai/backend/**'
       - 'invokeai/configs/**'
       - 'invokeai/frontend/dist/**'
@@ -112,7 +112,7 @@ jobs:
       - name: set INVOKEAI_OUTDIR
         run: >
           python -c
-          "import os;from ldm.invoke.globals import Globals;OUTDIR=os.path.join(Globals.root,str('outputs'));print(f'INVOKEAI_OUTDIR={OUTDIR}')"
+          "import os;from invokeai.backend.globals import Globals;OUTDIR=os.path.join(Globals.root,str('outputs'));print(f'INVOKEAI_OUTDIR={OUTDIR}')"
           >> ${{ matrix.github-env }}
 
       - name: run invokeai-configure

.gitignore

@@ -198,7 +198,7 @@ checkpoints
 .DS_Store
 
 # Let the frontend manage its own gitignore
-!invokeai/frontend/*
+!invokeai/frontend/web/*
 
 # Scratch folder
 .scratch/
@@ -213,11 +213,6 @@ gfpgan/
 # config file (will be created by installer)
 configs/models.yaml
 
-# weights (will be created by installer)
-models/ldm/stable-diffusion-v1/*.ckpt
-models/clipseg
-models/gfpgan
-
 # ignore initfile
 .invokeai
 
@@ -232,6 +227,3 @@ installer/install.bat
 installer/install.sh
 installer/update.bat
 installer/update.sh
-
-# no longer stored in source directory
-models

installer/create_installer.sh

@@ -11,10 +11,10 @@ if [[ -v "VIRTUAL_ENV" ]]; then
     exit -1
 fi
 
-VERSION=$(cd ..; python -c "from ldm.invoke import __version__ as version; print(version)")
+VERSION=$(cd ..; python -c "from invokeai.version import __version__ as version; print(version)")
 PATCH=""
 VERSION="v${VERSION}${PATCH}"
-LATEST_TAG="v2.3-latest"
+LATEST_TAG="v3.0-latest"
 
 echo Building installer for version $VERSION
 echo "Be certain that you're in the 'installer' directory before continuing."

installer/lib/installer.py

@@ -291,7 +291,7 @@ class InvokeAiInstance:
                     src = Path(__file__).parents[1].expanduser().resolve()
                     # if the above directory contains one of these files, we'll do a source install
                     next(src.glob("pyproject.toml"))
-                    next(src.glob("ldm"))
+                    next(src.glob("invokeai"))
                 except StopIteration:
                     print("Unable to find a wheel or perform a source install. Giving up.")
 
@@ -342,14 +342,14 @@ class InvokeAiInstance:
 
         introduction()
 
-        from ldm.invoke.config import invokeai_configure
+        from invokeai.frontend.install import invokeai_configure
 
         # NOTE: currently the config script does its own arg parsing! this means the command-line switches
         # from the installer will also automatically propagate down to the config script.
         # this may change in the future with config refactoring!
         succeeded = False
         try:
-            invokeai_configure.main()
+            invokeai_configure()
             succeeded = True
         except requests.exceptions.ConnectionError as e:
             print(f'\nA network error was encountered during configuration and download: {str(e)}')

invokeai/README

@@ -1,3 +1,11 @@
-After version 2.3 is released, the ldm/invoke modules will be migrated to this location
-so that we have a proper invokeai distribution. Currently it is only being used for
-data files.
+Organization of the source tree:
+
+app -- Home of nodes invocations and services
+assets -- Images and other data files used by InvokeAI
+backend -- Non-user facing libraries, including the rendering
+	core.
+configs -- Configuration files used at install and run times
+frontend -- User-facing scripts, including the CLI and the WebUI
+version -- Current InvokeAI version string, stored
+	in version/invokeai_version.py
+	

invokeai/app/api/dependencies.py

@@ -1,33 +1,31 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from argparse import Namespace
 import os
-
-from ..services.processor import DefaultInvocationProcessor
-
-from ..services.graph import GraphExecutionState
-from ..services.sqlite import SqliteItemStorage
+from argparse import Namespace
 
 from ...globals import Globals
-
+from ..services.generate_initializer import get_generate
+from ..services.graph import GraphExecutionState
 from ..services.image_storage import DiskImageStorage
 from ..services.invocation_queue import MemoryInvocationQueue
 from ..services.invocation_services import InvocationServices
 from ..services.invoker import Invoker
-from ..services.generate_initializer import get_generate
+from ..services.processor import DefaultInvocationProcessor
+from ..services.sqlite import SqliteItemStorage
 from .events import FastAPIEventService
 
 
 # TODO: is there a better way to achieve this?
-def check_internet()->bool:
-    '''
+def check_internet() -> bool:
+    """
     Return true if the internet is reachable.
     It does this by pinging huggingface.co.
-    '''
+    """
     import urllib.request
-    host = 'http://huggingface.co'
+
+    host = "http://huggingface.co"
     try:
-        urllib.request.urlopen(host,timeout=1)
+        urllib.request.urlopen(host, timeout=1)
         return True
     except:
         return False
@@ -35,14 +33,11 @@ def check_internet()->bool:
 
 class ApiDependencies:
     """Contains and initializes all dependencies for the API"""
+
     invoker: Invoker = None
 
     @staticmethod
-    def initialize(
-        args,
-        config,
-        event_handler_id: int
-    ):
+    def initialize(args, config, event_handler_id: int):
         Globals.try_patchmatch = args.patchmatch
         Globals.always_use_cpu = args.always_use_cpu
         Globals.internet_available = args.internet_available and check_internet()
@@ -50,26 +45,30 @@ class ApiDependencies:
         Globals.ckpt_convert = args.ckpt_convert
 
         # TODO: Use a logger
-        print(f'>> Internet connectivity is {Globals.internet_available}')
+        print(f">> Internet connectivity is {Globals.internet_available}")
 
         generate = get_generate(args, config)
 
         events = FastAPIEventService(event_handler_id)
 
-        output_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../../outputs'))
+        output_folder = os.path.abspath(
+            os.path.join(os.path.dirname(__file__), "../../../../outputs")
+        )
 
         images = DiskImageStorage(output_folder)
 
         # TODO: build a file/path manager?
-        db_location = os.path.join(output_folder, 'invokeai.db')
+        db_location = os.path.join(output_folder, "invokeai.db")
 
         services = InvocationServices(
-            generate = generate,
-            events   = events,
-            images   = images,
-            queue                   = MemoryInvocationQueue(),
-            graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = db_location, table_name = 'graph_executions'),
-            processor               = DefaultInvocationProcessor()
+            generate=generate,
+            events=events,
+            images=images,
+            queue=MemoryInvocationQueue(),
+            graph_execution_manager=SqliteItemStorage[GraphExecutionState](
+                filename=db_location, table_name="graph_executions"
+            ),
+            processor=DefaultInvocationProcessor(),
         )
 
         ApiDependencies.invoker = Invoker(services)

invokeai/app/api/events.py

@@ -1,11 +1,14 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import asyncio
+import threading
 from queue import Empty, Queue
 from typing import Any
+
 from fastapi_events.dispatcher import dispatch
+
 from ..services.events import EventServiceBase
-import threading
+
 
 class FastAPIEventService(EventServiceBase):
     event_handler_id: int
@@ -16,39 +19,34 @@ class FastAPIEventService(EventServiceBase):
         self.event_handler_id = event_handler_id
         self.__queue = Queue()
         self.__stop_event = threading.Event()
-        asyncio.create_task(self.__dispatch_from_queue(stop_event = self.__stop_event))
+        asyncio.create_task(self.__dispatch_from_queue(stop_event=self.__stop_event))
 
         super().__init__()
 
-
     def stop(self, *args, **kwargs):
         self.__stop_event.set()
         self.__queue.put(None)
 
-
     def dispatch(self, event_name: str, payload: Any) -> None:
-        self.__queue.put(dict(
-            event_name = event_name,
-            payload = payload
-        ))
-
+        self.__queue.put(dict(event_name=event_name, payload=payload))
 
     async def __dispatch_from_queue(self, stop_event: threading.Event):
         """Get events on from the queue and dispatch them, from the correct thread"""
         while not stop_event.is_set():
             try:
-                event = self.__queue.get(block = False)
-                if not event: # Probably stopping
+                event = self.__queue.get(block=False)
+                if not event:  # Probably stopping
                     continue
 
                 dispatch(
-                    event.get('event_name'),
-                    payload       = event.get('payload'),
-                    middleware_id = self.event_handler_id)
+                    event.get("event_name"),
+                    payload=event.get("payload"),
+                    middleware_id=self.event_handler_id,
+                )
 
             except Empty:
                 await asyncio.sleep(0.001)
                 pass
 
             except asyncio.CancelledError as e:
-                raise e # Raise a proper error
+                raise e  # Raise a proper error

invokeai/app/api/routers/images.py

@@ -0,0 +1,56 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from datetime import datetime, timezone
+
+from fastapi import Path, Request, UploadFile
+from fastapi.responses import FileResponse, Response
+from fastapi.routing import APIRouter
+from PIL import Image
+
+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"),
+):
+    """Gets a result"""
+    # TODO: This is not really secure at all. At least make sure only output results are served
+    filename = ApiDependencies.invoker.services.images.get_path(image_type, image_name)
+    return FileResponse(filename)
+
+
+@images_router.post(
+    "/uploads/",
+    operation_id="upload_image",
+    responses={
+        201: {"description": "The image was uploaded successfully"},
+        404: {"description": "Session not found"},
+    },
+)
+async def upload_image(file: UploadFile, request: Request):
+    if not file.content_type.startswith("image"):
+        return Response(status_code=415)
+
+    contents = await file.read()
+    try:
+        im = Image.open(contents)
+    except:
+        # Error opening the image
+        return Response(status_code=415)
+
+    filename = f"{str(int(datetime.now(timezone.utc).timestamp()))}.png"
+    ApiDependencies.invoker.services.images.save(ImageType.UPLOAD, filename, im)
+
+    return Response(
+        status_code=201,
+        headers={
+            "Location": request.url_for(
+                "get_image", image_type=ImageType.UPLOAD, image_name=filename
+            )
+        },
+    )

invokeai/app/api/routers/sessions.py

@@ -0,0 +1,271 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from typing import Annotated, List, Optional, Union
+
+from fastapi import Body, Path, Query
+from fastapi.responses import Response
+from fastapi.routing import APIRouter
+from pydantic.fields import Field
+
+from ...invocations import *
+from ...invocations.baseinvocation import BaseInvocation
+from ...services.graph import (
+    EdgeConnection,
+    Graph,
+    GraphExecutionState,
+    NodeAlreadyExecutedError,
+)
+from ...services.item_storage import PaginatedResults
+from ..dependencies import ApiDependencies
+
+session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
+
+
+@session_router.post(
+    "/",
+    operation_id="create_session",
+    responses={
+        200: {"model": GraphExecutionState},
+        400: {"description": "Invalid json"},
+    },
+)
+async def create_session(
+    graph: Optional[Graph] = Body(
+        default=None, description="The graph to initialize the session with"
+    )
+) -> GraphExecutionState:
+    """Creates a new session, optionally initializing it with an invocation graph"""
+    session = ApiDependencies.invoker.create_execution_state(graph)
+    return session
+
+
+@session_router.get(
+    "/",
+    operation_id="list_sessions",
+    responses={200: {"model": PaginatedResults[GraphExecutionState]}},
+)
+async def list_sessions(
+    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"),
+    query: str = Query(default="", description="The query string to search for"),
+) -> PaginatedResults[GraphExecutionState]:
+    """Gets a list of sessions, optionally searching"""
+    if filter == "":
+        result = ApiDependencies.invoker.services.graph_execution_manager.list(
+            page, per_page
+        )
+    else:
+        result = ApiDependencies.invoker.services.graph_execution_manager.search(
+            query, page, per_page
+        )
+    return result
+
+
+@session_router.get(
+    "/{session_id}",
+    operation_id="get_session",
+    responses={
+        200: {"model": GraphExecutionState},
+        404: {"description": "Session not found"},
+    },
+)
+async def get_session(
+    session_id: str = Path(description="The id of the session to get"),
+) -> GraphExecutionState:
+    """Gets a session"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code=404)
+    else:
+        return session
+
+
+@session_router.post(
+    "/{session_id}/nodes",
+    operation_id="add_node",
+    responses={
+        200: {"model": str},
+        400: {"description": "Invalid node or link"},
+        404: {"description": "Session not found"},
+    },
+)
+async def add_node(
+    session_id: str = Path(description="The id of the session"),
+    node: Annotated[
+        Union[BaseInvocation.get_invocations()], Field(discriminator="type")
+    ] = Body(description="The node to add"),
+) -> str:
+    """Adds a node to the graph"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code=404)
+
+    try:
+        session.add_node(node)
+        ApiDependencies.invoker.services.graph_execution_manager.set(
+            session
+        )  # TODO: can this be done automatically, or add node through an API?
+        return session.id
+    except NodeAlreadyExecutedError:
+        return Response(status_code=400)
+    except IndexError:
+        return Response(status_code=400)
+
+
+@session_router.put(
+    "/{session_id}/nodes/{node_path}",
+    operation_id="update_node",
+    responses={
+        200: {"model": GraphExecutionState},
+        400: {"description": "Invalid node or link"},
+        404: {"description": "Session not found"},
+    },
+)
+async def update_node(
+    session_id: str = Path(description="The id of the session"),
+    node_path: str = Path(description="The path to the node in the graph"),
+    node: Annotated[
+        Union[BaseInvocation.get_invocations()], Field(discriminator="type")
+    ] = Body(description="The new node"),
+) -> GraphExecutionState:
+    """Updates a node in the graph and removes all linked edges"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code=404)
+
+    try:
+        session.update_node(node_path, node)
+        ApiDependencies.invoker.services.graph_execution_manager.set(
+            session
+        )  # TODO: can this be done automatically, or add node through an API?
+        return session
+    except NodeAlreadyExecutedError:
+        return Response(status_code=400)
+    except IndexError:
+        return Response(status_code=400)
+
+
+@session_router.delete(
+    "/{session_id}/nodes/{node_path}",
+    operation_id="delete_node",
+    responses={
+        200: {"model": GraphExecutionState},
+        400: {"description": "Invalid node or link"},
+        404: {"description": "Session not found"},
+    },
+)
+async def delete_node(
+    session_id: str = Path(description="The id of the session"),
+    node_path: str = Path(description="The path to the node to delete"),
+) -> GraphExecutionState:
+    """Deletes a node in the graph and removes all linked edges"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code=404)
+
+    try:
+        session.delete_node(node_path)
+        ApiDependencies.invoker.services.graph_execution_manager.set(
+            session
+        )  # TODO: can this be done automatically, or add node through an API?
+        return session
+    except NodeAlreadyExecutedError:
+        return Response(status_code=400)
+    except IndexError:
+        return Response(status_code=400)
+
+
+@session_router.post(
+    "/{session_id}/edges",
+    operation_id="add_edge",
+    responses={
+        200: {"model": GraphExecutionState},
+        400: {"description": "Invalid node or link"},
+        404: {"description": "Session not found"},
+    },
+)
+async def add_edge(
+    session_id: str = Path(description="The id of the session"),
+    edge: tuple[EdgeConnection, EdgeConnection] = Body(description="The edge to add"),
+) -> GraphExecutionState:
+    """Adds an edge to the graph"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code=404)
+
+    try:
+        session.add_edge(edge)
+        ApiDependencies.invoker.services.graph_execution_manager.set(
+            session
+        )  # TODO: can this be done automatically, or add node through an API?
+        return session
+    except NodeAlreadyExecutedError:
+        return Response(status_code=400)
+    except IndexError:
+        return Response(status_code=400)
+
+
+# TODO: the edge being in the path here is really ugly, find a better solution
+@session_router.delete(
+    "/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}",
+    operation_id="delete_edge",
+    responses={
+        200: {"model": GraphExecutionState},
+        400: {"description": "Invalid node or link"},
+        404: {"description": "Session not found"},
+    },
+)
+async def delete_edge(
+    session_id: str = Path(description="The id of the session"),
+    from_node_id: str = Path(description="The id of the node the edge is coming from"),
+    from_field: str = Path(description="The field of the node the edge is coming from"),
+    to_node_id: str = Path(description="The id of the node the edge is going to"),
+    to_field: str = Path(description="The field of the node the edge is going to"),
+) -> GraphExecutionState:
+    """Deletes an edge from the graph"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code=404)
+
+    try:
+        edge = (
+            EdgeConnection(node_id=from_node_id, field=from_field),
+            EdgeConnection(node_id=to_node_id, field=to_field),
+        )
+        session.delete_edge(edge)
+        ApiDependencies.invoker.services.graph_execution_manager.set(
+            session
+        )  # TODO: can this be done automatically, or add node through an API?
+        return session
+    except NodeAlreadyExecutedError:
+        return Response(status_code=400)
+    except IndexError:
+        return Response(status_code=400)
+
+
+@session_router.put(
+    "/{session_id}/invoke",
+    operation_id="invoke_session",
+    responses={
+        200: {"model": None},
+        202: {"description": "The invocation is queued"},
+        400: {"description": "The session has no invocations ready to invoke"},
+        404: {"description": "Session not found"},
+    },
+)
+async def invoke_session(
+    session_id: str = Path(description="The id of the session to invoke"),
+    all: bool = Query(
+        default=False, description="Whether or not to invoke all remaining invocations"
+    ),
+) -> None:
+    """Invokes a session"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code=404)
+
+    if session.is_complete():
+        return Response(status_code=400)
+
+    ApiDependencies.invoker.invoke(session, invoke_all=all)
+    return Response(status_code=202)

invokeai/app/api/sockets.py

@@ -1,36 +1,38 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from fastapi import FastAPI
-from fastapi_socketio import SocketManager
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.typing import Event
+from fastapi_socketio import SocketManager
+
 from ..services.events import EventServiceBase
 
+
 class SocketIO:
     __sio: SocketManager
 
     def __init__(self, app: FastAPI):
-        self.__sio = SocketManager(app = app)
-        self.__sio.on('subscribe', handler=self._handle_sub)
-        self.__sio.on('unsubscribe', handler=self._handle_unsub)
+        self.__sio = SocketManager(app=app)
+        self.__sio.on("subscribe", handler=self._handle_sub)
+        self.__sio.on("unsubscribe", handler=self._handle_unsub)
 
         local_handler.register(
-            event_name = EventServiceBase.session_event,
-            _func=self._handle_session_event
+            event_name=EventServiceBase.session_event, _func=self._handle_session_event
         )
 
     async def _handle_session_event(self, event: Event):
         await self.__sio.emit(
-            event = event[1]['event'],
-            data = event[1]['data'],
-            room = event[1]['data']['graph_execution_state_id']
+            event=event[1]["event"],
+            data=event[1]["data"],
+            room=event[1]["data"]["graph_execution_state_id"],
         )
 
     async def _handle_sub(self, sid, data, *args, **kwargs):
-        if 'session' in data:
-            self.__sio.enter_room(sid, data['session'])
+        if "session" in data:
+            self.__sio.enter_room(sid, data["session"])
 
         # @app.sio.on('unsubscribe')
+
     async def _handle_unsub(self, sid, data, *args, **kwargs):
-        if 'session' in data:
-            self.__sio.leave_room(sid, data['session'])
+        if "session" in data:
+            self.__sio.leave_room(sid, data["session"])

invokeai/app/api_app.py

@@ -2,36 +2,37 @@
 
 import asyncio
 from inspect import signature
-from fastapi import FastAPI
-from fastapi.openapi.utils import get_openapi
-from fastapi.openapi.docs import get_swagger_ui_html, get_redoc_html
-from fastapi.staticfiles import StaticFiles
-from fastapi_events.middleware import EventHandlerASGIMiddleware
-from fastapi_events.handlers.local import local_handler
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic.schema import schema
+
 import uvicorn
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
+from fastapi.openapi.utils import get_openapi
+from fastapi.staticfiles import StaticFiles
+from fastapi_events.handlers.local import local_handler
+from fastapi_events.middleware import EventHandlerASGIMiddleware
+from pydantic.schema import schema
+
+from ..args import Args
+from .api.dependencies import ApiDependencies
+from .api.routers import images, sessions
 from .api.sockets import SocketIO
 from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
-from .api.routers import images, sessions
-from .api.dependencies import ApiDependencies
-from ..args import Args
 
 # 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
-)
+app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None)
 
 # Add event handler
 event_handler_id: int = id(app)
 app.add_middleware(
     EventHandlerASGIMiddleware,
-    handlers      = [local_handler], # TODO: consider doing this in services to support different configurations
-    middleware_id = event_handler_id)
+    handlers=[
+        local_handler
+    ],  # TODO: consider doing this in services to support different configurations
+    middleware_id=event_handler_id,
+)
 
 # Add CORS
 # TODO: use configuration for this
@@ -48,38 +49,34 @@ socket_io = SocketIO(app)
 
 config = {}
 
+
 # Add startup event to load dependencies
-@app.on_event('startup')
+@app.on_event("startup")
 async def startup_event():
     args = Args()
     config = args.parse_args()
 
     ApiDependencies.initialize(
-        args             = args,
-        config           = config,
-        event_handler_id = event_handler_id
+        args=args, config=config, event_handler_id=event_handler_id
     )
 
+
 # Shut down threads
-@app.on_event('shutdown')
+@app.on_event("shutdown")
 async def shutdown_event():
     ApiDependencies.shutdown()
 
+
 # Include all routers
 # TODO: REMOVE
 # app.include_router(
 #     invocation.invocation_router,
 #     prefix = '/api')
 
-app.include_router(
-    sessions.session_router,
-    prefix = '/api'
-)
+app.include_router(sessions.session_router, prefix="/api")
+
+app.include_router(images.images_router, prefix="/api")
 
-app.include_router(
-    images.images_router,
-    prefix = '/api'
-)
 
 # Build a custom OpenAPI to include all outputs
 # TODO: can outputs be included on metadata of invocation schemas somehow?
@@ -87,10 +84,10 @@ def custom_openapi():
     if app.openapi_schema:
         return app.openapi_schema
     openapi_schema = get_openapi(
-        title       = app.title,
-        description = "An API for invoking AI image operations",
-        version     = "1.0.0",
-        routes      = app.routes
+        title=app.title,
+        description="An API for invoking AI image operations",
+        version="1.0.0",
+        routes=app.routes,
     )
 
     # Add all outputs
@@ -102,12 +99,12 @@ def custom_openapi():
         output_types.add(output_type)
 
     output_schemas = schema(output_types, ref_prefix="#/components/schemas/")
-    for schema_key, output_schema in output_schemas['definitions'].items():
+    for schema_key, output_schema in output_schemas["definitions"].items():
         openapi_schema["components"]["schemas"][schema_key] = output_schema
 
         # TODO: note that we assume the schema_key here is the TYPE.__name__
         # This could break in some cases, figure out a better way to do it
-        output_type_titles[schema_key] = output_schema['title']
+        output_type_titles[schema_key] = output_schema["title"]
 
     # Add a reference to the output type to additionalProperties of the invoker schema
     for invoker in all_invocations:
@@ -115,46 +112,46 @@ def custom_openapi():
         output_type = signature(invoker.invoke).return_annotation
         output_type_title = output_type_titles[output_type.__name__]
         invoker_schema = openapi_schema["components"]["schemas"][invoker_name]
-        outputs_ref = { '$ref': f'#/components/schemas/{output_type_title}' }
-        if 'additionalProperties' not in invoker_schema:
-            invoker_schema['additionalProperties'] = {}
+        outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
+        if "additionalProperties" not in invoker_schema:
+            invoker_schema["additionalProperties"] = {}
 
-        invoker_schema['additionalProperties']['outputs'] = outputs_ref
+        invoker_schema["additionalProperties"]["outputs"] = outputs_ref
 
     app.openapi_schema = openapi_schema
     return app.openapi_schema
 
+
 app.openapi = custom_openapi
 
 # Override API doc favicons
-app.mount('/static', StaticFiles(directory='static/dream_web'), name='static')
+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(
+    return get_swagger_ui_html(
         openapi_url=app.openapi_url,
         title=app.title,
-        swagger_favicon_url="/static/favicon.ico"
+        swagger_favicon_url="/static/favicon.ico",
     )
 
+
 @app.get("/redoc", include_in_schema=False)
 def overridden_redoc():
-	return get_redoc_html(
+    return get_redoc_html(
         openapi_url=app.openapi_url,
         title=app.title,
-        redoc_favicon_url="/static/favicon.ico"
+        redoc_favicon_url="/static/favicon.ico",
     )
 
+
 def invoke_api():
     # Start our own event loop for eventing usage
     # TODO: determine if there's a better way to do this
     loop = asyncio.new_event_loop()
-    config = uvicorn.Config(
-        app = app,
-        host = "0.0.0.0",
-        port = 9090,
-        loop = loop)
-        # Use access_log to turn off logging
+    config = uvicorn.Config(app=app, host="0.0.0.0", port=9090, loop=loop)
+    # Use access_log to turn off logging
 
     server = uvicorn.Server(config)
     loop.run_until_complete(server.serve())

invokeai/app/cli_app.py

@@ -1,33 +1,40 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import argparse
-import shlex
 import os
+import shlex
 import time
-from typing import Any, Dict, Iterable, Literal, Union, get_args, get_origin, get_type_hints
+from typing import (
+    Any,
+    Dict,
+    Iterable,
+    Literal,
+    Union,
+    get_args,
+    get_origin,
+    get_type_hints,
+)
+
 from pydantic import BaseModel
 from pydantic.fields import Field
 
-from .services.processor import DefaultInvocationProcessor
-
-from .services.graph import EdgeConnection, GraphExecutionState
-
-from .services.sqlite import SqliteItemStorage
-
+from ..args import Args
+from .invocations import *
+from .invocations.baseinvocation import BaseInvocation
 from .invocations.image import ImageField
+from .services.events import EventServiceBase
 from .services.generate_initializer import get_generate
+from .services.graph import EdgeConnection, GraphExecutionState
 from .services.image_storage import DiskImageStorage
 from .services.invocation_queue import MemoryInvocationQueue
-from .invocations.baseinvocation import BaseInvocation
 from .services.invocation_services import InvocationServices
 from .services.invoker import Invoker
-from .invocations import *
-from ..args import Args
-from .services.events import EventServiceBase
+from .services.processor import DefaultInvocationProcessor
+from .services.sqlite import SqliteItemStorage
 
 
 class InvocationCommand(BaseModel):
-    invocation: Union[BaseInvocation.get_invocations()] = Field(discriminator="type") # type: ignore
+    invocation: Union[BaseInvocation.get_invocations()] = Field(discriminator="type")  # type: ignore
 
 
 class InvalidArgs(Exception):
@@ -35,47 +42,69 @@ class InvalidArgs(Exception):
 
 
 def get_invocation_parser() -> argparse.ArgumentParser:
-
     # Create invocation parser
     parser = argparse.ArgumentParser()
+
     def exit(*args, **kwargs):
         raise InvalidArgs
+
     parser.exit = exit
 
-    subparsers = parser.add_subparsers(dest='type')
+    subparsers = parser.add_subparsers(dest="type")
     invocation_parsers = dict()
 
     # Add history parser
-    history_parser = subparsers.add_parser('history', help="Shows the invocation history")
-    history_parser.add_argument('count', nargs='?', default=5, type=int, help="The number of history entries to show")
+    history_parser = subparsers.add_parser(
+        "history", help="Shows the invocation history"
+    )
+    history_parser.add_argument(
+        "count",
+        nargs="?",
+        default=5,
+        type=int,
+        help="The number of history entries to show",
+    )
 
     # Add default parser
-    default_parser = subparsers.add_parser('default', help="Define a default value for all inputs with a specified name")
-    default_parser.add_argument('input', type=str, help="The input field")
-    default_parser.add_argument('value', help="The default value")
+    default_parser = subparsers.add_parser(
+        "default", help="Define a default value for all inputs with a specified name"
+    )
+    default_parser.add_argument("input", type=str, help="The input field")
+    default_parser.add_argument("value", help="The default value")
 
-    default_parser = subparsers.add_parser('reset_default', help="Resets a default value")
-    default_parser.add_argument('input', type=str, help="The input field")
+    default_parser = subparsers.add_parser(
+        "reset_default", help="Resets a default value"
+    )
+    default_parser.add_argument("input", type=str, help="The input field")
 
     # Create subparsers for each invocation
     invocations = BaseInvocation.get_all_subclasses()
     for invocation in invocations:
         hints = get_type_hints(invocation)
-        cmd_name = get_args(hints['type'])[0]
+        cmd_name = get_args(hints["type"])[0]
         command_parser = subparsers.add_parser(cmd_name, help=invocation.__doc__)
         invocation_parsers[cmd_name] = command_parser
 
         # Add linking capability
-        command_parser.add_argument('--link', '-l', action='append', nargs=3,
-            help="A link in the format 'dest_field source_node source_field'. source_node can be relative to history (e.g. -1)")
+        command_parser.add_argument(
+            "--link",
+            "-l",
+            action="append",
+            nargs=3,
+            help="A link in the format 'dest_field source_node source_field'. source_node can be relative to history (e.g. -1)",
+        )
 
-        command_parser.add_argument('--link_node', '-ln', action='append',
-            help="A link from all fields in the specified node. Node can be relative to history (e.g. -1)")
+        command_parser.add_argument(
+            "--link_node",
+            "-ln",
+            action="append",
+            help="A link from all fields in the specified node. Node can be relative to history (e.g. -1)",
+        )
 
         # Convert all fields to arguments
         fields = invocation.__fields__
         for name, field in fields.items():
-            if name in ['id', 'type']:
+            if name in ["id", "type"]:
                 continue
 
             if get_origin(field.type_) == Literal:
@@ -84,15 +113,15 @@ def get_invocation_parser() -> argparse.ArgumentParser:
                 for val in allowed_values:
                     allowed_types.add(type(val))
                 allowed_types_list = list(allowed_types)
-                field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list] # type: ignore
+                field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list]  # type: ignore
 
                 command_parser.add_argument(
                     f"--{name}",
                     dest=name,
                     type=field_type,
                     default=field.default,
-                    choices = allowed_values,                    
-                    help=field.field_info.description
+                    choices=allowed_values,
+                    help=field.field_info.description,
                 )
             else:
                 command_parser.add_argument(
@@ -100,7 +129,7 @@ def get_invocation_parser() -> argparse.ArgumentParser:
                     dest=name,
                     type=field.type_,
                     default=field.default,
-                    help=field.field_info.description
+                    help=field.field_info.description,
                 )
 
     return parser
@@ -110,8 +139,8 @@ def get_invocation_command(invocation) -> str:
     fields = invocation.__fields__.items()
     type_hints = get_type_hints(type(invocation))
     command = [invocation.type]
-    for name,field in fields:
-        if name in ['id', 'type']:
+    for name, field in fields:
+        if name in ["id", "type"]:
             continue
 
         # TODO: add links
@@ -127,17 +156,25 @@ def get_invocation_command(invocation) -> str:
             if type_hint is str or str in get_args(type_hint):
                 command.append(f'--{name} "{field_value}"')
             else:
-                command.append(f'--{name} {field_value}')
+                command.append(f"--{name} {field_value}")
 
-    return ' '.join(command)
+    return " ".join(command)
 
 
-def get_graph_execution_history(graph_execution_state: GraphExecutionState) -> Iterable[str]:
+def get_graph_execution_history(
+    graph_execution_state: GraphExecutionState,
+) -> Iterable[str]:
     """Gets the history of fully-executed invocations for a graph execution"""
-    return (n for n in reversed(graph_execution_state.executed_history) if n in graph_execution_state.graph.nodes)
+    return (
+        n
+        for n in reversed(graph_execution_state.executed_history)
+        if n in graph_execution_state.graph.nodes
+    )
 
 
-def generate_matching_edges(a: BaseInvocation, b: BaseInvocation) -> list[tuple[EdgeConnection, EdgeConnection]]:
+def generate_matching_edges(
+    a: BaseInvocation, b: BaseInvocation
+) -> list[tuple[EdgeConnection, EdgeConnection]]:
     """Generates all possible edges between two invocations"""
     atype = type(a)
     btype = type(b)
@@ -150,10 +187,16 @@ def generate_matching_edges(a: BaseInvocation, b: BaseInvocation) -> list[tuple[
     matching_fields = set(afields.keys()).intersection(bfields.keys())
 
     # Remove invalid fields
-    invalid_fields = set(['type', 'id'])
+    invalid_fields = set(["type", "id"])
     matching_fields = matching_fields.difference(invalid_fields)
 
-    edges = [(EdgeConnection(node_id = a.id, field = field), EdgeConnection(node_id = b.id, field = field)) for field in matching_fields]
+    edges = [
+        (
+            EdgeConnection(node_id=a.id, field=field),
+            EdgeConnection(node_id=b.id, field=field),
+        )
+        for field in matching_fields
+    ]
     return edges
 
 
@@ -165,22 +208,26 @@ def invoke_cli():
 
     # NOTE: load model on first use, uncomment to load at startup
     # TODO: Make this a config option?
-    #generate.load_model()
+    # generate.load_model()
 
     events = EventServiceBase()
 
-    output_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../outputs'))
+    output_folder = os.path.abspath(
+        os.path.join(os.path.dirname(__file__), "../../../outputs")
+    )
 
     # TODO: build a file/path manager?
-    db_location = os.path.join(output_folder, 'invokeai.db')
+    db_location = os.path.join(output_folder, "invokeai.db")
 
     services = InvocationServices(
-        generate                = generate,
-        events                  = events,
-        images                  = DiskImageStorage(output_folder),
-        queue                   = MemoryInvocationQueue(),
-        graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = db_location, table_name = 'graph_executions'),
-        processor               = DefaultInvocationProcessor()
+        generate=generate,
+        events=events,
+        images=DiskImageStorage(output_folder),
+        queue=MemoryInvocationQueue(),
+        graph_execution_manager=SqliteItemStorage[GraphExecutionState](
+            filename=db_location, table_name="graph_executions"
+        ),
+        processor=DefaultInvocationProcessor(),
     )
 
     invoker = Invoker(services)
@@ -201,10 +248,10 @@ def invoke_cli():
             # Ctrl-c exits
             break
 
-        if cmd_input in ['exit','q']:
-            break;
+        if cmd_input in ["exit", "q"]:
+            break
 
-        if cmd_input in ['--help','help','h','?']:
+        if cmd_input in ["--help", "help", "h", "?"]:
             parser.print_help()
             continue
 
@@ -214,65 +261,82 @@ def invoke_cli():
             history = list(get_graph_execution_history(session))
 
             # Split the command for piping
-            cmds = cmd_input.split('|')
+            cmds = cmd_input.split("|")
             start_id = len(history)
             current_id = start_id
             new_invocations = list()
             for cmd in cmds:
-                if cmd is None or cmd.strip() == '':
-                    raise InvalidArgs('Empty command')
+                if cmd is None or cmd.strip() == "":
+                    raise InvalidArgs("Empty command")
 
                 # Parse args to create invocation
                 args = vars(parser.parse_args(shlex.split(cmd.strip())))
 
                 # Check for special commands
                 # TODO: These might be better as Pydantic models, similar to the invocations
-                if args['type'] == 'history':
-                    history_count = args['count'] or 5
+                if args["type"] == "history":
+                    history_count = args["count"] or 5
                     for i in range(min(history_count, len(history))):
                         entry_id = history[-1 - i]
                         entry = session.graph.get_node(entry_id)
-                        print(f'{entry_id}: {get_invocation_command(entry.invocation)}')
+                        print(f"{entry_id}: {get_invocation_command(entry.invocation)}")
                     continue
 
-                if args['type'] == 'reset_default':
-                    if args['input'] in defaults:
-                        del defaults[args['input']]
+                if args["type"] == "reset_default":
+                    if args["input"] in defaults:
+                        del defaults[args["input"]]
                     continue
 
-                if args['type'] == 'default':
-                    field = args['input']
-                    field_value = args['value']
+                if args["type"] == "default":
+                    field = args["input"]
+                    field_value = args["value"]
                     defaults[field] = field_value
                     continue
 
                 # Override defaults
-                for field_name,field_default in defaults.items():
+                for field_name, field_default in defaults.items():
                     if field_name in args:
                         args[field_name] = field_default
 
                 # Parse invocation
-                args['id'] = current_id
-                command = InvocationCommand(invocation = args)
+                args["id"] = current_id
+                command = InvocationCommand(invocation=args)
 
                 # Pipe previous command output (if there was a previous command)
                 edges = []
                 if len(history) > 0 or current_id != start_id:
-                    from_id = history[0] if current_id == start_id else str(current_id - 1)
-                    from_node = next(filter(lambda n: n[0].id == from_id, new_invocations))[0] if current_id != start_id else session.graph.get_node(from_id)
-                    matching_edges = generate_matching_edges(from_node, command.invocation)
+                    from_id = (
+                        history[0] if current_id == start_id else str(current_id - 1)
+                    )
+                    from_node = (
+                        next(filter(lambda n: n[0].id == from_id, new_invocations))[0]
+                        if current_id != start_id
+                        else session.graph.get_node(from_id)
+                    )
+                    matching_edges = generate_matching_edges(
+                        from_node, command.invocation
+                    )
                     edges.extend(matching_edges)
 
                 # Parse provided links
-                if 'link_node' in args and args['link_node']:
-                    for link in args['link_node']:
+                if "link_node" in args and args["link_node"]:
+                    for link in args["link_node"]:
                         link_node = session.graph.get_node(link)
-                        matching_edges = generate_matching_edges(link_node, command.invocation)
+                        matching_edges = generate_matching_edges(
+                            link_node, command.invocation
+                        )
                         edges.extend(matching_edges)
 
-                if 'link' in args and args['link']:
-                    for link in args['link']:
-                        edges.append((EdgeConnection(node_id = link[1], field = link[0]), EdgeConnection(node_id = command.invocation.id, field = link[2])))
+                if "link" in args and args["link"]:
+                    for link in args["link"]:
+                        edges.append(
+                            (
+                                EdgeConnection(node_id=link[1], field=link[0]),
+                                EdgeConnection(
+                                    node_id=command.invocation.id, field=link[2]
+                                ),
+                            )
+                        )
 
                 new_invocations.append((command.invocation, edges))
 
@@ -286,7 +350,7 @@ def invoke_cli():
                     session.add_edge(edge)
 
             # Execute all available invocations
-            invoker.invoke(session, invoke_all = True)
+            invoker.invoke(session, invoke_all=True)
             while not session.is_complete():
                 # Wait some time
                 session = invoker.services.graph_execution_manager.get(session.id)
@@ -295,7 +359,9 @@ def invoke_cli():
             # Print any errors
             if session.has_error():
                 for n in session.errors:
-                    print(f'Error in node {n} (source node {session.prepared_source_mapping[n]}): {session.errors[n]}')
+                    print(
+                        f"Error in node {n} (source node {session.prepared_source_mapping[n]}): {session.errors[n]}"
+                    )
 
                 # Start a new session
                 print("Creating a new session")

invokeai/app/invocations/__init__.py

@@ -4,5 +4,9 @@ __all__ = []
 
 dirname = os.path.dirname(os.path.abspath(__file__))
 for f in os.listdir(dirname):
-    if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py":
+    if (
+        f != "__init__.py"
+        and os.path.isfile("%s/%s" % (dirname, f))
+        and f[-3:] == ".py"
+    ):
         __all__.append(f[:-3])

invokeai/app/invocations/baseinvocation.py

@@ -3,7 +3,9 @@
 from abc import ABC, abstractmethod
 from inspect import signature
 from typing import get_args, get_type_hints
+
 from pydantic import BaseModel, Field
+
 from ..services.invocation_services import InvocationServices
 
 

invokeai/app/invocations/cv.py

@@ -1,30 +1,37 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from typing import Literal
-import numpy
-from pydantic import Field
-from PIL import Image, ImageOps
+
 import cv2 as cv
-from .image import ImageField, ImageOutput
-from .baseinvocation import BaseInvocation, InvocationContext
+import numpy
+from PIL import Image, ImageOps
+from pydantic import Field
+
 from ..services.image_storage import ImageType
+from .baseinvocation import BaseInvocation, InvocationContext
+from .image import ImageField, ImageOutput
 
 
 class CvInpaintInvocation(BaseInvocation):
     """Simple inpaint using opencv."""
-    type: Literal['cv_inpaint'] = 'cv_inpaint'
+
+    type: Literal["cv_inpaint"] = "cv_inpaint"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to inpaint")
-    mask: ImageField = Field(default=None, description="The mask to use when inpainting")
+    mask: ImageField = Field(
+        default=None, description="The mask to use when inpainting"
+    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
         mask = context.services.images.get(self.mask.image_type, self.mask.image_name)
 
         # Convert to cv image/mask
         # TODO: consider making these utility functions
-        cv_image = cv.cvtColor(numpy.array(image.convert('RGB')), cv.COLOR_RGB2BGR)
+        cv_image = cv.cvtColor(numpy.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
         cv_mask = numpy.array(ImageOps.invert(mask))
 
         # Inpaint
@@ -35,8 +42,10 @@ class CvInpaintInvocation(BaseInvocation):
         image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
 
         image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
         context.services.images.save(image_type, image_name, image_inpainted)
         return ImageOutput(
-            image = ImageField(image_type = image_type, image_name = image_name)
+            image=ImageField(image_type=image_type, image_name=image_name)
         )

invokeai/app/invocations/generate.py

@@ -0,0 +1,211 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from datetime import datetime, timezone
+from typing import Any, Literal, Optional, Union
+
+import numpy as np
+from PIL import Image
+from pydantic import Field
+from skimage.exposure.histogram_matching import match_histograms
+
+from ..services.image_storage import ImageType
+from ..services.invocation_services import InvocationServices
+from .baseinvocation import BaseInvocation, InvocationContext
+from .image import ImageField, ImageOutput
+
+SAMPLER_NAME_VALUES = Literal[
+    "ddim", "plms", "k_lms", "k_dpm_2", "k_dpm_2_a", "k_euler", "k_euler_a", "k_heun"
+]
+
+
+# Text to image
+class TextToImageInvocation(BaseInvocation):
+    """Generates an image using text2img."""
+
+    type: Literal["txt2img"] = "txt2img"
+
+    # Inputs
+    # TODO: consider making prompt optional to enable providing prompt through a link
+    # fmt: off
+    prompt: Optional[str] = Field(description="The prompt to generate an image from")
+    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
+    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
+    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
+    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image", )
+    cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
+    sampler_name: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The sampler to use" )
+    seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
+    model:       str = Field(default="", description="The model to use (currently ignored)")
+    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
+    # fmt: on
+
+    # TODO: pass this an emitter method or something? or a session for dispatching?
+    def dispatch_progress(
+        self, context: InvocationContext, sample: Any = None, step: int = 0
+    ) -> None:
+        context.services.events.emit_generator_progress(
+            context.graph_execution_state_id,
+            self.id,
+            step,
+            float(step) / float(self.steps),
+        )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        def step_callback(sample, step=0):
+            self.dispatch_progress(context, sample, step)
+
+        # Handle invalid model parameter
+        # TODO: figure out if this can be done via a validator that uses the model_cache
+        # TODO: How to get the default model name now?
+        if self.model is None or self.model == "":
+            self.model = context.services.generate.model_name
+
+        # Set the model (if already cached, this does nothing)
+        context.services.generate.set_model(self.model)
+
+        results = context.services.generate.prompt2image(
+            prompt=self.prompt,
+            step_callback=step_callback,
+            **self.dict(
+                exclude={"prompt"}
+            ),  # Shorthand for passing all of the parameters above manually
+        )
+
+        # 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
+        )
+        context.services.images.save(image_type, image_name, results[0][0])
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
+        )
+
+
+class ImageToImageInvocation(TextToImageInvocation):
+    """Generates an image using img2img."""
+
+    type: Literal["img2img"] = "img2img"
+
+    # Inputs
+    image: Union[ImageField, None] = Field(description="The input image")
+    strength: float = Field(
+        default=0.75, gt=0, le=1, description="The strength of the original image"
+    )
+    fit: bool = Field(
+        default=True,
+        description="Whether or not the result should be fit to the aspect ratio of the input image",
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = (
+            None
+            if self.image is None
+            else context.services.images.get(
+                self.image.image_type, self.image.image_name
+            )
+        )
+        mask = None
+
+        def step_callback(sample, step=0):
+            self.dispatch_progress(context, sample, step)
+
+        # Handle invalid model parameter
+        # TODO: figure out if this can be done via a validator that uses the model_cache
+        # TODO: How to get the default model name now?
+        if self.model is None or self.model == "":
+            self.model = context.services.generate.model_name
+
+        # Set the model (if already cached, this does nothing)
+        context.services.generate.set_model(self.model)
+
+        results = context.services.generate.prompt2image(
+            prompt=self.prompt,
+            init_img=image,
+            init_mask=mask,
+            step_callback=step_callback,
+            **self.dict(
+                exclude={"prompt", "image", "mask"}
+            ),  # Shorthand for passing all of the parameters above manually
+        )
+
+        result_image = results[0][0]
+
+        # 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
+        )
+        context.services.images.save(image_type, image_name, result_image)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
+        )
+
+
+class InpaintInvocation(ImageToImageInvocation):
+    """Generates an image using inpaint."""
+
+    type: Literal["inpaint"] = "inpaint"
+
+    # Inputs
+    mask: Union[ImageField, None] = Field(description="The mask")
+    inpaint_replace: float = Field(
+        default=0.0,
+        ge=0.0,
+        le=1.0,
+        description="The amount by which to replace masked areas with latent noise",
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = (
+            None
+            if self.image is None
+            else context.services.images.get(
+                self.image.image_type, self.image.image_name
+            )
+        )
+        mask = (
+            None
+            if self.mask is None
+            else context.services.images.get(self.mask.image_type, self.mask.image_name)
+        )
+
+        def step_callback(sample, step=0):
+            self.dispatch_progress(context, sample, step)
+
+        # Handle invalid model parameter
+        # TODO: figure out if this can be done via a validator that uses the model_cache
+        # TODO: How to get the default model name now?
+        if self.model is None or self.model == "":
+            self.model = context.services.generate.model_name
+
+        # Set the model (if already cached, this does nothing)
+        context.services.generate.set_model(self.model)
+
+        results = context.services.generate.prompt2image(
+            prompt=self.prompt,
+            init_img=image,
+            init_mask=mask,
+            step_callback=step_callback,
+            **self.dict(
+                exclude={"prompt", "image", "mask"}
+            ),  # Shorthand for passing all of the parameters above manually
+        )
+
+        result_image = results[0][0]
+
+        # 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
+        )
+        context.services.images.save(image_type, image_name, result_image)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
+        )

invokeai/app/invocations/image.py

@@ -2,30 +2,37 @@
 
 from datetime import datetime, timezone
 from typing import Literal, Optional
+
 import numpy
-from pydantic import Field, BaseModel
-from PIL import Image, ImageOps, ImageFilter
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
+from PIL import Image, ImageFilter, ImageOps
+from pydantic import BaseModel, Field
+
 from ..services.image_storage import ImageType
 from ..services.invocation_services import InvocationServices
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
 
 
 class ImageField(BaseModel):
     """An image field used for passing image objects between invocations"""
-    image_type: str = Field(default=ImageType.RESULT, description="The type of the image")
+
+    image_type: str = Field(
+        default=ImageType.RESULT, description="The type of the image"
+    )
     image_name: Optional[str] = Field(default=None, description="The name of the image")
 
 
 class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
-    type: Literal['image'] = 'image'
+
+    type: Literal["image"] = "image"
 
     image: ImageField = Field(default=None, description="The output image")
 
 
 class MaskOutput(BaseInvocationOutput):
     """Base class for invocations that output a mask"""
-    type: Literal['mask'] = 'mask'
+
+    type: Literal["mask"] = "mask"
 
     mask: ImageField = Field(default=None, description="The output mask")
 
@@ -33,7 +40,8 @@ class MaskOutput(BaseInvocationOutput):
 # TODO: this isn't really necessary anymore
 class LoadImageInvocation(BaseInvocation):
     """Load an image from a filename and provide it as output."""
-    type: Literal['load_image'] = 'load_image'
+
+    type: Literal["load_image"] = "load_image"
 
     # Inputs
     image_type: ImageType = Field(description="The type of the image")
@@ -41,69 +49,100 @@ class LoadImageInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         return ImageOutput(
-            image = ImageField(image_type = self.image_type, image_name = self.image_name)
+            image=ImageField(image_type=self.image_type, image_name=self.image_name)
         )
 
 
 class ShowImageInvocation(BaseInvocation):
     """Displays a provided image, and passes it forward in the pipeline."""
-    type: Literal['show_image'] = 'show_image'
+
+    type: Literal["show_image"] = "show_image"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to show")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
         if image:
             image.show()
 
         # TODO: how to handle failure?
 
         return ImageOutput(
-            image = ImageField(image_type = self.image.image_type, image_name = self.image.image_name)
+            image=ImageField(
+                image_type=self.image.image_type, image_name=self.image.image_name
+            )
         )
 
 
 class CropImageInvocation(BaseInvocation):
     """Crops an image to a specified box. The box can be outside of the image."""
-    type: Literal['crop'] = 'crop'
+
+    type: Literal["crop"] = "crop"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to crop")
-    x: int      = Field(default=0, description="The left x coordinate of the crop rectangle")
-    y: int      = Field(default=0, description="The top y coordinate of the crop rectangle")
-    width: int  = Field(default=512, gt=0, description="The width of the crop rectangle")
-    height: int = Field(default=512, gt=0, description="The height of the crop rectangle")
+    x: int = Field(default=0, description="The left x coordinate of the crop rectangle")
+    y: int = Field(default=0, description="The top y coordinate of the crop rectangle")
+    width: int = Field(default=512, gt=0, description="The width of the crop rectangle")
+    height: int = Field(
+        default=512, gt=0, description="The height of the crop rectangle"
+    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
 
-        image_crop = Image.new(mode = 'RGBA', size = (self.width, self.height), color = (0, 0, 0, 0))
+        image_crop = Image.new(
+            mode="RGBA", size=(self.width, self.height), color=(0, 0, 0, 0)
+        )
         image_crop.paste(image, (-self.x, -self.y))
 
         image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
         context.services.images.save(image_type, image_name, image_crop)
         return ImageOutput(
-            image = ImageField(image_type = image_type, image_name = image_name)
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
 
 
 class PasteImageInvocation(BaseInvocation):
     """Pastes an image into another image."""
-    type: Literal['paste'] = 'paste'
+
+    type: Literal["paste"] = "paste"
 
     # Inputs
-    base_image: ImageField     = Field(default=None, description="The base image")
-    image: ImageField          = Field(default=None, description="The image to paste")
-    mask: Optional[ImageField] = Field(default=None, description="The mask to use when pasting")
-    x: int                     = Field(default=0, description="The left x coordinate at which to paste the image")
-    y: int                     = Field(default=0, description="The top y coordinate at which to paste the image")
+    base_image: ImageField = Field(default=None, description="The base image")
+    image: ImageField = Field(default=None, description="The image to paste")
+    mask: Optional[ImageField] = Field(
+        default=None, description="The mask to use when pasting"
+    )
+    x: int = Field(
+        default=0, description="The left x coordinate at which to paste the image"
+    )
+    y: int = Field(
+        default=0, description="The top y coordinate at which to paste the image"
+    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        base_image = context.services.images.get(self.base_image.image_type, self.base_image.image_name)
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
-        mask = None if self.mask is None else ImageOps.invert(services.images.get(self.mask.image_type, self.mask.image_name))
+        base_image = context.services.images.get(
+            self.base_image.image_type, self.base_image.image_name
+        )
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+        mask = (
+            None
+            if self.mask is None
+            else ImageOps.invert(
+                services.images.get(self.mask.image_type, self.mask.image_name)
+            )
+        )
         # TODO: probably shouldn't invert mask here... should user be required to do it?
 
         min_x = min(0, self.x)
@@ -111,67 +150,88 @@ class PasteImageInvocation(BaseInvocation):
         max_x = max(base_image.width, image.width + self.x)
         max_y = max(base_image.height, image.height + self.y)
 
-        new_image = Image.new(mode = 'RGBA', size = (max_x - min_x, max_y - min_y), color = (0, 0, 0, 0))
+        new_image = Image.new(
+            mode="RGBA", size=(max_x - min_x, max_y - min_y), color=(0, 0, 0, 0)
+        )
         new_image.paste(base_image, (abs(min_x), abs(min_y)))
-        new_image.paste(image, (max(0, self.x), max(0, self.y)), mask = mask)
+        new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
 
         image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
         context.services.images.save(image_type, image_name, new_image)
         return ImageOutput(
-            image = ImageField(image_type = image_type, image_name = image_name)
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
 
 
 class MaskFromAlphaInvocation(BaseInvocation):
     """Extracts the alpha channel of an image as a mask."""
-    type: Literal['tomask'] = 'tomask'
+
+    type: Literal["tomask"] = "tomask"
 
     # Inputs
-    image: ImageField = Field(default=None, description="The image to create the mask from")
+    image: ImageField = Field(
+        default=None, description="The image to create the mask from"
+    )
     invert: bool = Field(default=False, description="Whether or not to invert the mask")
 
     def invoke(self, context: InvocationContext) -> MaskOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
 
         image_mask = image.split()[-1]
         if self.invert:
             image_mask = ImageOps.invert(image_mask)
 
         image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
-        context.services.images.save(image_type, image_name, image_mask)
-        return MaskOutput(
-            mask = ImageField(image_type = image_type, image_name = image_name)
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
+        context.services.images.save(image_type, image_name, image_mask)
+        return MaskOutput(mask=ImageField(image_type=image_type, image_name=image_name))
 
 
 class BlurInvocation(BaseInvocation):
     """Blurs an image"""
-    type: Literal['blur'] = 'blur'
+
+    type: Literal["blur"] = "blur"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to blur")
-    radius: float     = Field(default=8.0, ge=0, description="The blur radius")
-    blur_type: Literal['gaussian', 'box'] = Field(default='gaussian', description="The type of blur")
+    radius: float = Field(default=8.0, ge=0, description="The blur radius")
+    blur_type: Literal["gaussian", "box"] = Field(
+        default="gaussian", description="The type of blur"
+    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
 
-        blur = ImageFilter.GaussianBlur(self.radius) if self.blur_type == 'gaussian' else ImageFilter.BoxBlur(self.radius)
+        blur = (
+            ImageFilter.GaussianBlur(self.radius)
+            if self.blur_type == "gaussian"
+            else ImageFilter.BoxBlur(self.radius)
+        )
         blur_image = image.filter(blur)
 
         image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
         context.services.images.save(image_type, image_name, blur_image)
         return ImageOutput(
-            image = ImageField(image_type = image_type, image_name = image_name)
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
 
 
 class LerpInvocation(BaseInvocation):
     """Linear interpolation of all pixels of an image"""
-    type: Literal['lerp'] = 'lerp'
+
+    type: Literal["lerp"] = "lerp"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to lerp")
@@ -179,7 +239,9 @@ class LerpInvocation(BaseInvocation):
     max: int = Field(default=255, ge=0, le=255, description="The maximum output value")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
 
         image_arr = numpy.asarray(image, dtype=numpy.float32) / 255
         image_arr = image_arr * (self.max - self.min) + self.max
@@ -187,33 +249,46 @@ class LerpInvocation(BaseInvocation):
         lerp_image = Image.fromarray(numpy.uint8(image_arr))
 
         image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
         context.services.images.save(image_type, image_name, lerp_image)
         return ImageOutput(
-            image = ImageField(image_type = image_type, image_name = image_name)
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
 
 
 class InverseLerpInvocation(BaseInvocation):
     """Inverse linear interpolation of all pixels of an image"""
-    type: Literal['ilerp'] = 'ilerp'
+    #fmt: off
+    type: Literal["ilerp"] = "ilerp"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to lerp")
     min: int = Field(default=0, ge=0, le=255, description="The minimum input value")
     max: int = Field(default=255, ge=0, le=255, description="The maximum input value")
+    #fmt: on
     
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
 
         image_arr = numpy.asarray(image, dtype=numpy.float32)
-        image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255
+        image_arr = (
+            numpy.minimum(
+                numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1
+            )
+            * 255
+        )
 
         ilerp_image = Image.fromarray(numpy.uint8(image_arr))
 
         image_type = ImageType.INTERMEDIATE
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
         context.services.images.save(image_type, image_name, ilerp_image)
         return ImageOutput(
-            image = ImageField(image_type = image_type, image_name = image_name)
+            image=ImageField(image_type=image_type, image_name=image_name)
         )

invokeai/app/invocations/prompt.py

@@ -1,9 +1,14 @@
 from typing import Literal
+
 from pydantic.fields import Field
+
 from .baseinvocation import BaseInvocationOutput
 
+
 class PromptOutput(BaseInvocationOutput):
     """Base class for invocations that output a prompt"""
-    type: Literal['prompt'] = 'prompt'
+    #fmt: off
+    type: Literal["prompt"] = "prompt"
 
     prompt: str = Field(default=None, description="The output prompt")
+    #fmt: on

invokeai/app/invocations/reconstruct.py

@@ -1,36 +1,43 @@
 from datetime import datetime, timezone
 from typing import Literal, Union
+
 from pydantic import Field
-from .image import ImageField, ImageOutput
-from .baseinvocation import BaseInvocation, InvocationContext
+
 from ..services.image_storage import ImageType
 from ..services.invocation_services import InvocationServices
+from .baseinvocation import BaseInvocation, InvocationContext
+from .image import ImageField, ImageOutput
 
 
 class RestoreFaceInvocation(BaseInvocation):
     """Restores faces in an image."""
-    type: Literal['restore_face'] = 'restore_face'
+    #fmt: off
+    type:  Literal["restore_face"] = "restore_face"
 
     # Inputs
-    image: Union[ImageField,None] = Field(description="The input image")
-    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength of the restoration")
-
+    image: Union[ImageField, None] = Field(description="The input image")
+    strength:                float = Field(default=0.75, gt=0, le=1, description="The strength of the restoration"  )
+    #fmt: on
     
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
         results = context.services.generate.upscale_and_reconstruct(
-            image_list     = [[image, 0]],
-            upscale        = None,
-            strength       = self.strength, # GFPGAN strength
-            save_original  = False,
-            image_callback = None,
+            image_list=[[image, 0]],
+            upscale=None,
+            strength=self.strength,  # GFPGAN strength
+            save_original=False,
+            image_callback=None,
         )
 
         # Results are image and seed, unwrap for now
         # TODO: can this return multiple results?
         image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
         context.services.images.save(image_type, image_name, results[0][0])
         return ImageOutput(
-            image = ImageField(image_type = image_type, image_name = image_name)
+            image=ImageField(image_type=image_type, image_name=image_name)
         )

invokeai/app/invocations/upscale.py

@@ -2,37 +2,45 @@
 
 from datetime import datetime, timezone
 from typing import Literal, Union
+
 from pydantic import Field
-from .image import ImageField, ImageOutput
-from .baseinvocation import BaseInvocation, InvocationContext
+
 from ..services.image_storage import ImageType
 from ..services.invocation_services import InvocationServices
+from .baseinvocation import BaseInvocation, InvocationContext
+from .image import ImageField, ImageOutput
 
 
 class UpscaleInvocation(BaseInvocation):
     """Upscales an image."""
-    type: Literal['upscale'] = 'upscale'
+    #fmt: off
+    type: Literal["upscale"] = "upscale"
 
     # Inputs
-    image: Union[ImageField,None] = Field(description="The input image", default=None)
-    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
-    level: Literal[2,4]           = Field(default=2, description = "The upscale level")
+    image: Union[ImageField, None] = Field(description="The input image", default=None)
+    strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
+    level: Literal[2, 4] = Field(default=2, description="The upscale level")
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
         results = context.services.generate.upscale_and_reconstruct(
-            image_list     = [[image, 0]],
-            upscale        = (self.level, self.strength),
-            strength       = 0.0, # GFPGAN strength
-            save_original  = False,
-            image_callback = None,
+            image_list=[[image, 0]],
+            upscale=(self.level, self.strength),
+            strength=0.0,  # GFPGAN strength
+            save_original=False,
+            image_callback=None,
         )
 
         # Results are image and seed, unwrap for now
         # TODO: can this return multiple results?
         image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
         context.services.images.save(image_type, image_name, results[0][0])
         return ImageOutput(
-            image = ImageField(image_type = image_type, image_name = image_name)
+            image=ImageField(image_type=image_type, image_name=image_name)
         )

invokeai/app/services/events.py

@@ -0,0 +1,83 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from typing import Any, Dict
+
+
+class EventServiceBase:
+    session_event: str = "session_event"
+
+    """Basic event bus, to have an empty stand-in when not needed"""
+
+    def dispatch(self, event_name: str, payload: Any) -> None:
+        pass
+
+    def __emit_session_event(self, event_name: str, payload: Dict) -> None:
+        self.dispatch(
+            event_name=EventServiceBase.session_event,
+            payload=dict(event=event_name, data=payload),
+        )
+
+    # Define events here for every event in the system.
+    # This will make them easier to integrate until we find a schema generator.
+    def emit_generator_progress(
+        self,
+        graph_execution_state_id: str,
+        invocation_id: str,
+        step: int,
+        percent: float,
+    ) -> None:
+        """Emitted when there is generation progress"""
+        self.__emit_session_event(
+            event_name="generator_progress",
+            payload=dict(
+                graph_execution_state_id=graph_execution_state_id,
+                invocation_id=invocation_id,
+                step=step,
+                percent=percent,
+            ),
+        )
+
+    def emit_invocation_complete(
+        self, graph_execution_state_id: str, invocation_id: str, result: Dict
+    ) -> None:
+        """Emitted when an invocation has completed"""
+        self.__emit_session_event(
+            event_name="invocation_complete",
+            payload=dict(
+                graph_execution_state_id=graph_execution_state_id,
+                invocation_id=invocation_id,
+                result=result,
+            ),
+        )
+
+    def emit_invocation_error(
+        self, graph_execution_state_id: str, invocation_id: str, error: str
+    ) -> None:
+        """Emitted when an invocation has completed"""
+        self.__emit_session_event(
+            event_name="invocation_error",
+            payload=dict(
+                graph_execution_state_id=graph_execution_state_id,
+                invocation_id=invocation_id,
+                error=error,
+            ),
+        )
+
+    def emit_invocation_started(
+        self, graph_execution_state_id: str, invocation_id: str
+    ) -> None:
+        """Emitted when an invocation has started"""
+        self.__emit_session_event(
+            event_name="invocation_started",
+            payload=dict(
+                graph_execution_state_id=graph_execution_state_id,
+                invocation_id=invocation_id,
+            ),
+        )
+
+    def emit_graph_execution_complete(self, graph_execution_state_id: str) -> None:
+        """Emitted when a session has completed all invocations"""
+        self.__emit_session_event(
+            event_name="graph_execution_state_complete",
+            payload=dict(graph_execution_state_id=graph_execution_state_id),
+        )

invokeai/app/services/generate_initializer.py

@@ -1,42 +1,47 @@
-from argparse import Namespace
 import os
 import sys
 import traceback
+from argparse import Namespace
 
-from ...model_manager import ModelManager
+import invokeai.version
+from invokeai.backend import Generate, ModelManager
 
 from ...globals import Globals
-from ....generate import Generate
-import ldm.invoke
 
 
 # TODO: most of this code should be split into individual services as the Generate.py code is deprecated
 def get_generate(args, config) -> Generate:
     if not args.conf:
-        config_file = os.path.join(Globals.root,'configs','models.yaml')
+        config_file = os.path.join(Globals.root, "configs", "models.yaml")
         if not os.path.exists(config_file):
-            report_model_error(args, FileNotFoundError(f"The file {config_file} could not be found."))
+            report_model_error(
+                args, FileNotFoundError(f"The file {config_file} could not be found.")
+            )
 
-    print(f'>> {ldm.invoke.__app_name__}, version {ldm.invoke.__version__}')
+    print(f">> {invokeai.version.__app_name__}, version {invokeai.version.__version__}")
     print(f'>> InvokeAI runtime directory is "{Globals.root}"')
 
     # these two lines prevent a horrible warning message from appearing
     # when the frozen CLIP tokenizer is imported
     import transformers  # type: ignore
+
     transformers.logging.set_verbosity_error()
     import diffusers
+
     diffusers.logging.set_verbosity_error()
 
     # Loading Face Restoration and ESRGAN Modules
-    gfpgan,codeformer,esrgan = load_face_restoration(args)
+    gfpgan, codeformer, esrgan = load_face_restoration(args)
 
     # normalize the config directory relative to root
     if not os.path.isabs(args.conf):
-        args.conf = os.path.normpath(os.path.join(Globals.root,args.conf))
+        args.conf = os.path.normpath(os.path.join(Globals.root, args.conf))
 
     if args.embeddings:
         if not os.path.isabs(args.embedding_path):
-            embedding_path = os.path.normpath(os.path.join(Globals.root,args.embedding_path))
+            embedding_path = os.path.normpath(
+                os.path.join(Globals.root, args.embedding_path)
+            )
         else:
             embedding_path = args.embedding_path
     else:
@@ -49,35 +54,35 @@ def get_generate(args, config) -> Generate:
     if args.infile:
         try:
             if os.path.isfile(args.infile):
-                infile = open(args.infile, 'r', encoding='utf-8')
-            elif args.infile == '-':  # stdin
+                infile = open(args.infile, "r", encoding="utf-8")
+            elif args.infile == "-":  # stdin
                 infile = sys.stdin
             else:
-                raise FileNotFoundError(f'{args.infile} not found.')
+                raise FileNotFoundError(f"{args.infile} not found.")
         except (FileNotFoundError, IOError) as e:
-            print(f'{e}. Aborting.')
+            print(f"{e}. Aborting.")
             sys.exit(-1)
 
     # creating a Generate object:
     try:
         gen = Generate(
-            conf              = args.conf,
-            model             = args.model,
-            sampler_name      = args.sampler_name,
-            embedding_path    = embedding_path,
-            full_precision    = args.full_precision,
-            precision         = args.precision,
-            gfpgan            = gfpgan,
-            codeformer        = codeformer,
-            esrgan            = esrgan,
-            free_gpu_mem      = args.free_gpu_mem,
-            safety_checker    = args.safety_checker,
-            max_loaded_models = args.max_loaded_models,
-            )
+            conf=args.conf,
+            model=args.model,
+            sampler_name=args.sampler_name,
+            embedding_path=embedding_path,
+            full_precision=args.full_precision,
+            precision=args.precision,
+            gfpgan=gfpgan,
+            codeformer=codeformer,
+            esrgan=esrgan,
+            free_gpu_mem=args.free_gpu_mem,
+            safety_checker=args.safety_checker,
+            max_loaded_models=args.max_loaded_models,
+        )
     except (FileNotFoundError, TypeError, AssertionError) as e:
-        report_model_error(opt,e)
+        report_model_error(opt, e)
     except (IOError, KeyError) as e:
-        print(f'{e}. Aborting.')
+        print(f"{e}. Aborting.")
         sys.exit(-1)
 
     if args.seamless:
@@ -106,51 +111,61 @@ def load_face_restoration(opt):
     try:
         gfpgan, codeformer, esrgan = None, None, None
         if opt.restore or opt.esrgan:
-            from ldm.invoke.restoration import Restoration
+            from invokeai.backend.restoration import Restoration
+
             restoration = Restoration()
             if opt.restore:
-                gfpgan, codeformer = restoration.load_face_restore_models(opt.gfpgan_model_path)
+                gfpgan, codeformer = restoration.load_face_restore_models(
+                    opt.gfpgan_model_path
+                )
             else:
-                print('>> Face restoration disabled')
+                print(">> Face restoration disabled")
             if opt.esrgan:
                 esrgan = restoration.load_esrgan(opt.esrgan_bg_tile)
             else:
-                print('>> Upscaling disabled')
+                print(">> Upscaling disabled")
         else:
-            print('>> Face restoration and upscaling disabled')
+            print(">> Face restoration and upscaling disabled")
     except (ModuleNotFoundError, ImportError):
         print(traceback.format_exc(), file=sys.stderr)
-        print('>> You may need to install the ESRGAN and/or GFPGAN modules')
-    return gfpgan,codeformer,esrgan
+        print(">> You may need to install the ESRGAN and/or GFPGAN modules")
+    return gfpgan, codeformer, esrgan
 
 
-def report_model_error(opt:Namespace, e:Exception):
+def report_model_error(opt: Namespace, e: Exception):
     print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
-    print('** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models.')
-    yes_to_all = os.environ.get('INVOKE_MODEL_RECONFIGURE')
+    print(
+        "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
+    )
+    yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE")
     if yes_to_all:
-        print('** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE')
+        print(
+            "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
+        )
     else:
-        response = input('Do you want to run invokeai-configure script to select and/or reinstall models? [y] ')
-        if response.startswith(('n', 'N')):
+        response = input(
+            "Do you want to run invokeai-configure script to select and/or reinstall models? [y] "
+        )
+        if response.startswith(("n", "N")):
             return
 
-    print('invokeai-configure is launching....\n')
+    print("invokeai-configure is launching....\n")
 
     # Match arguments that were set on the CLI
     # only the arguments accepted by the configuration script are parsed
     root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else []
     config = ["--config", opt.conf] if opt.conf is not None else []
     previous_args = sys.argv
-    sys.argv = [ 'invokeai-configure' ]
+    sys.argv = ["invokeai-configure"]
     sys.argv.extend(root_dir)
     sys.argv.extend(config)
     if yes_to_all is not None:
         for arg in yes_to_all.split():
             sys.argv.append(arg)
 
-    from ldm.invoke.config import invokeai_configure
-    invokeai_configure.main()
+    from invokeai.frontend.install import invokeai_configure
+
+    invokeai_configure()
     # TODO: Figure out how to restart
     # print('** InvokeAI will now restart')
     # sys.argv = previous_args
@@ -161,10 +176,12 @@ def report_model_error(opt:Namespace, e:Exception):
 # Temporary initializer for Generate until we migrate off of it
 def old_get_generate(args, config) -> Generate:
     # TODO: Remove the need for globals
-    from ldm.invoke.globals import Globals
+    from invokeai.backend.globals import Globals
 
     # alert - setting globals here
-    Globals.root = os.path.expanduser(args.root_dir or os.environ.get('INVOKEAI_ROOT') or os.path.abspath('.'))
+    Globals.root = os.path.expanduser(
+        args.root_dir or os.environ.get("INVOKEAI_ROOT") or os.path.abspath(".")
+    )
     Globals.try_patchmatch = args.patchmatch
 
     print(f'>> InvokeAI runtime directory is "{Globals.root}"')
@@ -172,6 +189,7 @@ def old_get_generate(args, config) -> Generate:
     # these two lines prevent a horrible warning message from appearing
     # when the frozen CLIP tokenizer is imported
     import transformers
+
     transformers.logging.set_verbosity_error()
 
     # Loading Face Restoration and ESRGAN Modules
@@ -179,53 +197,57 @@ def old_get_generate(args, config) -> Generate:
     try:
         if config.restore or config.esrgan:
             from ldm.invoke.restoration import Restoration
+
             restoration = Restoration()
             if config.restore:
-                gfpgan, codeformer = restoration.load_face_restore_models(config.gfpgan_model_path)
+                gfpgan, codeformer = restoration.load_face_restore_models(
+                    config.gfpgan_model_path
+                )
             else:
-                print('>> Face restoration disabled')
+                print(">> Face restoration disabled")
             if config.esrgan:
                 esrgan = restoration.load_esrgan(config.esrgan_bg_tile)
             else:
-                print('>> Upscaling disabled')
+                print(">> Upscaling disabled")
         else:
-            print('>> Face restoration and upscaling disabled')
+            print(">> Face restoration and upscaling disabled")
     except (ModuleNotFoundError, ImportError):
         print(traceback.format_exc(), file=sys.stderr)
-        print('>> You may need to install the ESRGAN and/or GFPGAN modules')
+        print(">> You may need to install the ESRGAN and/or GFPGAN modules")
 
     # normalize the config directory relative to root
     if not os.path.isabs(config.conf):
-        config.conf = os.path.normpath(os.path.join(Globals.root,config.conf))
+        config.conf = os.path.normpath(os.path.join(Globals.root, config.conf))
 
     if config.embeddings:
         if not os.path.isabs(config.embedding_path):
-            embedding_path = os.path.normpath(os.path.join(Globals.root,config.embedding_path))
+            embedding_path = os.path.normpath(
+                os.path.join(Globals.root, config.embedding_path)
+            )
     else:
         embedding_path = None
 
-
     # TODO: lazy-initialize this by wrapping it
     try:
         generate = Generate(
-            conf              = config.conf,
-            model             = config.model,
-            sampler_name      = config.sampler_name,
-            embedding_path    = embedding_path,
-            full_precision    = config.full_precision,
-            precision         = config.precision,
-            gfpgan            = gfpgan,
-            codeformer        = codeformer,
-            esrgan            = esrgan,
-            free_gpu_mem      = config.free_gpu_mem,
-            safety_checker    = config.safety_checker,
-            max_loaded_models = config.max_loaded_models,
+            conf=config.conf,
+            model=config.model,
+            sampler_name=config.sampler_name,
+            embedding_path=embedding_path,
+            full_precision=config.full_precision,
+            precision=config.precision,
+            gfpgan=gfpgan,
+            codeformer=codeformer,
+            esrgan=esrgan,
+            free_gpu_mem=config.free_gpu_mem,
+            safety_checker=config.safety_checker,
+            max_loaded_models=config.max_loaded_models,
         )
     except (FileNotFoundError, TypeError, AssertionError):
-        #emergency_model_reconfigure() # TODO?
+        # emergency_model_reconfigure() # TODO?
         sys.exit(-1)
     except (IOError, KeyError) as e:
-        print(f'{e}. Aborting.')
+        print(f"{e}. Aborting.")
         sys.exit(-1)
 
     generate.free_gpu_mem = config.free_gpu_mem

invokeai/app/services/image_storage.py

@@ -1,20 +1,22 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from abc import ABC, abstractmethod
-from enum import Enum
 import datetime
 import os
+from abc import ABC, abstractmethod
+from enum import Enum
 from pathlib import Path
 from queue import Queue
 from typing import Dict
+
 from PIL.Image import Image
-from ...pngwriter import PngWriter
+
+from invokeai.backend.image_util import PngWriter
 
 
 class ImageType(str, Enum):
-    RESULT = 'results'
-    INTERMEDIATE = 'intermediates'
-    UPLOAD = 'uploads'
+    RESULT = "results"
+    INTERMEDIATE = "intermediates"
+    UPLOAD = "uploads"
 
 
 class ImageStorageBase(ABC):
@@ -38,14 +40,15 @@ class ImageStorageBase(ABC):
         pass
 
     def create_name(self, context_id: str, node_id: str) -> str:
-        return f'{context_id}_{node_id}_{str(int(datetime.datetime.now(datetime.timezone.utc).timestamp()))}.png'
+        return f"{context_id}_{node_id}_{str(int(datetime.datetime.now(datetime.timezone.utc).timestamp()))}.png"
 
 
 class DiskImageStorage(ImageStorageBase):
     """Stores images on disk"""
+
     __output_folder: str
     __pngWriter: PngWriter
-    __cache_ids: Queue # TODO: this is an incredibly naive cache
+    __cache_ids: Queue  # TODO: this is an incredibly naive cache
     __cache: Dict[str, Image]
     __max_cache_size: int
 
@@ -54,13 +57,15 @@ class DiskImageStorage(ImageStorageBase):
         self.__pngWriter = PngWriter(output_folder)
         self.__cache = dict()
         self.__cache_ids = Queue()
-        self.__max_cache_size = 10 # TODO: get this from config
+        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)).mkdir(
+                parents=True, exist_ok=True
+            )
 
     def get(self, image_type: ImageType, image_name: str) -> Image:
         image_path = self.get_path(image_type, image_name)
@@ -79,7 +84,9 @@ class DiskImageStorage(ImageStorageBase):
 
     def save(self, image_type: ImageType, image_name: str, image: Image) -> None:
         image_subpath = os.path.join(image_type, image_name)
-        self.__pngWriter.save_image_and_prompt_to_png(image, "", image_subpath, None) # TODO: just pass full path to png writer
+        self.__pngWriter.save_image_and_prompt_to_png(
+            image, "", image_subpath, None
+        )  # TODO: just pass full path to png writer
 
         image_path = self.get_path(image_type, image_name)
         self.__set_cache(image_path, image)
@@ -98,7 +105,9 @@ class DiskImageStorage(ImageStorageBase):
     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
+            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()
                 del self.__cache[cache_id]

invokeai/app/services/invocation_queue.py

@@ -6,17 +6,19 @@ from queue import Queue
 
 # TODO: make this serializable
 class InvocationQueueItem:
-    #session_id: str
+    # session_id: str
     graph_execution_state_id: str
     invocation_id: str
     invoke_all: bool
 
-    def __init__(self,
-        #session_id: str,
+    def __init__(
+        self,
+        # session_id: str,
         graph_execution_state_id: str,
         invocation_id: str,
-        invoke_all: bool = False):
-        #self.session_id = session_id
+        invoke_all: bool = False,
+    ):
+        # self.session_id = session_id
         self.graph_execution_state_id = graph_execution_state_id
         self.invocation_id = invocation_id
         self.invoke_all = invoke_all
@@ -24,12 +26,13 @@ class InvocationQueueItem:
 
 class InvocationQueueABC(ABC):
     """Abstract base class for all invocation queues"""
+
     @abstractmethod
     def get(self) -> InvocationQueueItem:
         pass
 
     @abstractmethod
-    def put(self, item: InvocationQueueItem|None) -> None:
+    def put(self, item: InvocationQueueItem | None) -> None:
         pass
 
 
@@ -42,5 +45,5 @@ class MemoryInvocationQueue(InvocationQueueABC):
     def get(self) -> InvocationQueueItem:
         return self.__queue.get()
 
-    def put(self, item: InvocationQueueItem|None) -> None:
+    def put(self, item: InvocationQueueItem | None) -> None:
         self.__queue.put(item)

invokeai/app/services/invocation_services.py

@@ -1,29 +1,32 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+from invokeai.backend import Generate
+
+from .events import EventServiceBase
+from .image_storage import ImageStorageBase
 from .invocation_queue import InvocationQueueABC
 from .item_storage import ItemStorageABC
-from .image_storage import ImageStorageBase
-from .events import EventServiceBase
-from ....generate import Generate
 
 
-class InvocationServices():
+class InvocationServices:
     """Services that can be used by invocations"""
-    generate: Generate # TODO: wrap Generate, or split it up from model?
+
+    generate: Generate  # TODO: wrap Generate, or split it up from model?
     events: EventServiceBase
     images: ImageStorageBase
     queue: InvocationQueueABC
 
     # NOTE: we must forward-declare any types that include invocations, since invocations can use services
-    graph_execution_manager: ItemStorageABC['GraphExecutionState']
-    processor: 'InvocationProcessorABC'
+    graph_execution_manager: ItemStorageABC["GraphExecutionState"]
+    processor: "InvocationProcessorABC"
 
-    def __init__(self,
+    def __init__(
+        self,
         generate: Generate,
         events: EventServiceBase,
         images: ImageStorageBase,
         queue: InvocationQueueABC,
-        graph_execution_manager: ItemStorageABC['GraphExecutionState'],
-        processor: 'InvocationProcessorABC'
+        graph_execution_manager: ItemStorageABC["GraphExecutionState"],
+        processor: "InvocationProcessorABC",
     ):
         self.generate = generate
         self.events = events

invokeai/app/services/invoker.py

@@ -2,11 +2,12 @@
 
 from abc import ABC
 from threading import Event, Thread
-from .graph import Graph, GraphExecutionState
-from .item_storage import ItemStorageABC
+
 from ..invocations.baseinvocation import InvocationContext
-from .invocation_services import InvocationServices
+from .graph import Graph, GraphExecutionState
 from .invocation_queue import InvocationQueueABC, InvocationQueueItem
+from .invocation_services import InvocationServices
+from .item_storage import ItemStorageABC
 
 
 class Invoker:
@@ -14,14 +15,13 @@ class Invoker:
 
     services: InvocationServices
 
-    def __init__(self,
-        services: InvocationServices
-    ):
+    def __init__(self, services: InvocationServices):
         self.services = services
         self._start()
 
-
-    def invoke(self, graph_execution_state: GraphExecutionState, invoke_all: bool = False) -> str|None:
+    def invoke(
+        self, graph_execution_state: GraphExecutionState, invoke_all: bool = False
+    ) -> str | None:
         """Determines the next node to invoke and returns the id of the invoked node, or None if there are no nodes to execute"""
 
         # Get the next invocation
@@ -33,38 +33,36 @@ class Invoker:
         self.services.graph_execution_manager.set(graph_execution_state)
 
         # Queue the invocation
-        print(f'queueing item {invocation.id}')
-        self.services.queue.put(InvocationQueueItem(
-            #session_id    = session.id,
-            graph_execution_state_id = graph_execution_state.id,
-            invocation_id = invocation.id,
-            invoke_all    = invoke_all
-        ))
+        print(f"queueing item {invocation.id}")
+        self.services.queue.put(
+            InvocationQueueItem(
+                # session_id    = session.id,
+                graph_execution_state_id=graph_execution_state.id,
+                invocation_id=invocation.id,
+                invoke_all=invoke_all,
+            )
+        )
 
         return invocation.id
 
-
-    def create_execution_state(self, graph: Graph|None = None) -> GraphExecutionState:
+    def create_execution_state(self, graph: Graph | None = None) -> GraphExecutionState:
         """Creates a new execution state for the given graph"""
-        new_state = GraphExecutionState(graph = Graph() if graph is None else graph)
+        new_state = GraphExecutionState(graph=Graph() if graph is None else graph)
         self.services.graph_execution_manager.set(new_state)
         return new_state
 
-
     def __start_service(self, service) -> None:
         # Call start() method on any services that have it
-        start_op = getattr(service, 'start', None)
+        start_op = getattr(service, "start", None)
         if callable(start_op):
             start_op(self)
 
-
     def __stop_service(self, service) -> None:
         # Call stop() method on any services that have it
-        stop_op = getattr(service, 'stop', None)
+        stop_op = getattr(service, "stop", None)
         if callable(stop_op):
             stop_op(self)
 
-    
     def _start(self) -> None:
         """Starts the invoker. This is called automatically when the invoker is created."""
         for service in vars(self.services):
@@ -73,7 +71,6 @@ class Invoker:
         for service in vars(self.services):
             self.__start_service(getattr(self.services, service))
 
-
     def stop(self) -> None:
         """Stops the invoker. A new invoker will have to be created to execute further."""
         # First stop all services

invokeai/app/services/item_storage.py

@@ -1,19 +1,21 @@
+from abc import ABC, abstractmethod
+from typing import Callable, Generic, TypeVar
 
-from typing import Callable, TypeVar, Generic
 from pydantic import BaseModel, Field
 from pydantic.generics import GenericModel
-from abc import ABC, abstractmethod
 
-T = TypeVar('T', bound=BaseModel)
+T = TypeVar("T", bound=BaseModel)
+
 
 class PaginatedResults(GenericModel, Generic[T]):
     """Paginated results"""
-    items: list[T]   = Field(description = "Items")
-    page: int        = Field(description = "Current Page")
-    pages: int       = Field(description = "Total number of pages")
-    per_page: int    = Field(description = "Number of items per page")
-    total: int       = Field(description = "Total number of items in result")
-
+    #fmt: off
+    items: list[T] = Field(description="Items")
+    page: int = Field(description="Current Page")
+    pages: int = Field(description="Total number of pages")
+    per_page: int = Field(description="Number of items per page")
+    total: int = Field(description="Total number of items in result")
+    #fmt: on
 
 class ItemStorageABC(ABC, Generic[T]):
     _on_changed_callbacks: list[Callable[[T], None]]
@@ -24,6 +26,7 @@ class ItemStorageABC(ABC, Generic[T]):
         self._on_deleted_callbacks = list()
 
     """Base item storage class"""
+
     @abstractmethod
     def get(self, item_id: str) -> T:
         pass
@@ -37,7 +40,9 @@ class ItemStorageABC(ABC, Generic[T]):
         pass
 
     @abstractmethod
-    def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
+    def search(
+        self, query: str, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[T]:
         pass
 
     def on_changed(self, on_changed: Callable[[T], None]) -> None:

invokeai/app/services/processor.py

@@ -1,5 +1,6 @@
-from threading import Event, Thread
 import traceback
+from threading import Event, Thread
+
 from ..invocations.baseinvocation import InvocationContext
 from .invocation_queue import InvocationQueueItem
 from .invoker import InvocationProcessorABC, Invoker
@@ -14,52 +15,62 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
         self.__invoker = invoker
         self.__stop_event = Event()
         self.__invoker_thread = Thread(
-            name = "invoker_processor",
-            target = self.__process,
-            kwargs = dict(stop_event = self.__stop_event)
+            name="invoker_processor",
+            target=self.__process,
+            kwargs=dict(stop_event=self.__stop_event),
+        )
+        self.__invoker_thread.daemon = (
+            True  # TODO: probably better to just not use threads?
         )
-        self.__invoker_thread.daemon = True # TODO: probably better to just not use threads?
         self.__invoker_thread.start()
 
-
     def stop(self, *args, **kwargs) -> None:
         self.__stop_event.set()
 
-
     def __process(self, stop_event: Event):
         try:
             while not stop_event.is_set():
                 queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
-                if not queue_item: # Probably stopping
+                if not queue_item:  # Probably stopping
                     continue
 
-                graph_execution_state = self.__invoker.services.graph_execution_manager.get(queue_item.graph_execution_state_id)
-                invocation = graph_execution_state.execution_graph.get_node(queue_item.invocation_id)
+                graph_execution_state = (
+                    self.__invoker.services.graph_execution_manager.get(
+                        queue_item.graph_execution_state_id
+                    )
+                )
+                invocation = graph_execution_state.execution_graph.get_node(
+                    queue_item.invocation_id
+                )
 
                 # Send starting event
                 self.__invoker.services.events.emit_invocation_started(
-                    graph_execution_state_id = graph_execution_state.id,
-                    invocation_id = invocation.id
+                    graph_execution_state_id=graph_execution_state.id,
+                    invocation_id=invocation.id,
                 )
 
                 # Invoke
                 try:
-                    outputs = invocation.invoke(InvocationContext(
-                        services = self.__invoker.services,
-                        graph_execution_state_id = graph_execution_state.id
-                    ))
+                    outputs = invocation.invoke(
+                        InvocationContext(
+                            services=self.__invoker.services,
+                            graph_execution_state_id=graph_execution_state.id,
+                        )
+                    )
 
                     # Save outputs and history
                     graph_execution_state.complete(invocation.id, outputs)
 
                     # Save the state changes
-                    self.__invoker.services.graph_execution_manager.set(graph_execution_state)
+                    self.__invoker.services.graph_execution_manager.set(
+                        graph_execution_state
+                    )
 
                     # Send complete event
                     self.__invoker.services.events.emit_invocation_complete(
-                        graph_execution_state_id = graph_execution_state.id,
-                        invocation_id = invocation.id,
-                        result = outputs.dict()
+                        graph_execution_state_id=graph_execution_state.id,
+                        invocation_id=invocation.id,
+                        result=outputs.dict(),
                     )
 
                 except KeyboardInterrupt:
@@ -72,24 +83,27 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     graph_execution_state.set_node_error(invocation.id, error)
 
                     # Save the state changes
-                    self.__invoker.services.graph_execution_manager.set(graph_execution_state)
+                    self.__invoker.services.graph_execution_manager.set(
+                        graph_execution_state
+                    )
 
                     # Send error event
                     self.__invoker.services.events.emit_invocation_error(
-                        graph_execution_state_id = graph_execution_state.id,
-                        invocation_id = invocation.id,
-                        error = error
+                        graph_execution_state_id=graph_execution_state.id,
+                        invocation_id=invocation.id,
+                        error=error,
                     )
 
                     pass
 
-
                 # Queue any further commands if invoking all
                 is_complete = graph_execution_state.is_complete()
                 if queue_item.invoke_all and not is_complete:
-                    self.__invoker.invoke(graph_execution_state, invoke_all = True)
+                    self.__invoker.invoke(graph_execution_state, invoke_all=True)
                 elif is_complete:
-                    self.__invoker.services.events.emit_graph_execution_complete(graph_execution_state.id)
+                    self.__invoker.services.events.emit_graph_execution_complete(
+                        graph_execution_state.id
+                    )
 
         except KeyboardInterrupt:
-            ... # Log something?
+            ...  # Log something?

invokeai/app/services/sqlite.py

@@ -1,12 +1,15 @@
 import sqlite3
 from threading import Lock
 from typing import Generic, TypeVar, Union, get_args
+
 from pydantic import BaseModel, parse_raw_as
+
 from .item_storage import ItemStorageABC, PaginatedResults
 
-T = TypeVar('T', bound=BaseModel)
+T = TypeVar("T", bound=BaseModel)
+
+sqlite_memory = ":memory:"
 
-sqlite_memory = ':memory:'
 
 class SqliteItemStorage(ItemStorageABC, Generic[T]):
     _filename: str
@@ -16,15 +19,17 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     _id_field: str
     _lock: Lock
 
-    def __init__(self, filename: str, table_name: str, id_field: str = 'id'):
+    def __init__(self, filename: str, table_name: str, id_field: str = "id"):
         super().__init__()
 
         self._filename = filename
         self._table_name = table_name
-        self._id_field = id_field # TODO: validate that T has this field
+        self._id_field = id_field  # TODO: validate that T has this field
         self._lock = Lock()
 
-        self._conn = sqlite3.connect(self._filename, check_same_thread=False) # TODO: figure out a better threading solution
+        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()
@@ -32,10 +37,14 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     def _create_table(self):
         try:
             self._lock.acquire()
-            self._cursor.execute(f'''CREATE TABLE IF NOT EXISTS {self._table_name} (
+            self._cursor.execute(
+                f"""CREATE TABLE IF NOT EXISTS {self._table_name} (
                 item TEXT,
-                id TEXT GENERATED ALWAYS AS (json_extract(item, '$.{self._id_field}')) VIRTUAL NOT NULL);''')
-            self._cursor.execute(f'''CREATE UNIQUE INDEX IF NOT EXISTS {self._table_name}_id ON {self._table_name}(id);''')
+                id TEXT GENERATED ALWAYS AS (json_extract(item, '$.{self._id_field}')) VIRTUAL NOT NULL);"""
+            )
+            self._cursor.execute(
+                f"""CREATE UNIQUE INDEX IF NOT EXISTS {self._table_name}_id ON {self._table_name}(id);"""
+            )
         finally:
             self._lock.release()
 
@@ -46,7 +55,10 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     def set(self, item: T):
         try:
             self._lock.acquire()
-            self._cursor.execute(f'''INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);''', (item.json(),))
+            self._cursor.execute(
+                f"""INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);""",
+                (item.json(),),
+            )
         finally:
             self._lock.release()
         self._on_changed(item)
@@ -54,7 +66,9 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     def get(self, id: str) -> Union[T, None]:
         try:
             self._lock.acquire()
-            self._cursor.execute(f'''SELECT item FROM {self._table_name} WHERE id = ?;''', (str(id),))
+            self._cursor.execute(
+                f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),)
+            )
             result = self._cursor.fetchone()
         finally:
             self._lock.release()
@@ -67,7 +81,9 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     def delete(self, id: str):
         try:
             self._lock.acquire()
-            self._cursor.execute(f'''DELETE FROM {self._table_name} WHERE id = ?;''', (str(id),))
+            self._cursor.execute(
+                f"""DELETE FROM {self._table_name} WHERE id = ?;""", (str(id),)
+            )
         finally:
             self._lock.release()
         self._on_deleted(id)
@@ -75,12 +91,15 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
         try:
             self._lock.acquire()
-            self._cursor.execute(f'''SELECT item FROM {self._table_name} LIMIT ? OFFSET ?;''', (per_page, page * per_page))
+            self._cursor.execute(
+                f"""SELECT item FROM {self._table_name} LIMIT ? OFFSET ?;""",
+                (per_page, page * per_page),
+            )
             result = self._cursor.fetchall()
 
             items = list(map(lambda r: self._parse_item(r[0]), result))
 
-            self._cursor.execute(f'''SELECT count(*) FROM {self._table_name};''')
+            self._cursor.execute(f"""SELECT count(*) FROM {self._table_name};""")
             count = self._cursor.fetchone()[0]
         finally:
             self._lock.release()
@@ -88,22 +107,26 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
         pageCount = int(count / per_page) + 1
 
         return PaginatedResults[T](
-            items = items,
-            page = page,
-            pages = pageCount,
-            per_page = per_page,
-            total = count
+            items=items, page=page, pages=pageCount, per_page=per_page, total=count
         )
 
-    def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
+    def search(
+        self, query: str, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[T]:
         try:
             self._lock.acquire()
-            self._cursor.execute(f'''SELECT item FROM {self._table_name} WHERE item LIKE ? LIMIT ? OFFSET ?;''', (f'%{query}%', per_page, page * per_page))
+            self._cursor.execute(
+                f"""SELECT item FROM {self._table_name} WHERE item LIKE ? LIMIT ? OFFSET ?;""",
+                (f"%{query}%", per_page, page * per_page),
+            )
             result = self._cursor.fetchall()
 
             items = list(map(lambda r: self._parse_item(r[0]), result))
 
-            self._cursor.execute(f'''SELECT count(*) FROM {self._table_name} WHERE item LIKE ?;''', (f'%{query}%',))
+            self._cursor.execute(
+                f"""SELECT count(*) FROM {self._table_name} WHERE item LIKE ?;""",
+                (f"%{query}%",),
+            )
             count = self._cursor.fetchone()[0]
         finally:
             self._lock.release()
@@ -111,9 +134,5 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
         pageCount = int(count / per_page) + 1
 
         return PaginatedResults[T](
-            items = items,
-            page = page,
-            pages = pageCount,
-            per_page = per_page,
-            total = count
+            items=items, page=page, pages=pageCount, per_page=per_page, total=count
         )

invokeai/backend/__init__.py

@@ -1,5 +1,5 @@
-'''
+"""
 Initialization file for invokeai.backend
-'''
-from .invoke_ai_web_server import InvokeAIWebServer
-
+"""
+from .generate import Generate
+from .model_management import ModelManager

invokeai/backend/config/invokeai_configure.py

@@ -6,19 +6,20 @@
 #
 # Coauthor: Kevin Turner http://github.com/keturn
 #
-print("Loading Python libraries...\n")
+import sys
+print("Loading Python libraries...\n",file=sys.stderr)
+
 import argparse
 import io
 import os
 import re
 import shutil
-import sys
 import traceback
 import warnings
 from argparse import Namespace
 from pathlib import Path
-from urllib import request
 from shutil import get_terminal_size
+from urllib import request
 
 import npyscreen
 import torch
@@ -37,17 +38,20 @@ from transformers import (
 
 import invokeai.configs as configs
 
+from ...frontend.install.model_install import addModelsForm, process_and_execute
+from ...frontend.install.widgets import (
+    CenteredButtonPress,
+    IntTitleSlider,
+    set_min_terminal_size,
+)
 from ..args import PRECISION_CHOICES, Args
-from ..globals import Globals, global_config_dir, global_config_file, global_cache_dir
-from .model_install import addModelsForm, process_and_execute
+from ..globals import Globals, global_cache_dir, global_config_dir, global_config_file
 from .model_install_backend import (
     default_dataset,
     download_from_hf,
-    recommended_datasets,
     hf_download_with_resume,
+    recommended_datasets,
 )
-from .widgets import IntTitleSlider, CenteredButtonPress, set_min_terminal_size
-
 
 warnings.filterwarnings("ignore")
 
@@ -82,6 +86,7 @@ INIT_FILE_PREAMBLE = """# InvokeAI initialization file
 # -Ak_euler_a -C10.0
 """
 
+
 # --------------------------------------------
 def postscript(errors: None):
     if not any(errors):
@@ -180,13 +185,11 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
 # ---------------------------------------------
 # this will preload the Bert tokenizer fles
 def download_bert():
-    print(
-        "Installing bert tokenizer...",
-        file=sys.stderr
-    )
+    print("Installing bert tokenizer...", file=sys.stderr)
     with warnings.catch_warnings():
         warnings.filterwarnings("ignore", category=DeprecationWarning)
         from transformers import BertTokenizerFast
+
         download_from_hf(BertTokenizerFast, "bert-base-uncased")
 
 
@@ -197,12 +200,14 @@ def download_sd1_clip():
     download_from_hf(CLIPTokenizer, version)
     download_from_hf(CLIPTextModel, version)
 
+
 # ---------------------------------------------
 def download_sd2_clip():
-    version = 'stabilityai/stable-diffusion-2'
+    version = "stabilityai/stable-diffusion-2"
     print("Installing SD2 clip model...", file=sys.stderr)
-    download_from_hf(CLIPTokenizer, version, subfolder='tokenizer')
-    download_from_hf(CLIPTextModel, version, subfolder='text_encoder')
+    download_from_hf(CLIPTokenizer, version, subfolder="tokenizer")
+    download_from_hf(CLIPTextModel, version, subfolder="text_encoder")
+
 
 # ---------------------------------------------
 def download_realesrgan():
@@ -329,7 +334,7 @@ class editOptsForm(npyscreen.FormMultiPage):
         old_opts = self.parentApp.invokeai_opts
         first_time = not (Globals.root / Globals.initfile).exists()
         access_token = HfFolder.get_token()
-        window_width,window_height = get_terminal_size()
+        window_width, window_height = get_terminal_size()
         for i in [
             "Configure startup settings. You can come back and change these later.",
             "Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.",
@@ -681,6 +686,7 @@ def run_console_ui(
     else:
         return (editApp.new_opts, editApp.user_selections)
 
+
 # -------------------------------------
 def write_opts(opts: Namespace, init_file: Path):
     """
@@ -701,8 +707,8 @@ def write_opts(opts: Namespace, init_file: Path):
         "^--?(o|out|no-xformer|xformer|no-ckpt|ckpt|free|no-nsfw|nsfw|prec|max_load|embed|always|ckpt|free_gpu)"
     )
     # fix windows paths
-    opts.outdir = opts.outdir.replace('\\','/')
-    opts.embedding_path = opts.embedding_path.replace('\\','/')
+    opts.outdir = opts.outdir.replace("\\", "/")
+    opts.embedding_path = opts.embedding_path.replace("\\", "/")
     new_file = f"{init_file}.new"
     try:
         lines = [x.strip() for x in open(init_file, "r").readlines()]
@@ -855,6 +861,7 @@ def main():
     except KeyboardInterrupt:
         print("\nGoodbye! Come back soon.")
 
+
 # -------------------------------------
 if __name__ == "__main__":
     main()

invokeai/backend/config/model_install_backend.py

@@ -8,6 +8,7 @@ import sys
 import warnings
 from pathlib import Path
 from tempfile import TemporaryFile
+from typing import List
 
 import requests
 from diffusers import AutoencoderKL
@@ -15,12 +16,12 @@ from huggingface_hub import hf_hub_url
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
 from tqdm import tqdm
-from typing import List
 
 import invokeai.configs as configs
-from ..generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
+
 from ..globals import Globals, global_cache_dir, global_config_dir
-from ..model_manager import ModelManager
+from ..model_management import ModelManager
+from ..stable_diffusion import StableDiffusionGeneratorPipeline
 
 warnings.filterwarnings("ignore")
 
@@ -44,42 +45,46 @@ Config_preamble = """
 # was trained on.
 """
 
+
 def default_config_file():
     return Path(global_config_dir()) / "models.yaml"
 
+
 def sd_configs():
     return Path(global_config_dir()) / "stable-diffusion"
 
+
 def initial_models():
     global Datasets
     if Datasets:
         return Datasets
     return (Datasets := OmegaConf.load(Dataset_path))
 
-def install_requested_models(
-        install_initial_models: List[str] = None,
-        remove_models: List[str] = None,
-        scan_directory: Path = None,
-        external_models: List[str] = None,
-        scan_at_startup: bool = False,
-        convert_to_diffusers: bool = False,
-        precision: str = "float16",
-        purge_deleted: bool = False,
-        config_file_path: Path = None,
-):
-    '''
-    Entry point for installing/deleting starter models, or installing external models.
-    '''
-    config_file_path=config_file_path or default_config_file()
-    if not config_file_path.exists():
-        open(config_file_path,'w')
 
-    model_manager= ModelManager(OmegaConf.load(config_file_path),precision=precision)
+def install_requested_models(
+    install_initial_models: List[str] = None,
+    remove_models: List[str] = None,
+    scan_directory: Path = None,
+    external_models: List[str] = None,
+    scan_at_startup: bool = False,
+    convert_to_diffusers: bool = False,
+    precision: str = "float16",
+    purge_deleted: bool = False,
+    config_file_path: Path = None,
+):
+    """
+    Entry point for installing/deleting starter models, or installing external models.
+    """
+    config_file_path = config_file_path or default_config_file()
+    if not config_file_path.exists():
+        open(config_file_path, "w")
+
+    model_manager = ModelManager(OmegaConf.load(config_file_path), precision=precision)
 
     if remove_models and len(remove_models) > 0:
         print("== DELETING UNCHECKED STARTER MODELS ==")
         for model in remove_models:
-            print(f'{model}...')
+            print(f"{model}...")
             model_manager.del_model(model, delete_files=purge_deleted)
         model_manager.commit(config_file_path)
 
@@ -96,20 +101,20 @@ def install_requested_models(
 
     # due to above, we have to reload the model manager because conf file
     # was changed behind its back
-    model_manager= ModelManager(OmegaConf.load(config_file_path),precision=precision)
+    model_manager = ModelManager(OmegaConf.load(config_file_path), precision=precision)
 
     external_models = external_models or list()
     if scan_directory:
         external_models.append(str(scan_directory))
 
-    if len(external_models)>0:
+    if len(external_models) > 0:
         print("== INSTALLING EXTERNAL MODELS ==")
         for path_url_or_repo in external_models:
             try:
                 model_manager.heuristic_import(
                     path_url_or_repo,
                     convert=convert_to_diffusers,
-                    commit_to_conf=config_file_path
+                    commit_to_conf=config_file_path,
                 )
             except KeyboardInterrupt:
                 sys.exit(-1)
@@ -117,17 +122,18 @@ def install_requested_models(
                 pass
 
     if scan_at_startup and scan_directory.is_dir():
-        argument = '--autoconvert' if convert_to_diffusers else '--autoimport'
+        argument = "--autoconvert" if convert_to_diffusers else "--autoimport"
         initfile = Path(Globals.root, Globals.initfile)
-        replacement = Path(Globals.root, f'{Globals.initfile}.new')
-        directory = str(scan_directory).replace('\\','/')
-        with open(initfile,'r') as input:
-            with open(replacement,'w') as output:
+        replacement = Path(Globals.root, f"{Globals.initfile}.new")
+        directory = str(scan_directory).replace("\\", "/")
+        with open(initfile, "r") as input:
+            with open(replacement, "w") as output:
                 while line := input.readline():
                     if not line.startswith(argument):
                         output.writelines([line])
-                output.writelines([f'{argument} {directory}'])
-        os.replace(replacement,initfile)
+                output.writelines([f"{argument} {directory}"])
+        os.replace(replacement, initfile)
+
 
 # -------------------------------------
 def yes_or_no(prompt: str, default_yes=True):
@@ -183,7 +189,9 @@ def migrate_models_ckpt():
     if not os.path.exists(os.path.join(model_path, "model.ckpt")):
         return
     new_name = initial_models()["stable-diffusion-1.4"]["file"]
-    print('The Stable Diffusion v4.1 "model.ckpt" is already installed. The name will be changed to {new_name} to avoid confusion.')
+    print(
+        'The Stable Diffusion v4.1 "model.ckpt" is already installed. The name will be changed to {new_name} to avoid confusion.'
+    )
     print(f"model.ckpt => {new_name}")
     os.replace(
         os.path.join(model_path, "model.ckpt"), os.path.join(model_path, new_name)
@@ -383,7 +391,8 @@ def update_config_file(successfully_downloaded: dict, config_file: Path):
 
 # ---------------------------------------------
 def new_config_file_contents(
-        successfully_downloaded: dict, config_file: Path,
+    successfully_downloaded: dict,
+    config_file: Path,
 ) -> str:
     if config_file.exists():
         conf = OmegaConf.load(str(config_file.expanduser().resolve()))
@@ -413,7 +422,9 @@ def new_config_file_contents(
             stanza["weights"] = os.path.relpath(
                 successfully_downloaded[model], start=Globals.root
             )
-            stanza["config"] = os.path.normpath(os.path.join(sd_configs(), mod["config"]))
+            stanza["config"] = os.path.normpath(
+                os.path.join(sd_configs(), mod["config"])
+            )
         if "vae" in mod:
             if "file" in mod["vae"]:
                 stanza["vae"] = os.path.normpath(

invokeai/backend/generate.py

@@ -25,21 +25,19 @@ from omegaconf import OmegaConf
 from PIL import Image, ImageOps
 from pytorch_lightning import logging, seed_everything
 
-import ldm.invoke.conditioning
-from ldm.invoke.args import metadata_from_png
-from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
-from ldm.invoke.conditioning import get_uc_and_c_and_ec
-from ldm.invoke.devices import choose_precision, choose_torch_device
-from ldm.invoke.generator.inpaint import infill_methods
-from ldm.invoke.globals import Globals, global_cache_dir
-from ldm.invoke.image_util import InitImageResizer
-from ldm.invoke.model_manager import ModelManager
-from ldm.invoke.pngwriter import PngWriter
-from ldm.invoke.seamless import configure_model_padding
-from ldm.invoke.txt2mask import Txt2Mask
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.ksampler import KSampler
-from ldm.models.diffusion.plms import PLMSSampler
+from .model_management import ModelManager
+from .args import metadata_from_png
+from .generator import infill_methods
+from .globals import Globals, global_cache_dir
+from .image_util import InitImageResizer, PngWriter, Txt2Mask, configure_model_padding
+from .prompting import get_uc_and_c_and_ec
+from .stable_diffusion import (
+    DDIMSampler,
+    HuggingFaceConceptsLibrary,
+    KSampler,
+    PLMSSampler,
+)
+from .util import choose_precision, choose_torch_device
 
 
 def fix_func(orig):
@@ -328,8 +326,8 @@ class Generate:
         variation_amount=0.0,
         threshold=0.0,
         perlin=0.0,
-        h_symmetry_time_pct = None,
-        v_symmetry_time_pct = None,
+        h_symmetry_time_pct=None,
+        v_symmetry_time_pct=None,
         karras_max=None,
         outdir=None,
         # these are specific to img2img and inpaint
@@ -717,7 +715,7 @@ class Generate:
             prompt,
             model=self.model,
             skip_normalize_legacy_blend=opt.skip_normalize,
-            log_tokens=ldm.invoke.conditioning.log_tokenization,
+            log_tokens=invokeai.backend.prompting.conditioning.log_tokenization,
         )
 
         if tool in ("gfpgan", "codeformer", "upscale"):
@@ -741,7 +739,7 @@ class Generate:
             )
 
         elif tool == "outcrop":
-            from ldm.invoke.restoration.outcrop import Outcrop
+            from .restoration.outcrop import Outcrop
 
             extend_instructions = {}
             for direction, pixels in _pairwise(opt.outcrop):
@@ -794,7 +792,7 @@ class Generate:
                 clear_cuda_cache=self.clear_cuda_cache,
             )
         elif tool == "outpaint":
-            from ldm.invoke.restoration.outpaint import Outpaint
+            from .restoration.outpaint import Outpaint
 
             restorer = Outpaint(image, self)
             return restorer.process(opt, args, image_callback=callback, prefix=prefix)
@@ -816,17 +814,12 @@ class Generate:
         hires_fix: bool = False,
         force_outpaint: bool = False,
     ):
-        inpainting_model_in_use = self.sampler.uses_inpainting_model()
-
         if hires_fix:
             return self._make_txt2img2img()
 
         if embiggen is not None:
             return self._make_embiggen()
 
-        if inpainting_model_in_use:
-            return self._make_omnibus()
-
         if ((init_image is not None) and (mask_image is not None)) or force_outpaint:
             return self._make_inpaint()
 
@@ -903,16 +896,9 @@ class Generate:
     def _make_inpaint(self):
         return self._load_generator(".inpaint", "Inpaint")
 
-    def _make_omnibus(self):
-        return self._load_generator(".omnibus", "Omnibus")
-
     def _load_generator(self, module, class_name):
-        if self.is_legacy_model(self.model_name):
-            mn = f"ldm.invoke.ckpt_generator{module}"
-            cn = f"Ckpt{class_name}"
-        else:
-            mn = f"ldm.invoke.generator{module}"
-            cn = class_name
+        mn = f"invokeai.backend.generator{module}"
+        cn = class_name
         module = importlib.import_module(mn)
         constructor = getattr(module, cn)
         return constructor(self.model, self.precision)
@@ -975,7 +961,7 @@ class Generate:
 
         seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
         if self.embedding_path is not None:
-            print(f'>> Loading embeddings from {self.embedding_path}')
+            print(f">> Loading embeddings from {self.embedding_path}")
             for root, _, files in os.walk(self.embedding_path):
                 for name in files:
                     ti_path = os.path.join(root, name)
@@ -1030,7 +1016,6 @@ class Generate:
         image_callback=None,
         prefix=None,
     ):
-
         results = []
         for r in image_list:
             image, seed = r

invokeai/backend/generator/__init__.py

@@ -0,0 +1,5 @@
+"""
+Initialization file for the invokeai.generator package
+"""
+from .base import Generator
+from .inpaint import infill_methods

invokeai/backend/generator/base.py

@@ -1,7 +1,7 @@
-'''
-Base class for ldm.invoke.generator.*
+"""
+Base class for invokeai.backend.generator.*
 including img2img, txt2img, and inpaint
-'''
+"""
 from __future__ import annotations
 
 import os
@@ -9,24 +9,25 @@ import os.path as osp
 import random
 import traceback
 from contextlib import nullcontext
+from pathlib import Path
 
 import cv2
 import numpy as np
 import torch
-
-from PIL import Image, ImageFilter, ImageChops
 from diffusers import DiffusionPipeline
 from einops import rearrange
-from pathlib import Path
+from PIL import Image, ImageChops, ImageFilter
 from pytorch_lightning import seed_everything
 from tqdm import trange
 
 import invokeai.assets.web as web_assets
-from ldm.models.diffusion.ddpm import DiffusionWrapper
-from ldm.util import rand_perlin_2d
+
+from ..stable_diffusion.diffusion.ddpm import DiffusionWrapper
+from ..util.util import rand_perlin_2d
 
 downsampling = 8
-CAUTION_IMG = 'caution.png'
+CAUTION_IMG = "caution.png"
+
 
 class Generator:
     downsampling_factor: int
@@ -39,7 +40,7 @@ class Generator:
         self.precision = precision
         self.seed = None
         self.latent_channels = model.channels
-        self.downsampling_factor = downsampling   # BUG: should come from model or config
+        self.downsampling_factor = downsampling  # BUG: should come from model or config
         self.safety_checker = None
         self.perlin = 0.0
         self.threshold = 0
@@ -50,56 +51,73 @@ class Generator:
         self.caution_img = None
 
     # this is going to be overridden in img2img.py, txt2img.py and inpaint.py
-    def get_make_image(self,prompt,**kwargs):
+    def get_make_image(self, prompt, **kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it
         """
-        raise NotImplementedError("image_iterator() must be implemented in a descendent class")
+        raise NotImplementedError(
+            "image_iterator() must be implemented in a descendent class"
+        )
 
     def set_variation(self, seed, variation_amount, with_variations):
-        self.seed             = seed
+        self.seed = seed
         self.variation_amount = variation_amount
-        self.with_variations  = with_variations
+        self.with_variations = with_variations
 
-    def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
-                 image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
-                 h_symmetry_time_pct=None, v_symmetry_time_pct=None,
-                 safety_checker:dict=None,
-                 free_gpu_mem: bool=False,
-                 **kwargs):
+    def generate(
+        self,
+        prompt,
+        init_image,
+        width,
+        height,
+        sampler,
+        iterations=1,
+        seed=None,
+        image_callback=None,
+        step_callback=None,
+        threshold=0.0,
+        perlin=0.0,
+        h_symmetry_time_pct=None,
+        v_symmetry_time_pct=None,
+        safety_checker: dict = None,
+        free_gpu_mem: bool = False,
+        **kwargs,
+    ):
         scope = nullcontext
         self.safety_checker = safety_checker
         self.free_gpu_mem = free_gpu_mem
         attention_maps_images = []
-        attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
+        attention_maps_callback = lambda saver: attention_maps_images.append(
+            saver.get_stacked_maps_image()
+        )
         make_image = self.get_make_image(
             prompt,
-            sampler = sampler,
-            init_image    = init_image,
-            width         = width,
-            height        = height,
-            step_callback = step_callback,
-            threshold     = threshold,
-            perlin        = perlin,
-            h_symmetry_time_pct     = h_symmetry_time_pct,
-            v_symmetry_time_pct     = v_symmetry_time_pct,
-            attention_maps_callback = attention_maps_callback,
-            **kwargs
+            sampler=sampler,
+            init_image=init_image,
+            width=width,
+            height=height,
+            step_callback=step_callback,
+            threshold=threshold,
+            perlin=perlin,
+            h_symmetry_time_pct=h_symmetry_time_pct,
+            v_symmetry_time_pct=v_symmetry_time_pct,
+            attention_maps_callback=attention_maps_callback,
+            **kwargs,
         )
-        results             = []
-        seed                = seed if seed is not None and seed >= 0 else self.new_seed()
-        first_seed          = seed
+        results = []
+        seed = seed if seed is not None and seed >= 0 else self.new_seed()
+        first_seed = seed
         seed, initial_noise = self.generate_initial_noise(seed, width, height)
 
         # There used to be an additional self.model.ema_scope() here, but it breaks
         # the inpaint-1.5 model. Not sure what it did.... ?
         with scope(self.model.device.type):
-            for n in trange(iterations, desc='Generating'):
+            for n in trange(iterations, desc="Generating"):
                 x_T = None
                 if self.variation_amount > 0:
                     seed_everything(seed)
-                    target_noise = self.get_noise(width,height)
+                    target_noise = self.get_noise(width, height)
                     x_T = self.slerp(self.variation_amount, initial_noise, target_noise)
                 elif initial_noise is not None:
                     # i.e. we specified particular variations
@@ -107,9 +125,9 @@ class Generator:
                 else:
                     seed_everything(seed)
                     try:
-                        x_T = self.get_noise(width,height)
+                        x_T = self.get_noise(width, height)
                     except:
-                        print('** An error occurred while getting initial noise **')
+                        print("** An error occurred while getting initial noise **")
                         print(traceback.format_exc())
 
                 image = make_image(x_T)
@@ -120,19 +138,30 @@ class Generator:
                 results.append([image, seed])
 
                 if image_callback is not None:
-                    attention_maps_image = None if len(attention_maps_images)==0 else attention_maps_images[-1]
-                    image_callback(image, seed, first_seed=first_seed, attention_maps_image=attention_maps_image)
+                    attention_maps_image = (
+                        None
+                        if len(attention_maps_images) == 0
+                        else attention_maps_images[-1]
+                    )
+                    image_callback(
+                        image,
+                        seed,
+                        first_seed=first_seed,
+                        attention_maps_image=attention_maps_image,
+                    )
 
                 seed = self.new_seed()
 
                 # Free up memory from the last generation.
-                clear_cuda_cache = kwargs['clear_cuda_cache'] if 'clear_cuda_cache' in kwargs else None
+                clear_cuda_cache = (
+                    kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None
+                )
                 if clear_cuda_cache is not None:
                     clear_cuda_cache()
 
         return results
 
-    def sample_to_image(self,samples)->Image.Image:
+    def sample_to_image(self, samples) -> Image.Image:
         """
         Given samples returned from a sampler, converts
         it into a PIL Image
@@ -141,18 +170,30 @@ class Generator:
             image = self.model.decode_latents(samples)
         return self.model.numpy_to_pil(image)[0]
 
-    def repaste_and_color_correct(self, result: Image.Image, init_image: Image.Image, init_mask: Image.Image, mask_blur_radius: int = 8) -> Image.Image:
+    def repaste_and_color_correct(
+        self,
+        result: Image.Image,
+        init_image: Image.Image,
+        init_mask: Image.Image,
+        mask_blur_radius: int = 8,
+    ) -> Image.Image:
         if init_image is None or init_mask is None:
             return result
 
         # Get the original alpha channel of the mask if there is one.
         # Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
-        pil_init_mask = init_mask.getchannel('A') if init_mask.mode == 'RGBA' else init_mask.convert('L')
-        pil_init_image = init_image.convert('RGBA') # Add an alpha channel if one doesn't exist
+        pil_init_mask = (
+            init_mask.getchannel("A")
+            if init_mask.mode == "RGBA"
+            else init_mask.convert("L")
+        )
+        pil_init_image = init_image.convert(
+            "RGBA"
+        )  # Add an alpha channel if one doesn't exist
 
         # Build an image with only visible pixels from source to use as reference for color-matching.
-        init_rgb_pixels = np.asarray(init_image.convert('RGB'), dtype=np.uint8)
-        init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8)
+        init_rgb_pixels = np.asarray(init_image.convert("RGB"), dtype=np.uint8)
+        init_a_pixels = np.asarray(pil_init_image.getchannel("A"), dtype=np.uint8)
         init_mask_pixels = np.asarray(pil_init_mask, dtype=np.uint8)
 
         # Get numpy version of result
@@ -171,44 +212,70 @@ class Generator:
 
             # Color correct
             np_matched_result = np_image.copy()
-            np_matched_result[:,:,:] = (((np_matched_result[:,:,:].astype(np.float32) - gen_means[None,None,:]) / gen_std[None,None,:]) * init_std[None,None,:] + init_means[None,None,:]).clip(0, 255).astype(np.uint8)
-            matched_result = Image.fromarray(np_matched_result, mode='RGB')
+            np_matched_result[:, :, :] = (
+                (
+                    (
+                        (
+                            np_matched_result[:, :, :].astype(np.float32)
+                            - gen_means[None, None, :]
+                        )
+                        / gen_std[None, None, :]
+                    )
+                    * init_std[None, None, :]
+                    + init_means[None, None, :]
+                )
+                .clip(0, 255)
+                .astype(np.uint8)
+            )
+            matched_result = Image.fromarray(np_matched_result, mode="RGB")
         else:
-            matched_result = Image.fromarray(np_image, mode='RGB')
+            matched_result = Image.fromarray(np_image, mode="RGB")
 
         # Blur the mask out (into init image) by specified amount
         if mask_blur_radius > 0:
             nm = np.asarray(pil_init_mask, dtype=np.uint8)
-            nmd = cv2.erode(nm, kernel=np.ones((3,3), dtype=np.uint8), iterations=int(mask_blur_radius / 2))
-            pmd = Image.fromarray(nmd, mode='L')
+            nmd = cv2.erode(
+                nm,
+                kernel=np.ones((3, 3), dtype=np.uint8),
+                iterations=int(mask_blur_radius / 2),
+            )
+            pmd = Image.fromarray(nmd, mode="L")
             blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius))
         else:
             blurred_init_mask = pil_init_mask
 
-        multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1])
+        multiplied_blurred_init_mask = ImageChops.multiply(
+            blurred_init_mask, self.pil_image.split()[-1]
+        )
 
         # Paste original on color-corrected generation (using blurred mask)
-        matched_result.paste(init_image, (0,0), mask = multiplied_blurred_init_mask)
+        matched_result.paste(init_image, (0, 0), mask=multiplied_blurred_init_mask)
         return matched_result
 
-    def sample_to_lowres_estimated_image(self,samples):
+    def sample_to_lowres_estimated_image(self, samples):
         # origingally adapted from code by @erucipe and @keturn here:
         # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
 
         # these updated numbers for v1.5 are from @torridgristle
-        v1_5_latent_rgb_factors = torch.tensor([
-            #    R        G        B
-            [ 0.3444,  0.1385,  0.0670], # L1
-            [ 0.1247,  0.4027,  0.1494], # L2
-            [-0.3192,  0.2513,  0.2103], # L3
-            [-0.1307, -0.1874, -0.7445]  # L4
-        ], dtype=samples.dtype, device=samples.device)
+        v1_5_latent_rgb_factors = torch.tensor(
+            [
+                #    R        G        B
+                [0.3444, 0.1385, 0.0670],  # L1
+                [0.1247, 0.4027, 0.1494],  # L2
+                [-0.3192, 0.2513, 0.2103],  # L3
+                [-0.1307, -0.1874, -0.7445],  # L4
+            ],
+            dtype=samples.dtype,
+            device=samples.device,
+        )
 
         latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
-        latents_ubyte = (((latent_image + 1) / 2)
-                         .clamp(0, 1)  # change scale from -1..1 to 0..1
-                         .mul(0xFF)  # to 0..255
-                         .byte()).cpu()
+        latents_ubyte = (
+            ((latent_image + 1) / 2)
+            .clamp(0, 1)  # change scale from -1..1 to 0..1
+            .mul(0xFF)  # to 0..255
+            .byte()
+        ).cpu()
 
         return Image.fromarray(latents_ubyte.numpy())
 
@@ -217,38 +284,45 @@ class Generator:
         if self.variation_amount > 0 or len(self.with_variations) > 0:
             # use fixed initial noise plus random noise per iteration
             seed_everything(seed)
-            initial_noise = self.get_noise(width,height)
+            initial_noise = self.get_noise(width, height)
             for v_seed, v_weight in self.with_variations:
                 seed = v_seed
                 seed_everything(seed)
-                next_noise = self.get_noise(width,height)
+                next_noise = self.get_noise(width, height)
                 initial_noise = self.slerp(v_weight, initial_noise, next_noise)
             if self.variation_amount > 0:
-                random.seed() # reset RNG to an actually random state, so we can get a random seed for variations
-                seed = random.randrange(0,np.iinfo(np.uint32).max)
+                random.seed()  # reset RNG to an actually random state, so we can get a random seed for variations
+                seed = random.randrange(0, np.iinfo(np.uint32).max)
             return (seed, initial_noise)
         else:
             return (seed, None)
 
     # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self,width,height):
+    def get_noise(self, width, height):
         """
         Returns a tensor filled with random numbers, either form a normal distribution
         (txt2img) or from the latent image (img2img, inpaint)
         """
-        raise NotImplementedError("get_noise() must be implemented in a descendent class")
+        raise NotImplementedError(
+            "get_noise() must be implemented in a descendent class"
+        )
 
-    def get_perlin_noise(self,width,height):
-        fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
+    def get_perlin_noise(self, width, height):
+        fixdevice = "cpu" if (self.model.device.type == "mps") else self.model.device
         # limit noise to only the diffusion image channels, not the mask channels
         input_channels = min(self.latent_channels, 4)
         # round up to the nearest block of 8
         temp_width = int((width + 7) / 8) * 8
         temp_height = int((height + 7) / 8) * 8
-        noise = torch.stack([
-            rand_perlin_2d((temp_height, temp_width),
-                           (8, 8),
-                           device = self.model.device).to(fixdevice) for _ in range(input_channels)], dim=0).to(self.model.device)
+        noise = torch.stack(
+            [
+                rand_perlin_2d(
+                    (temp_height, temp_width), (8, 8), device=self.model.device
+                ).to(fixdevice)
+                for _ in range(input_channels)
+            ],
+            dim=0,
+        ).to(self.model.device)
         return noise[0:4, 0:height, 0:width]
 
     def new_seed(self):
@@ -256,7 +330,7 @@ class Generator:
         return self.seed
 
     def slerp(self, t, v0, v1, DOT_THRESHOLD=0.9995):
-        '''
+        """
         Spherical linear interpolation
         Args:
             t (float/np.ndarray): Float value between 0.0 and 1.0
@@ -266,7 +340,7 @@ class Generator:
                                 colineal. Not recommended to alter this.
         Returns:
             v2 (np.ndarray): Interpolation vector between v0 and v1
-        '''
+        """
         inputs_are_torch = False
         if not isinstance(v0, np.ndarray):
             inputs_are_torch = True
@@ -292,15 +366,15 @@ class Generator:
 
         return v2
 
-    def safety_check(self,image:Image.Image):
-        '''
+    def safety_check(self, image: Image.Image):
+        """
         If the CompViz safety checker flags an NSFW image, we
         blur it out.
-        '''
+        """
         import diffusers
 
-        checker = self.safety_checker['checker']
-        extractor = self.safety_checker['extractor']
+        checker = self.safety_checker["checker"]
+        extractor = self.safety_checker["extractor"]
         features = extractor([image], return_tensors="pt")
         features.to(self.model.device)
 
@@ -309,19 +383,23 @@ class Generator:
         x_image = x_image[None].transpose(0, 3, 1, 2)
 
         diffusers.logging.set_verbosity_error()
-        checked_image, has_nsfw_concept = checker(images=x_image, clip_input=features.pixel_values)
+        checked_image, has_nsfw_concept = checker(
+            images=x_image, clip_input=features.pixel_values
+        )
         if has_nsfw_concept[0]:
-            print('** An image with potential non-safe content has been detected. A blurred image will be returned. **')
+            print(
+                "** An image with potential non-safe content has been detected. A blurred image will be returned. **"
+            )
             return self.blur(image)
         else:
             return image
 
-    def blur(self,input):
+    def blur(self, input):
         blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32))
         try:
             caution = self.get_caution_img()
             if caution:
-                blurry.paste(caution,(0,0),caution)
+                blurry.paste(caution, (0, 0), caution)
         except FileNotFoundError:
             pass
         return blurry
@@ -332,43 +410,52 @@ class Generator:
             return self.caution_img
         path = Path(web_assets.__path__[0]) / CAUTION_IMG
         caution = Image.open(path)
-        self.caution_img = caution.resize((caution.width // 2, caution.height //2))
+        self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
         return self.caution_img
 
     # this is a handy routine for debugging use. Given a generated sample,
     # convert it into a PNG image and store it at the indicated path
     def save_sample(self, sample, filepath):
         image = self.sample_to_image(sample)
-        dirname = os.path.dirname(filepath) or '.'
+        dirname = os.path.dirname(filepath) or "."
         if not os.path.exists(dirname):
-            print(f'** creating directory {dirname}')
+            print(f"** creating directory {dirname}")
             os.makedirs(dirname, exist_ok=True)
-        image.save(filepath,'PNG')
+        image.save(filepath, "PNG")
 
-
-    def torch_dtype(self)->torch.dtype:
-        return torch.float16 if self.precision == 'float16' else torch.float32
+    def torch_dtype(self) -> torch.dtype:
+        return torch.float16 if self.precision == "float16" else torch.float32
 
     # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self,width,height):
-        device         = self.model.device
+    def get_noise(self, width, height):
+        device = self.model.device
         # limit noise to only the diffusion image channels, not the mask channels
         input_channels = min(self.latent_channels, 4)
-        if self.use_mps_noise or device.type == 'mps':
-            x = torch.randn([1,
-                             input_channels,
-                             height // self.downsampling_factor,
-                             width  // self.downsampling_factor],
-                            dtype=self.torch_dtype(),
-                            device='cpu').to(device)
+        if self.use_mps_noise or device.type == "mps":
+            x = torch.randn(
+                [
+                    1,
+                    input_channels,
+                    height // self.downsampling_factor,
+                    width // self.downsampling_factor,
+                ],
+                dtype=self.torch_dtype(),
+                device="cpu",
+            ).to(device)
         else:
-            x = torch.randn([1,
-                             input_channels,
-                             height // self.downsampling_factor,
-                             width  // self.downsampling_factor],
-                            dtype=self.torch_dtype(),
-                            device=device)
+            x = torch.randn(
+                [
+                    1,
+                    input_channels,
+                    height // self.downsampling_factor,
+                    width // self.downsampling_factor,
+                ],
+                dtype=self.torch_dtype(),
+                device=device,
+            )
         if self.perlin > 0.0:
-            perlin_noise = self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
-            x = (1-self.perlin)*x + self.perlin*perlin_noise
+            perlin_noise = self.get_perlin_noise(
+                width // self.downsampling_factor, height // self.downsampling_factor
+            )
+            x = (1 - self.perlin) * x + self.perlin * perlin_noise
         return x

invokeai/backend/generator/embiggen.py

@@ -1,37 +1,38 @@
-'''
-ldm.invoke.generator.embiggen descends from ldm.invoke.generator
-and generates with ldm.invoke.generator.img2img
-'''
+"""
+invokeai.backend.generator.embiggen descends from .generator
+and generates with .generator.img2img
+"""
 
 import numpy as np
 import torch
 from PIL import Image
 from tqdm import trange
 
-from ldm.invoke.generator.base import Generator
-from ldm.invoke.generator.img2img import Img2Img
+from .base import Generator
+from .img2img import Img2Img
 
 
 class Embiggen(Generator):
     def __init__(self, model, precision):
         super().__init__(model, precision)
-        self.init_latent         = None
+        self.init_latent = None
 
     # Replace generate because Embiggen doesn't need/use most of what it does normallly
-    def generate(self,prompt,iterations=1,seed=None,
-                 image_callback=None, step_callback=None,
-                 **kwargs):
-
-        make_image = self.get_make_image(
-            prompt,
-            step_callback = step_callback,
-            **kwargs
-        )
-        results             = []
-        seed                = seed if seed else self.new_seed()
+    def generate(
+        self,
+        prompt,
+        iterations=1,
+        seed=None,
+        image_callback=None,
+        step_callback=None,
+        **kwargs,
+    ):
+        make_image = self.get_make_image(prompt, step_callback=step_callback, **kwargs)
+        results = []
+        seed = seed if seed else self.new_seed()
 
         # Noise will be generated by the Img2Img generator when called
-        for _ in trange(iterations, desc='Generating'):
+        for _ in trange(iterations, desc="Generating"):
             # make_image will call Img2Img which will do the equivalent of get_noise itself
             image = make_image()
             results.append([image, seed])
@@ -56,13 +57,15 @@ class Embiggen(Generator):
         embiggen,
         embiggen_tiles,
         step_callback=None,
-        **kwargs
+        **kwargs,
     ):
         """
         Returns a function returning an image derived from the prompt and multi-stage twice-baked potato layering over the img2img on the initial image
         Return value depends on the seed at the time you call it
         """
-        assert not sampler.uses_inpainting_model(), "--embiggen is not supported by inpainting models"
+        assert (
+            not sampler.uses_inpainting_model()
+        ), "--embiggen is not supported by inpainting models"
 
         # Construct embiggen arg array, and sanity check arguments
         if embiggen == None:  # embiggen can also be called with just embiggen_tiles
@@ -70,48 +73,57 @@ class Embiggen(Generator):
         elif embiggen[0] < 0:
             embiggen[0] = 1.0
             print(
-                '>> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !')
+                ">> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !"
+            )
         if len(embiggen) < 2:
             embiggen.append(0.75)
         elif embiggen[1] > 1.0 or embiggen[1] < 0:
             embiggen[1] = 0.75
-            print('>> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !')
+            print(
+                ">> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !"
+            )
         if len(embiggen) < 3:
             embiggen.append(0.25)
         elif embiggen[2] < 0:
             embiggen[2] = 0.25
-            print('>> Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !')
+            print(
+                ">> Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !"
+            )
 
         # Convert tiles from their user-freindly count-from-one to count-from-zero, because we need to do modulo math
         # and then sort them, because... people.
         if embiggen_tiles:
-            embiggen_tiles = list(map(lambda n: n-1, embiggen_tiles))
+            embiggen_tiles = list(map(lambda n: n - 1, embiggen_tiles))
             embiggen_tiles.sort()
 
         if strength >= 0.5:
-            print(f'* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45.')
+            print(
+                f"* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
+            )
 
         # Prep img2img generator, since we wrap over it
-        gen_img2img = Img2Img(self.model,self.precision)
+        gen_img2img = Img2Img(self.model, self.precision)
 
         # Open original init image (not a tensor) to manipulate
         initsuperimage = Image.open(init_img)
 
         with Image.open(init_img) as img:
-            initsuperimage = img.convert('RGB')
+            initsuperimage = img.convert("RGB")
 
         # Size of the target super init image in pixels
         initsuperwidth, initsuperheight = initsuperimage.size
 
         # Increase by scaling factor if not already resized, using ESRGAN as able
         if embiggen[0] != 1.0:
-            initsuperwidth = round(initsuperwidth*embiggen[0])
-            initsuperheight = round(initsuperheight*embiggen[0])
+            initsuperwidth = round(initsuperwidth * embiggen[0])
+            initsuperheight = round(initsuperheight * embiggen[0])
             if embiggen[1] > 0:  # No point in ESRGAN upscaling if strength is set zero
-                from ldm.invoke.restoration.realesrgan import ESRGAN
+                from ..restoration.realesrgan import ESRGAN
+
                 esrgan = ESRGAN()
                 print(
-                    f'>> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}')
+                    f">> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
+                )
                 if embiggen[0] > 2:
                     initsuperimage = esrgan.process(
                         initsuperimage,
@@ -130,7 +142,8 @@ class Embiggen(Generator):
             #   but from personal experiance it doesn't greatly improve anything after 4x
             # Resize to target scaling factor resolution
             initsuperimage = initsuperimage.resize(
-                (initsuperwidth, initsuperheight), Image.Resampling.LANCZOS)
+                (initsuperwidth, initsuperheight), Image.Resampling.LANCZOS
+            )
 
         # Use width and height as tile widths and height
         # Determine buffer size in pixels
@@ -153,23 +166,24 @@ class Embiggen(Generator):
         emb_tiles_x = 1
         emb_tiles_y = 1
         if (initsuperwidth - width) > 0:
-            emb_tiles_x = ceildiv(initsuperwidth - width,
-                                  width - overlap_size_x) + 1
+            emb_tiles_x = ceildiv(initsuperwidth - width, width - overlap_size_x) + 1
         if (initsuperheight - height) > 0:
-            emb_tiles_y = ceildiv(initsuperheight - height,
-                                  height - overlap_size_y) + 1
+            emb_tiles_y = ceildiv(initsuperheight - height, height - overlap_size_y) + 1
         # Sanity
-        assert emb_tiles_x > 1 or emb_tiles_y > 1, f'ERROR: Based on the requested dimensions of {initsuperwidth}x{initsuperheight} and tiles of {width}x{height} you don\'t need to Embiggen! Check your arguments.'
+        assert (
+            emb_tiles_x > 1 or emb_tiles_y > 1
+        ), f"ERROR: Based on the requested dimensions of {initsuperwidth}x{initsuperheight} and tiles of {width}x{height} you don't need to Embiggen! Check your arguments."
 
         # Prep alpha layers --------------
         # https://stackoverflow.com/questions/69321734/how-to-create-different-transparency-like-gradient-with-python-pil
         # agradientL is Left-side transparent
-        agradientL = Image.linear_gradient('L').rotate(
-            90).resize((overlap_size_x, height))
+        agradientL = (
+            Image.linear_gradient("L").rotate(90).resize((overlap_size_x, height))
+        )
         # agradientT is Top-side transparent
-        agradientT = Image.linear_gradient('L').resize((width, overlap_size_y))
+        agradientT = Image.linear_gradient("L").resize((width, overlap_size_y))
         # radial corner is the left-top corner, made full circle then cut to just the left-top quadrant
-        agradientC = Image.new('L', (256, 256))
+        agradientC = Image.new("L", (256, 256))
         for y in range(256):
             for x in range(256):
                 # Find distance to lower right corner (numpy takes arrays)
@@ -177,16 +191,16 @@ class Embiggen(Generator):
                 # Clamp values to max 255
                 if distanceToLR > 255:
                     distanceToLR = 255
-                #Place the pixel as invert of distance
+                # Place the pixel as invert of distance
                 agradientC.putpixel((x, y), round(255 - distanceToLR))
 
         # Create alternative asymmetric diagonal corner to use on "tailing" intersections to prevent hard edges
         # Fits for a left-fading gradient on the bottom side and full opacity on the right side.
-        agradientAsymC = Image.new('L', (256, 256))
+        agradientAsymC = Image.new("L", (256, 256))
         for y in range(256):
             for x in range(256):
-                value = round(max(0, x-(255-y)) * (255 / max(1,y)))
-                #Clamp values
+                value = round(max(0, x - (255 - y)) * (255 / max(1, y)))
+                # Clamp values
                 value = max(0, value)
                 value = min(255, value)
                 agradientAsymC.putpixel((x, y), value)
@@ -204,80 +218,91 @@ class Embiggen(Generator):
         # make masks with an asymmetric upper-right corner so when the curved transparent corner of the next tile
         # to its right is placed it doesn't reveal a hard trailing semi-transparent edge in the overlapping space
         alphaLayerTaC = alphaLayerT.copy()
-        alphaLayerTaC.paste(agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+        alphaLayerTaC.paste(
+            agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)),
+            (width - overlap_size_x, 0),
+        )
         alphaLayerLTaC = alphaLayerLTC.copy()
-        alphaLayerLTaC.paste(agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+        alphaLayerLTaC.paste(
+            agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)),
+            (width - overlap_size_x, 0),
+        )
 
         if embiggen_tiles:
             # Individual unconnected sides
             alphaLayerR = Image.new("L", (width, height), 255)
-            alphaLayerR.paste(agradientL.rotate(
-                180), (width - overlap_size_x, 0))
+            alphaLayerR.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
             alphaLayerB = Image.new("L", (width, height), 255)
-            alphaLayerB.paste(agradientT.rotate(
-                180), (0, height - overlap_size_y))
+            alphaLayerB.paste(agradientT.rotate(180), (0, height - overlap_size_y))
             alphaLayerTB = Image.new("L", (width, height), 255)
             alphaLayerTB.paste(agradientT, (0, 0))
-            alphaLayerTB.paste(agradientT.rotate(
-                180), (0, height - overlap_size_y))
+            alphaLayerTB.paste(agradientT.rotate(180), (0, height - overlap_size_y))
             alphaLayerLR = Image.new("L", (width, height), 255)
             alphaLayerLR.paste(agradientL, (0, 0))
-            alphaLayerLR.paste(agradientL.rotate(
-                180), (width - overlap_size_x, 0))
+            alphaLayerLR.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
 
             # Sides and corner Layers
             alphaLayerRBC = Image.new("L", (width, height), 255)
-            alphaLayerRBC.paste(agradientL.rotate(
-                180), (width - overlap_size_x, 0))
-            alphaLayerRBC.paste(agradientT.rotate(
-                180), (0, height - overlap_size_y))
-            alphaLayerRBC.paste(agradientC.rotate(180).resize(
-                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
+            alphaLayerRBC.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
+            alphaLayerRBC.paste(agradientT.rotate(180), (0, height - overlap_size_y))
+            alphaLayerRBC.paste(
+                agradientC.rotate(180).resize((overlap_size_x, overlap_size_y)),
+                (width - overlap_size_x, height - overlap_size_y),
+            )
             alphaLayerLBC = Image.new("L", (width, height), 255)
             alphaLayerLBC.paste(agradientL, (0, 0))
-            alphaLayerLBC.paste(agradientT.rotate(
-                180), (0, height - overlap_size_y))
-            alphaLayerLBC.paste(agradientC.rotate(90).resize(
-                (overlap_size_x, overlap_size_y)), (0, height - overlap_size_y))
+            alphaLayerLBC.paste(agradientT.rotate(180), (0, height - overlap_size_y))
+            alphaLayerLBC.paste(
+                agradientC.rotate(90).resize((overlap_size_x, overlap_size_y)),
+                (0, height - overlap_size_y),
+            )
             alphaLayerRTC = Image.new("L", (width, height), 255)
-            alphaLayerRTC.paste(agradientL.rotate(
-                180), (width - overlap_size_x, 0))
+            alphaLayerRTC.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
             alphaLayerRTC.paste(agradientT, (0, 0))
-            alphaLayerRTC.paste(agradientC.rotate(270).resize(
-                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+            alphaLayerRTC.paste(
+                agradientC.rotate(270).resize((overlap_size_x, overlap_size_y)),
+                (width - overlap_size_x, 0),
+            )
 
             # All but X layers
             alphaLayerABT = Image.new("L", (width, height), 255)
             alphaLayerABT.paste(alphaLayerLBC, (0, 0))
-            alphaLayerABT.paste(agradientL.rotate(
-                180), (width - overlap_size_x, 0))
-            alphaLayerABT.paste(agradientC.rotate(180).resize(
-                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
+            alphaLayerABT.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
+            alphaLayerABT.paste(
+                agradientC.rotate(180).resize((overlap_size_x, overlap_size_y)),
+                (width - overlap_size_x, height - overlap_size_y),
+            )
             alphaLayerABL = Image.new("L", (width, height), 255)
             alphaLayerABL.paste(alphaLayerRTC, (0, 0))
-            alphaLayerABL.paste(agradientT.rotate(
-                180), (0, height - overlap_size_y))
-            alphaLayerABL.paste(agradientC.rotate(180).resize(
-                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
+            alphaLayerABL.paste(agradientT.rotate(180), (0, height - overlap_size_y))
+            alphaLayerABL.paste(
+                agradientC.rotate(180).resize((overlap_size_x, overlap_size_y)),
+                (width - overlap_size_x, height - overlap_size_y),
+            )
             alphaLayerABR = Image.new("L", (width, height), 255)
             alphaLayerABR.paste(alphaLayerLBC, (0, 0))
             alphaLayerABR.paste(agradientT, (0, 0))
-            alphaLayerABR.paste(agradientC.resize(
-                (overlap_size_x, overlap_size_y)), (0, 0))
+            alphaLayerABR.paste(
+                agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0)
+            )
             alphaLayerABB = Image.new("L", (width, height), 255)
             alphaLayerABB.paste(alphaLayerRTC, (0, 0))
             alphaLayerABB.paste(agradientL, (0, 0))
-            alphaLayerABB.paste(agradientC.resize(
-                (overlap_size_x, overlap_size_y)), (0, 0))
+            alphaLayerABB.paste(
+                agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0)
+            )
 
             # All-around layer
             alphaLayerAA = Image.new("L", (width, height), 255)
             alphaLayerAA.paste(alphaLayerABT, (0, 0))
             alphaLayerAA.paste(agradientT, (0, 0))
-            alphaLayerAA.paste(agradientC.resize(
-                (overlap_size_x, overlap_size_y)), (0, 0))
-            alphaLayerAA.paste(agradientC.rotate(270).resize(
-                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+            alphaLayerAA.paste(
+                agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0)
+            )
+            alphaLayerAA.paste(
+                agradientC.rotate(270).resize((overlap_size_x, overlap_size_y)),
+                (width - overlap_size_x, 0),
+            )
 
         # Clean up temporary gradients
         del agradientL
@@ -287,17 +312,20 @@ class Embiggen(Generator):
         def make_image():
             # Make main tiles -------------------------------------------------
             if embiggen_tiles:
-                print(f'>> Making {len(embiggen_tiles)} Embiggen tiles...')
+                print(f">> Making {len(embiggen_tiles)} Embiggen tiles...")
             else:
                 print(
-                    f'>> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})...')
+                    f">> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
+                )
 
             emb_tile_store = []
             # Although we could use the same seed for every tile for determinism, at higher strengths this may
             # produce duplicated structures for each tile and make the tiling effect more obvious
             # instead track and iterate a local seed we pass to Img2Img
             seed = self.seed
-            seedintlimit = np.iinfo(np.uint32).max - 1 # only retreive this one from numpy
+            seedintlimit = (
+                np.iinfo(np.uint32).max - 1
+            )  # only retreive this one from numpy
 
             for tile in range(emb_tiles_x * emb_tiles_y):
                 # Don't iterate on first tile
@@ -334,37 +362,38 @@ class Embiggen(Generator):
 
                 if embiggen_tiles:
                     print(
-                        f'Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)')
+                        f"Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)"
+                    )
                 else:
-                    print(
-                        f'Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles')
+                    print(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles")
 
                 # create a torch tensor from an Image
-                newinitimage = np.array(
-                    newinitimage).astype(np.float32) / 255.0
+                newinitimage = np.array(newinitimage).astype(np.float32) / 255.0
                 newinitimage = newinitimage[None].transpose(0, 3, 1, 2)
                 newinitimage = torch.from_numpy(newinitimage)
                 newinitimage = 2.0 * newinitimage - 1.0
                 newinitimage = newinitimage.to(self.model.device)
-                clear_cuda_cache = kwargs['clear_cuda_cache'] if 'clear_cuda_cache' in kwargs else None
+                clear_cuda_cache = (
+                    kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None
+                )
 
                 tile_results = gen_img2img.generate(
                     prompt,
-                    iterations     = 1,
-                    seed           = seed,
-                    sampler        = sampler,
-                    steps          = steps,
-                    cfg_scale      = cfg_scale,
-                    conditioning   = conditioning,
-                    ddim_eta       = ddim_eta,
-                    image_callback = None,  # called only after the final image is generated
-                    step_callback  = step_callback,   # called after each intermediate image is generated
-                    width          = width,
-                    height         = height,
-                    init_image     = newinitimage,    # notice that init_image is different from init_img
-                    mask_image     = None,
-                    strength       = strength,
-                    clear_cuda_cache     = clear_cuda_cache
+                    iterations=1,
+                    seed=seed,
+                    sampler=sampler,
+                    steps=steps,
+                    cfg_scale=cfg_scale,
+                    conditioning=conditioning,
+                    ddim_eta=ddim_eta,
+                    image_callback=None,  # called only after the final image is generated
+                    step_callback=step_callback,  # called after each intermediate image is generated
+                    width=width,
+                    height=height,
+                    init_image=newinitimage,  # notice that init_image is different from init_img
+                    mask_image=None,
+                    strength=strength,
+                    clear_cuda_cache=clear_cuda_cache,
                 )
 
                 emb_tile_store.append(tile_results[0][0])
@@ -373,12 +402,14 @@ class Embiggen(Generator):
                 del newinitimage
 
             # Sanity check we have them all
-            if len(emb_tile_store) == (emb_tiles_x * emb_tiles_y) or (embiggen_tiles != [] and len(emb_tile_store) == len(embiggen_tiles)):
-                outputsuperimage = Image.new(
-                    "RGBA", (initsuperwidth, initsuperheight))
+            if len(emb_tile_store) == (emb_tiles_x * emb_tiles_y) or (
+                embiggen_tiles != [] and len(emb_tile_store) == len(embiggen_tiles)
+            ):
+                outputsuperimage = Image.new("RGBA", (initsuperwidth, initsuperheight))
                 if embiggen_tiles:
                     outputsuperimage.alpha_composite(
-                        initsuperimage.convert('RGBA'), (0, 0))
+                        initsuperimage.convert("RGBA"), (0, 0)
+                    )
                 for tile in range(emb_tiles_x * emb_tiles_y):
                     if embiggen_tiles:
                         if tile in embiggen_tiles:
@@ -387,7 +418,7 @@ class Embiggen(Generator):
                             continue
                     else:
                         intileimage = emb_tile_store[tile]
-                    intileimage = intileimage.convert('RGBA')
+                    intileimage = intileimage.convert("RGBA")
                     # Get row and column entries
                     emb_row_i = tile // emb_tiles_x
                     emb_column_i = tile % emb_tiles_x
@@ -399,8 +430,7 @@ class Embiggen(Generator):
                         if emb_column_i + 1 == emb_tiles_x:
                             left = initsuperwidth - width
                         else:
-                            left = round(emb_column_i *
-                                         (width - overlap_size_x))
+                            left = round(emb_column_i * (width - overlap_size_x))
                         if emb_row_i + 1 == emb_tiles_y:
                             top = initsuperheight - height
                         else:
@@ -411,33 +441,43 @@ class Embiggen(Generator):
                             # top of image
                             if emb_row_i == 0:
                                 if emb_column_i == 0:
-                                    if (tile+1) in embiggen_tiles:  # Look-ahead right
-                                        if (tile+emb_tiles_x) not in embiggen_tiles:  # Look-ahead down
+                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
+                                        if (
+                                            tile + emb_tiles_x
+                                        ) not in embiggen_tiles:  # Look-ahead down
                                             intileimage.putalpha(alphaLayerB)
                                         # Otherwise do nothing on this tile
-                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                    elif (
+                                        tile + emb_tiles_x
+                                    ) in embiggen_tiles:  # Look-ahead down only
                                         intileimage.putalpha(alphaLayerR)
                                     else:
                                         intileimage.putalpha(alphaLayerRBC)
                                 elif emb_column_i == emb_tiles_x - 1:
-                                    if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
+                                    if (
+                                        tile + emb_tiles_x
+                                    ) in embiggen_tiles:  # Look-ahead down
                                         intileimage.putalpha(alphaLayerL)
                                     else:
                                         intileimage.putalpha(alphaLayerLBC)
                                 else:
-                                    if (tile+1) in embiggen_tiles:  # Look-ahead right
-                                        if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
+                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
+                                        if (
+                                            tile + emb_tiles_x
+                                        ) in embiggen_tiles:  # Look-ahead down
                                             intileimage.putalpha(alphaLayerL)
                                         else:
                                             intileimage.putalpha(alphaLayerLBC)
-                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                    elif (
+                                        tile + emb_tiles_x
+                                    ) in embiggen_tiles:  # Look-ahead down only
                                         intileimage.putalpha(alphaLayerLR)
                                     else:
                                         intileimage.putalpha(alphaLayerABT)
                             # bottom of image
                             elif emb_row_i == emb_tiles_y - 1:
                                 if emb_column_i == 0:
-                                    if (tile+1) in embiggen_tiles: # Look-ahead right
+                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
                                         intileimage.putalpha(alphaLayerTaC)
                                     else:
                                         intileimage.putalpha(alphaLayerRTC)
@@ -445,34 +485,44 @@ class Embiggen(Generator):
                                     # No tiles to look ahead to
                                     intileimage.putalpha(alphaLayerLTC)
                                 else:
-                                    if (tile+1) in embiggen_tiles: # Look-ahead right
+                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
                                         intileimage.putalpha(alphaLayerLTaC)
                                     else:
                                         intileimage.putalpha(alphaLayerABB)
                             # vertical middle of image
                             else:
                                 if emb_column_i == 0:
-                                    if (tile+1) in embiggen_tiles: # Look-ahead right
-                                        if (tile+emb_tiles_x) in embiggen_tiles: # Look-ahead down
+                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
+                                        if (
+                                            tile + emb_tiles_x
+                                        ) in embiggen_tiles:  # Look-ahead down
                                             intileimage.putalpha(alphaLayerTaC)
                                         else:
                                             intileimage.putalpha(alphaLayerTB)
-                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                    elif (
+                                        tile + emb_tiles_x
+                                    ) in embiggen_tiles:  # Look-ahead down only
                                         intileimage.putalpha(alphaLayerRTC)
                                     else:
                                         intileimage.putalpha(alphaLayerABL)
                                 elif emb_column_i == emb_tiles_x - 1:
-                                    if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
+                                    if (
+                                        tile + emb_tiles_x
+                                    ) in embiggen_tiles:  # Look-ahead down
                                         intileimage.putalpha(alphaLayerLTC)
                                     else:
                                         intileimage.putalpha(alphaLayerABR)
                                 else:
-                                    if (tile+1) in embiggen_tiles: # Look-ahead right
-                                        if (tile+emb_tiles_x) in embiggen_tiles: # Look-ahead down
+                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
+                                        if (
+                                            tile + emb_tiles_x
+                                        ) in embiggen_tiles:  # Look-ahead down
                                             intileimage.putalpha(alphaLayerLTaC)
                                         else:
                                             intileimage.putalpha(alphaLayerABR)
-                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                    elif (
+                                        tile + emb_tiles_x
+                                    ) in embiggen_tiles:  # Look-ahead down only
                                         intileimage.putalpha(alphaLayerABB)
                                     else:
                                         intileimage.putalpha(alphaLayerAA)
@@ -481,21 +531,28 @@ class Embiggen(Generator):
                             if emb_row_i == 0 and emb_column_i >= 1:
                                 intileimage.putalpha(alphaLayerL)
                             elif emb_row_i >= 1 and emb_column_i == 0:
-                                if emb_column_i + 1 == emb_tiles_x: # If we don't have anything that can be placed to the right
+                                if (
+                                    emb_column_i + 1 == emb_tiles_x
+                                ):  # If we don't have anything that can be placed to the right
                                     intileimage.putalpha(alphaLayerT)
                                 else:
                                     intileimage.putalpha(alphaLayerTaC)
                             else:
-                                if emb_column_i + 1 == emb_tiles_x: # If we don't have anything that can be placed to the right
+                                if (
+                                    emb_column_i + 1 == emb_tiles_x
+                                ):  # If we don't have anything that can be placed to the right
                                     intileimage.putalpha(alphaLayerLTC)
                                 else:
                                     intileimage.putalpha(alphaLayerLTaC)
                     # Layer tile onto final image
                     outputsuperimage.alpha_composite(intileimage, (left, top))
             else:
-                print('Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation.')
+                print(
+                    "Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
+                )
 
             # after internal loops and patching up return Embiggen image
             return outputsuperimage
+
         # end of function declaration
         return make_image

invokeai/backend/generator/img2img.py

@@ -0,0 +1,97 @@
+"""
+invokeai.backend.generator.img2img descends from .generator
+"""
+
+import torch
+from diffusers import logging
+
+from ..stable_diffusion import (
+    ConditioningData,
+    PostprocessingSettings,
+    StableDiffusionGeneratorPipeline,
+)
+from .base import Generator
+
+
+class Img2Img(Generator):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+        self.init_latent = None  # by get_noise()
+
+    def get_make_image(
+        self,
+        prompt,
+        sampler,
+        steps,
+        cfg_scale,
+        ddim_eta,
+        conditioning,
+        init_image,
+        strength,
+        step_callback=None,
+        threshold=0.0,
+        warmup=0.2,
+        perlin=0.0,
+        h_symmetry_time_pct=None,
+        v_symmetry_time_pct=None,
+        attention_maps_callback=None,
+        **kwargs,
+    ):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it.
+        """
+        self.perlin = perlin
+
+        # noinspection PyTypeChecker
+        pipeline: StableDiffusionGeneratorPipeline = self.model
+        pipeline.scheduler = sampler
+
+        uc, c, extra_conditioning_info = conditioning
+        conditioning_data = ConditioningData(
+            uc,
+            c,
+            cfg_scale,
+            extra_conditioning_info,
+            postprocessing_settings=PostprocessingSettings(
+                threshold=threshold,
+                warmup=warmup,
+                h_symmetry_time_pct=h_symmetry_time_pct,
+                v_symmetry_time_pct=v_symmetry_time_pct,
+            ),
+        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
+
+        def make_image(x_T):
+            # FIXME: use x_T for initial seeded noise
+            # We're not at the moment because the pipeline automatically resizes init_image if
+            # necessary, which the x_T input might not match.
+            logging.set_verbosity_error()  # quench safety check warnings
+            pipeline_output = pipeline.img2img_from_embeddings(
+                init_image,
+                strength,
+                steps,
+                conditioning_data,
+                noise_func=self.get_noise_like,
+                callback=step_callback,
+            )
+            if (
+                pipeline_output.attention_map_saver is not None
+                and attention_maps_callback is not None
+            ):
+                attention_maps_callback(pipeline_output.attention_map_saver)
+            return pipeline.numpy_to_pil(pipeline_output.images)[0]
+
+        return make_image
+
+    def get_noise_like(self, like: torch.Tensor):
+        device = like.device
+        if device.type == "mps":
+            x = torch.randn_like(like, device="cpu").to(device)
+        else:
+            x = torch.randn_like(like, device=device)
+        if self.perlin > 0.0:
+            shape = like.shape
+            x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(
+                shape[3], shape[2]
+            )
+        return x

invokeai/backend/generator/inpaint.py

@@ -1,32 +1,35 @@
-'''
-ldm.invoke.generator.inpaint descends from ldm.invoke.generator
-'''
+"""
+invokeai.backend.generator.inpaint descends from .generator
+"""
 from __future__ import annotations
 
 import math
 
-import PIL
 import cv2
 import numpy as np
+import PIL
 import torch
-from PIL import Image, ImageFilter, ImageOps, ImageChops
+from PIL import Image, ImageChops, ImageFilter, ImageOps
 
-from ldm.invoke.generator.diffusers_pipeline import image_resized_to_grid_as_tensor, StableDiffusionGeneratorPipeline, \
-    ConditioningData
-from ldm.invoke.generator.img2img import Img2Img
-from ldm.invoke.patchmatch import PatchMatch
-from ldm.util import debug_image
+from ..image_util import PatchMatch, debug_image
+from ..stable_diffusion.diffusers_pipeline import (
+    ConditioningData,
+    StableDiffusionGeneratorPipeline,
+    image_resized_to_grid_as_tensor,
+)
+from .img2img import Img2Img
 
 
-def infill_methods()->list[str]:
+def infill_methods() -> list[str]:
     methods = [
         "tile",
         "solid",
     ]
     if PatchMatch.patchmatch_available():
-        methods.insert(0, 'patchmatch')
+        methods.insert(0, "patchmatch")
     return methods
 
+
 class Inpaint(Img2Img):
     def __init__(self, model, precision):
         self.inpaint_height = 0
@@ -53,11 +56,11 @@ class Inpaint(Img2Img):
             np.ravel(image),
             shape=(nrows, ncols, height, width, depth),
             strides=(height * _strides[0], width * _strides[1], *_strides),
-            writeable=False
+            writeable=False,
         )
 
     def infill_patchmatch(self, im: Image.Image) -> Image:
-        if im.mode != 'RGBA':
+        if im.mode != "RGBA":
             return im
 
         # Skip patchmatch if patchmatch isn't available
@@ -65,13 +68,17 @@ class Inpaint(Img2Img):
             return im
 
         # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
-        im_patched_np = PatchMatch.inpaint(im.convert('RGB'), ImageOps.invert(im.split()[-1]), patch_size = 3)
-        im_patched = Image.fromarray(im_patched_np, mode = 'RGB')
+        im_patched_np = PatchMatch.inpaint(
+            im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3
+        )
+        im_patched = Image.fromarray(im_patched_np, mode="RGB")
         return im_patched
 
-    def tile_fill_missing(self, im: Image.Image, tile_size: int = 16, seed: int = None) -> Image:
+    def tile_fill_missing(
+        self, im: Image.Image, tile_size: int = 16, seed: int = None
+    ) -> Image:
         # Only fill if there's an alpha layer
-        if im.mode != 'RGBA':
+        if im.mode != "RGBA":
             return im
 
         a = np.asarray(im, dtype=np.uint8)
@@ -79,21 +86,21 @@ class Inpaint(Img2Img):
         tile_size = (tile_size, tile_size)
 
         # Get the image as tiles of a specified size
-        tiles = self.get_tile_images(a,*tile_size).copy()
+        tiles = self.get_tile_images(a, *tile_size).copy()
 
         # Get the mask as tiles
-        tiles_mask = tiles[:,:,:,:,3]
+        tiles_mask = tiles[:, :, :, :, 3]
 
         # Find any mask tiles with any fully transparent pixels (we will be replacing these later)
         tmask_shape = tiles_mask.shape
         tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
-        n,ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
-        tiles_mask = (tiles_mask > 0)
-        tiles_mask = tiles_mask.reshape((n,ny)).all(axis = 1)
+        n, ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
+        tiles_mask = tiles_mask > 0
+        tiles_mask = tiles_mask.reshape((n, ny)).all(axis=1)
 
         # Get RGB tiles in single array and filter by the mask
         tshape = tiles.shape
-        tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), * tiles.shape[2:]))
+        tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), *tiles.shape[2:]))
         filtered_tiles = tiles_all[tiles_mask]
 
         if len(filtered_tiles) == 0:
@@ -101,23 +108,32 @@ class Inpaint(Img2Img):
 
         # Find all invalid tiles and replace with a random valid tile
         replace_count = (tiles_mask == False).sum()
-        rng = np.random.default_rng(seed = seed)
-        tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[rng.choice(filtered_tiles.shape[0], replace_count),:,:,:]
+        rng = np.random.default_rng(seed=seed)
+        tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[
+            rng.choice(filtered_tiles.shape[0], replace_count), :, :, :
+        ]
 
         # Convert back to an image
         tiles_all = tiles_all.reshape(tshape)
-        tiles_all = tiles_all.swapaxes(1,2)
-        st = tiles_all.reshape((math.prod(tiles_all.shape[0:2]), math.prod(tiles_all.shape[2:4]), tiles_all.shape[4]))
-        si = Image.fromarray(st, mode='RGBA')
+        tiles_all = tiles_all.swapaxes(1, 2)
+        st = tiles_all.reshape(
+            (
+                math.prod(tiles_all.shape[0:2]),
+                math.prod(tiles_all.shape[2:4]),
+                tiles_all.shape[4],
+            )
+        )
+        si = Image.fromarray(st, mode="RGBA")
 
         return si
 
-
     def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
         npimg = np.asarray(mask, dtype=np.uint8)
 
         # Detect any partially transparent regions
-        npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0)))
+        npgradient = np.uint8(
+            255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0))
+        )
 
         # Detect hard edges
         npedge = cv2.Canny(npimg, threshold1=100, threshold2=200)
@@ -126,7 +142,9 @@ class Inpaint(Img2Img):
         npmask = npgradient + npedge
 
         # Expand
-        npmask = cv2.dilate(npmask, np.ones((3,3), np.uint8), iterations = int(edge_size / 2))
+        npmask = cv2.dilate(
+            npmask, np.ones((3, 3), np.uint8), iterations=int(edge_size / 2)
+        )
 
         new_mask = Image.fromarray(npmask)
 
@@ -135,9 +153,22 @@ class Inpaint(Img2Img):
 
         return ImageOps.invert(new_mask)
 
-
-    def seam_paint(self, im: Image.Image, seam_size: int, seam_blur: int, prompt, sampler, steps, cfg_scale, ddim_eta,
-                   conditioning, strength, noise, infill_method, step_callback) -> Image.Image:
+    def seam_paint(
+        self,
+        im: Image.Image,
+        seam_size: int,
+        seam_blur: int,
+        prompt,
+        sampler,
+        steps,
+        cfg_scale,
+        ddim_eta,
+        conditioning,
+        strength,
+        noise,
+        infill_method,
+        step_callback,
+    ) -> Image.Image:
         hard_mask = self.pil_image.split()[-1].copy()
         mask = self.mask_edge(hard_mask, seam_size, seam_blur)
 
@@ -148,15 +179,15 @@ class Inpaint(Img2Img):
             cfg_scale,
             ddim_eta,
             conditioning,
-            init_image = im.copy().convert('RGBA'),
-            mask_image = mask,
-            strength = strength,
-            mask_blur_radius = 0,
-            seam_size = 0,
-            step_callback = step_callback,
-            inpaint_width = im.width,
-            inpaint_height = im.height,
-            infill_method = infill_method
+            init_image=im.copy().convert("RGBA"),
+            mask_image=mask,
+            strength=strength,
+            mask_blur_radius=0,
+            seam_size=0,
+            step_callback=step_callback,
+            inpaint_width=im.width,
+            inpaint_height=im.height,
+            infill_method=infill_method,
         )
 
         seam_noise = self.get_noise(im.width, im.height)
@@ -165,28 +196,35 @@ class Inpaint(Img2Img):
 
         return result
 
-
     @torch.no_grad()
-    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,
-                       init_image: PIL.Image.Image | torch.FloatTensor,
-                       mask_image: PIL.Image.Image | torch.FloatTensor,
-                       strength: float,
-                       mask_blur_radius: int = 8,
-                       # Seam settings - when 0, doesn't fill seam
-                       seam_size: int = 0,
-                       seam_blur: int = 0,
-                       seam_strength: float = 0.7,
-                       seam_steps: int = 10,
-                       tile_size: int = 32,
-                       step_callback=None,
-                       inpaint_replace=False, enable_image_debugging=False,
-                       infill_method = None,
-                       inpaint_width=None,
-                       inpaint_height=None,
-                       inpaint_fill:tuple(int)=(0x7F, 0x7F, 0x7F, 0xFF),
-                       attention_maps_callback=None,
-                       **kwargs):
+    def get_make_image(
+        self,
+        prompt,
+        sampler,
+        steps,
+        cfg_scale,
+        ddim_eta,
+        conditioning,
+        init_image: PIL.Image.Image | torch.FloatTensor,
+        mask_image: PIL.Image.Image | torch.FloatTensor,
+        strength: float,
+        mask_blur_radius: int = 8,
+        # Seam settings - when 0, doesn't fill seam
+        seam_size: int = 0,
+        seam_blur: int = 0,
+        seam_strength: float = 0.7,
+        seam_steps: int = 10,
+        tile_size: int = 32,
+        step_callback=None,
+        inpaint_replace=False,
+        enable_image_debugging=False,
+        infill_method=None,
+        inpaint_width=None,
+        inpaint_height=None,
+        inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
+        attention_maps_callback=None,
+        **kwargs,
+    ):
         """
         Returns a function returning an image derived from the prompt and
         the initial image + mask.  Return value depends on the seed at
@@ -204,33 +242,39 @@ class Inpaint(Img2Img):
             self.pil_image = init_image.copy()
 
             # Do infill
-            if infill_method == 'patchmatch' and PatchMatch.patchmatch_available():
+            if infill_method == "patchmatch" and PatchMatch.patchmatch_available():
                 init_filled = self.infill_patchmatch(self.pil_image.copy())
-            elif infill_method == 'tile':
+            elif infill_method == "tile":
                 init_filled = self.tile_fill_missing(
-                    self.pil_image.copy(),
-                    seed = self.seed,
-                    tile_size = tile_size
+                    self.pil_image.copy(), seed=self.seed, tile_size=tile_size
                 )
-            elif infill_method == 'solid':
+            elif infill_method == "solid":
                 solid_bg = PIL.Image.new("RGBA", init_image.size, inpaint_fill)
                 init_filled = PIL.Image.alpha_composite(solid_bg, init_image)
             else:
-                raise ValueError(f"Non-supported infill type {infill_method}", infill_method)
-            init_filled.paste(init_image, (0,0), init_image.split()[-1])
+                raise ValueError(
+                    f"Non-supported infill type {infill_method}", infill_method
+                )
+            init_filled.paste(init_image, (0, 0), init_image.split()[-1])
 
             # Resize if requested for inpainting
             if inpaint_width and inpaint_height:
                 init_filled = init_filled.resize((inpaint_width, inpaint_height))
 
-            debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging)
+            debug_image(
+                init_filled, "init_filled", debug_status=self.enable_image_debugging
+            )
 
             # Create init tensor
-            init_image = image_resized_to_grid_as_tensor(init_filled.convert('RGB'))
+            init_image = image_resized_to_grid_as_tensor(init_filled.convert("RGB"))
 
         if isinstance(mask_image, PIL.Image.Image):
             self.pil_mask = mask_image.copy()
-            debug_image(mask_image, "mask_image BEFORE multiply with pil_image", debug_status=self.enable_image_debugging)
+            debug_image(
+                mask_image,
+                "mask_image BEFORE multiply with pil_image",
+                debug_status=self.enable_image_debugging,
+            )
 
             init_alpha = self.pil_image.getchannel("A")
             if mask_image.mode != "L":
@@ -243,8 +287,14 @@ class Inpaint(Img2Img):
             if inpaint_width and inpaint_height:
                 mask_image = mask_image.resize((inpaint_width, inpaint_height))
 
-            debug_image(mask_image, "mask_image AFTER multiply with pil_image", debug_status=self.enable_image_debugging)
-            mask: torch.FloatTensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+            debug_image(
+                mask_image,
+                "mask_image AFTER multiply with pil_image",
+                debug_status=self.enable_image_debugging,
+            )
+            mask: torch.FloatTensor = image_resized_to_grid_as_tensor(
+                mask_image, normalize=False
+            )
         else:
             mask: torch.FloatTensor = mask_image
 
@@ -256,9 +306,9 @@ class Inpaint(Img2Img):
 
         # todo: support cross-attention control
         uc, c, _ = conditioning
-        conditioning_data = (ConditioningData(uc, c, cfg_scale)
-                             .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
-
+        conditioning_data = ConditioningData(
+            uc, c, cfg_scale
+        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
 
         def make_image(x_T):
             pipeline_output = pipeline.inpaint_from_embeddings(
@@ -271,43 +321,71 @@ class Inpaint(Img2Img):
                 callback=step_callback,
             )
 
-            if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
+            if (
+                pipeline_output.attention_map_saver is not None
+                and attention_maps_callback is not None
+            ):
                 attention_maps_callback(pipeline_output.attention_map_saver)
 
-            result = self.postprocess_size_and_mask(pipeline.numpy_to_pil(pipeline_output.images)[0])
+            result = self.postprocess_size_and_mask(
+                pipeline.numpy_to_pil(pipeline_output.images)[0]
+            )
 
             # Seam paint if this is our first pass (seam_size set to 0 during seam painting)
             if seam_size > 0:
                 old_image = self.pil_image or init_image
                 old_mask = self.pil_mask or mask_image
 
-                result = self.seam_paint(result, seam_size, seam_blur, prompt, sampler, seam_steps, cfg_scale, ddim_eta,
-                                         conditioning, seam_strength, x_T, infill_method, step_callback)
+                result = self.seam_paint(
+                    result,
+                    seam_size,
+                    seam_blur,
+                    prompt,
+                    sampler,
+                    seam_steps,
+                    cfg_scale,
+                    ddim_eta,
+                    conditioning,
+                    seam_strength,
+                    x_T,
+                    infill_method,
+                    step_callback,
+                )
 
                 # Restore original settings
-                self.get_make_image(prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,
-                       old_image,
-                       old_mask,
-                       strength,
-                       mask_blur_radius, seam_size, seam_blur, seam_strength,
-                       seam_steps, tile_size, step_callback,
-                       inpaint_replace, enable_image_debugging,
-                       inpaint_width = inpaint_width,
-                       inpaint_height = inpaint_height,
-                       infill_method = infill_method,
-                       **kwargs)
+                self.get_make_image(
+                    prompt,
+                    sampler,
+                    steps,
+                    cfg_scale,
+                    ddim_eta,
+                    conditioning,
+                    old_image,
+                    old_mask,
+                    strength,
+                    mask_blur_radius,
+                    seam_size,
+                    seam_blur,
+                    seam_strength,
+                    seam_steps,
+                    tile_size,
+                    step_callback,
+                    inpaint_replace,
+                    enable_image_debugging,
+                    inpaint_width=inpaint_width,
+                    inpaint_height=inpaint_height,
+                    infill_method=infill_method,
+                    **kwargs,
+                )
 
             return result
 
         return make_image
 
-
-    def sample_to_image(self, samples)->Image.Image:
-        gen_result = super().sample_to_image(samples).convert('RGB')
+    def sample_to_image(self, samples) -> Image.Image:
+        gen_result = super().sample_to_image(samples).convert("RGB")
         return self.postprocess_size_and_mask(gen_result)
 
-
     def postprocess_size_and_mask(self, gen_result: Image.Image) -> Image.Image:
         debug_image(gen_result, "gen_result", debug_status=self.enable_image_debugging)
 
@@ -318,7 +396,13 @@ class Inpaint(Img2Img):
         if self.pil_image is None or self.pil_mask is None:
             return gen_result
 
-        corrected_result = self.repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
-        debug_image(corrected_result, "corrected_result", debug_status=self.enable_image_debugging)
+        corrected_result = self.repaste_and_color_correct(
+            gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius
+        )
+        debug_image(
+            corrected_result,
+            "corrected_result",
+            debug_status=self.enable_image_debugging,
+        )
 
         return corrected_result

invokeai/backend/generator/txt2img.py

@@ -0,0 +1,81 @@
+"""
+invokeai.backend.generator.txt2img inherits from invokeai.backend.generator
+"""
+import PIL.Image
+import torch
+
+from ..stable_diffusion import (
+    ConditioningData,
+    PostprocessingSettings,
+    StableDiffusionGeneratorPipeline,
+)
+from .base import Generator
+
+
+class Txt2Img(Generator):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+
+    @torch.no_grad()
+    def get_make_image(
+        self,
+        prompt,
+        sampler,
+        steps,
+        cfg_scale,
+        ddim_eta,
+        conditioning,
+        width,
+        height,
+        step_callback=None,
+        threshold=0.0,
+        warmup=0.2,
+        perlin=0.0,
+        h_symmetry_time_pct=None,
+        v_symmetry_time_pct=None,
+        attention_maps_callback=None,
+        **kwargs,
+    ):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it
+        kwargs are 'width' and 'height'
+        """
+        self.perlin = perlin
+
+        # noinspection PyTypeChecker
+        pipeline: StableDiffusionGeneratorPipeline = self.model
+        pipeline.scheduler = sampler
+
+        uc, c, extra_conditioning_info = conditioning
+        conditioning_data = ConditioningData(
+            uc,
+            c,
+            cfg_scale,
+            extra_conditioning_info,
+            postprocessing_settings=PostprocessingSettings(
+                threshold=threshold,
+                warmup=warmup,
+                h_symmetry_time_pct=h_symmetry_time_pct,
+                v_symmetry_time_pct=v_symmetry_time_pct,
+            ),
+        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
+
+        def make_image(x_T) -> PIL.Image.Image:
+            pipeline_output = pipeline.image_from_embeddings(
+                latents=torch.zeros_like(x_T, dtype=self.torch_dtype()),
+                noise=x_T,
+                num_inference_steps=steps,
+                conditioning_data=conditioning_data,
+                callback=step_callback,
+            )
+
+            if (
+                pipeline_output.attention_map_saver is not None
+                and attention_maps_callback is not None
+            ):
+                attention_maps_callback(pipeline_output.attention_map_saver)
+
+            return pipeline.numpy_to_pil(pipeline_output.images)[0]
+
+        return make_image

invokeai/backend/generator/txt2img2img.py

@@ -1,6 +1,6 @@
-'''
-ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
-'''
+"""
+invokeai.backend.generator.txt2img inherits from invokeai.backend.generator
+"""
 
 import math
 from typing import Callable, Optional
@@ -8,21 +8,40 @@ from typing import Callable, Optional
 import torch
 from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_error
 
-from ldm.invoke.generator.base import Generator
-from ldm.invoke.generator.diffusers_pipeline import trim_to_multiple_of, StableDiffusionGeneratorPipeline, \
-    ConditioningData
-from ldm.models.diffusion.shared_invokeai_diffusion import PostprocessingSettings
+from ..models import PostprocessingSettings
+from .base import Generator
+from .diffusers_pipeline import (
+    ConditioningData,
+    StableDiffusionGeneratorPipeline,
+    trim_to_multiple_of,
+)
 
 
 class Txt2Img2Img(Generator):
     def __init__(self, model, precision):
         super().__init__(model, precision)
-        self.init_latent = None    # for get_noise()
+        self.init_latent = None  # for get_noise()
 
-    def get_make_image(self, prompt:str, sampler, steps:int, cfg_scale:float, ddim_eta,
-                       conditioning, width:int, height:int, strength:float,
-                       step_callback:Optional[Callable]=None, threshold=0.0, warmup=0.2, perlin=0.0,
-                       h_symmetry_time_pct=None, v_symmetry_time_pct=None, attention_maps_callback=None, **kwargs):
+    def get_make_image(
+        self,
+        prompt: str,
+        sampler,
+        steps: int,
+        cfg_scale: float,
+        ddim_eta,
+        conditioning,
+        width: int,
+        height: int,
+        strength: float,
+        step_callback: Optional[Callable] = None,
+        threshold=0.0,
+        warmup=0.2,
+        perlin=0.0,
+        h_symmetry_time_pct=None,
+        v_symmetry_time_pct=None,
+        attention_maps_callback=None,
+        **kwargs,
+    ):
         """
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it
@@ -35,19 +54,20 @@ class Txt2Img2Img(Generator):
         pipeline.scheduler = sampler
 
         uc, c, extra_conditioning_info = conditioning
-        conditioning_data = (
-            ConditioningData(
-                uc, c, cfg_scale, extra_conditioning_info,
-                postprocessing_settings = PostprocessingSettings(
-                    threshold=threshold,
-                    warmup=0.2,
-                    h_symmetry_time_pct=h_symmetry_time_pct,
-                    v_symmetry_time_pct=v_symmetry_time_pct
-                )
-            ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+        conditioning_data = ConditioningData(
+            uc,
+            c,
+            cfg_scale,
+            extra_conditioning_info,
+            postprocessing_settings=PostprocessingSettings(
+                threshold=threshold,
+                warmup=0.2,
+                h_symmetry_time_pct=h_symmetry_time_pct,
+                v_symmetry_time_pct=v_symmetry_time_pct,
+            ),
+        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
 
         def make_image(x_T):
-
             first_pass_latent_output, _ = pipeline.latents_from_embeddings(
                 latents=torch.zeros_like(x_T),
                 num_inference_steps=steps,
@@ -61,28 +81,40 @@ class Txt2Img2Img(Generator):
             init_width = first_pass_latent_output.size()[3] * self.downsampling_factor
             init_height = first_pass_latent_output.size()[2] * self.downsampling_factor
             print(
-                  f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
-                 )
+                f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
+            )
 
             # resizing
             resized_latents = torch.nn.functional.interpolate(
                 first_pass_latent_output,
-                size=(height // self.downsampling_factor, width // self.downsampling_factor),
-                mode="bilinear"
+                size=(
+                    height // self.downsampling_factor,
+                    width // self.downsampling_factor,
+                ),
+                mode="bilinear",
             )
 
             # Free up memory from the last generation.
-            clear_cuda_cache = kwargs['clear_cuda_cache'] or None
+            clear_cuda_cache = kwargs["clear_cuda_cache"] or None
             if clear_cuda_cache is not None:
                 clear_cuda_cache()
 
-            second_pass_noise = self.get_noise_like(resized_latents, override_perlin=True)
+            second_pass_noise = self.get_noise_like(
+                resized_latents, override_perlin=True
+            )
 
             # Clear symmetry for the second pass
             from dataclasses import replace
-            new_postprocessing_settings = replace(conditioning_data.postprocessing_settings, h_symmetry_time_pct=None)
-            new_postprocessing_settings = replace(new_postprocessing_settings, v_symmetry_time_pct=None)
-            new_conditioning_data = replace(conditioning_data, postprocessing_settings=new_postprocessing_settings)
+
+            new_postprocessing_settings = replace(
+                conditioning_data.postprocessing_settings, h_symmetry_time_pct=None
+            )
+            new_postprocessing_settings = replace(
+                new_postprocessing_settings, v_symmetry_time_pct=None
+            )
+            new_conditioning_data = replace(
+                conditioning_data, postprocessing_settings=new_postprocessing_settings
+            )
 
             verbosity = get_verbosity()
             set_verbosity_error()
@@ -92,15 +124,18 @@ class Txt2Img2Img(Generator):
                 conditioning_data=new_conditioning_data,
                 strength=strength,
                 noise=second_pass_noise,
-                callback=step_callback)
+                callback=step_callback,
+            )
             set_verbosity(verbosity)
 
-            if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
+            if (
+                pipeline_output.attention_map_saver is not None
+                and attention_maps_callback is not None
+            ):
                 attention_maps_callback(pipeline_output.attention_map_saver)
 
             return pipeline.numpy_to_pil(pipeline_output.images)[0]
 
-
         # FIXME: do we really need something entirely different for the inpainting model?
 
         # in the case of the inpainting model being loaded, the trick of
@@ -111,19 +146,23 @@ class Txt2Img2Img(Generator):
 
         return make_image
 
-    def get_noise_like(self, like: torch.Tensor, override_perlin: bool=False):
+    def get_noise_like(self, like: torch.Tensor, override_perlin: bool = False):
         device = like.device
-        if device.type == 'mps':
-            x = torch.randn_like(like, device='cpu', dtype=self.torch_dtype()).to(device)
+        if device.type == "mps":
+            x = torch.randn_like(like, device="cpu", dtype=self.torch_dtype()).to(
+                device
+            )
         else:
             x = torch.randn_like(like, device=device, dtype=self.torch_dtype())
         if self.perlin > 0.0 and override_perlin == False:
             shape = like.shape
-            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
+            x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(
+                shape[3], shape[2]
+            )
         return x
 
     # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self,width,height,scale = True):
+    def get_noise(self, width, height, scale=True):
         # print(f"Get noise: {width}x{height}")
         if scale:
             # Scale the input width and height for the initial generation
@@ -133,7 +172,9 @@ class Txt2Img2Img(Generator):
             aspect = width / height
             dimension = self.model.unet.config.sample_size * self.model.vae_scale_factor
             min_dimension = math.floor(dimension * 0.5)
-            model_area = dimension * dimension # hardcoded for now since all models are trained on square images
+            model_area = (
+                dimension * dimension
+            )  # hardcoded for now since all models are trained on square images
 
             if aspect > 1.0:
                 init_height = max(min_dimension, math.sqrt(model_area / aspect))
@@ -142,7 +183,9 @@ class Txt2Img2Img(Generator):
                 init_width = max(min_dimension, math.sqrt(model_area * aspect))
                 init_height = init_width / aspect
 
-            scaled_width, scaled_height = trim_to_multiple_of(math.floor(init_width), math.floor(init_height))
+            scaled_width, scaled_height = trim_to_multiple_of(
+                math.floor(init_width), math.floor(init_height)
+            )
 
         else:
             scaled_width = width
@@ -152,10 +195,14 @@ class Txt2Img2Img(Generator):
         channels = self.latent_channels
         if channels == 9:
             channels = 4  # we don't really want noise for all the mask channels
-        shape = (1, channels,
-                 scaled_height // self.downsampling_factor, scaled_width // self.downsampling_factor)
-        if self.use_mps_noise or device.type == 'mps':
-            tensor = torch.empty(size=shape, device='cpu')
+        shape = (
+            1,
+            channels,
+            scaled_height // self.downsampling_factor,
+            scaled_width // self.downsampling_factor,
+        )
+        if self.use_mps_noise or device.type == "mps":
+            tensor = torch.empty(size=shape, device="cpu")
             tensor = self.get_noise_like(like=tensor).to(device)
         else:
             tensor = torch.empty(size=shape, device=device)

invokeai/backend/globals.py

@@ -1,5 +1,5 @@
-'''
-ldm.invoke.globals defines a small number of global variables that would
+"""
+invokeai.backend.globals defines a small number of global variables that would
 otherwise have to be passed through long and complex call chains.
 
 It defines a Namespace object named "Globals" that contains
@@ -9,7 +9,7 @@ the attributes:
   - initfile       - path to the initialization file
   - try_patchmatch - option to globally disable loading of 'patchmatch' module
   - always_use_cpu - force use of CPU even if GPU is available
-'''
+"""
 
 import os
 import os.path as osp
@@ -20,12 +20,12 @@ from typing import Union
 Globals = Namespace()
 
 # Where to look for the initialization file and other key components
-Globals.initfile = 'invokeai.init'
-Globals.models_file = 'models.yaml'
-Globals.models_dir = 'models'
-Globals.config_dir = 'configs'
-Globals.autoscan_dir = 'weights'
-Globals.converted_ckpts_dir = 'converted_ckpts'
+Globals.initfile = "invokeai.init"
+Globals.models_file = "models.yaml"
+Globals.models_dir = "models"
+Globals.config_dir = "configs"
+Globals.autoscan_dir = "weights"
+Globals.converted_ckpts_dir = "converted_ckpts"
 
 # Set the default root directory. This can be overwritten by explicitly
 # passing the `--root <directory>` argument on the command line.
@@ -34,12 +34,15 @@ Globals.converted_ckpts_dir = 'converted_ckpts'
 # 2) use VIRTUAL_ENV environment variable, with a check for initfile being there
 # 3) use ~/invokeai
 
-if os.environ.get('INVOKEAI_ROOT'):
-    Globals.root = osp.abspath(os.environ.get('INVOKEAI_ROOT'))
-elif os.environ.get('VIRTUAL_ENV') and Path(os.environ.get('VIRTUAL_ENV'),'..',Globals.initfile).exists():
-    Globals.root = osp.abspath(osp.join(os.environ.get('VIRTUAL_ENV'), '..'))
+if os.environ.get("INVOKEAI_ROOT"):
+    Globals.root = osp.abspath(os.environ.get("INVOKEAI_ROOT"))
+elif (
+    os.environ.get("VIRTUAL_ENV")
+    and Path(os.environ.get("VIRTUAL_ENV"), "..", Globals.initfile).exists()
+):
+    Globals.root = osp.abspath(osp.join(os.environ.get("VIRTUAL_ENV"), ".."))
 else:
-    Globals.root = osp.abspath(osp.expanduser('~/invokeai'))
+    Globals.root = osp.abspath(osp.expanduser("~/invokeai"))
 
 # Try loading patchmatch
 Globals.try_patchmatch = True
@@ -61,31 +64,38 @@ Globals.sequential_guidance = False
 Globals.full_precision = False
 
 # whether we should convert ckpt files into diffusers models on the fly
-Globals.ckpt_convert = False
+Globals.ckpt_convert = True
 
 # logging tokenization everywhere
 Globals.log_tokenization = False
 
-def global_config_file()->Path:
+
+def global_config_file() -> Path:
     return Path(Globals.root, Globals.config_dir, Globals.models_file)
 
-def global_config_dir()->Path:
+
+def global_config_dir() -> Path:
     return Path(Globals.root, Globals.config_dir)
 
-def global_models_dir()->Path:
+
+def global_models_dir() -> Path:
     return Path(Globals.root, Globals.models_dir)
 
-def global_autoscan_dir()->Path:
+
+def global_autoscan_dir() -> Path:
     return Path(Globals.root, Globals.autoscan_dir)
 
-def global_converted_ckpts_dir()->Path:
+
+def global_converted_ckpts_dir() -> Path:
     return Path(global_models_dir(), Globals.converted_ckpts_dir)
 
-def global_set_root(root_dir:Union[str,Path]):
+
+def global_set_root(root_dir: Union[str, Path]):
     Globals.root = root_dir
 
-def global_cache_dir(subdir:Union[str,Path]='')->Path:
-    '''
+
+def global_cache_dir(subdir: Union[str, Path] = "") -> Path:
+    """
     Returns Path to the model cache directory. If a subdirectory
     is provided, it will be appended to the end of the path, allowing
     for huggingface-style conventions:
@@ -98,18 +108,18 @@ def global_cache_dir(subdir:Union[str,Path]='')->Path:
     One other caveat is that HuggingFace is moving some diffusers models
     into the "hub" subdirectory as well, so this will need to be revisited
     from time to time.
-    '''
-    home: str = os.getenv('HF_HOME')
+    """
+    home: str = os.getenv("HF_HOME")
 
     if home is None:
-        home = os.getenv('XDG_CACHE_HOME')
+        home = os.getenv("XDG_CACHE_HOME")
 
         if home is not None:
             # Set `home` to $XDG_CACHE_HOME/huggingface, which is the default location mentioned in HuggingFace Hub Client Library.
             # See: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#xdgcachehome
-            home += os.sep + 'huggingface'
+            home += os.sep + "huggingface"
 
     if home is not None:
-        return Path(home,subdir)
+        return Path(home, subdir)
     else:
-        return Path(Globals.root,'models',subdir)
+        return Path(Globals.root, "models", subdir)

invokeai/backend/image_util/__init__.py

@@ -0,0 +1,24 @@
+"""
+Initialization file for invokeai.backend.image_util methods.
+"""
+from .patchmatch import PatchMatch
+from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata
+from .seamless import configure_model_padding
+from .txt2mask import Txt2Mask
+from .util import InitImageResizer, make_grid
+
+
+def debug_image(
+    debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False
+):
+    if not debug_status:
+        return
+
+    image_copy = debug_image.copy().convert("RGBA")
+    ImageDraw.Draw(image_copy).text((5, 5), debug_text, (255, 0, 0))
+
+    if debug_show:
+        image_copy.show()
+
+    if debug_result:
+        return image_copy

invokeai/backend/image_util/patchmatch.py

@@ -1,19 +1,21 @@
-'''
+"""
 This module defines a singleton object, "patchmatch" that
 wraps the actual patchmatch object. It respects the global
 "try_patchmatch" attribute, so that patchmatch loading can
 be suppressed or deferred
-'''
-from ldm.invoke.globals import Globals
-import numpy as  np
+"""
+import numpy as np
+
+from invokeai.backend.globals import Globals
+
 
 class PatchMatch:
-    '''
+    """
     Thin class wrapper around the patchmatch function.
-    '''
+    """
 
     patch_match = None
-    tried_load:bool = False
+    tried_load: bool = False
 
     def __init__(self):
         super().__init__()
@@ -24,21 +26,22 @@ class PatchMatch:
             return
         if Globals.try_patchmatch:
             from patchmatch import patch_match as pm
+
             if pm.patchmatch_available:
-                print('>> Patchmatch initialized')
+                print(">> Patchmatch initialized")
             else:
-                print('>> Patchmatch not loaded (nonfatal)')
+                print(">> Patchmatch not loaded (nonfatal)")
             self.patch_match = pm
         else:
-            print('>> Patchmatch loading disabled')
+            print(">> Patchmatch loading disabled")
         self.tried_load = True
 
     @classmethod
-    def patchmatch_available(self)->bool:
+    def patchmatch_available(self) -> bool:
         self._load_patch_match()
         return self.patch_match and self.patch_match.patchmatch_available
 
     @classmethod
-    def inpaint(self,*args,**kwargs)->np.ndarray:
+    def inpaint(self, *args, **kwargs) -> np.ndarray:
         if self.patchmatch_available():
-            return self.patch_match.inpaint(*args,**kwargs)
+            return self.patch_match.inpaint(*args, **kwargs)

invokeai/backend/image_util/pngwriter.py

@@ -6,10 +6,11 @@ PngWriter -- Converts Images generated by T2I into PNGs, finds
 
 Exports function retrieve_metadata(path)
 """
+import json
 import os
 import re
-import json
-from PIL import PngImagePlugin, Image
+
+from PIL import Image, PngImagePlugin
 
 # -------------------image generation utils-----
 
@@ -25,52 +26,57 @@ class PngWriter:
         dirlist = sorted(os.listdir(self.outdir), reverse=True)
         # find the first filename that matches our pattern or return 000000.0.png
         existing_name = next(
-            (f for f in dirlist if re.match('^(\d+)\..*\.png', f)),
-            '0000000.0.png',
+            (f for f in dirlist if re.match("^(\d+)\..*\.png", f)),
+            "0000000.0.png",
         )
-        basecount = int(existing_name.split('.', 1)[0]) + 1
-        return f'{basecount:06}'
+        basecount = int(existing_name.split(".", 1)[0]) + 1
+        return f"{basecount:06}"
 
     # saves image named _image_ to outdir/name, writing metadata from prompt
     # returns full path of output
-    def save_image_and_prompt_to_png(self, image, dream_prompt, name, metadata=None, compress_level=6):
+    def save_image_and_prompt_to_png(
+        self, image, dream_prompt, name, metadata=None, compress_level=6
+    ):
         path = os.path.join(self.outdir, name)
         info = PngImagePlugin.PngInfo()
-        info.add_text('Dream', dream_prompt)
+        info.add_text("Dream", dream_prompt)
         if metadata:
-            info.add_text('sd-metadata', json.dumps(metadata))
-        image.save(path, 'PNG', pnginfo=info, compress_level=compress_level)
+            info.add_text("sd-metadata", json.dumps(metadata))
+        image.save(path, "PNG", pnginfo=info, compress_level=compress_level)
         return path
 
-    def retrieve_metadata(self,img_basename):
-        '''
+    def retrieve_metadata(self, img_basename):
+        """
         Given a PNG filename stored in outdir, returns the "sd-metadata"
         metadata stored there, as a dict
-        '''
-        path = os.path.join(self.outdir,img_basename)
+        """
+        path = os.path.join(self.outdir, img_basename)
         all_metadata = retrieve_metadata(path)
-        return all_metadata['sd-metadata']
+        return all_metadata["sd-metadata"]
+
 
 def retrieve_metadata(img_path):
-    '''
+    """
     Given a path to a PNG image, returns the "sd-metadata"
     metadata stored there, as a dict
-    '''
+    """
     im = Image.open(img_path)
-    if hasattr(im, 'text'):
-        md = im.text.get('sd-metadata', '{}')
-        dream_prompt = im.text.get('Dream', '')
+    if hasattr(im, "text"):
+        md = im.text.get("sd-metadata", "{}")
+        dream_prompt = im.text.get("Dream", "")
     else:
         # When trying to retrieve metadata from images without a 'text' payload, such as JPG images.
-        md = '{}'
-        dream_prompt = ''
-    return {'sd-metadata': json.loads(md), 'Dream': dream_prompt}
+        md = "{}"
+        dream_prompt = ""
+    return {"sd-metadata": json.loads(md), "Dream": dream_prompt}
 
-def write_metadata(img_path:str, meta:dict):
+
+def write_metadata(img_path: str, meta: dict):
     im = Image.open(img_path)
     info = PngImagePlugin.PngInfo()
-    info.add_text('sd-metadata', json.dumps(meta))
-    im.save(img_path,'PNG',pnginfo=info)
+    info.add_text("sd-metadata", json.dumps(meta))
+    im.save(img_path, "PNG", pnginfo=info)
+
 
 class PromptFormatter:
     def __init__(self, t2i, opt):
@@ -86,28 +92,30 @@ class PromptFormatter:
 
         switches = list()
         switches.append(f'"{opt.prompt}"')
-        switches.append(f'-s{opt.steps        or t2i.steps}')
-        switches.append(f'-W{opt.width        or t2i.width}')
-        switches.append(f'-H{opt.height       or t2i.height}')
-        switches.append(f'-C{opt.cfg_scale    or t2i.cfg_scale}')
-        switches.append(f'-A{opt.sampler_name or t2i.sampler_name}')
-# to do: put model name into the t2i object
-#        switches.append(f'--model{t2i.model_name}')
+        switches.append(f"-s{opt.steps        or t2i.steps}")
+        switches.append(f"-W{opt.width        or t2i.width}")
+        switches.append(f"-H{opt.height       or t2i.height}")
+        switches.append(f"-C{opt.cfg_scale    or t2i.cfg_scale}")
+        switches.append(f"-A{opt.sampler_name or t2i.sampler_name}")
+        # to do: put model name into the t2i object
+        #        switches.append(f'--model{t2i.model_name}')
         if opt.seamless or t2i.seamless:
-            switches.append(f'--seamless')
+            switches.append(f"--seamless")
         if opt.init_img:
-            switches.append(f'-I{opt.init_img}')
+            switches.append(f"-I{opt.init_img}")
         if opt.fit:
-            switches.append(f'--fit')
+            switches.append(f"--fit")
         if opt.strength and opt.init_img is not None:
-            switches.append(f'-f{opt.strength or t2i.strength}')
+            switches.append(f"-f{opt.strength or t2i.strength}")
         if opt.gfpgan_strength:
-            switches.append(f'-G{opt.gfpgan_strength}')
+            switches.append(f"-G{opt.gfpgan_strength}")
         if opt.upscale:
             switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
         if opt.variation_amount > 0:
-            switches.append(f'-v{opt.variation_amount}')
+            switches.append(f"-v{opt.variation_amount}")
         if opt.with_variations:
-            formatted_variations = ','.join(f'{seed}:{weight}' for seed, weight in opt.with_variations)
-            switches.append(f'-V{formatted_variations}')
-        return ' '.join(switches)
+            formatted_variations = ",".join(
+                f"{seed}:{weight}" for seed, weight in opt.with_variations
+            )
+            switches.append(f"-V{formatted_variations}")
+        return " ".join(switches)

invokeai/backend/image_util/seamless.py

@@ -0,0 +1,59 @@
+import torch.nn as nn
+
+
+def _conv_forward_asymmetric(self, input, weight, bias):
+    """
+    Patch for Conv2d._conv_forward that supports asymmetric padding
+    """
+    working = nn.functional.pad(
+        input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"]
+    )
+    working = nn.functional.pad(
+        working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"]
+    )
+    return nn.functional.conv2d(
+        working,
+        weight,
+        bias,
+        self.stride,
+        nn.modules.utils._pair(0),
+        self.dilation,
+        self.groups,
+    )
+
+
+def configure_model_padding(model, seamless, seamless_axes):
+    """
+    Modifies the 2D convolution layers to use a circular padding mode based on the `seamless` and `seamless_axes` options.
+    """
+    # TODO: get an explicit interface for this in diffusers: https://github.com/huggingface/diffusers/issues/556
+    for m in model.modules():
+        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+            if seamless:
+                m.asymmetric_padding_mode = {}
+                m.asymmetric_padding = {}
+                m.asymmetric_padding_mode["x"] = (
+                    "circular" if ("x" in seamless_axes) else "constant"
+                )
+                m.asymmetric_padding["x"] = (
+                    m._reversed_padding_repeated_twice[0],
+                    m._reversed_padding_repeated_twice[1],
+                    0,
+                    0,
+                )
+                m.asymmetric_padding_mode["y"] = (
+                    "circular" if ("y" in seamless_axes) else "constant"
+                )
+                m.asymmetric_padding["y"] = (
+                    0,
+                    0,
+                    m._reversed_padding_repeated_twice[2],
+                    m._reversed_padding_repeated_twice[3],
+                )
+                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+            else:
+                m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
+                if hasattr(m, "asymmetric_padding_mode"):
+                    del m.asymmetric_padding_mode
+                if hasattr(m, "asymmetric_padding"):
+                    del m.asymmetric_padding

invokeai/backend/image_util/txt2mask.py

@@ -1,9 +1,9 @@
-'''Makes available the Txt2Mask class, which assists in the automatic
+"""Makes available the Txt2Mask class, which assists in the automatic
 assignment of masks via text prompt using clipseg.
 
 Here is typical usage:
 
-    from ldm.invoke.txt2mask import Txt2Mask, SegmentedGrayscale
+    from invokeai.backend.image_util.txt2mask import Txt2Mask, SegmentedGrayscale
     from PIL import Image
 
     txt2mask = Txt2Mask(self.device)
@@ -25,31 +25,39 @@ the mask that exceed the indicated confidence threshold. Values range
 from 0.0 to 1.0. The higher the threshold, the more confident the
 algorithm is. In limited testing, I have found that values around 0.5
 work fine.
-'''
+"""
 
+import numpy as np
 import torch
-import numpy as  np
-from transformers import AutoProcessor, CLIPSegForImageSegmentation
 from PIL import Image, ImageOps
 from torchvision import transforms
-from ldm.invoke.globals import global_cache_dir
+from transformers import AutoProcessor, CLIPSegForImageSegmentation
 
-CLIPSEG_MODEL = 'CIDAS/clipseg-rd64-refined'
+from invokeai.backend.globals import global_cache_dir
+
+CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
 CLIPSEG_SIZE = 352
 
+
 class SegmentedGrayscale(object):
-    def __init__(self, image:Image, heatmap:torch.Tensor):
+    def __init__(self, image: Image, heatmap: torch.Tensor):
         self.heatmap = heatmap
         self.image = image
 
-    def to_grayscale(self,invert:bool=False)->Image:
-        return self._rescale(Image.fromarray(np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)))
+    def to_grayscale(self, invert: bool = False) -> Image:
+        return self._rescale(
+            Image.fromarray(
+                np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)
+            )
+        )
 
-    def to_mask(self,threshold:float=0.5)->Image:
+    def to_mask(self, threshold: float = 0.5) -> Image:
         discrete_heatmap = self.heatmap.lt(threshold).int()
-        return self._rescale(Image.fromarray(np.uint8(discrete_heatmap*255),mode='L'))
+        return self._rescale(
+            Image.fromarray(np.uint8(discrete_heatmap * 255), mode="L")
+        )
 
-    def to_transparent(self,invert:bool=False)->Image:
+    def to_transparent(self, invert: bool = False) -> Image:
         transparent_image = self.image.copy()
         # For img2img, we want the selected regions to be transparent,
         # but to_grayscale() returns the opposite. Thus invert.
@@ -58,70 +66,77 @@ class SegmentedGrayscale(object):
         return transparent_image
 
     # unscales and uncrops the 352x352 heatmap so that it matches the image again
-    def _rescale(self, heatmap:Image)->Image:
-        size = self.image.width if (self.image.width > self.image.height) else self.image.height
-        resized_image = heatmap.resize(
-            (size,size),
-            resample=Image.Resampling.LANCZOS
+    def _rescale(self, heatmap: Image) -> Image:
+        size = (
+            self.image.width
+            if (self.image.width > self.image.height)
+            else self.image.height
         )
-        return resized_image.crop((0,0,self.image.width,self.image.height))
+        resized_image = heatmap.resize((size, size), resample=Image.Resampling.LANCZOS)
+        return resized_image.crop((0, 0, self.image.width, self.image.height))
+
 
 class Txt2Mask(object):
-    '''
+    """
     Create new Txt2Mask object. The optional device argument can be one of
     'cuda', 'mps' or 'cpu'.
-    '''
-    def __init__(self,device='cpu',refined=False):
-        print('>> Initializing clipseg model for text to mask inference')
+    """
+
+    def __init__(self, device="cpu", refined=False):
+        print(">> Initializing clipseg model for text to mask inference")
 
         # BUG: we are not doing anything with the device option at this time
         self.device = device
-        self.processor = AutoProcessor.from_pretrained(CLIPSEG_MODEL,
-                                                       cache_dir=global_cache_dir('hub')
-                                                       )
-        self.model = CLIPSegForImageSegmentation.from_pretrained(CLIPSEG_MODEL,
-                                                                 cache_dir=global_cache_dir('hub')
-                                                                 )
+        self.processor = AutoProcessor.from_pretrained(
+            CLIPSEG_MODEL, cache_dir=global_cache_dir("hub")
+        )
+        self.model = CLIPSegForImageSegmentation.from_pretrained(
+            CLIPSEG_MODEL, cache_dir=global_cache_dir("hub")
+        )
 
     @torch.no_grad()
-    def segment(self, image, prompt:str) -> SegmentedGrayscale:
-        '''
+    def segment(self, image, prompt: str) -> SegmentedGrayscale:
+        """
         Given a prompt string such as "a bagel", tries to identify the object in the
         provided image and returns a SegmentedGrayscale object in which the brighter
         pixels indicate where the object is inferred to be.
-        '''
-        transform = transforms.Compose([
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-            transforms.Resize((CLIPSEG_SIZE, CLIPSEG_SIZE)), # must be multiple of 64...
-        ])
+        """
+        transform = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize(
+                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+                ),
+                transforms.Resize(
+                    (CLIPSEG_SIZE, CLIPSEG_SIZE)
+                ),  # must be multiple of 64...
+            ]
+        )
 
         if type(image) is str:
-            image = Image.open(image).convert('RGB')
+            image = Image.open(image).convert("RGB")
 
         image = ImageOps.exif_transpose(image)
         img = self._scale_and_crop(image)
 
-        inputs = self.processor(text=[prompt],
-                                images=[img],
-                                padding=True,
-                                return_tensors='pt')
+        inputs = self.processor(
+            text=[prompt], images=[img], padding=True, return_tensors="pt"
+        )
         outputs = self.model(**inputs)
         heatmap = torch.sigmoid(outputs.logits)
         return SegmentedGrayscale(image, heatmap)
 
-    def _scale_and_crop(self, image:Image)->Image:
-        scaled_image = Image.new('RGB',(CLIPSEG_SIZE,CLIPSEG_SIZE))
-        if image.width > image.height: # width is constraint
+    def _scale_and_crop(self, image: Image) -> Image:
+        scaled_image = Image.new("RGB", (CLIPSEG_SIZE, CLIPSEG_SIZE))
+        if image.width > image.height:  # width is constraint
             scale = CLIPSEG_SIZE / image.width
         else:
             scale = CLIPSEG_SIZE / image.height
         scaled_image.paste(
             image.resize(
-                (int(scale * image.width),
-                 int(scale * image.height)
-                ),
-                resample=Image.Resampling.LANCZOS
-            ),box=(0,0)
+                (int(scale * image.width), int(scale * image.height)),
+                resample=Image.Resampling.LANCZOS,
+            ),
+            box=(0, 0),
         )
         return scaled_image

invokeai/backend/image_util/util.py

@@ -1,12 +1,15 @@
-from math import sqrt, floor, ceil
+from math import ceil, floor, sqrt
+
 from PIL import Image
 
-class InitImageResizer():
+
+class InitImageResizer:
     """Simple class to create resized copies of an Image while preserving the aspect ratio."""
-    def __init__(self,Image):
+
+    def __init__(self, Image):
         self.image = Image
 
-    def resize(self,width=None,height=None) -> Image:
+    def resize(self, width=None, height=None) -> Image:
         """
         Return a copy of the image resized to fit within
         a box width x height. The aspect ratio is
@@ -18,37 +21,36 @@ class InitImageResizer():
         Everything is floored to the nearest multiple of 64 so
         that it can be passed to img2img()
         """
-        im    = self.image
+        im = self.image
 
-        ar = im.width/float(im.height)
+        ar = im.width / float(im.height)
 
         # Infer missing values from aspect ratio
-        if not(width or height): # both missing
-            width  = im.width
+        if not (width or height):  # both missing
+            width = im.width
             height = im.height
-        elif not height:           # height missing
-            height = int(width/ar)
-        elif not width:            # width missing
-            width  = int(height*ar)
+        elif not height:  # height missing
+            height = int(width / ar)
+        elif not width:  # width missing
+            width = int(height * ar)
 
-        w_scale = width/im.width
-        h_scale = height/im.height
-        scale = min(w_scale,h_scale)
-        (rw,rh) = (int(scale*im.width),int(scale*im.height))
+        w_scale = width / im.width
+        h_scale = height / im.height
+        scale = min(w_scale, h_scale)
+        (rw, rh) = (int(scale * im.width), int(scale * im.height))
 
-        #round everything to multiples of 64
-        width,height,rw,rh = map(
-            lambda x: x-x%64, (width,height,rw,rh)
-        )
+        # round everything to multiples of 64
+        width, height, rw, rh = map(lambda x: x - x % 64, (width, height, rw, rh))
 
         # no resize necessary, but return a copy
         if im.width == width and im.height == height:
             return im.copy()
 
         # otherwise resize the original image so that it fits inside the bounding box
-        resized_image = self.image.resize((rw,rh),resample=Image.Resampling.LANCZOS)
+        resized_image = self.image.resize((rw, rh), resample=Image.Resampling.LANCZOS)
         return resized_image
 
+
 def make_grid(image_list, rows=None, cols=None):
     image_cnt = len(image_list)
     if None in (rows, cols):
@@ -57,7 +59,7 @@ def make_grid(image_list, rows=None, cols=None):
     width = image_list[0].width
     height = image_list[0].height
 
-    grid_img = Image.new('RGB', (width * cols, height * rows))
+    grid_img = Image.new("RGB", (width * cols, height * rows))
     i = 0
     for r in range(0, rows):
         for c in range(0, cols):
@@ -67,4 +69,3 @@ def make_grid(image_list, rows=None, cols=None):
             i = i + 1
 
     return grid_img
-

invokeai/backend/model_management/__init__.py

@@ -0,0 +1,10 @@
+"""
+Initialization file for invokeai.backend.model_management
+"""
+from .convert_ckpt_to_diffusers import (
+    convert_ckpt_to_diffusers,
+    load_pipeline_from_original_stable_diffusion_ckpt,
+)
+from .model_manager import ModelManager
+from invokeai.frontend.merge import merge_diffusion_models
+

invokeai/backend/model_management/model_manager.py

@@ -9,7 +9,6 @@ from __future__ import annotations
 import contextlib
 import gc
 import hashlib
-import io
 import os
 import re
 import sys
@@ -32,15 +31,10 @@ from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
 from picklescan.scanner import scan_file_path
 
-from ldm.invoke.devices import CPU_DEVICE
-from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from ldm.invoke.globals import Globals, global_cache_dir
-from ldm.util import (
-    ask_user,
-    download_with_resume,
-    instantiate_from_config,
-    url_attachment_name,
-)
+from invokeai.backend.globals import Globals, global_cache_dir
+
+from ..stable_diffusion import StableDiffusionGeneratorPipeline
+from ..util import CPU_DEVICE, ask_user, download_with_resume
 
 
 class SDLegacyType(Enum):
@@ -341,22 +335,9 @@ class ModelManager(object):
 
         tic = time.time()
 
-        # this does the work
-        model_format = mconfig.get("format", "ckpt")
-        if model_format == "ckpt":
-            weights = mconfig.weights
-            print(f">> Loading {model_name} from {weights}")
-            model, width, height, model_hash = self._load_ckpt_model(
-                model_name, mconfig
-            )
-        elif model_format == "diffusers":
-            with warnings.catch_warnings():
-                warnings.simplefilter("ignore")
-                model, width, height, model_hash = self._load_diffusers_model(mconfig)
-        else:
-            raise NotImplementedError(
-                f"Unknown model format {model_name}: {model_format}"
-            )
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            model, width, height, model_hash = self._load_diffusers_model(mconfig)
 
         # usage statistics
         toc = time.time()
@@ -370,125 +351,6 @@ class ModelManager(object):
             )
         return model, width, height, model_hash
 
-    def _load_ckpt_model(self, model_name, mconfig):
-        config = mconfig.config
-        weights = mconfig.weights
-        vae = mconfig.get("vae")
-        width = mconfig.width
-        height = mconfig.height
-
-        if not os.path.isabs(config):
-            config = os.path.join(Globals.root, config)
-        if not os.path.isabs(weights):
-            weights = os.path.normpath(os.path.join(Globals.root, weights))
-
-        # if converting automatically to diffusers, then we do the conversion and return
-        # a diffusers pipeline
-        if Globals.ckpt_convert:
-            print(
-                f">> Converting legacy checkpoint {model_name} into a diffusers model..."
-            )
-            from ldm.invoke.ckpt_to_diffuser import (
-                load_pipeline_from_original_stable_diffusion_ckpt,
-            )
-
-            self.offload_model(self.current_model)
-            if vae_config := self._choose_diffusers_vae(model_name):
-                vae = self._load_vae(vae_config)
-            if self._has_cuda():
-                torch.cuda.empty_cache()
-            pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
-                checkpoint_path=weights,
-                original_config_file=config,
-                vae=vae,
-                return_generator_pipeline=True,
-                precision=torch.float16
-                if self.precision == "float16"
-                else torch.float32,
-            )
-            if self.sequential_offload:
-                pipeline.enable_offload_submodels(self.device)
-            else:
-                pipeline.to(self.device)
-
-            return (
-                pipeline,
-                width,
-                height,
-                "NOHASH",
-            )
-
-        # scan model
-        self.scan_model(model_name, weights)
-
-        print(f">> Loading {model_name} from {weights}")
-
-        # for usage statistics
-        if self._has_cuda():
-            torch.cuda.reset_peak_memory_stats()
-            torch.cuda.empty_cache()
-
-        # this does the work
-        if not os.path.isabs(config):
-            config = os.path.join(Globals.root, config)
-        omega_config = OmegaConf.load(config)
-        with open(weights, "rb") as f:
-            weight_bytes = f.read()
-        model_hash = self._cached_sha256(weights, weight_bytes)
-        sd = None
-        if weights.endswith(".safetensors"):
-            sd = safetensors.torch.load(weight_bytes)
-        else:
-            sd = torch.load(io.BytesIO(weight_bytes), map_location="cpu")
-        del weight_bytes
-        # merged models from auto11 merge board are flat for some reason
-        if "state_dict" in sd:
-            sd = sd["state_dict"]
-
-        print("   | Forcing garbage collection prior to loading new model")
-        gc.collect()
-        model = instantiate_from_config(omega_config.model)
-        model.load_state_dict(sd, strict=False)
-
-        if self.precision == "float16":
-            print("   | Using faster float16 precision")
-            model = model.to(torch.float16)
-        else:
-            print("   | Using more accurate float32 precision")
-
-        # look and load a matching vae file. Code borrowed from AUTOMATIC1111 modules/sd_models.py
-        if vae:
-            if not os.path.isabs(vae):
-                vae = os.path.normpath(os.path.join(Globals.root, vae))
-            if os.path.exists(vae):
-                print(f"   | Loading VAE weights from: {vae}")
-                vae_ckpt = None
-                vae_dict = None
-                if vae.endswith(".safetensors"):
-                    vae_ckpt = safetensors.torch.load_file(vae)
-                    vae_dict = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss"}
-                else:
-                    vae_ckpt = torch.load(vae, map_location="cpu")
-                    vae_dict = {
-                        k: v
-                        for k, v in vae_ckpt["state_dict"].items()
-                        if k[0:4] != "loss"
-                    }
-                model.first_stage_model.load_state_dict(vae_dict, strict=False)
-            else:
-                print(f"   | VAE file {vae} not found. Skipping.")
-
-        model.to(self.device)
-        # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
-        model.cond_stage_model.device = self.device
-
-        model.eval()
-
-        for module in model.modules():
-            if isinstance(module, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
-                module._orig_padding_mode = module.padding_mode
-        return model, width, height, model_hash
-
     def _load_diffusers_model(self, mconfig):
         name_or_path = self.model_name_or_path(mconfig)
         using_fp16 = self.precision == "float16"
@@ -553,6 +415,47 @@ class ModelManager(object):
 
         return pipeline, width, height, model_hash
 
+    def _load_ckpt_model(self, model_name, mconfig):
+        config = mconfig.config
+        weights = mconfig.weights
+        vae = mconfig.get("vae")
+        width = mconfig.width
+        height = mconfig.height
+
+        if not os.path.isabs(config):
+            config = os.path.join(Globals.root, config)
+        if not os.path.isabs(weights):
+            weights = os.path.normpath(os.path.join(Globals.root, weights))
+
+        # Convert to diffusers and return a diffusers pipeline
+        print(f">> Converting legacy checkpoint {model_name} into a diffusers model...")
+
+        from . import load_pipeline_from_original_stable_diffusion_ckpt
+
+        self.offload_model(self.current_model)
+        if vae_config := self._choose_diffusers_vae(model_name):
+            vae = self._load_vae(vae_config)
+        if self._has_cuda():
+            torch.cuda.empty_cache()
+        pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
+            checkpoint_path=weights,
+            original_config_file=config,
+            vae=vae,
+            return_generator_pipeline=True,
+            precision=torch.float16 if self.precision == "float16" else torch.float32,
+        )
+        if self.sequential_offload:
+            pipeline.enable_offload_submodels(self.device)
+        else:
+            pipeline.to(self.device)
+
+        return (
+            pipeline,
+            width,
+            height,
+            "NOHASH",
+        )
+
     def model_name_or_path(self, model_name: Union[str, DictConfig]) -> str | Path:
         if isinstance(model_name, DictConfig) or isinstance(model_name, dict):
             mconfig = model_name
@@ -640,7 +543,9 @@ class ModelManager(object):
         models.yaml file.
         """
         model_name = model_name or Path(repo_or_path).stem
-        model_description = model_description or f"Imported diffusers model {model_name}"
+        model_description = (
+            model_description or f"Imported diffusers model {model_name}"
+        )
         new_config = dict(
             description=model_description,
             vae=vae,
@@ -656,66 +561,6 @@ class ModelManager(object):
             self.commit(commit_to_conf)
         return model_name
 
-    def import_ckpt_model(
-        self,
-        weights: Union[str, Path],
-        config: Union[str, Path] = "configs/stable-diffusion/v1-inference.yaml",
-        vae: Union[str, Path] = None,
-        model_name: str = None,
-        model_description: str = None,
-        commit_to_conf: Path = None,
-    ) -> str:
-        """
-        Attempts to install the indicated ckpt file and returns True if successful.
-
-        "weights" can be either a path-like object corresponding to a local .ckpt file
-        or a http/https URL pointing to a remote model.
-
-        "vae" is a Path or str object pointing to a ckpt or safetensors file to be used
-        as the VAE for this model.
-
-        "config" is the model config file to use with this ckpt file. It defaults to
-        v1-inference.yaml. If a URL is provided, the config will be downloaded.
-
-        You can optionally provide a model name and/or description. If not provided,
-        then these will be derived from the weight file name. If you provide a commit_to_conf
-        path to the configuration file, then the new entry will be committed to the
-        models.yaml file.
-
-        Return value is the name of the imported file, or None if an error occurred.
-        """
-        if str(weights).startswith(("http:", "https:")):
-            model_name = model_name or url_attachment_name(weights)
-
-        weights_path = self._resolve_path(weights, "models/ldm/stable-diffusion-v1")
-        config_path = self._resolve_path(config, "configs/stable-diffusion")
-
-        if weights_path is None or not weights_path.exists():
-            return
-        if config_path is None or not config_path.exists():
-            return
-
-        model_name = (
-            model_name or Path(weights).stem
-        )  # note this gives ugly pathnames if used on a URL without a Content-Disposition header
-        model_description = (
-            model_description or f"Imported stable diffusion weights file {model_name}"
-        )
-        new_config = dict(
-            weights=str(weights_path),
-            config=str(config_path),
-            description=model_description,
-            format="ckpt",
-            width=512,
-            height=512,
-        )
-        if vae:
-            new_config["vae"] = vae
-        self.add_model(model_name, new_config, True)
-        if commit_to_conf:
-            self.commit(commit_to_conf)
-        return model_name
-
     @classmethod
     def probe_model_type(self, checkpoint: dict) -> SDLegacyType:
         """
@@ -748,7 +593,7 @@ class ModelManager(object):
     def heuristic_import(
         self,
         path_url_or_repo: str,
-        convert: bool = False,
+        convert: bool = True,
         model_name: str = None,
         description: str = None,
         commit_to_conf: Path = None,
@@ -883,35 +728,18 @@ class ModelManager(object):
             )
             return
 
-        if convert:
-            diffuser_path = Path(
-                Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
-            )
-            model_name = self.convert_and_import(
-                model_path,
-                diffusers_path=diffuser_path,
-                vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
-                model_name=model_name,
-                model_description=description,
-                original_config_file=model_config_file,
-                commit_to_conf=commit_to_conf,
-            )
-        else:
-            model_name = self.import_ckpt_model(
-                model_path,
-                config=model_config_file,
-                model_name=model_name,
-                model_description=description,
-                vae=str(
-                    Path(
-                        Globals.root,
-                        "models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt",
-                    )
-                ),
-                commit_to_conf=commit_to_conf,
-            )
-        if commit_to_conf:
-            self.commit(commit_to_conf)
+        diffuser_path = Path(
+            Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
+        )
+        model_name = self.convert_and_import(
+            model_path,
+            diffusers_path=diffuser_path,
+            vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
+            model_name=model_name,
+            model_description=description,
+            original_config_file=model_config_file,
+            commit_to_conf=commit_to_conf,
+        )
         return model_name
 
     def convert_and_import(
@@ -936,7 +764,7 @@ class ModelManager(object):
 
         new_config = None
 
-        from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffuser
+        from . import convert_ckpt_to_diffusers
 
         if diffusers_path.exists():
             print(
@@ -951,7 +779,7 @@ class ModelManager(object):
             # By passing the specified VAE to the conversion function, the autoencoder
             # will be built into the model rather than tacked on afterward via the config file
             vae_model = self._load_vae(vae) if vae else None
-            convert_ckpt_to_diffuser(
+            convert_ckpt_to_diffusers(
                 ckpt_path,
                 diffusers_path,
                 extract_ema=True,

invokeai/backend/prompting/__init__.py

@@ -0,0 +1,10 @@
+"""
+Initialization file for invokeai.backend.prompting
+"""
+from .conditioning import (
+    get_prompt_structure,
+    get_tokenizer,
+    get_tokens_for_prompt_object,
+    get_uc_and_c_and_ec,
+    split_weighted_subprompts,
+)

invokeai/backend/prompting/conditioning.py

@@ -1,31 +1,46 @@
-'''
+"""
 This module handles the generation of the conditioning tensors.
 
 Useful function exports:
 
 get_uc_and_c_and_ec()           get the conditioned and unconditioned latent, and edited conditioning if we're doing cross-attention control
 
-'''
+"""
 import re
-from typing import Union, Optional, Any
-
-from transformers import CLIPTokenizer, CLIPTextModel
+from typing import Any, Optional, Union
 
 from compel import Compel
-from compel.prompt_parser import FlattenedPrompt, Blend, Fragment, CrossAttentionControlSubstitute, PromptParser
-from .devices import torch_dtype
-from ..models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-from ldm.invoke.globals import Globals
+from compel.prompt_parser import (
+    Blend,
+    CrossAttentionControlSubstitute,
+    FlattenedPrompt,
+    Fragment,
+    PromptParser,
+)
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from invokeai.backend.globals import Globals
+
+from ..stable_diffusion import InvokeAIDiffuserComponent
+from ..util import torch_dtype
+
 
 def get_tokenizer(model) -> CLIPTokenizer:
     # TODO remove legacy ckpt fallback handling
-    return (getattr(model, 'tokenizer', None) # diffusers
-            or model.cond_stage_model.tokenizer) # ldm
+    return (
+        getattr(model, "tokenizer", None)  # diffusers
+        or model.cond_stage_model.tokenizer
+    )  # ldm
+
 
 def get_text_encoder(model) -> Any:
     # TODO remove legacy ckpt fallback handling
-    return (getattr(model, 'text_encoder', None)  # diffusers
-            or UnsqueezingLDMTransformer(model.cond_stage_model.transformer)) # ldm
+    return getattr(
+        model, "text_encoder", None
+    ) or UnsqueezingLDMTransformer(  # diffusers
+        model.cond_stage_model.transformer
+    )  # ldm
+
 
 class UnsqueezingLDMTransformer:
     def __init__(self, ldm_transformer):
@@ -40,28 +55,41 @@ class UnsqueezingLDMTransformer:
         return insufficiently_unsqueezed_tensor.unsqueeze(0)
 
 
-def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False):
+def get_uc_and_c_and_ec(
+    prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False
+):
     # lazy-load any deferred textual inversions.
     # this might take a couple of seconds the first time a textual inversion is used.
-    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(prompt_string)
+    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
+        prompt_string
+    )
 
     tokenizer = get_tokenizer(model)
     text_encoder = get_text_encoder(model)
-    compel = Compel(tokenizer=tokenizer,
-                    text_encoder=text_encoder,
-                    textual_inversion_manager=model.textual_inversion_manager,
-                    dtype_for_device_getter=torch_dtype)
+    compel = Compel(
+        tokenizer=tokenizer,
+        text_encoder=text_encoder,
+        textual_inversion_manager=model.textual_inversion_manager,
+        dtype_for_device_getter=torch_dtype,
+    )
 
     # get rid of any newline characters
     prompt_string = prompt_string.replace("\n", " ")
-    positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
-    legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-    positive_prompt: FlattenedPrompt|Blend
+    (
+        positive_prompt_string,
+        negative_prompt_string,
+    ) = split_prompt_to_positive_and_negative(prompt_string)
+    legacy_blend = try_parse_legacy_blend(
+        positive_prompt_string, skip_normalize_legacy_blend
+    )
+    positive_prompt: FlattenedPrompt | Blend
     if legacy_blend is not None:
         positive_prompt = legacy_blend
     else:
         positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: FlattenedPrompt|Blend = Compel.parse_prompt_string(negative_prompt_string)
+    negative_prompt: FlattenedPrompt | Blend = Compel.parse_prompt_string(
+        negative_prompt_string
+    )
 
     if log_tokens or getattr(Globals, "log_tokenization", False):
         log_tokenization(positive_prompt, negative_prompt, tokenizer=tokenizer)
@@ -71,42 +99,70 @@ def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_l
 
     tokens_count = get_max_token_count(tokenizer, positive_prompt)
 
-    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
-                                                         cross_attention_control_args=options.get(
-                                                             'cross_attention_control', None))
+    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+        tokens_count_including_eos_bos=tokens_count,
+        cross_attention_control_args=options.get("cross_attention_control", None),
+    )
     return uc, c, ec
 
 
-def get_prompt_structure(prompt_string, skip_normalize_legacy_blend: bool = False) -> (
-    Union[FlattenedPrompt, Blend], FlattenedPrompt):
-    positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
-    legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-    positive_prompt: FlattenedPrompt|Blend
+def get_prompt_structure(
+    prompt_string, skip_normalize_legacy_blend: bool = False
+) -> (Union[FlattenedPrompt, Blend], FlattenedPrompt):
+    (
+        positive_prompt_string,
+        negative_prompt_string,
+    ) = split_prompt_to_positive_and_negative(prompt_string)
+    legacy_blend = try_parse_legacy_blend(
+        positive_prompt_string, skip_normalize_legacy_blend
+    )
+    positive_prompt: FlattenedPrompt | Blend
     if legacy_blend is not None:
         positive_prompt = legacy_blend
     else:
         positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: FlattenedPrompt|Blend = Compel.parse_prompt_string(negative_prompt_string)
+    negative_prompt: FlattenedPrompt | Blend = Compel.parse_prompt_string(
+        negative_prompt_string
+    )
 
     return positive_prompt, negative_prompt
 
-def get_max_token_count(tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=True) -> int:
+
+def get_max_token_count(
+    tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=True
+) -> int:
     if type(prompt) is Blend:
         blend: Blend = prompt
-        return max([get_max_token_count(tokenizer, c, truncate_if_too_long) for c in blend.prompts])
+        return max(
+            [
+                get_max_token_count(tokenizer, c, truncate_if_too_long)
+                for c in blend.prompts
+            ]
+        )
     else:
-        return len(get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long))
+        return len(
+            get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long)
+        )
 
 
-def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True) -> [str]:
-
+def get_tokens_for_prompt_object(
+    tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
+) -> [str]:
     if type(parsed_prompt) is Blend:
-        raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
+        raise ValueError(
+            "Blend is not supported here - you need to get tokens for each of its .children"
+        )
 
-    text_fragments = [x.text if type(x) is Fragment else
-                      (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else
-                       str(x))
-                      for x in parsed_prompt.children]
+    text_fragments = [
+        x.text
+        if type(x) is Fragment
+        else (
+            " ".join([f.text for f in x.original])
+            if type(x) is CrossAttentionControlSubstitute
+            else str(x)
+        )
+        for x in parsed_prompt.children
+    ]
     text = " ".join(text_fragments)
     tokens = tokenizer.tokenize(text)
     if truncate_if_too_long:
@@ -116,39 +172,47 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
 
 
 def split_prompt_to_positive_and_negative(prompt_string_uncleaned: str):
-    unconditioned_words = ''
-    unconditional_regex = r'\[(.*?)\]'
+    unconditioned_words = ""
+    unconditional_regex = r"\[(.*?)\]"
     unconditionals = re.findall(unconditional_regex, prompt_string_uncleaned)
     if len(unconditionals) > 0:
-        unconditioned_words = ' '.join(unconditionals)
+        unconditioned_words = " ".join(unconditionals)
 
         # Remove Unconditioned Words From Prompt
         unconditional_regex_compile = re.compile(unconditional_regex)
-        clean_prompt = unconditional_regex_compile.sub(' ', prompt_string_uncleaned)
-        prompt_string_cleaned = re.sub(' +', ' ', clean_prompt)
+        clean_prompt = unconditional_regex_compile.sub(" ", prompt_string_uncleaned)
+        prompt_string_cleaned = re.sub(" +", " ", clean_prompt)
     else:
         prompt_string_cleaned = prompt_string_uncleaned
     return prompt_string_cleaned, unconditioned_words
 
 
-def log_tokenization(positive_prompt: Union[Blend, FlattenedPrompt],
-                     negative_prompt: Union[Blend, FlattenedPrompt],
-                     tokenizer):
+def log_tokenization(
+    positive_prompt: Union[Blend, FlattenedPrompt],
+    negative_prompt: Union[Blend, FlattenedPrompt],
+    tokenizer,
+):
     print(f"\n>> [TOKENLOG] Parsed Prompt: {positive_prompt}")
     print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
 
     log_tokenization_for_prompt_object(positive_prompt, tokenizer)
-    log_tokenization_for_prompt_object(negative_prompt, tokenizer, display_label_prefix="(negative prompt)")
+    log_tokenization_for_prompt_object(
+        negative_prompt, tokenizer, display_label_prefix="(negative prompt)"
+    )
 
 
-def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None):
+def log_tokenization_for_prompt_object(
+    p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
+):
     display_label_prefix = display_label_prefix or ""
     if type(p) is Blend:
         blend: Blend = p
         for i, c in enumerate(blend.prompts):
             log_tokenization_for_prompt_object(
-                c, tokenizer,
-                display_label_prefix=f"{display_label_prefix}(blend part {i + 1}, weight={blend.weights[i]})")
+                c,
+                tokenizer,
+                display_label_prefix=f"{display_label_prefix}(blend part {i + 1}, weight={blend.weights[i]})",
+            )
     elif type(p) is FlattenedPrompt:
         flattened_prompt: FlattenedPrompt = p
         if flattened_prompt.wants_cross_attention_control:
@@ -163,18 +227,26 @@ def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokeniz
                     edited_fragments.append(f)
 
             original_text = " ".join([x.text for x in original_fragments])
-            log_tokenization_for_text(original_text, tokenizer,
-                                      display_label=f"{display_label_prefix}(.swap originals)")
+            log_tokenization_for_text(
+                original_text,
+                tokenizer,
+                display_label=f"{display_label_prefix}(.swap originals)",
+            )
             edited_text = " ".join([x.text for x in edited_fragments])
-            log_tokenization_for_text(edited_text, tokenizer,
-                                      display_label=f"{display_label_prefix}(.swap replacements)")
+            log_tokenization_for_text(
+                edited_text,
+                tokenizer,
+                display_label=f"{display_label_prefix}(.swap replacements)",
+            )
         else:
             text = " ".join([x.text for x in flattened_prompt.children])
-            log_tokenization_for_text(text, tokenizer, display_label=display_label_prefix)
+            log_tokenization_for_text(
+                text, tokenizer, display_label=display_label_prefix
+            )
 
 
 def log_tokenization_for_text(text, tokenizer, display_label=None):
-    """ shows how the prompt is tokenized
+    """shows how the prompt is tokenized
     # usually tokens have '</w>' to indicate end-of-word,
     # but for readability it has been replaced with ' '
     """
@@ -185,7 +257,7 @@ def log_tokenization_for_text(text, tokenizer, display_label=None):
     totalTokens = len(tokens)
 
     for i in range(0, totalTokens):
-        token = tokens[i].replace('</w>', ' ')
+        token = tokens[i].replace("</w>", " ")
         # alternate color
         s = (usedTokens % 6) + 1
         if i < tokenizer.model_max_length:
@@ -196,14 +268,14 @@ def log_tokenization_for_text(text, tokenizer, display_label=None):
 
     if usedTokens > 0:
         print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
-        print(f'{tokenized}\x1b[0m')
+        print(f"{tokenized}\x1b[0m")
 
     if discarded != "":
-        print(f'\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):')
-        print(f'{discarded}\x1b[0m')
+        print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
+        print(f"{discarded}\x1b[0m")
 
 
-def try_parse_legacy_blend(text: str, skip_normalize: bool=False) -> Optional[Blend]:
+def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Blend]:
     weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
     if len(weighted_subprompts) <= 1:
         return None
@@ -214,10 +286,12 @@ def try_parse_legacy_blend(text: str, skip_normalize: bool=False) -> Optional[Bl
     parsed_conjunctions = [pp.parse_conjunction(x) for x in strings]
     flattened_prompts = [x.prompts[0] for x in parsed_conjunctions]
 
-    return Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)
+    return Blend(
+        prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize
+    )
 
 
-def split_weighted_subprompts(text, skip_normalize=False)->list:
+def split_weighted_subprompts(text, skip_normalize=False) -> list:
     """
     Legacy blend parsing.
 
@@ -226,7 +300,8 @@ def split_weighted_subprompts(text, skip_normalize=False)->list:
     if ':' has no value defined, defaults to 1.0
     repeats until no text remaining
     """
-    prompt_parser = re.compile("""
+    prompt_parser = re.compile(
+        """
             (?P<prompt>     # capture group for 'prompt'
             (?:\\\:|[^:])+  # match one or more non ':' characters or escaped colons '\:'
             )               # end 'prompt'
@@ -239,16 +314,20 @@ def split_weighted_subprompts(text, skip_normalize=False)->list:
             |               # OR
             $               # else, if no ':' then match end of line
             )               # end non-capture group
-            """, re.VERBOSE)
-    parsed_prompts = [(match.group("prompt").replace("\\:", ":"), float(
-        match.group("weight") or 1)) for match in re.finditer(prompt_parser, text)]
+            """,
+        re.VERBOSE,
+    )
+    parsed_prompts = [
+        (match.group("prompt").replace("\\:", ":"), float(match.group("weight") or 1))
+        for match in re.finditer(prompt_parser, text)
+    ]
     if skip_normalize:
         return parsed_prompts
     weight_sum = sum(map(lambda x: x[1], parsed_prompts))
     if weight_sum == 0:
         print(
-            "* Warning: Subprompt weights add up to zero. Discarding and using even weights instead.")
+            "* Warning: Subprompt weights add up to zero. Discarding and using even weights instead."
+        )
         equal_weight = 1 / max(len(parsed_prompts), 1)
         return [(x[0], equal_weight) for x in parsed_prompts]
     return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
-

invokeai/backend/restoration/__init__.py

@@ -0,0 +1,4 @@
+"""
+Initialization file for the invokeai.backend.restoration package
+"""
+from .base import Restoration

invokeai/backend/restoration/base.py

@@ -1,38 +1,43 @@
-class Restoration():
+class Restoration:
     def __init__(self) -> None:
         pass
 
-    def load_face_restore_models(self, gfpgan_model_path='./models/gfpgan/GFPGANv1.4.pth'):
+    def load_face_restore_models(
+        self, gfpgan_model_path="./models/gfpgan/GFPGANv1.4.pth"
+    ):
         # Load GFPGAN
         gfpgan = self.load_gfpgan(gfpgan_model_path)
         if gfpgan.gfpgan_model_exists:
-            print('>> GFPGAN Initialized')
+            print(">> GFPGAN Initialized")
         else:
-            print('>> GFPGAN Disabled')
+            print(">> GFPGAN Disabled")
             gfpgan = None
 
         # Load CodeFormer
         codeformer = self.load_codeformer()
         if codeformer.codeformer_model_exists:
-            print('>> CodeFormer Initialized')
+            print(">> CodeFormer Initialized")
         else:
-            print('>> CodeFormer Disabled')
+            print(">> CodeFormer Disabled")
             codeformer = None
 
         return gfpgan, codeformer
 
     # Face Restore Models
     def load_gfpgan(self, gfpgan_model_path):
-        from ldm.invoke.restoration.gfpgan import GFPGAN
+        from .gfpgan import GFPGAN
+
         return GFPGAN(gfpgan_model_path)
 
     def load_codeformer(self):
-        from ldm.invoke.restoration.codeformer import CodeFormerRestoration
+        from .codeformer import CodeFormerRestoration
+
         return CodeFormerRestoration()
 
     # Upscale Models
     def load_esrgan(self, esrgan_bg_tile=400):
-        from ldm.invoke.restoration.realesrgan import ESRGAN
+        from .realesrgan import ESRGAN
+
         esrgan = ESRGAN(esrgan_bg_tile)
-        print('>> ESRGAN Initialized')
-        return esrgan;
+        print(">> ESRGAN Initialized")
+        return esrgan

invokeai/backend/restoration/codeformer.py

@@ -1,17 +1,21 @@
 import os
-import torch
-import numpy as np
-import warnings
 import sys
-from ldm.invoke.globals import Globals
+import warnings
 
-pretrained_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
+import numpy as np
+import torch
 
-class CodeFormerRestoration():
-    def __init__(self,
-            codeformer_dir='models/codeformer',
-            codeformer_model_path='codeformer.pth') -> None:
+from ..globals import Globals
 
+pretrained_model_url = (
+    "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
+)
+
+
+class CodeFormerRestoration:
+    def __init__(
+        self, codeformer_dir="models/codeformer", codeformer_model_path="codeformer.pth"
+    ) -> None:
         if not os.path.isabs(codeformer_dir):
             codeformer_dir = os.path.join(Globals.root, codeformer_dir)
 
@@ -19,22 +23,23 @@ class CodeFormerRestoration():
         self.codeformer_model_exists = os.path.isfile(self.model_path)
 
         if not self.codeformer_model_exists:
-            print('## NOT FOUND: CodeFormer model not found at ' + self.model_path)
+            print("## NOT FOUND: CodeFormer model not found at " + self.model_path)
         sys.path.append(os.path.abspath(codeformer_dir))
 
     def process(self, image, strength, device, seed=None, fidelity=0.75):
         if seed is not None:
-            print(f'>> CodeFormer - Restoring Faces for image seed:{seed}')
+            print(f">> CodeFormer - Restoring Faces for image seed:{seed}")
         with warnings.catch_warnings():
-            warnings.filterwarnings('ignore', category=DeprecationWarning)
-            warnings.filterwarnings('ignore', category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=UserWarning)
 
-            from basicsr.utils.download_util import load_file_from_url
             from basicsr.utils import img2tensor, tensor2img
+            from basicsr.utils.download_util import load_file_from_url
             from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-            from ldm.invoke.restoration.codeformer_arch import CodeFormer
-            from torchvision.transforms.functional import normalize
             from PIL import Image
+            from torchvision.transforms.functional import normalize
+
+            from .codeformer_arch import CodeFormer
 
             cf_class = CodeFormer
 
@@ -43,28 +48,31 @@ class CodeFormerRestoration():
                 codebook_size=1024,
                 n_head=8,
                 n_layers=9,
-                connect_list=['32', '64', '128', '256']
+                connect_list=["32", "64", "128", "256"],
             ).to(device)
 
             # note that this file should already be downloaded and cached at
             # this point
-            checkpoint_path = load_file_from_url(url=pretrained_model_url,
-                                                 model_dir=os.path.abspath(os.path.dirname(self.model_path)),
-                                                 progress=True
+            checkpoint_path = load_file_from_url(
+                url=pretrained_model_url,
+                model_dir=os.path.abspath(os.path.dirname(self.model_path)),
+                progress=True,
             )
-            checkpoint = torch.load(checkpoint_path)['params_ema']
+            checkpoint = torch.load(checkpoint_path)["params_ema"]
             cf.load_state_dict(checkpoint)
             cf.eval()
 
-            image = image.convert('RGB')
+            image = image.convert("RGB")
             # Codeformer expects a BGR np array; make array and flip channels
-            bgr_image_array = np.array(image, dtype=np.uint8)[...,::-1]
+            bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
 
             face_helper = FaceRestoreHelper(
                 upscale_factor=1,
                 use_parse=True,
                 device=device,
-                model_rootpath=os.path.join(Globals.root,'models','gfpgan','weights'),
+                model_rootpath=os.path.join(
+                    Globals.root, "models", "gfpgan", "weights"
+                ),
             )
             face_helper.clean_all()
             face_helper.read_image(bgr_image_array)
@@ -72,30 +80,35 @@ class CodeFormerRestoration():
             face_helper.align_warp_face()
 
             for idx, cropped_face in enumerate(face_helper.cropped_faces):
-                cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-                normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+                cropped_face_t = img2tensor(
+                    cropped_face / 255.0, bgr2rgb=True, float32=True
+                )
+                normalize(
+                    cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True
+                )
                 cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
 
                 try:
                     with torch.no_grad():
                         output = cf(cropped_face_t, w=fidelity, adain=True)[0]
-                        restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
+                        restored_face = tensor2img(
+                            output.squeeze(0), rgb2bgr=True, min_max=(-1, 1)
+                        )
                     del output
                     torch.cuda.empty_cache()
                 except RuntimeError as error:
-                    print(f'\tFailed inference for CodeFormer: {error}.')
+                    print(f"\tFailed inference for CodeFormer: {error}.")
                     restored_face = cropped_face
 
-                restored_face = restored_face.astype('uint8')
+                restored_face = restored_face.astype("uint8")
                 face_helper.add_restored_face(restored_face)
 
-
             face_helper.get_inverse_affine(None)
 
             restored_img = face_helper.paste_faces_to_input_image()
 
             # Flip the channels back to RGB
-            res = Image.fromarray(restored_img[...,::-1])
+            res = Image.fromarray(restored_img[..., ::-1])
 
             if strength < 1.0:
                 # Resize the image to the new image if the sizes have changed

invokeai/backend/restoration/codeformer_arch.py

@@ -1,13 +1,15 @@
 import math
+from typing import List, Optional
+
 import numpy as np
 import torch
-from torch import nn, Tensor
 import torch.nn.functional as F
-from typing import Optional, List
-
-from ldm.invoke.restoration.vqgan_arch import *
 from basicsr.utils import get_root_logger
 from basicsr.utils.registry import ARCH_REGISTRY
+from torch import Tensor, nn
+
+from .vqgan_arch import *
+
 
 def calc_mean_std(feat, eps=1e-5):
     """Calculate mean and std for adaptive_instance_normalization.
@@ -18,7 +20,7 @@ def calc_mean_std(feat, eps=1e-5):
             divide-by-zero. Default: 1e-5.
     """
     size = feat.size()
-    assert len(size) == 4, 'The input feature should be 4D tensor.'
+    assert len(size) == 4, "The input feature should be 4D tensor."
     b, c = size[:2]
     feat_var = feat.view(b, c, -1).var(dim=2) + eps
     feat_std = feat_var.sqrt().view(b, c, 1, 1)
@@ -39,7 +41,9 @@ def adaptive_instance_normalization(content_feat, style_feat):
     size = content_feat.size()
     style_mean, style_std = calc_mean_std(style_feat)
     content_mean, content_std = calc_mean_std(content_feat)
-    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(
+        size
+    )
     return normalized_feat * style_std.expand(size) + style_mean.expand(size)
 
 
@@ -49,7 +53,9 @@ class PositionEmbeddingSine(nn.Module):
     used by the Attention is all you need paper, generalized to work on images.
     """
 
-    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+    def __init__(
+        self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
+    ):
         super().__init__()
         self.num_pos_feats = num_pos_feats
         self.temperature = temperature
@@ -62,7 +68,9 @@ class PositionEmbeddingSine(nn.Module):
 
     def forward(self, x, mask=None):
         if mask is None:
-            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
+            mask = torch.zeros(
+                (x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool
+            )
         not_mask = ~mask
         y_embed = not_mask.cumsum(1, dtype=torch.float32)
         x_embed = not_mask.cumsum(2, dtype=torch.float32)
@@ -85,6 +93,7 @@ class PositionEmbeddingSine(nn.Module):
         pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
         return pos
 
+
 def _get_activation_fn(activation):
     """Return an activation function given a string"""
     if activation == "relu":
@@ -93,11 +102,13 @@ def _get_activation_fn(activation):
         return F.gelu
     if activation == "glu":
         return F.glu
-    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
 
 
 class TransformerSALayer(nn.Module):
-    def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
+    def __init__(
+        self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"
+    ):
         super().__init__()
         self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
         # Implementation of Feedforward model - MLP
@@ -115,16 +126,19 @@ class TransformerSALayer(nn.Module):
     def with_pos_embed(self, tensor, pos: Optional[Tensor]):
         return tensor if pos is None else tensor + pos
 
-    def forward(self, tgt,
-                tgt_mask: Optional[Tensor] = None,
-                tgt_key_padding_mask: Optional[Tensor] = None,
-                query_pos: Optional[Tensor] = None):
-
+    def forward(
+        self,
+        tgt,
+        tgt_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+    ):
         # self attention
         tgt2 = self.norm1(tgt)
         q = k = self.with_pos_embed(tgt2, query_pos)
-        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
-                              key_padding_mask=tgt_key_padding_mask)[0]
+        tgt2 = self.self_attn(
+            q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
+        )[0]
         tgt = tgt + self.dropout1(tgt2)
 
         # ffn
@@ -133,20 +147,23 @@ class TransformerSALayer(nn.Module):
         tgt = tgt + self.dropout2(tgt2)
         return tgt
 
+
 class Fuse_sft_block(nn.Module):
     def __init__(self, in_ch, out_ch):
         super().__init__()
-        self.encode_enc = ResBlock(2*in_ch, out_ch)
+        self.encode_enc = ResBlock(2 * in_ch, out_ch)
 
         self.scale = nn.Sequential(
-                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
-                    nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
+            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
+        )
 
         self.shift = nn.Sequential(
-                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
-                    nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
+            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
+        )
 
     def forward(self, enc_feat, dec_feat, w=1):
         enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
@@ -159,11 +176,19 @@ class Fuse_sft_block(nn.Module):
 
 @ARCH_REGISTRY.register()
 class CodeFormer(VQAutoEncoder):
-    def __init__(self, dim_embd=512, n_head=8, n_layers=9,
-                codebook_size=1024, latent_size=256,
-                connect_list=['32', '64', '128', '256'],
-                fix_modules=['quantize','generator']):
-        super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
+    def __init__(
+        self,
+        dim_embd=512,
+        n_head=8,
+        n_layers=9,
+        codebook_size=1024,
+        latent_size=256,
+        connect_list=["32", "64", "128", "256"],
+        fix_modules=["quantize", "generator"],
+    ):
+        super(CodeFormer, self).__init__(
+            512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size
+        )
 
         if fix_modules is not None:
             for module in fix_modules:
@@ -173,33 +198,53 @@ class CodeFormer(VQAutoEncoder):
         self.connect_list = connect_list
         self.n_layers = n_layers
         self.dim_embd = dim_embd
-        self.dim_mlp = dim_embd*2
+        self.dim_mlp = dim_embd * 2
 
         self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
         self.feat_emb = nn.Linear(256, self.dim_embd)
 
         # transformer
-        self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
-                                    for _ in range(self.n_layers)])
+        self.ft_layers = nn.Sequential(
+            *[
+                TransformerSALayer(
+                    embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0
+                )
+                for _ in range(self.n_layers)
+            ]
+        )
 
         # logits_predict head
         self.idx_pred_layer = nn.Sequential(
-            nn.LayerNorm(dim_embd),
-            nn.Linear(dim_embd, codebook_size, bias=False))
+            nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False)
+        )
 
         self.channels = {
-            '16': 512,
-            '32': 256,
-            '64': 256,
-            '128': 128,
-            '256': 128,
-            '512': 64,
+            "16": 512,
+            "32": 256,
+            "64": 256,
+            "128": 128,
+            "256": 128,
+            "512": 64,
         }
 
         # after second residual block for > 16, before attn layer for ==16
-        self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
+        self.fuse_encoder_block = {
+            "512": 2,
+            "256": 5,
+            "128": 8,
+            "64": 11,
+            "32": 14,
+            "16": 18,
+        }
         # after first residual block for > 16, before attn layer for ==16
-        self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}
+        self.fuse_generator_block = {
+            "16": 6,
+            "32": 9,
+            "64": 12,
+            "128": 15,
+            "256": 18,
+            "512": 21,
+        }
 
         # fuse_convs_dict
         self.fuse_convs_dict = nn.ModuleDict()
@@ -228,20 +273,20 @@ class CodeFormer(VQAutoEncoder):
         lq_feat = x
         # ################# Transformer ###################
         # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
-        pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
+        pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1)
         # BCHW -> BC(HW) -> (HW)BC
-        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
+        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1))
         query_emb = feat_emb
         # Transformer encoder
         for layer in self.ft_layers:
             query_emb = layer(query_emb, query_pos=pos_emb)
 
         # output logits
-        logits = self.idx_pred_layer(query_emb) # (hw)bn
-        logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n
+        logits = self.idx_pred_layer(query_emb)  # (hw)bn
+        logits = logits.permute(1, 0, 2)  # (hw)bn -> b(hw)n
 
-        if code_only: # for training stage II
-          # logits doesn't need softmax before cross_entropy loss
+        if code_only:  # for training stage II
+            # logits doesn't need softmax before cross_entropy loss
             return logits, lq_feat
 
         # ################# Quantization ###################
@@ -252,12 +297,14 @@ class CodeFormer(VQAutoEncoder):
         # ------------
         soft_one_hot = F.softmax(logits, dim=2)
         _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
-        quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
+        quant_feat = self.quantize.get_codebook_feat(
+            top_idx, shape=[x.shape[0], 16, 16, 256]
+        )
         # preserve gradients
         # quant_feat = lq_feat + (quant_feat - lq_feat).detach()
 
         if detach_16:
-            quant_feat = quant_feat.detach() # for training stage III
+            quant_feat = quant_feat.detach()  # for training stage III
         if adain:
             quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
 
@@ -267,10 +314,12 @@ class CodeFormer(VQAutoEncoder):
 
         for i, block in enumerate(self.generator.blocks):
             x = block(x)
-            if i in fuse_list: # fuse after i-th block
+            if i in fuse_list:  # fuse after i-th block
                 f_size = str(x.shape[-1])
-                if w>0:
-                    x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
+                if w > 0:
+                    x = self.fuse_convs_dict[f_size](
+                        enc_feat_dict[f_size].detach(), x, w
+                    )
         out = x
         # logits doesn't need softmax before cross_entropy loss
         return out, logits, lq_feat

invokeai/backend/restoration/gfpgan.py

@@ -1,26 +1,25 @@
-import torch
-import warnings
 import os
 import sys
-import numpy as np
-from ldm.invoke.globals import Globals
+import warnings
 
+import numpy as np
+import torch
 from PIL import Image
 
+from invokeai.backend.globals import Globals
 
-class GFPGAN():
-    def __init__(
-            self,
-            gfpgan_model_path='models/gfpgan/GFPGANv1.4.pth'
-    ) -> None:
 
+class GFPGAN:
+    def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
         if not os.path.isabs(gfpgan_model_path):
-            gfpgan_model_path=os.path.abspath(os.path.join(Globals.root,gfpgan_model_path))
+            gfpgan_model_path = os.path.abspath(
+                os.path.join(Globals.root, gfpgan_model_path)
+            )
         self.model_path = gfpgan_model_path
         self.gfpgan_model_exists = os.path.isfile(self.model_path)
 
         if not self.gfpgan_model_exists:
-            print('## NOT FOUND: GFPGAN model not found at ' + self.model_path)
+            print("## NOT FOUND: GFPGAN model not found at " + self.model_path)
             return None
 
     def model_exists(self):
@@ -28,40 +27,40 @@ class GFPGAN():
 
     def process(self, image, strength: float, seed: str = None):
         if seed is not None:
-            print(f'>> GFPGAN - Restoring Faces for image seed:{seed}')
+            print(f">> GFPGAN - Restoring Faces for image seed:{seed}")
 
         with warnings.catch_warnings():
-            warnings.filterwarnings('ignore', category=DeprecationWarning)
-            warnings.filterwarnings('ignore', category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=UserWarning)
             cwd = os.getcwd()
-            os.chdir(os.path.join(Globals.root,'models'))
+            os.chdir(os.path.join(Globals.root, "models"))
             try:
                 from gfpgan import GFPGANer
+
                 self.gfpgan = GFPGANer(
                     model_path=self.model_path,
                     upscale=1,
-                    arch='clean',
+                    arch="clean",
                     channel_multiplier=2,
                     bg_upsampler=None,
                 )
             except Exception:
                 import traceback
-                print('>> Error loading GFPGAN:', file=sys.stderr)
+
+                print(">> Error loading GFPGAN:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
             os.chdir(cwd)
 
         if self.gfpgan is None:
+            print(f">> WARNING: GFPGAN not initialized.")
             print(
-                f'>> WARNING: GFPGAN not initialized.'
-            )
-            print(
-                f'>> Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}'
+                f">> Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
             )
 
-        image = image.convert('RGB')
+        image = image.convert("RGB")
 
         # GFPGAN expects a BGR np array; make array and flip channels
-        bgr_image_array = np.array(image, dtype=np.uint8)[...,::-1]
+        bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
 
         _, _, restored_img = self.gfpgan.enhance(
             bgr_image_array,
@@ -71,7 +70,7 @@ class GFPGAN():
         )
 
         # Flip the channels back to RGB
-        res = Image.fromarray(restored_img[...,::-1])
+        res = Image.fromarray(restored_img[..., ::-1])
 
         if strength < 1.0:
             # Resize the image to the new image if the sizes have changed
@@ -79,7 +78,6 @@ class GFPGAN():
                 image = image.resize(res.size)
             res = Image.blend(image, res, strength)
 
-
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         self.gfpgan = None

invokeai/backend/restoration/outcrop.py

@@ -0,0 +1,119 @@
+import math
+import warnings
+
+from PIL import Image, ImageFilter
+
+
+class Outcrop(object):
+    def __init__(
+        self,
+        image,
+        generate,  # current generate object
+    ):
+        self.image = image
+        self.generate = generate
+
+    def process(
+        self,
+        extents: dict,
+        opt,  # current options
+        orig_opt,  # ones originally used to generate the image
+        image_callback=None,
+        prefix=None,
+    ):
+        # grow and mask the image
+        extended_image = self._extend_all(extents)
+
+        # switch samplers temporarily
+        curr_sampler = self.generate.sampler
+        self.generate.sampler_name = opt.sampler_name
+        self.generate._set_sampler()
+
+        def wrapped_callback(img, seed, **kwargs):
+            preferred_seed = (
+                orig_opt.seed
+                if orig_opt.seed is not None and orig_opt.seed >= 0
+                else seed
+            )
+            image_callback(img, preferred_seed, use_prefix=prefix, **kwargs)
+
+        result = self.generate.prompt2image(
+            opt.prompt,
+            seed=opt.seed or orig_opt.seed,
+            sampler=self.generate.sampler,
+            steps=opt.steps,
+            cfg_scale=opt.cfg_scale,
+            ddim_eta=self.generate.ddim_eta,
+            width=extended_image.width,
+            height=extended_image.height,
+            init_img=extended_image,
+            strength=0.90,
+            image_callback=wrapped_callback if image_callback else None,
+            seam_size=opt.seam_size or 96,
+            seam_blur=opt.seam_blur or 16,
+            seam_strength=opt.seam_strength or 0.7,
+            seam_steps=20,
+            tile_size=32,
+            color_match=True,
+            force_outpaint=True,  # this just stops the warning about erased regions
+        )
+
+        # swap sampler back
+        self.generate.sampler = curr_sampler
+        return result
+
+    def _extend_all(
+        self,
+        extents: dict,
+    ) -> Image:
+        """
+        Extend the image in direction ('top','bottom','left','right') by
+        the indicated value. The image canvas is extended, and the empty
+        rectangular section will be filled with a blurred copy of the
+        adjacent image.
+        """
+        image = self.image
+        for direction in extents:
+            assert direction in [
+                "top",
+                "left",
+                "bottom",
+                "right",
+            ], 'Direction must be one of "top", "left", "bottom", "right"'
+            pixels = extents[direction]
+            # round pixels up to the nearest 64
+            pixels = math.ceil(pixels / 64) * 64
+            print(f">> extending image {direction}ward by {pixels} pixels")
+            image = self._rotate(image, direction)
+            image = self._extend(image, pixels)
+            image = self._rotate(image, direction, reverse=True)
+        return image
+
+    def _rotate(self, image: Image, direction: str, reverse=False) -> Image:
+        """
+        Rotates image so that the area to extend is always at the top top.
+        Simplifies logic later. The reverse argument, if true, will undo the
+        previous transpose.
+        """
+        transposes = {
+            "right": ["ROTATE_90", "ROTATE_270"],
+            "bottom": ["ROTATE_180", "ROTATE_180"],
+            "left": ["ROTATE_270", "ROTATE_90"],
+        }
+        if direction not in transposes:
+            return image
+        transpose = transposes[direction][1 if reverse else 0]
+        return image.transpose(Image.Transpose.__dict__[transpose])
+
+    def _extend(self, image: Image, pixels: int) -> Image:
+        extended_img = Image.new("RGBA", (image.width, image.height + pixels))
+
+        extended_img.paste((0, 0, 0), [0, 0, image.width, image.height + pixels])
+        extended_img.paste(image, box=(0, pixels))
+
+        # now make the top part transparent to use as a mask
+        alpha = extended_img.getchannel("A")
+        alpha.paste(0, (0, 0, extended_img.width, pixels))
+        extended_img.putalpha(alpha)
+
+        return extended_img

invokeai/backend/restoration/outpaint.py

@@ -1,39 +1,43 @@
-import warnings
 import math
+import warnings
+
 from PIL import Image, ImageFilter
 
+
 class Outpaint(object):
     def __init__(self, image, generate):
-        self.image     = image
-        self.generate  = generate
+        self.image = image
+        self.generate = generate
 
-    def process(self, opt, old_opt, image_callback = None, prefix = None):
+    def process(self, opt, old_opt, image_callback=None, prefix=None):
         image = self._create_outpaint_image(self.image, opt.out_direction)
 
-        seed   = old_opt.seed
+        seed = old_opt.seed
         prompt = old_opt.prompt
 
-        def wrapped_callback(img,seed,**kwargs):
-            image_callback(img,seed,use_prefix=prefix,**kwargs)
-
+        def wrapped_callback(img, seed, **kwargs):
+            image_callback(img, seed, use_prefix=prefix, **kwargs)
 
         return self.generate.prompt2image(
             prompt,
-            seed           = seed,
-            sampler        = self.generate.sampler,
-            steps          = opt.steps,
-            cfg_scale      = opt.cfg_scale,
-            ddim_eta       = self.generate.ddim_eta,
-            width          = opt.width,
-            height         = opt.height,
-            init_img       = image,
-            strength       = 0.83,
-            image_callback = wrapped_callback,
-            prefix         = prefix,
+            seed=seed,
+            sampler=self.generate.sampler,
+            steps=opt.steps,
+            cfg_scale=opt.cfg_scale,
+            ddim_eta=self.generate.ddim_eta,
+            width=opt.width,
+            height=opt.height,
+            init_img=image,
+            strength=0.83,
+            image_callback=wrapped_callback,
+            prefix=prefix,
         )
 
     def _create_outpaint_image(self, image, direction_args):
-        assert len(direction_args) in [1, 2], 'Direction (-D) must have exactly one or two arguments.'
+        assert len(direction_args) in [
+            1,
+            2,
+        ], "Direction (-D) must have exactly one or two arguments."
 
         if len(direction_args) == 1:
             direction = direction_args[0]
@@ -42,19 +46,26 @@ class Outpaint(object):
             direction = direction_args[0]
             pixels = int(direction_args[1])
 
-        assert direction in ['top', 'left', 'bottom', 'right'], 'Direction (-D) must be one of "top", "left", "bottom", "right"'
+        assert direction in [
+            "top",
+            "left",
+            "bottom",
+            "right",
+        ], 'Direction (-D) must be one of "top", "left", "bottom", "right"'
 
         image = image.convert("RGBA")
         # we always extend top, but rotate to extend along the requested side
-        if direction == 'left':
+        if direction == "left":
             image = image.transpose(Image.Transpose.ROTATE_270)
-        elif direction == 'bottom':
+        elif direction == "bottom":
             image = image.transpose(Image.Transpose.ROTATE_180)
-        elif direction == 'right':
+        elif direction == "right":
             image = image.transpose(Image.Transpose.ROTATE_90)
 
-        pixels = image.height//2 if pixels is None else int(pixels)
-        assert 0 < pixels < image.height, 'Direction (-D) pixels length must be in the range 0 - image.size'
+        pixels = image.height // 2 if pixels is None else int(pixels)
+        assert (
+            0 < pixels < image.height
+        ), "Direction (-D) pixels length must be in the range 0 - image.size"
 
         # the top part of the image is taken from the source image mirrored
         # coordinates (0,0) are the upper left corner of an image
@@ -74,19 +85,18 @@ class Outpaint(object):
         new_img.paste(bottom, (0, pixels))
 
         # create a 10% dither in the middle
-        dither = min(image.height//10, pixels)
+        dither = min(image.height // 10, pixels)
         for x in range(0, image.width, 2):
             for y in range(pixels - dither, pixels + dither):
                 (r, g, b, a) = new_img.getpixel((x, y))
                 new_img.putpixel((x, y), (r, g, b, 0))
 
         # let's rotate back again
-        if direction == 'left':
+        if direction == "left":
             new_img = new_img.transpose(Image.Transpose.ROTATE_90)
-        elif direction == 'bottom':
+        elif direction == "bottom":
             new_img = new_img.transpose(Image.Transpose.ROTATE_180)
-        elif direction == 'right':
+        elif direction == "right":
             new_img = new_img.transpose(Image.Transpose.ROTATE_270)
 
         return new_img
-

invokeai/backend/restoration/realesrgan.py

@@ -1,13 +1,15 @@
-import torch
-import warnings
-import numpy as np
 import os
+import warnings
 
-from ldm.invoke.globals import Globals
+import numpy as np
+import torch
 from PIL import Image
 from PIL.Image import Image as ImageType
 
-class ESRGAN():
+from invokeai.backend.globals import Globals
+
+
+class ESRGAN:
     def __init__(self, bg_tile_size=400) -> None:
         self.bg_tile_size = bg_tile_size
 
@@ -22,12 +24,23 @@ class ESRGAN():
         else:
             use_half_precision = True
 
-        from realesrgan.archs.srvgg_arch import SRVGGNetCompact
         from realesrgan import RealESRGANer
+        from realesrgan.archs.srvgg_arch import SRVGGNetCompact
 
-        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-        model_path = os.path.join(Globals.root, 'models/realesrgan/realesr-general-x4v3.pth')
-        wdn_model_path = os.path.join(Globals.root, 'models/realesrgan/realesr-general-wdn-x4v3.pth')
+        model = SRVGGNetCompact(
+            num_in_ch=3,
+            num_out_ch=3,
+            num_feat=64,
+            num_conv=32,
+            upscale=4,
+            act_type="prelu",
+        )
+        model_path = os.path.join(
+            Globals.root, "models/realesrgan/realesr-general-x4v3.pth"
+        )
+        wdn_model_path = os.path.join(
+            Globals.root, "models/realesrgan/realesr-general-wdn-x4v3.pth"
+        )
         scale = 4
 
         bg_upsampler = RealESRGANer(
@@ -43,41 +56,49 @@ class ESRGAN():
 
         return bg_upsampler
 
-    def process(self, image: ImageType, strength: float, seed: str = None, upsampler_scale: int = 2, denoise_str: float = 0.75):
+    def process(
+        self,
+        image: ImageType,
+        strength: float,
+        seed: str = None,
+        upsampler_scale: int = 2,
+        denoise_str: float = 0.75,
+    ):
         with warnings.catch_warnings():
-            warnings.filterwarnings('ignore', category=DeprecationWarning)
-            warnings.filterwarnings('ignore', category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=UserWarning)
 
             try:
                 upsampler = self.load_esrgan_bg_upsampler(denoise_str)
             except Exception:
-                import traceback
                 import sys
-                print('>> Error loading Real-ESRGAN:', file=sys.stderr)
+                import traceback
+
+                print(">> Error loading Real-ESRGAN:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
         if upsampler_scale == 0:
-            print('>> Real-ESRGAN: Invalid scaling option. Image not upscaled.')
+            print(">> Real-ESRGAN: Invalid scaling option. Image not upscaled.")
             return image
 
         if seed is not None:
             print(
-                f'>> Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}'
+                f">> Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
             )
         # ESRGAN outputs images with partial transparency if given RGBA images; convert to RGB
         image = image.convert("RGB")
 
         # REALSRGAN expects a BGR np array; make array and flip channels
-        bgr_image_array = np.array(image, dtype=np.uint8)[...,::-1]
+        bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
 
         output, _ = upsampler.enhance(
             bgr_image_array,
             outscale=upsampler_scale,
-            alpha_upsampler='realesrgan',
+            alpha_upsampler="realesrgan",
         )
 
         # Flip the channels back to RGB
-        res = Image.fromarray(output[...,::-1])
+        res = Image.fromarray(output[..., ::-1])
 
         if strength < 1.0:
             # Resize the image to the new image if the sizes have changed

invokeai/backend/restoration/vqgan_arch.py

@@ -1,23 +1,27 @@
-'''
+"""
 VQGAN code, adapted from the original created by the Unleashing Transformers authors:
 https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
 
-'''
+"""
+import copy
+
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import copy
 from basicsr.utils import get_root_logger
 from basicsr.utils.registry import ARCH_REGISTRY
 
+
 def normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+    return torch.nn.GroupNorm(
+        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+    )
 
 
 @torch.jit.script
 def swish(x):
-    return x*torch.sigmoid(x)
+    return x * torch.sigmoid(x)
 
 
 #  Define VQVAE classes
@@ -28,7 +32,9 @@ class VectorQuantizer(nn.Module):
         self.emb_dim = emb_dim  # dimension of embedding
         self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
         self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
+        self.embedding.weight.data.uniform_(
+            -1.0 / self.codebook_size, 1.0 / self.codebook_size
+        )
 
     def forward(self, z):
         # reshape z -> (batch, height, width, channel) and flatten
@@ -36,23 +42,32 @@ class VectorQuantizer(nn.Module):
         z_flattened = z.view(-1, self.emb_dim)
 
         # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-        d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
-            2 * torch.matmul(z_flattened, self.embedding.weight.t())
+        d = (
+            (z_flattened**2).sum(dim=1, keepdim=True)
+            + (self.embedding.weight**2).sum(1)
+            - 2 * torch.matmul(z_flattened, self.embedding.weight.t())
+        )
 
         mean_distance = torch.mean(d)
         # find closest encodings
         # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
-        min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
+        min_encoding_scores, min_encoding_indices = torch.topk(
+            d, 1, dim=1, largest=False
+        )
         # [0-1], higher score, higher confidence
-        min_encoding_scores = torch.exp(-min_encoding_scores/10)
+        min_encoding_scores = torch.exp(-min_encoding_scores / 10)
 
-        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
+        min_encodings = torch.zeros(
+            min_encoding_indices.shape[0], self.codebook_size
+        ).to(z)
         min_encodings.scatter_(1, min_encoding_indices, 1)
 
         # get quantized latent vectors
         z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
         # compute loss for embedding
-        loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
+        loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(
+            (z_q - z.detach()) ** 2
+        )
         # preserve gradients
         z_q = z + (z_q - z).detach()
 
@@ -62,18 +77,22 @@ class VectorQuantizer(nn.Module):
         # reshape back to match original input shape
         z_q = z_q.permute(0, 3, 1, 2).contiguous()
 
-        return z_q, loss, {
-            "perplexity": perplexity,
-            "min_encodings": min_encodings,
-            "min_encoding_indices": min_encoding_indices,
-            "min_encoding_scores": min_encoding_scores,
-            "mean_distance": mean_distance
-            }
+        return (
+            z_q,
+            loss,
+            {
+                "perplexity": perplexity,
+                "min_encodings": min_encodings,
+                "min_encoding_indices": min_encoding_indices,
+                "min_encoding_scores": min_encoding_scores,
+                "mean_distance": mean_distance,
+            },
+        )
 
     def get_codebook_feat(self, indices, shape):
         # input indices: batch*token_num -> (batch*token_num)*1
         # shape: batch, height, width, channel
-        indices = indices.view(-1,1)
+        indices = indices.view(-1, 1)
         min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
         min_encodings.scatter_(1, indices, 1)
         # get quantized latent vectors
@@ -86,14 +105,24 @@ class VectorQuantizer(nn.Module):
 
 
 class GumbelQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
+    def __init__(
+        self,
+        codebook_size,
+        emb_dim,
+        num_hiddens,
+        straight_through=False,
+        kl_weight=5e-4,
+        temp_init=1.0,
+    ):
         super().__init__()
         self.codebook_size = codebook_size  # number of embeddings
         self.emb_dim = emb_dim  # dimension of embedding
         self.straight_through = straight_through
         self.temperature = temp_init
         self.kl_weight = kl_weight
-        self.proj = nn.Conv2d(num_hiddens, codebook_size, 1)  # projects last encoder layer to quantized logits
+        self.proj = nn.Conv2d(
+            num_hiddens, codebook_size, 1
+        )  # projects last encoder layer to quantized logits
         self.embed = nn.Embedding(codebook_size, emb_dim)
 
     def forward(self, z):
@@ -107,18 +136,21 @@ class GumbelQuantizer(nn.Module):
 
         # + kl divergence to the prior loss
         qy = F.softmax(logits, dim=1)
-        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
+        diff = (
+            self.kl_weight
+            * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
+        )
         min_encoding_indices = soft_one_hot.argmax(dim=1)
 
-        return z_q, diff, {
-            "min_encoding_indices": min_encoding_indices
-        }
+        return z_q, diff, {"min_encoding_indices": min_encoding_indices}
 
 
 class Downsample(nn.Module):
     def __init__(self, in_channels):
         super().__init__()
-        self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+        self.conv = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=3, stride=2, padding=0
+        )
 
     def forward(self, x):
         pad = (0, 1, 0, 1)
@@ -130,7 +162,9 @@ class Downsample(nn.Module):
 class Upsample(nn.Module):
     def __init__(self, in_channels):
         super().__init__()
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+        self.conv = nn.Conv2d(
+            in_channels, in_channels, kernel_size=3, stride=1, padding=1
+        )
 
     def forward(self, x):
         x = F.interpolate(x, scale_factor=2.0, mode="nearest")
@@ -145,11 +179,17 @@ class ResBlock(nn.Module):
         self.in_channels = in_channels
         self.out_channels = in_channels if out_channels is None else out_channels
         self.norm1 = normalize(in_channels)
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
         self.norm2 = normalize(out_channels)
-        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
         if self.in_channels != self.out_channels:
-            self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+            self.conv_out = nn.Conv2d(
+                in_channels, out_channels, kernel_size=1, stride=1, padding=0
+            )
 
     def forward(self, x_in):
         x = x_in
@@ -172,32 +212,16 @@ class AttnBlock(nn.Module):
 
         self.norm = normalize(in_channels)
         self.q = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
         )
         self.k = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
         )
         self.v = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
         )
         self.proj_out = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
         )
 
     def forward(self, x):
@@ -209,26 +233,35 @@ class AttnBlock(nn.Module):
 
         # compute attention
         b, c, h, w = q.shape
-        q = q.reshape(b, c, h*w)
+        q = q.reshape(b, c, h * w)
         q = q.permute(0, 2, 1)
-        k = k.reshape(b, c, h*w)
+        k = k.reshape(b, c, h * w)
         w_ = torch.bmm(q, k)
-        w_ = w_ * (int(c)**(-0.5))
+        w_ = w_ * (int(c) ** (-0.5))
         w_ = F.softmax(w_, dim=2)
 
         # attend to values
-        v = v.reshape(b, c, h*w)
+        v = v.reshape(b, c, h * w)
         w_ = w_.permute(0, 2, 1)
         h_ = torch.bmm(v, w_)
         h_ = h_.reshape(b, c, h, w)
 
         h_ = self.proj_out(h_)
 
-        return x+h_
+        return x + h_
 
 
 class Encoder(nn.Module):
-    def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
+    def __init__(
+        self,
+        in_channels,
+        nf,
+        emb_dim,
+        ch_mult,
+        num_res_blocks,
+        resolution,
+        attn_resolutions,
+    ):
         super().__init__()
         self.nf = nf
         self.num_resolutions = len(ch_mult)
@@ -237,7 +270,7 @@ class Encoder(nn.Module):
         self.attn_resolutions = attn_resolutions
 
         curr_res = self.resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
+        in_ch_mult = (1,) + tuple(ch_mult)
 
         blocks = []
         # initial convultion
@@ -264,7 +297,9 @@ class Encoder(nn.Module):
 
         # normalise and convert to latent size
         blocks.append(normalize(block_in_ch))
-        blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
+        blocks.append(
+            nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1)
+        )
         self.blocks = nn.ModuleList(blocks)
 
     def forward(self, x):
@@ -286,11 +321,13 @@ class Generator(nn.Module):
         self.in_channels = emb_dim
         self.out_channels = 3
         block_in_ch = self.nf * self.ch_mult[-1]
-        curr_res = self.resolution // 2 ** (self.num_resolutions-1)
+        curr_res = self.resolution // 2 ** (self.num_resolutions - 1)
 
         blocks = []
         # initial conv
-        blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
+        blocks.append(
+            nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)
+        )
 
         # non-local attention block
         blocks.append(ResBlock(block_in_ch, block_in_ch))
@@ -312,11 +349,14 @@ class Generator(nn.Module):
                 curr_res = curr_res * 2
 
         blocks.append(normalize(block_in_ch))
-        blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
+        blocks.append(
+            nn.Conv2d(
+                block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1
+            )
+        )
 
         self.blocks = nn.ModuleList(blocks)
 
-
     def forward(self, x):
         for block in self.blocks:
             x = block(x)
@@ -326,8 +366,21 @@ class Generator(nn.Module):
 
 @ARCH_REGISTRY.register()
 class VQAutoEncoder(nn.Module):
-    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
-                beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
+    def __init__(
+        self,
+        img_size,
+        nf,
+        ch_mult,
+        quantizer="nearest",
+        res_blocks=2,
+        attn_resolutions=[16],
+        codebook_size=1024,
+        emb_dim=256,
+        beta=0.25,
+        gumbel_straight_through=False,
+        gumbel_kl_weight=1e-8,
+        model_path=None,
+    ):
         super().__init__()
         logger = get_root_logger()
         self.in_channels = 3
@@ -346,11 +399,13 @@ class VQAutoEncoder(nn.Module):
             self.ch_mult,
             self.n_blocks,
             self.resolution,
-            self.attn_resolutions
+            self.attn_resolutions,
         )
         if self.quantizer_type == "nearest":
-            self.beta = beta #0.25
-            self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
+            self.beta = beta  # 0.25
+            self.quantize = VectorQuantizer(
+                self.codebook_size, self.embed_dim, self.beta
+            )
         elif self.quantizer_type == "gumbel":
             self.gumbel_num_hiddens = emb_dim
             self.straight_through = gumbel_straight_through
@@ -360,7 +415,7 @@ class VQAutoEncoder(nn.Module):
                 self.embed_dim,
                 self.gumbel_num_hiddens,
                 self.straight_through,
-                self.kl_weight
+                self.kl_weight,
             )
         self.generator = Generator(
             self.nf,
@@ -368,20 +423,23 @@ class VQAutoEncoder(nn.Module):
             self.ch_mult,
             self.n_blocks,
             self.resolution,
-            self.attn_resolutions
+            self.attn_resolutions,
         )
 
         if model_path is not None:
-            chkpt = torch.load(model_path, map_location='cpu')
-            if 'params_ema' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
-                logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
-            elif 'params' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
-                logger.info(f'vqgan is loaded from: {model_path} [params]')
+            chkpt = torch.load(model_path, map_location="cpu")
+            if "params_ema" in chkpt:
+                self.load_state_dict(
+                    torch.load(model_path, map_location="cpu")["params_ema"]
+                )
+                logger.info(f"vqgan is loaded from: {model_path} [params_ema]")
+            elif "params" in chkpt:
+                self.load_state_dict(
+                    torch.load(model_path, map_location="cpu")["params"]
+                )
+                logger.info(f"vqgan is loaded from: {model_path} [params]")
             else:
-                raise ValueError(f'Wrong params!')
-
+                raise ValueError(f"Wrong params!")
 
     def forward(self, x):
         x = self.encoder(x)
@@ -390,46 +448,67 @@ class VQAutoEncoder(nn.Module):
         return x, codebook_loss, quant_stats
 
 
-
 # patch based discriminator
 @ARCH_REGISTRY.register()
 class VQGANDiscriminator(nn.Module):
     def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
         super().__init__()
 
-        layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
+        layers = [
+            nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1),
+            nn.LeakyReLU(0.2, True),
+        ]
         ndf_mult = 1
         ndf_mult_prev = 1
         for n in range(1, n_layers):  # gradually increase the number of filters
             ndf_mult_prev = ndf_mult
-            ndf_mult = min(2 ** n, 8)
+            ndf_mult = min(2**n, 8)
             layers += [
-                nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
+                nn.Conv2d(
+                    ndf * ndf_mult_prev,
+                    ndf * ndf_mult,
+                    kernel_size=4,
+                    stride=2,
+                    padding=1,
+                    bias=False,
+                ),
                 nn.BatchNorm2d(ndf * ndf_mult),
-                nn.LeakyReLU(0.2, True)
+                nn.LeakyReLU(0.2, True),
             ]
 
         ndf_mult_prev = ndf_mult
-        ndf_mult = min(2 ** n_layers, 8)
+        ndf_mult = min(2**n_layers, 8)
 
         layers += [
-            nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
+            nn.Conv2d(
+                ndf * ndf_mult_prev,
+                ndf * ndf_mult,
+                kernel_size=4,
+                stride=1,
+                padding=1,
+                bias=False,
+            ),
             nn.BatchNorm2d(ndf * ndf_mult),
-            nn.LeakyReLU(0.2, True)
+            nn.LeakyReLU(0.2, True),
         ]
 
         layers += [
-            nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)]  # output 1 channel prediction map
+            nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)
+        ]  # output 1 channel prediction map
         self.main = nn.Sequential(*layers)
 
         if model_path is not None:
-            chkpt = torch.load(model_path, map_location='cpu')
-            if 'params_d' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
-            elif 'params' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
+            chkpt = torch.load(model_path, map_location="cpu")
+            if "params_d" in chkpt:
+                self.load_state_dict(
+                    torch.load(model_path, map_location="cpu")["params_d"]
+                )
+            elif "params" in chkpt:
+                self.load_state_dict(
+                    torch.load(model_path, map_location="cpu")["params"]
+                )
             else:
-                raise ValueError(f'Wrong params!')
+                raise ValueError(f"Wrong params!")
 
     def forward(self, x):
         return self.main(x)

invokeai/backend/stable_diffusion/__init__.py

@@ -0,0 +1,16 @@
+"""
+Initialization file for the invokeai.backend.stable_diffusion package
+"""
+from .concepts_lib import HuggingFaceConceptsLibrary
+from .diffusers_pipeline import (
+    ConditioningData,
+    PipelineIntermediateState,
+    StableDiffusionGeneratorPipeline,
+)
+from .diffusion import InvokeAIDiffuserComponent
+from .diffusion.cross_attention_map_saving import AttentionMapSaver
+from .diffusion.ddim import DDIMSampler
+from .diffusion.ksampler import KSampler
+from .diffusion.plms import PLMSSampler
+from .diffusion.shared_invokeai_diffusion import PostprocessingSettings
+from .textual_inversion_manager import TextualInversionManager

invokeai/backend/stable_diffusion/attention.py

@@ -1,21 +1,22 @@
-from inspect import isfunction
 import math
+from inspect import isfunction
 from typing import Callable, Optional
 
 import torch
 import torch.nn.functional as F
-from torch import nn, einsum
 from einops import rearrange, repeat
+from torch import einsum, nn
+
+from .diffusion import InvokeAICrossAttentionMixin
+from .diffusionmodules.util import checkpoint
 
-from ldm.models.diffusion.cross_attention_control import InvokeAICrossAttentionMixin
-from ldm.modules.diffusionmodules.util import checkpoint
 
 def exists(val):
     return val is not None
 
 
 def uniq(arr):
-    return{el: True for el in arr}.keys()
+    return {el: True for el in arr}.keys()
 
 
 def default(val, d):
@@ -47,19 +48,18 @@ class GEGLU(nn.Module):
 
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
         super().__init__()
         inner_dim = int(dim * mult)
         dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
+        project_in = (
+            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
+            if not glu
+            else GEGLU(dim, inner_dim)
+        )
 
         self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
+            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
         )
 
     def forward(self, x):
@@ -76,7 +76,9 @@ def zero_module(module):
 
 
 def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+    return torch.nn.GroupNorm(
+        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+    )
 
 
 class LinearAttention(nn.Module):
@@ -84,17 +86,21 @@ class LinearAttention(nn.Module):
         super().__init__()
         self.heads = heads
         hidden_dim = dim_head * heads
-        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
         self.to_out = nn.Conv2d(hidden_dim, dim, 1)
 
     def forward(self, x):
         b, c, h, w = x.shape
         qkv = self.to_qkv(x)
-        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
+        q, k, v = rearrange(
+            qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
+        )
         k = k.softmax(dim=-1)
-        context = torch.einsum('bhdn,bhen->bhde', k, v)
-        out = torch.einsum('bhde,bhdn->bhen', context, q)
-        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
+        context = torch.einsum("bhdn,bhen->bhde", k, v)
+        out = torch.einsum("bhde,bhdn->bhen", context, q)
+        out = rearrange(
+            out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
+        )
         return self.to_out(out)
 
 
@@ -104,26 +110,18 @@ class SpatialSelfAttention(nn.Module):
         self.in_channels = in_channels
 
         self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
+        self.q = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
 
     def forward(self, x):
         h_ = x
@@ -133,43 +131,45 @@ class SpatialSelfAttention(nn.Module):
         v = self.v(h_)
 
         # compute attention
-        b,c,h,w = q.shape
-        q = rearrange(q, 'b c h w -> b (h w) c')
-        k = rearrange(k, 'b c h w -> b c (h w)')
-        w_ = torch.einsum('bij,bjk->bik', q, k)
+        b, c, h, w = q.shape
+        q = rearrange(q, "b c h w -> b (h w) c")
+        k = rearrange(k, "b c h w -> b c (h w)")
+        w_ = torch.einsum("bij,bjk->bik", q, k)
 
-        w_ = w_ * (int(c)**(-0.5))
+        w_ = w_ * (int(c) ** (-0.5))
         w_ = torch.nn.functional.softmax(w_, dim=2)
 
         # attend to values
-        v = rearrange(v, 'b c h w -> b c (h w)')
-        w_ = rearrange(w_, 'b i j -> b j i')
-        h_ = torch.einsum('bij,bjk->bik', v, w_)
-        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
+        v = rearrange(v, "b c h w -> b c (h w)")
+        w_ = rearrange(w_, "b i j -> b j i")
+        h_ = torch.einsum("bij,bjk->bik", v, w_)
+        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
         h_ = self.proj_out(h_)
 
-        return x+h_
+        return x + h_
+
 
 def get_mem_free_total(device):
-    #only on cuda
+    # only on cuda
     if not torch.cuda.is_available():
         return None
     stats = torch.cuda.memory_stats(device)
-    mem_active = stats['active_bytes.all.current']
-    mem_reserved = stats['reserved_bytes.all.current']
+    mem_active = stats["active_bytes.all.current"]
+    mem_reserved = stats["reserved_bytes.all.current"]
     mem_free_cuda, _ = torch.cuda.mem_get_info(device)
     mem_free_torch = mem_reserved - mem_active
     mem_free_total = mem_free_cuda + mem_free_torch
     return mem_free_total
 
+
 class CrossAttention(nn.Module, InvokeAICrossAttentionMixin):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
         super().__init__()
         InvokeAICrossAttentionMixin.__init__(self)
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
 
-        self.scale = dim_head ** -0.5
+        self.scale = dim_head**-0.5
         self.heads = heads
 
         self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
@@ -177,8 +177,7 @@ class CrossAttention(nn.Module, InvokeAICrossAttentionMixin):
         self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
 
         self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, query_dim),
-            nn.Dropout(dropout)
+            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
         )
 
     def forward(self, x, context=None, mask=None):
@@ -190,7 +189,7 @@ class CrossAttention(nn.Module, InvokeAICrossAttentionMixin):
         v = self.to_v(context)
         del context, x
 
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
 
         # don't apply scale twice
         cached_scale = self.scale
@@ -198,29 +197,45 @@ class CrossAttention(nn.Module, InvokeAICrossAttentionMixin):
         r = self.get_invokeai_attention_mem_efficient(q, k, v)
         self.scale = cached_scale
 
-        hidden_states = rearrange(r, '(b h) n d -> b n (h d)', h=h)
+        hidden_states = rearrange(r, "(b h) n d -> b n (h d)", h=h)
         return self.to_out(hidden_states)
 
 
-
-
 class BasicTransformerBlock(nn.Module):
-    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        dropout=0.0,
+        context_dim=None,
+        gated_ff=True,
+        checkpoint=True,
+    ):
         super().__init__()
-        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)  # is a self-attention
+        self.attn1 = CrossAttention(
+            query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
+        )  # is a self-attention
         self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
-        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
-                                    heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
+        self.attn2 = CrossAttention(
+            query_dim=dim,
+            context_dim=context_dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+        )  # is self-attn if context is none
         self.norm1 = nn.LayerNorm(dim)
         self.norm2 = nn.LayerNorm(dim)
         self.norm3 = nn.LayerNorm(dim)
         self.checkpoint = checkpoint
 
     def forward(self, x, context=None):
-        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+        return checkpoint(
+            self._forward, (x, context), self.parameters(), self.checkpoint
+        )
 
     def _forward(self, x, context=None):
-        x = x.contiguous() if x.device.type == 'mps' else x
+        x = x.contiguous() if x.device.type == "mps" else x
         x += self.attn1(self.norm1(x.clone()))
         x += self.attn2(self.norm2(x.clone()), context=context)
         x += self.ff(self.norm3(x.clone()))
@@ -235,29 +250,31 @@ class SpatialTransformer(nn.Module):
     Then apply standard transformer action.
     Finally, reshape to image
     """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None):
+
+    def __init__(
+        self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None
+    ):
         super().__init__()
         self.in_channels = in_channels
         inner_dim = n_heads * d_head
         self.norm = Normalize(in_channels)
 
-        self.proj_in = nn.Conv2d(in_channels,
-                                 inner_dim,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
-                for d in range(depth)]
+        self.proj_in = nn.Conv2d(
+            in_channels, inner_dim, kernel_size=1, stride=1, padding=0
         )
 
-        self.proj_out = zero_module(nn.Conv2d(inner_dim,
-                                              in_channels,
-                                              kernel_size=1,
-                                              stride=1,
-                                              padding=0))
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim
+                )
+                for d in range(depth)
+            ]
+        )
+
+        self.proj_out = zero_module(
+            nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
+        )
 
     def forward(self, x, context=None):
         # note: if no context is given, cross-attention defaults to self-attention
@@ -265,9 +282,9 @@ class SpatialTransformer(nn.Module):
         x_in = x
         x = self.norm(x)
         x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c')
+        x = rearrange(x, "b c h w -> b (h w) c")
         for block in self.transformer_blocks:
             x = block(x, context=context)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
+        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
         x = self.proj_out(x)
         return x + x_in

invokeai/backend/stable_diffusion/autoencoder.py

@@ -1,16 +1,13 @@
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
 from contextlib import contextmanager
 
+import pytorch_lightning as pl
+import torch
+import torch.nn.functional as F
 from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
 
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import (
-    DiagonalGaussianDistribution,
-)
-
-from ldm.util import instantiate_from_config
+from ..util import instantiate_from_config
+from .diffusionmodules.model import Decoder, Encoder
+from .distributions.distributions import DiagonalGaussianDistribution
 
 
 class VQModel(pl.LightningModule):
@@ -22,7 +19,7 @@ class VQModel(pl.LightningModule):
         embed_dim,
         ckpt_path=None,
         ignore_keys=[],
-        image_key='image',
+        image_key="image",
         colorize_nlabels=None,
         monitor=None,
         batch_resize_range=None,
@@ -46,27 +43,23 @@ class VQModel(pl.LightningModule):
             remap=remap,
             sane_index_shape=sane_index_shape,
         )
-        self.quant_conv = torch.nn.Conv2d(ddconfig['z_channels'], embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(
-            embed_dim, ddconfig['z_channels'], 1
-        )
+        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
         if colorize_nlabels is not None:
             assert type(colorize_nlabels) == int
-            self.register_buffer(
-                'colorize', torch.randn(3, colorize_nlabels, 1, 1)
-            )
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
         if monitor is not None:
             self.monitor = monitor
         self.batch_resize_range = batch_resize_range
         if self.batch_resize_range is not None:
             print(
-                f'{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.'
+                f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}."
             )
 
         self.use_ema = use_ema
         if self.use_ema:
             self.model_ema = LitEma(self)
-            print(f'>> Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
+            print(f">> Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
 
         if ckpt_path is not None:
             self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
@@ -79,30 +72,30 @@ class VQModel(pl.LightningModule):
             self.model_ema.store(self.parameters())
             self.model_ema.copy_to(self)
             if context is not None:
-                print(f'{context}: Switched to EMA weights')
+                print(f"{context}: Switched to EMA weights")
         try:
             yield None
         finally:
             if self.use_ema:
                 self.model_ema.restore(self.parameters())
                 if context is not None:
-                    print(f'{context}: Restored training weights')
+                    print(f"{context}: Restored training weights")
 
     def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location='cpu')['state_dict']
+        sd = torch.load(path, map_location="cpu")["state_dict"]
         keys = list(sd.keys())
         for k in keys:
             for ik in ignore_keys:
                 if k.startswith(ik):
-                    print('Deleting key {} from state_dict.'.format(k))
+                    print("Deleting key {} from state_dict.".format(k))
                     del sd[k]
         missing, unexpected = self.load_state_dict(sd, strict=False)
         print(
-            f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
+            f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
         )
         if len(missing) > 0:
-            print(f'Missing Keys: {missing}')
-            print(f'Unexpected Keys: {unexpected}')
+            print(f"Missing Keys: {missing}")
+            print(f"Unexpected Keys: {unexpected}")
 
     def on_train_batch_end(self, *args, **kwargs):
         if self.use_ema:
@@ -140,11 +133,7 @@ class VQModel(pl.LightningModule):
         x = batch[k]
         if len(x.shape) == 3:
             x = x[..., None]
-        x = (
-            x.permute(0, 3, 1, 2)
-            .to(memory_format=torch.contiguous_format)
-            .float()
-        )
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
         if self.batch_resize_range is not None:
             lower_size = self.batch_resize_range[0]
             upper_size = self.batch_resize_range[1]
@@ -156,7 +145,7 @@ class VQModel(pl.LightningModule):
                     np.arange(lower_size, upper_size + 16, 16)
                 )
             if new_resize != x.shape[2]:
-                x = F.interpolate(x, size=new_resize, mode='bicubic')
+                x = F.interpolate(x, size=new_resize, mode="bicubic")
             x = x.detach()
         return x
 
@@ -175,7 +164,7 @@ class VQModel(pl.LightningModule):
                 optimizer_idx,
                 self.global_step,
                 last_layer=self.get_last_layer(),
-                split='train',
+                split="train",
                 predicted_indices=ind,
             )
 
@@ -197,7 +186,7 @@ class VQModel(pl.LightningModule):
                 optimizer_idx,
                 self.global_step,
                 last_layer=self.get_last_layer(),
-                split='train',
+                split="train",
             )
             self.log_dict(
                 log_dict_disc,
@@ -211,12 +200,10 @@ class VQModel(pl.LightningModule):
     def validation_step(self, batch, batch_idx):
         log_dict = self._validation_step(batch, batch_idx)
         with self.ema_scope():
-            log_dict_ema = self._validation_step(
-                batch, batch_idx, suffix='_ema'
-            )
+            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
         return log_dict
 
-    def _validation_step(self, batch, batch_idx, suffix=''):
+    def _validation_step(self, batch, batch_idx, suffix=""):
         x = self.get_input(batch, self.image_key)
         xrec, qloss, ind = self(x, return_pred_indices=True)
         aeloss, log_dict_ae = self.loss(
@@ -226,7 +213,7 @@ class VQModel(pl.LightningModule):
             0,
             self.global_step,
             last_layer=self.get_last_layer(),
-            split='val' + suffix,
+            split="val" + suffix,
             predicted_indices=ind,
         )
 
@@ -237,12 +224,12 @@ class VQModel(pl.LightningModule):
             1,
             self.global_step,
             last_layer=self.get_last_layer(),
-            split='val' + suffix,
+            split="val" + suffix,
             predicted_indices=ind,
         )
-        rec_loss = log_dict_ae[f'val{suffix}/rec_loss']
+        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
         self.log(
-            f'val{suffix}/rec_loss',
+            f"val{suffix}/rec_loss",
             rec_loss,
             prog_bar=True,
             logger=True,
@@ -251,7 +238,7 @@ class VQModel(pl.LightningModule):
             sync_dist=True,
         )
         self.log(
-            f'val{suffix}/aeloss',
+            f"val{suffix}/aeloss",
             aeloss,
             prog_bar=True,
             logger=True,
@@ -259,8 +246,8 @@ class VQModel(pl.LightningModule):
             on_epoch=True,
             sync_dist=True,
         )
-        if version.parse(pl.__version__) >= version.parse('1.4.0'):
-            del log_dict_ae[f'val{suffix}/rec_loss']
+        if version.parse(pl.__version__) >= version.parse("1.4.0"):
+            del log_dict_ae[f"val{suffix}/rec_loss"]
         self.log_dict(log_dict_ae)
         self.log_dict(log_dict_disc)
         return self.log_dict
@@ -268,8 +255,8 @@ class VQModel(pl.LightningModule):
     def configure_optimizers(self):
         lr_d = self.learning_rate
         lr_g = self.lr_g_factor * self.learning_rate
-        print('lr_d', lr_d)
-        print('lr_g', lr_g)
+        print("lr_d", lr_d)
+        print("lr_g", lr_g)
         opt_ae = torch.optim.Adam(
             list(self.encoder.parameters())
             + list(self.decoder.parameters())
@@ -286,21 +273,17 @@ class VQModel(pl.LightningModule):
         if self.scheduler_config is not None:
             scheduler = instantiate_from_config(self.scheduler_config)
 
-            print('Setting up LambdaLR scheduler...')
+            print("Setting up LambdaLR scheduler...")
             scheduler = [
                 {
-                    'scheduler': LambdaLR(
-                        opt_ae, lr_lambda=scheduler.schedule
-                    ),
-                    'interval': 'step',
-                    'frequency': 1,
+                    "scheduler": LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+                    "interval": "step",
+                    "frequency": 1,
                 },
                 {
-                    'scheduler': LambdaLR(
-                        opt_disc, lr_lambda=scheduler.schedule
-                    ),
-                    'interval': 'step',
-                    'frequency': 1,
+                    "scheduler": LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+                    "interval": "step",
+                    "frequency": 1,
                 },
             ]
             return [opt_ae, opt_disc], scheduler
@@ -314,7 +297,7 @@ class VQModel(pl.LightningModule):
         x = self.get_input(batch, self.image_key)
         x = x.to(self.device)
         if only_inputs:
-            log['inputs'] = x
+            log["inputs"] = x
             return log
         xrec, _ = self(x)
         if x.shape[1] > 3:
@@ -322,22 +305,20 @@ class VQModel(pl.LightningModule):
             assert xrec.shape[1] > 3
             x = self.to_rgb(x)
             xrec = self.to_rgb(xrec)
-        log['inputs'] = x
-        log['reconstructions'] = xrec
+        log["inputs"] = x
+        log["reconstructions"] = xrec
         if plot_ema:
             with self.ema_scope():
                 xrec_ema, _ = self(x)
                 if x.shape[1] > 3:
                     xrec_ema = self.to_rgb(xrec_ema)
-                log['reconstructions_ema'] = xrec_ema
+                log["reconstructions_ema"] = xrec_ema
         return log
 
     def to_rgb(self, x):
-        assert self.image_key == 'segmentation'
-        if not hasattr(self, 'colorize'):
-            self.register_buffer(
-                'colorize', torch.randn(3, x.shape[1], 1, 1).to(x)
-            )
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
         x = F.conv2d(x, weight=self.colorize)
         x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
         return x
@@ -372,7 +353,7 @@ class AutoencoderKL(pl.LightningModule):
         embed_dim,
         ckpt_path=None,
         ignore_keys=[],
-        image_key='image',
+        image_key="image",
         colorize_nlabels=None,
         monitor=None,
     ):
@@ -381,34 +362,28 @@ class AutoencoderKL(pl.LightningModule):
         self.encoder = Encoder(**ddconfig)
         self.decoder = Decoder(**ddconfig)
         self.loss = instantiate_from_config(lossconfig)
-        assert ddconfig['double_z']
-        self.quant_conv = torch.nn.Conv2d(
-            2 * ddconfig['z_channels'], 2 * embed_dim, 1
-        )
-        self.post_quant_conv = torch.nn.Conv2d(
-            embed_dim, ddconfig['z_channels'], 1
-        )
+        assert ddconfig["double_z"]
+        self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
         self.embed_dim = embed_dim
         if colorize_nlabels is not None:
             assert type(colorize_nlabels) == int
-            self.register_buffer(
-                'colorize', torch.randn(3, colorize_nlabels, 1, 1)
-            )
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
         if monitor is not None:
             self.monitor = monitor
         if ckpt_path is not None:
             self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
 
     def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location='cpu')['state_dict']
+        sd = torch.load(path, map_location="cpu")["state_dict"]
         keys = list(sd.keys())
         for k in keys:
             for ik in ignore_keys:
                 if k.startswith(ik):
-                    print('Deleting key {} from state_dict.'.format(k))
+                    print("Deleting key {} from state_dict.".format(k))
                     del sd[k]
         self.load_state_dict(sd, strict=False)
-        print(f'Restored from {path}')
+        print(f"Restored from {path}")
 
     def encode(self, x):
         h = self.encoder(x)
@@ -434,11 +409,7 @@ class AutoencoderKL(pl.LightningModule):
         x = batch[k]
         if len(x.shape) == 3:
             x = x[..., None]
-        x = (
-            x.permute(0, 3, 1, 2)
-            .to(memory_format=torch.contiguous_format)
-            .float()
-        )
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
         return x
 
     def training_step(self, batch, batch_idx, optimizer_idx):
@@ -454,10 +425,10 @@ class AutoencoderKL(pl.LightningModule):
                 optimizer_idx,
                 self.global_step,
                 last_layer=self.get_last_layer(),
-                split='train',
+                split="train",
             )
             self.log(
-                'aeloss',
+                "aeloss",
                 aeloss,
                 prog_bar=True,
                 logger=True,
@@ -482,11 +453,11 @@ class AutoencoderKL(pl.LightningModule):
                 optimizer_idx,
                 self.global_step,
                 last_layer=self.get_last_layer(),
-                split='train',
+                split="train",
             )
 
             self.log(
-                'discloss',
+                "discloss",
                 discloss,
                 prog_bar=True,
                 logger=True,
@@ -512,7 +483,7 @@ class AutoencoderKL(pl.LightningModule):
             0,
             self.global_step,
             last_layer=self.get_last_layer(),
-            split='val',
+            split="val",
         )
 
         discloss, log_dict_disc = self.loss(
@@ -522,10 +493,10 @@ class AutoencoderKL(pl.LightningModule):
             1,
             self.global_step,
             last_layer=self.get_last_layer(),
-            split='val',
+            split="val",
         )
 
-        self.log('val/rec_loss', log_dict_ae['val/rec_loss'])
+        self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
         self.log_dict(log_dict_ae)
         self.log_dict(log_dict_disc)
         return self.log_dict
@@ -560,17 +531,15 @@ class AutoencoderKL(pl.LightningModule):
                 assert xrec.shape[1] > 3
                 x = self.to_rgb(x)
                 xrec = self.to_rgb(xrec)
-            log['samples'] = self.decode(torch.randn_like(posterior.sample()))
-            log['reconstructions'] = xrec
-        log['inputs'] = x
+            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
+            log["reconstructions"] = xrec
+        log["inputs"] = x
         return log
 
     def to_rgb(self, x):
-        assert self.image_key == 'segmentation'
-        if not hasattr(self, 'colorize'):
-            self.register_buffer(
-                'colorize', torch.randn(3, x.shape[1], 1, 1).to(x)
-            )
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
         x = F.conv2d(x, weight=self.colorize)
         x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
         return x

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -8,32 +8,50 @@ import os
 import re
 import traceback
 from typing import Callable
-from urllib import request, error as ul_error
-from huggingface_hub import HfFolder, hf_hub_url, ModelSearchArguments, ModelFilter, HfApi
-from ldm.invoke.globals import Globals
+from urllib import error as ul_error
+from urllib import request
+
+from huggingface_hub import (
+    HfApi,
+    HfFolder,
+    ModelFilter,
+    ModelSearchArguments,
+    hf_hub_url,
+)
+
+from invokeai.backend.globals import Globals
+
 
 class HuggingFaceConceptsLibrary(object):
     def __init__(self, root=None):
-        '''
+        """
         Initialize the Concepts object. May optionally pass a root directory.
-        '''
+        """
         self.root = root or Globals.root
         self.hf_api = HfApi()
         self.local_concepts = dict()
         self.concept_list = None
         self.concepts_loaded = dict()
-        self.triggers = dict()            # concept name to trigger phrase
-        self.concept_names = dict()       # trigger phrase to concept name
-        self.match_trigger = re.compile('(<[\w\- >]+>)') # trigger is slightly less restrictive than HF concept name
-        self.match_concept = re.compile('<([\w\-]+)>') # HF concept name can only contain A-Za-z0-9_-
+        self.triggers = dict()  # concept name to trigger phrase
+        self.concept_names = dict()  # trigger phrase to concept name
+        self.match_trigger = re.compile(
+            "(<[\w\- >]+>)"
+        )  # trigger is slightly less restrictive than HF concept name
+        self.match_concept = re.compile(
+            "<([\w\-]+)>"
+        )  # HF concept name can only contain A-Za-z0-9_-
 
-    def list_concepts(self)->list:
-        '''
+    def list_concepts(self) -> list:
+        """
         Return a list of all the concepts by name, without the 'sd-concepts-library' part.
         Also adds local concepts in invokeai/embeddings folder.
-        '''
-        local_concepts_now = self.get_local_concepts(os.path.join(self.root, 'embeddings'))
-        local_concepts_to_add = set(local_concepts_now).difference(set(self.local_concepts))
+        """
+        local_concepts_now = self.get_local_concepts(
+            os.path.join(self.root, "embeddings")
+        )
+        local_concepts_to_add = set(local_concepts_now).difference(
+            set(self.local_concepts)
+        )
         self.local_concepts.update(local_concepts_now)
 
         if self.concept_list is not None:
@@ -43,83 +61,96 @@ class HuggingFaceConceptsLibrary(object):
             return self.concept_list
         else:
             try:
-                models = self.hf_api.list_models(filter=ModelFilter(model_name='sd-concepts-library/'))
-                self.concept_list = [a.id.split('/')[1] for a in models]
+                models = self.hf_api.list_models(
+                    filter=ModelFilter(model_name="sd-concepts-library/")
+                )
+                self.concept_list = [a.id.split("/")[1] for a in models]
                 # when init, add all in dir. when not init, add only concepts added between init and now
                 self.concept_list.extend(list(local_concepts_to_add))
             except Exception as e:
-                print(f' ** WARNING: Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}.')
-                print(' ** You may load .bin and .pt file(s) manually using the --embedding_directory argument.')
+                print(
+                    f" ** WARNING: Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}."
+                )
+                print(
+                    " ** You may load .bin and .pt file(s) manually using the --embedding_directory argument."
+                )
             return self.concept_list
 
-    def get_concept_model_path(self, concept_name:str)->str:
-        '''
+    def get_concept_model_path(self, concept_name: str) -> str:
+        """
         Returns the path to the 'learned_embeds.bin' file in
         the named concept. Returns None if invalid or cannot
         be downloaded.
-        '''
+        """
         if not concept_name in self.list_concepts():
-            print(f'This concept is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept.')
+            print(
+                f"This concept is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept."
+            )
             return None
-        return self.get_concept_file(concept_name.lower(),'learned_embeds.bin')
+        return self.get_concept_file(concept_name.lower(), "learned_embeds.bin")
 
-    def concept_to_trigger(self, concept_name:str)->str:
-        '''
+    def concept_to_trigger(self, concept_name: str) -> str:
+        """
         Given a concept name returns its trigger by looking in the
         "token_identifier.txt" file.
-        '''
+        """
         if concept_name in self.triggers:
             return self.triggers[concept_name]
         elif self.concept_is_local(concept_name):
-            trigger = f'<{concept_name}>'
+            trigger = f"<{concept_name}>"
             self.triggers[concept_name] = trigger
             self.concept_names[trigger] = concept_name
             return trigger
 
-        file = self.get_concept_file(concept_name, 'token_identifier.txt', local_only=True)
+        file = self.get_concept_file(
+            concept_name, "token_identifier.txt", local_only=True
+        )
         if not file:
             return None
-        with open(file,'r') as f:
+        with open(file, "r") as f:
             trigger = f.readline()
             trigger = trigger.strip()
         self.triggers[concept_name] = trigger
         self.concept_names[trigger] = concept_name
         return trigger
 
-    def trigger_to_concept(self, trigger:str)->str:
-        '''
+    def trigger_to_concept(self, trigger: str) -> str:
+        """
         Given a trigger phrase, maps it to the concept library name.
         Only works if concept_to_trigger() has previously been called
         on this library. There needs to be a persistent database for
         this.
-        '''
-        concept = self.concept_names.get(trigger,None)
-        return f'<{concept}>' if concept else f'{trigger}'
+        """
+        concept = self.concept_names.get(trigger, None)
+        return f"<{concept}>" if concept else f"{trigger}"
 
-    def replace_triggers_with_concepts(self, prompt:str)->str:
-        '''
+    def replace_triggers_with_concepts(self, prompt: str) -> str:
+        """
         Given a prompt string that contains <trigger> tags, replace these
         tags with the concept name. The reason for this is so that the
         concept names get stored in the prompt metadata. There is no
         controlling of colliding triggers in the SD library, so it is
         better to store the concept name (unique) than the concept trigger
         (not necessarily unique!)
-        '''
+        """
         if not prompt:
             return prompt
         triggers = self.match_trigger.findall(prompt)
         if not triggers:
             return prompt
 
-        def do_replace(match)->str:
-            return self.trigger_to_concept(match.group(1)) or f'<{match.group(1)}>'
+        def do_replace(match) -> str:
+            return self.trigger_to_concept(match.group(1)) or f"<{match.group(1)}>"
+
         return self.match_trigger.sub(do_replace, prompt)
 
-    def replace_concepts_with_triggers(self,
-                                       prompt:str,
-                                       load_concepts_callback: Callable[[list], any],
-                                       excluded_tokens:list[str])->str:
-        '''
+    def replace_concepts_with_triggers(
+        self,
+        prompt: str,
+        load_concepts_callback: Callable[[list], any],
+        excluded_tokens: list[str],
+    ) -> str:
+        """
         Given a prompt string that contains `<concept_name>` tags, replace
         these tags with the appropriate trigger.
 
@@ -128,20 +159,30 @@ class HuggingFaceConceptsLibrary(object):
 
         `excluded_tokens` are any tokens that should not be replaced, typically because they
         are trigger tokens from a locally-loaded embedding.
-        '''
+        """
         concepts = self.match_concept.findall(prompt)
         if not concepts:
             return prompt
         load_concepts_callback(concepts)
 
-        def do_replace(match)->str:
-            if excluded_tokens and f'<{match.group(1)}>' in excluded_tokens:
-                return f'<{match.group(1)}>'
-            return self.concept_to_trigger(match.group(1)) or f'<{match.group(1)}>'
+        def do_replace(match) -> str:
+            if excluded_tokens and f"<{match.group(1)}>" in excluded_tokens:
+                return f"<{match.group(1)}>"
+            return self.concept_to_trigger(match.group(1)) or f"<{match.group(1)}>"
+
         return self.match_concept.sub(do_replace, prompt)
 
-    def get_concept_file(self, concept_name:str, file_name:str='learned_embeds.bin' , local_only:bool=False)->str:
-        if not (self.concept_is_downloaded(concept_name) or self.concept_is_local(concept_name) or local_only):
+    def get_concept_file(
+        self,
+        concept_name: str,
+        file_name: str = "learned_embeds.bin",
+        local_only: bool = False,
+    ) -> str:
+        if not (
+            self.concept_is_downloaded(concept_name)
+            or self.concept_is_local(concept_name)
+            or local_only
+        ):
             self.download_concept(concept_name)
 
         # get local path in invokeai/embeddings if local concept
@@ -153,19 +194,19 @@ class HuggingFaceConceptsLibrary(object):
             path = os.path.join(concept_path, file_name)
         return path if os.path.exists(path) else None
 
-    def concept_is_local(self, concept_name)->bool:
+    def concept_is_local(self, concept_name) -> bool:
         return concept_name in self.local_concepts
 
-    def concept_is_downloaded(self, concept_name)->bool:
+    def concept_is_downloaded(self, concept_name) -> bool:
         concept_directory = self._concept_path(concept_name)
         return os.path.exists(concept_directory)
 
-    def download_concept(self,concept_name)->bool:
+    def download_concept(self, concept_name) -> bool:
         repo_id = self._concept_id(concept_name)
         dest = self._concept_path(concept_name)
 
         access_token = HfFolder.get_token()
-        header = [("Authorization", f'Bearer {access_token}')] if access_token else []
+        header = [("Authorization", f"Bearer {access_token}")] if access_token else []
         opener = request.build_opener()
         opener.addheaders = header
         request.install_opener(opener)
@@ -174,45 +215,59 @@ class HuggingFaceConceptsLibrary(object):
         succeeded = True
 
         bytes = 0
+
         def tally_download_size(chunk, size, total):
             nonlocal bytes
-            if chunk==0:
+            if chunk == 0:
                 bytes += total
 
-        print(f'>> Downloading {repo_id}...',end='')
+        print(f">> Downloading {repo_id}...", end="")
         try:
-            for file in ('README.md','learned_embeds.bin','token_identifier.txt','type_of_concept.txt'):
+            for file in (
+                "README.md",
+                "learned_embeds.bin",
+                "token_identifier.txt",
+                "type_of_concept.txt",
+            ):
                 url = hf_hub_url(repo_id, file)
-                request.urlretrieve(url, os.path.join(dest,file),reporthook=tally_download_size)
+                request.urlretrieve(
+                    url, os.path.join(dest, file), reporthook=tally_download_size
+                )
         except ul_error.HTTPError as e:
-            if e.code==404:
-                print(f'This concept is not known to the Hugging Face library. Generation will continue without the concept.')
+            if e.code == 404:
+                print(
+                    f"This concept is not known to the Hugging Face library. Generation will continue without the concept."
+                )
             else:
-                print(f'Failed to download {concept_name}/{file} ({str(e)}. Generation will continue without the concept.)')
+                print(
+                    f"Failed to download {concept_name}/{file} ({str(e)}. Generation will continue without the concept.)"
+                )
             os.rmdir(dest)
             return False
         except ul_error.URLError as e:
-            print(f'ERROR: {str(e)}. This may reflect a network issue. Generation will continue without the concept.')
+            print(
+                f"ERROR: {str(e)}. This may reflect a network issue. Generation will continue without the concept."
+            )
             os.rmdir(dest)
             return False
-        print('...{:.2f}Kb'.format(bytes/1024))
+        print("...{:.2f}Kb".format(bytes / 1024))
         return succeeded
 
-    def _concept_id(self, concept_name:str)->str:
-        return f'sd-concepts-library/{concept_name}'
+    def _concept_id(self, concept_name: str) -> str:
+        return f"sd-concepts-library/{concept_name}"
 
-    def _concept_path(self, concept_name:str)->str:
-        return os.path.join(self.root,'models','sd-concepts-library',concept_name)
+    def _concept_path(self, concept_name: str) -> str:
+        return os.path.join(self.root, "models", "sd-concepts-library", concept_name)
 
-    def _concept_local_path(self, concept_name:str)->str:
+    def _concept_local_path(self, concept_name: str) -> str:
         filename = self.local_concepts[concept_name]
-        return os.path.join(self.root,'embeddings',filename)
+        return os.path.join(self.root, "embeddings", filename)
 
-    def get_local_concepts(self, loc_dir:str):
+    def get_local_concepts(self, loc_dir: str):
         locs_dic = dict()
         if os.path.isdir(loc_dir):
             for file in os.listdir(loc_dir):
                 f = os.path.splitext(file)
-                if f[1] == '.bin' or f[1] == '.pt':
+                if f[1] == ".bin" or f[1] == ".pt":
                     locs_dic[f[0]] = file
         return locs_dic

invokeai/backend/stable_diffusion/data/base.py

@@ -1,10 +1,6 @@
 from abc import abstractmethod
-from torch.utils.data import (
-    Dataset,
-    ConcatDataset,
-    ChainDataset,
-    IterableDataset,
-)
+
+from torch.utils.data import ChainDataset, ConcatDataset, Dataset, IterableDataset
 
 
 class Txt2ImgIterableBaseDataset(IterableDataset):
@@ -19,9 +15,7 @@ class Txt2ImgIterableBaseDataset(IterableDataset):
         self.sample_ids = valid_ids
         self.size = size
 
-        print(
-            f'{self.__class__.__name__} dataset contains {self.__len__()} examples.'
-        )
+        print(f"{self.__class__.__name__} dataset contains {self.__len__()} examples.")
 
     def __len__(self):
         return self.num_records

invokeai/backend/stable_diffusion/data/imagenet.py

@@ -1,31 +1,32 @@
-import os, yaml, pickle, shutil, tarfile, glob
-import cv2
-import albumentations
-import PIL
-import numpy as np
-import torchvision.transforms.functional as TF
-from omegaconf import OmegaConf
+import glob
+import os
+import pickle
+import shutil
+import tarfile
 from functools import partial
-from PIL import Image
-from tqdm import tqdm
-from torch.utils.data import Dataset, Subset
 
+import albumentations
+import cv2
+import numpy as np
+import PIL
 import taming.data.utils as tdu
+import torchvision.transforms.functional as TF
+import yaml
+from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
+from omegaconf import OmegaConf
+from PIL import Image
 from taming.data.imagenet import (
-    str_to_indices,
-    give_synsets_from_indices,
+    ImagePaths,
     download,
+    give_synsets_from_indices,
     retrieve,
+    str_to_indices,
 )
-from taming.data.imagenet import ImagePaths
-
-from ldm.modules.image_degradation import (
-    degradation_fn_bsr,
-    degradation_fn_bsr_light,
-)
+from torch.utils.data import Dataset, Subset
+from tqdm import tqdm
 
 
-def synset2idx(path_to_yaml='data/index_synset.yaml'):
+def synset2idx(path_to_yaml="data/index_synset.yaml"):
     with open(path_to_yaml) as f:
         di2s = yaml.load(f)
     return dict((v, k) for k, v in di2s.items())
@@ -36,9 +37,7 @@ class ImageNetBase(Dataset):
         self.config = config or OmegaConf.create()
         if not type(self.config) == dict:
             self.config = OmegaConf.to_container(self.config)
-        self.keep_orig_class_label = self.config.get(
-            'keep_orig_class_label', False
-        )
+        self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
         self.process_images = True  # if False we skip loading & processing images and self.data contains filepaths
         self._prepare()
         self._prepare_synset_to_human()
@@ -58,21 +57,19 @@ class ImageNetBase(Dataset):
     def _filter_relpaths(self, relpaths):
         ignore = set(
             [
-                'n06596364_9591.JPEG',
+                "n06596364_9591.JPEG",
             ]
         )
-        relpaths = [
-            rpath for rpath in relpaths if not rpath.split('/')[-1] in ignore
-        ]
-        if 'sub_indices' in self.config:
-            indices = str_to_indices(self.config['sub_indices'])
+        relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
+        if "sub_indices" in self.config:
+            indices = str_to_indices(self.config["sub_indices"])
             synsets = give_synsets_from_indices(
                 indices, path_to_yaml=self.idx2syn
             )  # returns a list of strings
             self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
             files = []
             for rpath in relpaths:
-                syn = rpath.split('/')[0]
+                syn = rpath.split("/")[0]
                 if syn in synsets:
                     files.append(rpath)
             return files
@@ -81,8 +78,8 @@ class ImageNetBase(Dataset):
 
     def _prepare_synset_to_human(self):
         SIZE = 2655750
-        URL = 'https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1'
-        self.human_dict = os.path.join(self.root, 'synset_human.txt')
+        URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
+        self.human_dict = os.path.join(self.root, "synset_human.txt")
         if (
             not os.path.exists(self.human_dict)
             or not os.path.getsize(self.human_dict) == SIZE
@@ -90,64 +87,62 @@ class ImageNetBase(Dataset):
             download(URL, self.human_dict)
 
     def _prepare_idx_to_synset(self):
-        URL = 'https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1'
-        self.idx2syn = os.path.join(self.root, 'index_synset.yaml')
+        URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
+        self.idx2syn = os.path.join(self.root, "index_synset.yaml")
         if not os.path.exists(self.idx2syn):
             download(URL, self.idx2syn)
 
     def _prepare_human_to_integer_label(self):
-        URL = 'https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1'
+        URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
         self.human2integer = os.path.join(
-            self.root, 'imagenet1000_clsidx_to_labels.txt'
+            self.root, "imagenet1000_clsidx_to_labels.txt"
         )
         if not os.path.exists(self.human2integer):
             download(URL, self.human2integer)
-        with open(self.human2integer, 'r') as f:
+        with open(self.human2integer, "r") as f:
             lines = f.read().splitlines()
             assert len(lines) == 1000
             self.human2integer_dict = dict()
             for line in lines:
-                value, key = line.split(':')
+                value, key = line.split(":")
                 self.human2integer_dict[key] = int(value)
 
     def _load(self):
-        with open(self.txt_filelist, 'r') as f:
+        with open(self.txt_filelist, "r") as f:
             self.relpaths = f.read().splitlines()
             l1 = len(self.relpaths)
             self.relpaths = self._filter_relpaths(self.relpaths)
             print(
-                'Removed {} files from filelist during filtering.'.format(
+                "Removed {} files from filelist during filtering.".format(
                     l1 - len(self.relpaths)
                 )
             )
 
-        self.synsets = [p.split('/')[0] for p in self.relpaths]
+        self.synsets = [p.split("/")[0] for p in self.relpaths]
         self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
 
         unique_synsets = np.unique(self.synsets)
-        class_dict = dict(
-            (synset, i) for i, synset in enumerate(unique_synsets)
-        )
+        class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
         if not self.keep_orig_class_label:
             self.class_labels = [class_dict[s] for s in self.synsets]
         else:
             self.class_labels = [self.synset2idx[s] for s in self.synsets]
 
-        with open(self.human_dict, 'r') as f:
+        with open(self.human_dict, "r") as f:
             human_dict = f.read().splitlines()
             human_dict = dict(line.split(maxsplit=1) for line in human_dict)
 
         self.human_labels = [human_dict[s] for s in self.synsets]
 
         labels = {
-            'relpath': np.array(self.relpaths),
-            'synsets': np.array(self.synsets),
-            'class_label': np.array(self.class_labels),
-            'human_label': np.array(self.human_labels),
+            "relpath": np.array(self.relpaths),
+            "synsets": np.array(self.synsets),
+            "class_label": np.array(self.class_labels),
+            "human_label": np.array(self.human_labels),
         }
 
         if self.process_images:
-            self.size = retrieve(self.config, 'size', default=256)
+            self.size = retrieve(self.config, "size", default=256)
             self.data = ImagePaths(
                 self.abspaths,
                 labels=labels,
@@ -159,11 +154,11 @@ class ImageNetBase(Dataset):
 
 
 class ImageNetTrain(ImageNetBase):
-    NAME = 'ILSVRC2012_train'
-    URL = 'http://www.image-net.org/challenges/LSVRC/2012/'
-    AT_HASH = 'a306397ccf9c2ead27155983c254227c0fd938e2'
+    NAME = "ILSVRC2012_train"
+    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
+    AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
     FILES = [
-        'ILSVRC2012_img_train.tar',
+        "ILSVRC2012_img_train.tar",
     ]
     SIZES = [
         147897477120,
@@ -178,20 +173,18 @@ class ImageNetTrain(ImageNetBase):
         if self.data_root:
             self.root = os.path.join(self.data_root, self.NAME)
         else:
-            cachedir = os.environ.get(
-                'XDG_CACHE_HOME', os.path.expanduser('~/.cache')
-            )
-            self.root = os.path.join(cachedir, 'autoencoders/data', self.NAME)
+            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
+            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
 
-        self.datadir = os.path.join(self.root, 'data')
-        self.txt_filelist = os.path.join(self.root, 'filelist.txt')
+        self.datadir = os.path.join(self.root, "data")
+        self.txt_filelist = os.path.join(self.root, "filelist.txt")
         self.expected_length = 1281167
         self.random_crop = retrieve(
-            self.config, 'ImageNetTrain/random_crop', default=True
+            self.config, "ImageNetTrain/random_crop", default=True
         )
         if not tdu.is_prepared(self.root):
             # prep
-            print('Preparing dataset {} in {}'.format(self.NAME, self.root))
+            print("Preparing dataset {} in {}".format(self.NAME, self.root))
 
             datadir = self.datadir
             if not os.path.exists(datadir):
@@ -205,37 +198,37 @@ class ImageNetTrain(ImageNetBase):
                     atpath = at.get(self.AT_HASH, datastore=self.root)
                     assert atpath == path
 
-                print('Extracting {} to {}'.format(path, datadir))
+                print("Extracting {} to {}".format(path, datadir))
                 os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, 'r:') as tar:
+                with tarfile.open(path, "r:") as tar:
                     tar.extractall(path=datadir)
 
-                print('Extracting sub-tars.')
-                subpaths = sorted(glob.glob(os.path.join(datadir, '*.tar')))
+                print("Extracting sub-tars.")
+                subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
                 for subpath in tqdm(subpaths):
-                    subdir = subpath[: -len('.tar')]
+                    subdir = subpath[: -len(".tar")]
                     os.makedirs(subdir, exist_ok=True)
-                    with tarfile.open(subpath, 'r:') as tar:
+                    with tarfile.open(subpath, "r:") as tar:
                         tar.extractall(path=subdir)
 
-            filelist = glob.glob(os.path.join(datadir, '**', '*.JPEG'))
+            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
             filelist = [os.path.relpath(p, start=datadir) for p in filelist]
             filelist = sorted(filelist)
-            filelist = '\n'.join(filelist) + '\n'
-            with open(self.txt_filelist, 'w') as f:
+            filelist = "\n".join(filelist) + "\n"
+            with open(self.txt_filelist, "w") as f:
                 f.write(filelist)
 
             tdu.mark_prepared(self.root)
 
 
 class ImageNetValidation(ImageNetBase):
-    NAME = 'ILSVRC2012_validation'
-    URL = 'http://www.image-net.org/challenges/LSVRC/2012/'
-    AT_HASH = '5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5'
-    VS_URL = 'https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1'
+    NAME = "ILSVRC2012_validation"
+    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
+    AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
+    VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
     FILES = [
-        'ILSVRC2012_img_val.tar',
-        'validation_synset.txt',
+        "ILSVRC2012_img_val.tar",
+        "validation_synset.txt",
     ]
     SIZES = [
         6744924160,
@@ -251,19 +244,17 @@ class ImageNetValidation(ImageNetBase):
         if self.data_root:
             self.root = os.path.join(self.data_root, self.NAME)
         else:
-            cachedir = os.environ.get(
-                'XDG_CACHE_HOME', os.path.expanduser('~/.cache')
-            )
-            self.root = os.path.join(cachedir, 'autoencoders/data', self.NAME)
-        self.datadir = os.path.join(self.root, 'data')
-        self.txt_filelist = os.path.join(self.root, 'filelist.txt')
+            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
+            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
+        self.datadir = os.path.join(self.root, "data")
+        self.txt_filelist = os.path.join(self.root, "filelist.txt")
         self.expected_length = 50000
         self.random_crop = retrieve(
-            self.config, 'ImageNetValidation/random_crop', default=False
+            self.config, "ImageNetValidation/random_crop", default=False
         )
         if not tdu.is_prepared(self.root):
             # prep
-            print('Preparing dataset {} in {}'.format(self.NAME, self.root))
+            print("Preparing dataset {} in {}".format(self.NAME, self.root))
 
             datadir = self.datadir
             if not os.path.exists(datadir):
@@ -277,9 +268,9 @@ class ImageNetValidation(ImageNetBase):
                     atpath = at.get(self.AT_HASH, datastore=self.root)
                     assert atpath == path
 
-                print('Extracting {} to {}'.format(path, datadir))
+                print("Extracting {} to {}".format(path, datadir))
                 os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, 'r:') as tar:
+                with tarfile.open(path, "r:") as tar:
                     tar.extractall(path=datadir)
 
                 vspath = os.path.join(self.root, self.FILES[1])
@@ -289,11 +280,11 @@ class ImageNetValidation(ImageNetBase):
                 ):
                     download(self.VS_URL, vspath)
 
-                with open(vspath, 'r') as f:
+                with open(vspath, "r") as f:
                     synset_dict = f.read().splitlines()
                     synset_dict = dict(line.split() for line in synset_dict)
 
-                print('Reorganizing into synset folders')
+                print("Reorganizing into synset folders")
                 synsets = np.unique(list(synset_dict.values()))
                 for s in synsets:
                     os.makedirs(os.path.join(datadir, s), exist_ok=True)
@@ -302,11 +293,11 @@ class ImageNetValidation(ImageNetBase):
                     dst = os.path.join(datadir, v)
                     shutil.move(src, dst)
 
-            filelist = glob.glob(os.path.join(datadir, '**', '*.JPEG'))
+            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
             filelist = [os.path.relpath(p, start=datadir) for p in filelist]
             filelist = sorted(filelist)
-            filelist = '\n'.join(filelist) + '\n'
-            with open(self.txt_filelist, 'w') as f:
+            filelist = "\n".join(filelist) + "\n"
+            with open(self.txt_filelist, "w") as f:
                 f.write(filelist)
 
             tdu.mark_prepared(self.root)
@@ -356,32 +347,28 @@ class ImageNetSR(Dataset):
             False  # gets reset later if incase interp_op is from pillow
         )
 
-        if degradation == 'bsrgan':
-            self.degradation_process = partial(
-                degradation_fn_bsr, sf=downscale_f
-            )
+        if degradation == "bsrgan":
+            self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
 
-        elif degradation == 'bsrgan_light':
-            self.degradation_process = partial(
-                degradation_fn_bsr_light, sf=downscale_f
-            )
+        elif degradation == "bsrgan_light":
+            self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
 
         else:
             interpolation_fn = {
-                'cv_nearest': cv2.INTER_NEAREST,
-                'cv_bilinear': cv2.INTER_LINEAR,
-                'cv_bicubic': cv2.INTER_CUBIC,
-                'cv_area': cv2.INTER_AREA,
-                'cv_lanczos': cv2.INTER_LANCZOS4,
-                'pil_nearest': PIL.Image.NEAREST,
-                'pil_bilinear': PIL.Image.BILINEAR,
-                'pil_bicubic': PIL.Image.BICUBIC,
-                'pil_box': PIL.Image.BOX,
-                'pil_hamming': PIL.Image.HAMMING,
-                'pil_lanczos': PIL.Image.LANCZOS,
+                "cv_nearest": cv2.INTER_NEAREST,
+                "cv_bilinear": cv2.INTER_LINEAR,
+                "cv_bicubic": cv2.INTER_CUBIC,
+                "cv_area": cv2.INTER_AREA,
+                "cv_lanczos": cv2.INTER_LANCZOS4,
+                "pil_nearest": PIL.Image.NEAREST,
+                "pil_bilinear": PIL.Image.BILINEAR,
+                "pil_bicubic": PIL.Image.BICUBIC,
+                "pil_box": PIL.Image.BOX,
+                "pil_hamming": PIL.Image.HAMMING,
+                "pil_lanczos": PIL.Image.LANCZOS,
             }[degradation]
 
-            self.pil_interpolation = degradation.startswith('pil_')
+            self.pil_interpolation = degradation.startswith("pil_")
 
             if self.pil_interpolation:
                 self.degradation_process = partial(
@@ -400,10 +387,10 @@ class ImageNetSR(Dataset):
 
     def __getitem__(self, i):
         example = self.base[i]
-        image = Image.open(example['file_path_'])
+        image = Image.open(example["file_path_"])
 
-        if not image.mode == 'RGB':
-            image = image.convert('RGB')
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
 
         image = np.array(image).astype(np.uint8)
 
@@ -423,8 +410,8 @@ class ImageNetSR(Dataset):
                 height=crop_side_len, width=crop_side_len
             )
 
-        image = self.cropper(image=image)['image']
-        image = self.image_rescaler(image=image)['image']
+        image = self.cropper(image=image)["image"]
+        image = self.image_rescaler(image=image)["image"]
 
         if self.pil_interpolation:
             image_pil = PIL.Image.fromarray(image)
@@ -432,10 +419,10 @@ class ImageNetSR(Dataset):
             LR_image = np.array(LR_image).astype(np.uint8)
 
         else:
-            LR_image = self.degradation_process(image=image)['image']
+            LR_image = self.degradation_process(image=image)["image"]
 
-        example['image'] = (image / 127.5 - 1.0).astype(np.float32)
-        example['LR_image'] = (LR_image / 127.5 - 1.0).astype(np.float32)
+        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
+        example["LR_image"] = (LR_image / 127.5 - 1.0).astype(np.float32)
 
         return example
 
@@ -445,7 +432,7 @@ class ImageNetSRTrain(ImageNetSR):
         super().__init__(**kwargs)
 
     def get_base(self):
-        with open('data/imagenet_train_hr_indices.p', 'rb') as f:
+        with open("data/imagenet_train_hr_indices.p", "rb") as f:
             indices = pickle.load(f)
         dset = ImageNetTrain(
             process_images=False,
@@ -458,7 +445,7 @@ class ImageNetSRValidation(ImageNetSR):
         super().__init__(**kwargs)
 
     def get_base(self):
-        with open('data/imagenet_val_hr_indices.p', 'rb') as f:
+        with open("data/imagenet_val_hr_indices.p", "rb") as f:
             indices = pickle.load(f)
         dset = ImageNetValidation(
             process_images=False,

invokeai/backend/stable_diffusion/data/lsun.py

@@ -1,4 +1,5 @@
 import os
+
 import numpy as np
 import PIL
 from PIL import Image
@@ -12,27 +13,25 @@ class LSUNBase(Dataset):
         txt_file,
         data_root,
         size=None,
-        interpolation='bicubic',
+        interpolation="bicubic",
         flip_p=0.5,
     ):
         self.data_paths = txt_file
         self.data_root = data_root
-        with open(self.data_paths, 'r') as f:
+        with open(self.data_paths, "r") as f:
             self.image_paths = f.read().splitlines()
         self._length = len(self.image_paths)
         self.labels = {
-            'relative_file_path_': [l for l in self.image_paths],
-            'file_path_': [
-                os.path.join(self.data_root, l) for l in self.image_paths
-            ],
+            "relative_file_path_": [l for l in self.image_paths],
+            "file_path_": [os.path.join(self.data_root, l) for l in self.image_paths],
         }
 
         self.size = size
         self.interpolation = {
-            'linear': PIL.Image.LINEAR,
-            'bilinear': PIL.Image.BILINEAR,
-            'bicubic': PIL.Image.BICUBIC,
-            'lanczos': PIL.Image.LANCZOS,
+            "linear": PIL.Image.LINEAR,
+            "bilinear": PIL.Image.BILINEAR,
+            "bicubic": PIL.Image.BICUBIC,
+            "lanczos": PIL.Image.LANCZOS,
         }[interpolation]
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
@@ -41,14 +40,17 @@ class LSUNBase(Dataset):
 
     def __getitem__(self, i):
         example = dict((k, self.labels[k][i]) for k in self.labels)
-        image = Image.open(example['file_path_'])
-        if not image.mode == 'RGB':
-            image = image.convert('RGB')
+        image = Image.open(example["file_path_"])
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
 
         # default to score-sde preprocessing
         img = np.array(image).astype(np.uint8)
         crop = min(img.shape[0], img.shape[1])
-        h, w, = (
+        (
+            h,
+            w,
+        ) = (
             img.shape[0],
             img.shape[1],
         )
@@ -59,68 +61,64 @@ class LSUNBase(Dataset):
 
         image = Image.fromarray(img)
         if self.size is not None:
-            image = image.resize(
-                (self.size, self.size), resample=self.interpolation
-            )
+            image = image.resize((self.size, self.size), resample=self.interpolation)
 
         image = self.flip(image)
         image = np.array(image).astype(np.uint8)
-        example['image'] = (image / 127.5 - 1.0).astype(np.float32)
+        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
         return example
 
 
 class LSUNChurchesTrain(LSUNBase):
     def __init__(self, **kwargs):
         super().__init__(
-            txt_file='data/lsun/church_outdoor_train.txt',
-            data_root='data/lsun/churches',
-            **kwargs
+            txt_file="data/lsun/church_outdoor_train.txt",
+            data_root="data/lsun/churches",
+            **kwargs,
         )
 
 
 class LSUNChurchesValidation(LSUNBase):
     def __init__(self, flip_p=0.0, **kwargs):
         super().__init__(
-            txt_file='data/lsun/church_outdoor_val.txt',
-            data_root='data/lsun/churches',
+            txt_file="data/lsun/church_outdoor_val.txt",
+            data_root="data/lsun/churches",
             flip_p=flip_p,
-            **kwargs
+            **kwargs,
         )
 
 
 class LSUNBedroomsTrain(LSUNBase):
     def __init__(self, **kwargs):
         super().__init__(
-            txt_file='data/lsun/bedrooms_train.txt',
-            data_root='data/lsun/bedrooms',
-            **kwargs
+            txt_file="data/lsun/bedrooms_train.txt",
+            data_root="data/lsun/bedrooms",
+            **kwargs,
         )
 
 
 class LSUNBedroomsValidation(LSUNBase):
     def __init__(self, flip_p=0.0, **kwargs):
         super().__init__(
-            txt_file='data/lsun/bedrooms_val.txt',
-            data_root='data/lsun/bedrooms',
+            txt_file="data/lsun/bedrooms_val.txt",
+            data_root="data/lsun/bedrooms",
             flip_p=flip_p,
-            **kwargs
+            **kwargs,
         )
 
 
 class LSUNCatsTrain(LSUNBase):
     def __init__(self, **kwargs):
         super().__init__(
-            txt_file='data/lsun/cat_train.txt',
-            data_root='data/lsun/cats',
-            **kwargs
+            txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs
         )
 
 
 class LSUNCatsValidation(LSUNBase):
     def __init__(self, flip_p=0.0, **kwargs):
         super().__init__(
-            txt_file='data/lsun/cat_val.txt',
-            data_root='data/lsun/cats',
+            txt_file="data/lsun/cat_val.txt",
+            data_root="data/lsun/cats",
             flip_p=flip_p,
-            **kwargs
+            **kwargs,
         )

invokeai/backend/stable_diffusion/data/personalized.py

@@ -0,0 +1,199 @@
+import os
+import random
+
+import numpy as np
+import PIL
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+imagenet_templates_smallest = [
+    "a photo of a {}",
+]
+
+imagenet_templates_small = [
+    "a photo of a {}",
+    "a rendering of a {}",
+    "a cropped photo of the {}",
+    "the photo of a {}",
+    "a photo of a clean {}",
+    "a photo of a dirty {}",
+    "a dark photo of the {}",
+    "a photo of my {}",
+    "a photo of the cool {}",
+    "a close-up photo of a {}",
+    "a bright photo of the {}",
+    "a cropped photo of a {}",
+    "a photo of the {}",
+    "a good photo of the {}",
+    "a photo of one {}",
+    "a close-up photo of the {}",
+    "a rendition of the {}",
+    "a photo of the clean {}",
+    "a rendition of a {}",
+    "a photo of a nice {}",
+    "a good photo of a {}",
+    "a photo of the nice {}",
+    "a photo of the small {}",
+    "a photo of the weird {}",
+    "a photo of the large {}",
+    "a photo of a cool {}",
+    "a photo of a small {}",
+]
+
+imagenet_dual_templates_small = [
+    "a photo of a {} with {}",
+    "a rendering of a {} with {}",
+    "a cropped photo of the {} with {}",
+    "the photo of a {} with {}",
+    "a photo of a clean {} with {}",
+    "a photo of a dirty {} with {}",
+    "a dark photo of the {} with {}",
+    "a photo of my {} with {}",
+    "a photo of the cool {} with {}",
+    "a close-up photo of a {} with {}",
+    "a bright photo of the {} with {}",
+    "a cropped photo of a {} with {}",
+    "a photo of the {} with {}",
+    "a good photo of the {} with {}",
+    "a photo of one {} with {}",
+    "a close-up photo of the {} with {}",
+    "a rendition of the {} with {}",
+    "a photo of the clean {} with {}",
+    "a rendition of a {} with {}",
+    "a photo of a nice {} with {}",
+    "a good photo of a {} with {}",
+    "a photo of the nice {} with {}",
+    "a photo of the small {} with {}",
+    "a photo of the weird {} with {}",
+    "a photo of the large {} with {}",
+    "a photo of a cool {} with {}",
+    "a photo of a small {} with {}",
+]
+
+per_img_token_list = [
+    "א",
+    "ב",
+    "ג",
+    "ד",
+    "ה",
+    "ו",
+    "ז",
+    "ח",
+    "ט",
+    "י",
+    "כ",
+    "ל",
+    "מ",
+    "נ",
+    "ס",
+    "ע",
+    "פ",
+    "צ",
+    "ק",
+    "ר",
+    "ש",
+    "ת",
+]
+
+
+class PersonalizedBase(Dataset):
+    def __init__(
+        self,
+        data_root,
+        size=None,
+        repeats=100,
+        interpolation="bicubic",
+        flip_p=0.5,
+        set="train",
+        placeholder_token="*",
+        per_image_tokens=False,
+        center_crop=False,
+        mixing_prob=0.25,
+        coarse_class_text=None,
+    ):
+        self.data_root = data_root
+
+        self.image_paths = [
+            os.path.join(self.data_root, file_path)
+            for file_path in os.listdir(self.data_root)
+            if file_path != ".DS_Store"
+        ]
+
+        # self._length = len(self.image_paths)
+        self.num_images = len(self.image_paths)
+        self._length = self.num_images
+
+        self.placeholder_token = placeholder_token
+
+        self.per_image_tokens = per_image_tokens
+        self.center_crop = center_crop
+        self.mixing_prob = mixing_prob
+
+        self.coarse_class_text = coarse_class_text
+
+        if per_image_tokens:
+            assert self.num_images < len(
+                per_img_token_list
+            ), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
+
+        if set == "train":
+            self._length = self.num_images * repeats
+
+        self.size = size
+        self.interpolation = {
+            "linear": PIL.Image.LINEAR,
+            "bilinear": PIL.Image.BILINEAR,
+            "bicubic": PIL.Image.BICUBIC,
+            "lanczos": PIL.Image.LANCZOS,
+        }[interpolation]
+        self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, i):
+        example = {}
+        image = Image.open(self.image_paths[i % self.num_images])
+
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+
+        placeholder_string = self.placeholder_token
+        if self.coarse_class_text:
+            placeholder_string = f"{self.coarse_class_text} {placeholder_string}"
+
+        if self.per_image_tokens and np.random.uniform() < self.mixing_prob:
+            text = random.choice(imagenet_dual_templates_small).format(
+                placeholder_string, per_img_token_list[i % self.num_images]
+            )
+        else:
+            text = random.choice(imagenet_templates_small).format(placeholder_string)
+
+        example["caption"] = text
+
+        # default to score-sde preprocessing
+        img = np.array(image).astype(np.uint8)
+
+        if self.center_crop:
+            crop = min(img.shape[0], img.shape[1])
+            (
+                h,
+                w,
+            ) = (
+                img.shape[0],
+                img.shape[1],
+            )
+            img = img[
+                (h - crop) // 2 : (h + crop) // 2,
+                (w - crop) // 2 : (w + crop) // 2,
+            ]
+
+        image = Image.fromarray(img)
+        if self.size is not None:
+            image = image.resize((self.size, self.size), resample=self.interpolation)
+
+        image = self.flip(image)
+        image = np.array(image).astype(np.uint8)
+        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
+        return example

invokeai/backend/stable_diffusion/data/personalized_style.py

@@ -0,0 +1,170 @@
+import os
+import random
+
+import numpy as np
+import PIL
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+imagenet_templates_small = [
+    "a painting in the style of {}",
+    "a rendering in the style of {}",
+    "a cropped painting in the style of {}",
+    "the painting in the style of {}",
+    "a clean painting in the style of {}",
+    "a dirty painting in the style of {}",
+    "a dark painting in the style of {}",
+    "a picture in the style of {}",
+    "a cool painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a bright painting in the style of {}",
+    "a cropped painting in the style of {}",
+    "a good painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a rendition in the style of {}",
+    "a nice painting in the style of {}",
+    "a small painting in the style of {}",
+    "a weird painting in the style of {}",
+    "a large painting in the style of {}",
+]
+
+imagenet_dual_templates_small = [
+    "a painting in the style of {} with {}",
+    "a rendering in the style of {} with {}",
+    "a cropped painting in the style of {} with {}",
+    "the painting in the style of {} with {}",
+    "a clean painting in the style of {} with {}",
+    "a dirty painting in the style of {} with {}",
+    "a dark painting in the style of {} with {}",
+    "a cool painting in the style of {} with {}",
+    "a close-up painting in the style of {} with {}",
+    "a bright painting in the style of {} with {}",
+    "a cropped painting in the style of {} with {}",
+    "a good painting in the style of {} with {}",
+    "a painting of one {} in the style of {}",
+    "a nice painting in the style of {} with {}",
+    "a small painting in the style of {} with {}",
+    "a weird painting in the style of {} with {}",
+    "a large painting in the style of {} with {}",
+]
+
+per_img_token_list = [
+    "א",
+    "ב",
+    "ג",
+    "ד",
+    "ה",
+    "ו",
+    "ז",
+    "ח",
+    "ט",
+    "י",
+    "כ",
+    "ל",
+    "מ",
+    "נ",
+    "ס",
+    "ע",
+    "פ",
+    "צ",
+    "ק",
+    "ר",
+    "ש",
+    "ת",
+]
+
+
+class PersonalizedBase(Dataset):
+    def __init__(
+        self,
+        data_root,
+        size=None,
+        repeats=100,
+        interpolation="bicubic",
+        flip_p=0.5,
+        set="train",
+        placeholder_token="*",
+        per_image_tokens=False,
+        center_crop=False,
+    ):
+        self.data_root = data_root
+
+        self.image_paths = [
+            os.path.join(self.data_root, file_path)
+            for file_path in os.listdir(self.data_root)
+            if file_path != ".DS_Store"
+        ]
+
+        # self._length = len(self.image_paths)
+        self.num_images = len(self.image_paths)
+        self._length = self.num_images
+
+        self.placeholder_token = placeholder_token
+
+        self.per_image_tokens = per_image_tokens
+        self.center_crop = center_crop
+
+        if per_image_tokens:
+            assert self.num_images < len(
+                per_img_token_list
+            ), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
+
+        if set == "train":
+            self._length = self.num_images * repeats
+
+        self.size = size
+        self.interpolation = {
+            "linear": PIL.Image.LINEAR,
+            "bilinear": PIL.Image.BILINEAR,
+            "bicubic": PIL.Image.BICUBIC,
+            "lanczos": PIL.Image.LANCZOS,
+        }[interpolation]
+        self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, i):
+        example = {}
+        image = Image.open(self.image_paths[i % self.num_images])
+
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+
+        if self.per_image_tokens and np.random.uniform() < 0.25:
+            text = random.choice(imagenet_dual_templates_small).format(
+                self.placeholder_token, per_img_token_list[i % self.num_images]
+            )
+        else:
+            text = random.choice(imagenet_templates_small).format(
+                self.placeholder_token
+            )
+
+        example["caption"] = text
+
+        # default to score-sde preprocessing
+        img = np.array(image).astype(np.uint8)
+
+        if self.center_crop:
+            crop = min(img.shape[0], img.shape[1])
+            (
+                h,
+                w,
+            ) = (
+                img.shape[0],
+                img.shape[1],
+            )
+            img = img[
+                (h - crop) // 2 : (h + crop) // 2,
+                (w - crop) // 2 : (w + crop) // 2,
+            ]
+
+        image = Image.fromarray(img)
+        if self.size is not None:
+            image = image.resize((self.size, self.size), resample=self.interpolation)
+
+        image = self.flip(image)
+        image = np.array(image).astype(np.uint8)
+        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
+        return example

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -2,22 +2,28 @@ from __future__ import annotations
 
 import dataclasses
 import inspect
-import psutil
 import secrets
 from collections.abc import Sequence
 from dataclasses import dataclass, field
-from typing import List, Optional, Union, Callable, Type, TypeVar, Generic, Any
+from typing import Any, Callable, Generic, List, Optional, Type, TypeVar, Union
 
-import PIL.Image
 import einops
+import PIL.Image
 import psutil
 import torch
 import torchvision.transforms as T
+from compel import EmbeddingsProvider
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
+    StableDiffusionPipeline,
+)
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import (
+    StableDiffusionImg2ImgPipeline,
+)
+from diffusers.pipelines.stable_diffusion.safety_checker import (
+    StableDiffusionSafetyChecker,
+)
 from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
 from diffusers.utils.import_utils import is_xformers_available
@@ -26,13 +32,16 @@ from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from typing_extensions import ParamSpec
 
-from ldm.invoke.globals import Globals
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent, PostprocessingSettings
-from ldm.modules.textual_inversion_manager import TextualInversionManager
-from ..devices import normalize_device, CPU_DEVICE
-from ..offloading import LazilyLoadedModelGroup, FullyLoadedModelGroup, ModelGroup
-from ...models.diffusion.cross_attention_map_saving import AttentionMapSaver
-from compel import EmbeddingsProvider
+from invokeai.backend.globals import Globals
+
+from ..util import CPU_DEVICE, normalize_device
+from .diffusion import (
+    AttentionMapSaver,
+    InvokeAIDiffuserComponent,
+    PostprocessingSettings,
+)
+from .offloading import FullyLoadedModelGroup, LazilyLoadedModelGroup, ModelGroup
+from .textual_inversion_manager import TextualInversionManager
 
 
 @dataclass
@@ -51,7 +60,7 @@ _default_personalization_config_params = dict(
     initializer_wods=["sculpture"],
     per_image_tokens=False,
     num_vectors_per_token=1,
-    progressive_words=False
+    progressive_words=False,
 )
 
 
@@ -64,29 +73,34 @@ class AddsMaskLatents:
 
     This class assumes the same mask and base image should apply to all items in the batch.
     """
+
     forward: Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
     mask: torch.Tensor
     initial_image_latents: torch.Tensor
 
-    def __call__(self, latents: torch.Tensor, t: torch.Tensor, text_embeddings: torch.Tensor) -> torch.Tensor:
+    def __call__(
+        self, latents: torch.Tensor, t: torch.Tensor, text_embeddings: torch.Tensor
+    ) -> torch.Tensor:
         model_input = self.add_mask_channels(latents)
         return self.forward(model_input, t, text_embeddings)
 
     def add_mask_channels(self, latents):
         batch_size = latents.size(0)
         # duplicate mask and latents for each batch
-        mask = einops.repeat(self.mask, 'b c h w -> (repeat b) c h w', repeat=batch_size)
-        image_latents = einops.repeat(self.initial_image_latents, 'b c h w -> (repeat b) c h w', repeat=batch_size)
+        mask = einops.repeat(
+            self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size
+        )
+        image_latents = einops.repeat(
+            self.initial_image_latents, "b c h w -> (repeat b) c h w", repeat=batch_size
+        )
         # add mask and image as additional channels
-        model_input, _ = einops.pack([latents, mask, image_latents], 'b * h w')
+        model_input, _ = einops.pack([latents, mask, image_latents], "b * h w")
         return model_input
 
 
 def are_like_tensors(a: torch.Tensor, b: object) -> bool:
-    return (
-        isinstance(b, torch.Tensor)
-        and (a.size() == b.size())
-    )
+    return isinstance(b, torch.Tensor) and (a.size() == b.size())
+
 
 @dataclass
 class AddsMaskGuidance:
@@ -96,7 +110,9 @@ class AddsMaskGuidance:
     noise: torch.Tensor
     _debug: Optional[Callable] = None
 
-    def __call__(self, step_output: BaseOutput | SchedulerOutput, t: torch.Tensor, conditioning) -> BaseOutput:
+    def __call__(
+        self, step_output: BaseOutput | SchedulerOutput, t: torch.Tensor, conditioning
+    ) -> BaseOutput:
         output_class = step_output.__class__  # We'll create a new one with masked data.
 
         # The problem with taking SchedulerOutput instead of the model output is that we're less certain what's in it.
@@ -106,30 +122,41 @@ class AddsMaskGuidance:
         prev_sample = step_output[0]
         # Mask anything that has the same shape as prev_sample, return others as-is.
         return output_class(
-            {k: (self.apply_mask(v, self._t_for_field(k, t))
-                 if are_like_tensors(prev_sample, v) else v)
-            for k, v in step_output.items()}
+            {
+                k: (
+                    self.apply_mask(v, self._t_for_field(k, t))
+                    if are_like_tensors(prev_sample, v)
+                    else v
+                )
+                for k, v in step_output.items()
+            }
         )
 
-    def _t_for_field(self, field_name:str, t):
+    def _t_for_field(self, field_name: str, t):
         if field_name == "pred_original_sample":
             return torch.zeros_like(t, dtype=t.dtype)  # it represents t=0
         return t
 
     def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
         batch_size = latents.size(0)
-        mask = einops.repeat(self.mask, 'b c h w -> (repeat b) c h w', repeat=batch_size)
+        mask = einops.repeat(
+            self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size
+        )
         if t.dim() == 0:
             # some schedulers expect t to be one-dimensional.
             # TODO: file diffusers bug about inconsistency?
-            t = einops.repeat(t, '-> batch', batch=batch_size)
+            t = einops.repeat(t, "-> batch", batch=batch_size)
         # Noise shouldn't be re-randomized between steps here. The multistep schedulers
         # get very confused about what is happening from step to step when we do that.
         mask_latents = self.scheduler.add_noise(self.mask_latents, self.noise, t)
         # TODO: Do we need to also apply scheduler.scale_model_input? Or is add_noise appropriately scaled already?
         # mask_latents = self.scheduler.scale_model_input(mask_latents, t)
-        mask_latents = einops.repeat(mask_latents, 'b c h w -> (repeat b) c h w', repeat=batch_size)
-        masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype))
+        mask_latents = einops.repeat(
+            mask_latents, "b c h w -> (repeat b) c h w", repeat=batch_size
+        )
+        masked_input = torch.lerp(
+            mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype)
+        )
         if self._debug:
             self._debug(masked_input, f"t={t} lerped")
         return masked_input
@@ -139,7 +166,9 @@ def trim_to_multiple_of(*args, multiple_of=8):
     return tuple((x - x % multiple_of) for x in args)
 
 
-def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True, multiple_of=8) -> torch.FloatTensor:
+def image_resized_to_grid_as_tensor(
+    image: PIL.Image.Image, normalize: bool = True, multiple_of=8
+) -> torch.FloatTensor:
     """
 
     :param image: input image
@@ -147,10 +176,12 @@ def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True
     :param multiple_of: resize the input so both dimensions are a multiple of this
     """
     w, h = trim_to_multiple_of(*image.size)
-    transformation = T.Compose([
-        T.Resize((h, w), T.InterpolationMode.LANCZOS),
-        T.ToTensor(),
-    ])
+    transformation = T.Compose(
+        [
+            T.Resize((h, w), T.InterpolationMode.LANCZOS),
+            T.ToTensor(),
+        ]
+    )
     tensor = transformation(image)
     if normalize:
         tensor = tensor * 2.0 - 1.0
@@ -160,9 +191,11 @@ def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True
 def is_inpainting_model(unet: UNet2DConditionModel):
     return unet.conv_in.in_channels == 9
 
-CallbackType = TypeVar('CallbackType')
-ReturnType = TypeVar('ReturnType')
-ParamType = ParamSpec('ParamType')
+
+CallbackType = TypeVar("CallbackType")
+ReturnType = TypeVar("ReturnType")
+ParamType = ParamSpec("ParamType")
+
 
 @dataclass(frozen=True)
 class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]):
@@ -171,9 +204,12 @@ class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]):
     generator_method: Callable[ParamType, ReturnType]
     callback_arg_type: Type[CallbackType]
 
-    def __call__(self, *args: ParamType.args,
-                 callback:Callable[[CallbackType], Any]=None,
-                 **kwargs: ParamType.kwargs) -> ReturnType:
+    def __call__(
+        self,
+        *args: ParamType.args,
+        callback: Callable[[CallbackType], Any] = None,
+        **kwargs: ParamType.kwargs,
+    ) -> ReturnType:
         result = None
         for result in self.generator_method(*args, **kwargs):
             if callback is not None and isinstance(result, self.callback_arg_type):
@@ -218,6 +254,7 @@ class ConditioningData:
                 scheduler_args[name] = value
         return dataclasses.replace(self, scheduler_args=scheduler_args)
 
+
 @dataclass
 class InvokeAIStableDiffusionPipelineOutput(StableDiffusionPipelineOutput):
     r"""
@@ -275,10 +312,18 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         safety_checker: Optional[StableDiffusionSafetyChecker],
         feature_extractor: Optional[CLIPFeatureExtractor],
         requires_safety_checker: bool = False,
-        precision: str = 'float32',
+        precision: str = "float32",
     ):
-        super().__init__(vae, text_encoder, tokenizer, unet, scheduler,
-                         safety_checker, feature_extractor, requires_safety_checker)
+        super().__init__(
+            vae,
+            text_encoder,
+            tokenizer,
+            unet,
+            scheduler,
+            safety_checker,
+            feature_extractor,
+            requires_safety_checker,
+        )
 
         self.register_modules(
             vae=vae,
@@ -289,27 +334,34 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
         )
-        self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward, is_running_diffusers=True)
-        use_full_precision = (precision == 'float32' or precision == 'autocast')
-        self.textual_inversion_manager = TextualInversionManager(tokenizer=self.tokenizer,
-                                                                 text_encoder=self.text_encoder,
-                                                                 full_precision=use_full_precision)
+        self.invokeai_diffuser = InvokeAIDiffuserComponent(
+            self.unet, self._unet_forward, is_running_diffusers=True
+        )
+        use_full_precision = precision == "float32" or precision == "autocast"
+        self.textual_inversion_manager = TextualInversionManager(
+            tokenizer=self.tokenizer,
+            text_encoder=self.text_encoder,
+            full_precision=use_full_precision,
+        )
         # InvokeAI's interface for text embeddings and whatnot
         self.embeddings_provider = EmbeddingsProvider(
             tokenizer=self.tokenizer,
             text_encoder=self.text_encoder,
-            textual_inversion_manager=self.textual_inversion_manager
+            textual_inversion_manager=self.textual_inversion_manager,
         )
 
         self._model_group = FullyLoadedModelGroup(self.unet.device)
         self._model_group.install(*self._submodels)
 
-
     def _adjust_memory_efficient_attention(self, latents: torch.Tensor):
         """
         if xformers is available, use it, otherwise use sliced attention.
         """
-        if torch.cuda.is_available() and is_xformers_available() and not Globals.disable_xformers:
+        if (
+            torch.cuda.is_available()
+            and is_xformers_available()
+            and not Globals.disable_xformers
+        ):
             self.enable_xformers_memory_efficient_attention()
         else:
             if torch.backends.mps.is_available():
@@ -318,25 +370,32 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 # fix is in https://github.com/kulinseth/pytorch/pull/222 but no idea when it will get merged to pytorch mainline.
                 pass
             else:
-                if self.device.type == 'cpu' or self.device.type == 'mps':
+                if self.device.type == "cpu" or self.device.type == "mps":
                     mem_free = psutil.virtual_memory().free
-                elif self.device.type == 'cuda':
+                elif self.device.type == "cuda":
                     mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.device))
                 else:
                     raise ValueError(f"unrecognized device {self.device}")
                 # input tensor of [1, 4, h/8, w/8]
                 # output tensor of [16, (h/8 * w/8), (h/8 * w/8)]
-                bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4
-                max_size_required_for_baddbmm = \
-                    16 * \
-                    latents.size(dim=2) * latents.size(dim=3) * latents.size(dim=2) * latents.size(dim=3) * \
-                    bytes_per_element_needed_for_baddbmm_duplication
-                if max_size_required_for_baddbmm > (mem_free * 3.0 / 4.0): # 3.3 / 4.0 is from old Invoke code
-                    self.enable_attention_slicing(slice_size='max')
+                bytes_per_element_needed_for_baddbmm_duplication = (
+                    latents.element_size() + 4
+                )
+                max_size_required_for_baddbmm = (
+                    16
+                    * latents.size(dim=2)
+                    * latents.size(dim=3)
+                    * latents.size(dim=2)
+                    * latents.size(dim=3)
+                    * bytes_per_element_needed_for_baddbmm_duplication
+                )
+                if max_size_required_for_baddbmm > (
+                    mem_free * 3.0 / 4.0
+                ):  # 3.3 / 4.0 is from old Invoke code
+                    self.enable_attention_slicing(slice_size="max")
                 else:
                     self.disable_attention_slicing()
 
-
     def enable_offload_submodels(self, device: torch.device):
         """
         Offload each submodel when it's not in use.
@@ -398,12 +457,16 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         values = [getattr(self, name) for name in module_names.keys()]
         return [m for m in values if isinstance(m, torch.nn.Module)]
 
-    def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
-                              conditioning_data: ConditioningData,
-                              *,
-                              noise: torch.Tensor,
-                              callback: Callable[[PipelineIntermediateState], None]=None,
-                              run_id=None) -> InvokeAIStableDiffusionPipelineOutput:
+    def image_from_embeddings(
+        self,
+        latents: torch.Tensor,
+        num_inference_steps: int,
+        conditioning_data: ConditioningData,
+        *,
+        noise: torch.Tensor,
+        callback: Callable[[PipelineIntermediateState], None] = None,
+        run_id=None,
+    ) -> InvokeAIStableDiffusionPipelineOutput:
         r"""
         Function invoked when calling the pipeline for generation.
 
@@ -417,71 +480,104 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         :param run_id:
         """
         result_latents, result_attention_map_saver = self.latents_from_embeddings(
-            latents, num_inference_steps,
+            latents,
+            num_inference_steps,
             conditioning_data,
             noise=noise,
             run_id=run_id,
-            callback=callback)
+            callback=callback,
+        )
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
 
         with torch.inference_mode():
             image = self.decode_latents(result_latents)
-            output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_map_saver)
+            output = InvokeAIStableDiffusionPipelineOutput(
+                images=image,
+                nsfw_content_detected=[],
+                attention_map_saver=result_attention_map_saver,
+            )
             return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
-    def latents_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
-                                conditioning_data: ConditioningData,
-                                *,
-                                noise: torch.Tensor,
-                                timesteps=None,
-                                additional_guidance: List[Callable] = None, run_id=None,
-                                callback: Callable[[PipelineIntermediateState], None] = None
-                                ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
+    def latents_from_embeddings(
+        self,
+        latents: torch.Tensor,
+        num_inference_steps: int,
+        conditioning_data: ConditioningData,
+        *,
+        noise: torch.Tensor,
+        timesteps=None,
+        additional_guidance: List[Callable] = None,
+        run_id=None,
+        callback: Callable[[PipelineIntermediateState], None] = None,
+    ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
         if timesteps is None:
-            self.scheduler.set_timesteps(num_inference_steps, device=self._model_group.device_for(self.unet))
+            self.scheduler.set_timesteps(
+                num_inference_steps, device=self._model_group.device_for(self.unet)
+            )
             timesteps = self.scheduler.timesteps
-        infer_latents_from_embeddings = GeneratorToCallbackinator(self.generate_latents_from_embeddings, PipelineIntermediateState)
+        infer_latents_from_embeddings = GeneratorToCallbackinator(
+            self.generate_latents_from_embeddings, PipelineIntermediateState
+        )
         result: PipelineIntermediateState = infer_latents_from_embeddings(
-            latents, timesteps, conditioning_data,
+            latents,
+            timesteps,
+            conditioning_data,
             noise=noise,
             additional_guidance=additional_guidance,
             run_id=run_id,
-            callback=callback)
+            callback=callback,
+        )
         return result.latents, result.attention_map_saver
 
-    def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps,
-                                         conditioning_data: ConditioningData,
-                                         *,
-                                         noise: torch.Tensor,
-                                         run_id: str = None,
-                                         additional_guidance: List[Callable] = None):
+    def generate_latents_from_embeddings(
+        self,
+        latents: torch.Tensor,
+        timesteps,
+        conditioning_data: ConditioningData,
+        *,
+        noise: torch.Tensor,
+        run_id: str = None,
+        additional_guidance: List[Callable] = None,
+    ):
         self._adjust_memory_efficient_attention(latents)
         if run_id is None:
             run_id = secrets.token_urlsafe(self.ID_LENGTH)
         if additional_guidance is None:
             additional_guidance = []
         extra_conditioning_info = conditioning_data.extra
-        with self.invokeai_diffuser.custom_attention_context(extra_conditioning_info=extra_conditioning_info,
-                                                             step_count=len(self.scheduler.timesteps)
-                                                             ):
-
-            yield PipelineIntermediateState(run_id=run_id, step=-1, timestep=self.scheduler.num_train_timesteps,
-                                            latents=latents)
+        with self.invokeai_diffuser.custom_attention_context(
+            extra_conditioning_info=extra_conditioning_info,
+            step_count=len(self.scheduler.timesteps),
+        ):
+            yield PipelineIntermediateState(
+                run_id=run_id,
+                step=-1,
+                timestep=self.scheduler.num_train_timesteps,
+                latents=latents,
+            )
 
             batch_size = latents.shape[0]
-            batched_t = torch.full((batch_size,), timesteps[0],
-                                   dtype=timesteps.dtype, device=self._model_group.device_for(self.unet))
+            batched_t = torch.full(
+                (batch_size,),
+                timesteps[0],
+                dtype=timesteps.dtype,
+                device=self._model_group.device_for(self.unet),
+            )
             latents = self.scheduler.add_noise(latents, noise, batched_t)
 
             attention_map_saver: Optional[AttentionMapSaver] = None
 
             for i, t in enumerate(self.progress_bar(timesteps)):
                 batched_t.fill_(t)
-                step_output = self.step(batched_t, latents, conditioning_data,
-                                        step_index=i,
-                                        total_step_count=len(timesteps),
-                                        additional_guidance=additional_guidance)
+                step_output = self.step(
+                    batched_t,
+                    latents,
+                    conditioning_data,
+                    step_index=i,
+                    total_step_count=len(timesteps),
+                    additional_guidance=additional_guidance,
+                )
                 latents = step_output.prev_sample
 
                 latents = self.invokeai_diffuser.do_latent_postprocessing(
@@ -489,28 +585,39 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     latents=latents,
                     sigma=batched_t,
                     step_index=i,
-                    total_step_count=len(timesteps)
+                    total_step_count=len(timesteps),
                 )
 
-                predicted_original = getattr(step_output, 'pred_original_sample', None)
+                predicted_original = getattr(step_output, "pred_original_sample", None)
 
                 # TODO resuscitate attention map saving
-                #if i == len(timesteps)-1 and extra_conditioning_info is not None:
+                # if i == len(timesteps)-1 and extra_conditioning_info is not None:
                 #    eos_token_index = extra_conditioning_info.tokens_count_including_eos_bos - 1
                 #    attention_map_token_ids = range(1, eos_token_index)
                 #    attention_map_saver = AttentionMapSaver(token_ids=attention_map_token_ids, latents_shape=latents.shape[-2:])
                 #    self.invokeai_diffuser.setup_attention_map_saving(attention_map_saver)
 
-                yield PipelineIntermediateState(run_id=run_id, step=i, timestep=int(t), latents=latents,
-                                                predicted_original=predicted_original, attention_map_saver=attention_map_saver)
+                yield PipelineIntermediateState(
+                    run_id=run_id,
+                    step=i,
+                    timestep=int(t),
+                    latents=latents,
+                    predicted_original=predicted_original,
+                    attention_map_saver=attention_map_saver,
+                )
 
             return latents, attention_map_saver
 
     @torch.inference_mode()
-    def step(self, t: torch.Tensor, latents: torch.Tensor,
-             conditioning_data: ConditioningData,
-             step_index:int, total_step_count:int,
-             additional_guidance: List[Callable] = None):
+    def step(
+        self,
+        t: torch.Tensor,
+        latents: torch.Tensor,
+        conditioning_data: ConditioningData,
+        step_index: int,
+        total_step_count: int,
+        additional_guidance: List[Callable] = None,
+    ):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
 
@@ -523,16 +630,19 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         # predict the noise residual
         noise_pred = self.invokeai_diffuser.do_diffusion_step(
-            latent_model_input, t,
-            conditioning_data.unconditioned_embeddings, conditioning_data.text_embeddings,
+            latent_model_input,
+            t,
+            conditioning_data.unconditioned_embeddings,
+            conditioning_data.text_embeddings,
             conditioning_data.guidance_scale,
             step_index=step_index,
             total_step_count=total_step_count,
         )
 
         # compute the previous noisy sample x_t -> x_t-1
-        step_output = self.scheduler.step(noise_pred, timestep, latents,
-                                          **conditioning_data.scheduler_args)
+        step_output = self.scheduler.step(
+            noise_pred, timestep, latents, **conditioning_data.scheduler_args
+        )
 
         # TODO: this additional_guidance extension point feels redundant with InvokeAIDiffusionComponent.
         #    But the way things are now, scheduler runs _after_ that, so there was
@@ -542,7 +652,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         return step_output
 
-    def _unet_forward(self, latents, t, text_embeddings, cross_attention_kwargs: Optional[dict[str,Any]] = None):
+    def _unet_forward(
+        self,
+        latents,
+        t,
+        text_embeddings,
+        cross_attention_kwargs: Optional[dict[str, Any]] = None,
+    ):
         """predict the noise residual"""
         if is_inpainting_model(self.unet) and latents.size(1) == 4:
             # Pad out normal non-inpainting inputs for an inpainting model.
@@ -551,67 +667,100 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             #     use of AddsMaskLatents.
             latents = AddsMaskLatents(
                 self._unet_forward,
-                mask=torch.ones_like(latents[:1, :1], device=latents.device, dtype=latents.dtype),
-                initial_image_latents=torch.zeros_like(latents[:1], device=latents.device, dtype=latents.dtype)
+                mask=torch.ones_like(
+                    latents[:1, :1], device=latents.device, dtype=latents.dtype
+                ),
+                initial_image_latents=torch.zeros_like(
+                    latents[:1], device=latents.device, dtype=latents.dtype
+                ),
             ).add_mask_channels(latents)
 
         # First three args should be positional, not keywords, so torch hooks can see them.
-        return self.unet(latents, t, text_embeddings,
-                         cross_attention_kwargs=cross_attention_kwargs).sample
+        return self.unet(
+            latents, t, text_embeddings, cross_attention_kwargs=cross_attention_kwargs
+        ).sample
 
-    def img2img_from_embeddings(self,
-                                init_image: Union[torch.FloatTensor, PIL.Image.Image],
-                                strength: float,
-                                num_inference_steps: int,
-                                conditioning_data: ConditioningData,
-                                *, callback: Callable[[PipelineIntermediateState], None] = None,
-                                run_id=None,
-                                noise_func=None
-                                ) -> InvokeAIStableDiffusionPipelineOutput:
+    def img2img_from_embeddings(
+        self,
+        init_image: Union[torch.FloatTensor, PIL.Image.Image],
+        strength: float,
+        num_inference_steps: int,
+        conditioning_data: ConditioningData,
+        *,
+        callback: Callable[[PipelineIntermediateState], None] = None,
+        run_id=None,
+        noise_func=None,
+    ) -> InvokeAIStableDiffusionPipelineOutput:
         if isinstance(init_image, PIL.Image.Image):
-            init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
+            init_image = image_resized_to_grid_as_tensor(init_image.convert("RGB"))
 
         if init_image.dim() == 3:
-            init_image = einops.rearrange(init_image, 'c h w -> 1 c h w')
+            init_image = einops.rearrange(init_image, "c h w -> 1 c h w")
 
         # 6. Prepare latent variables
         initial_latents = self.non_noised_latents_from_image(
-            init_image, device=self._model_group.device_for(self.unet),
-            dtype=self.unet.dtype)
+            init_image,
+            device=self._model_group.device_for(self.unet),
+            dtype=self.unet.dtype,
+        )
         noise = noise_func(initial_latents)
 
-        return self.img2img_from_latents_and_embeddings(initial_latents, num_inference_steps,
-                                                        conditioning_data,
-                                                        strength,
-                                                        noise, run_id, callback)
+        return self.img2img_from_latents_and_embeddings(
+            initial_latents,
+            num_inference_steps,
+            conditioning_data,
+            strength,
+            noise,
+            run_id,
+            callback,
+        )
 
-    def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_steps,
-                                            conditioning_data: ConditioningData,
-                                            strength,
-                                            noise: torch.Tensor, run_id=None, callback=None
-                                            ) -> InvokeAIStableDiffusionPipelineOutput:
-        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength,
-                                                  device=self._model_group.device_for(self.unet))
+    def img2img_from_latents_and_embeddings(
+        self,
+        initial_latents,
+        num_inference_steps,
+        conditioning_data: ConditioningData,
+        strength,
+        noise: torch.Tensor,
+        run_id=None,
+        callback=None,
+    ) -> InvokeAIStableDiffusionPipelineOutput:
+        timesteps, _ = self.get_img2img_timesteps(
+            num_inference_steps,
+            strength,
+            device=self._model_group.device_for(self.unet),
+        )
         result_latents, result_attention_maps = self.latents_from_embeddings(
-            initial_latents, num_inference_steps, conditioning_data,
+            initial_latents,
+            num_inference_steps,
+            conditioning_data,
             timesteps=timesteps,
             noise=noise,
             run_id=run_id,
-            callback=callback)
+            callback=callback,
+        )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
 
         with torch.inference_mode():
             image = self.decode_latents(result_latents)
-            output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_maps)
+            output = InvokeAIStableDiffusionPipelineOutput(
+                images=image,
+                nsfw_content_detected=[],
+                attention_map_saver=result_attention_maps,
+            )
             return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
-    def get_img2img_timesteps(self, num_inference_steps: int, strength: float, device) -> (torch.Tensor, int):
+    def get_img2img_timesteps(
+        self, num_inference_steps: int, strength: float, device
+    ) -> (torch.Tensor, int):
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         assert img2img_pipeline.scheduler is self.scheduler
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps, adjusted_steps = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
+        timesteps, adjusted_steps = img2img_pipeline.get_timesteps(
+            num_inference_steps, strength, device=device
+        )
         # Workaround for low strength resulting in zero timesteps.
         # TODO: submit upstream fix for zero-step img2img
         if timesteps.numel() == 0:
@@ -620,21 +769,22 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         return timesteps, adjusted_steps
 
     def inpaint_from_embeddings(
-            self,
-            init_image: torch.FloatTensor,
-            mask: torch.FloatTensor,
-            strength: float,
-            num_inference_steps: int,
-            conditioning_data: ConditioningData,
-            *, callback: Callable[[PipelineIntermediateState], None] = None,
-            run_id=None,
-            noise_func=None,
-            ) -> InvokeAIStableDiffusionPipelineOutput:
+        self,
+        init_image: torch.FloatTensor,
+        mask: torch.FloatTensor,
+        strength: float,
+        num_inference_steps: int,
+        conditioning_data: ConditioningData,
+        *,
+        callback: Callable[[PipelineIntermediateState], None] = None,
+        run_id=None,
+        noise_func=None,
+    ) -> InvokeAIStableDiffusionPipelineOutput:
         device = self._model_group.device_for(self.unet)
         latents_dtype = self.unet.dtype
 
         if isinstance(init_image, PIL.Image.Image):
-            init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
+            init_image = image_resized_to_grid_as_tensor(init_image.convert("RGB"))
 
         init_image = init_image.to(device=device, dtype=latents_dtype)
         mask = mask.to(device=device, dtype=latents_dtype)
@@ -642,18 +792,23 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if init_image.dim() == 3:
             init_image = init_image.unsqueeze(0)
 
-        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength, device=device)
+        timesteps, _ = self.get_img2img_timesteps(
+            num_inference_steps, strength, device=device
+        )
 
         # 6. Prepare latent variables
         # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
         # because we have our own noise function
-        init_image_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
+        init_image_latents = self.non_noised_latents_from_image(
+            init_image, device=device, dtype=latents_dtype
+        )
         noise = noise_func(init_image_latents)
 
         if mask.dim() == 3:
             mask = mask.unsqueeze(0)
-        latent_mask = tv_resize(mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR) \
-            .to(device=device, dtype=latents_dtype)
+        latent_mask = tv_resize(
+            mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR
+        ).to(device=device, dtype=latents_dtype)
 
         guidance: List[Callable] = []
 
@@ -661,20 +816,30 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             # You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint
             # (that's why there's a mask!) but it seems to really want that blanked out.
             masked_init_image = init_image * torch.where(mask < 0.5, 1, 0)
-            masked_latents = self.non_noised_latents_from_image(masked_init_image, device=device, dtype=latents_dtype)
+            masked_latents = self.non_noised_latents_from_image(
+                masked_init_image, device=device, dtype=latents_dtype
+            )
 
             # TODO: we should probably pass this in so we don't have to try/finally around setting it.
-            self.invokeai_diffuser.model_forward_callback = \
-                AddsMaskLatents(self._unet_forward, latent_mask, masked_latents)
+            self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(
+                self._unet_forward, latent_mask, masked_latents
+            )
         else:
-            guidance.append(AddsMaskGuidance(latent_mask, init_image_latents, self.scheduler, noise))
+            guidance.append(
+                AddsMaskGuidance(latent_mask, init_image_latents, self.scheduler, noise)
+            )
 
         try:
             result_latents, result_attention_maps = self.latents_from_embeddings(
-                init_image_latents, num_inference_steps,
-                conditioning_data, noise=noise, timesteps=timesteps,
+                init_image_latents,
+                num_inference_steps,
+                conditioning_data,
+                noise=noise,
+                timesteps=timesteps,
                 additional_guidance=guidance,
-                run_id=run_id, callback=callback)
+                run_id=run_id,
+                callback=callback,
+            )
         finally:
             self.invokeai_diffuser.model_forward_callback = self._unet_forward
 
@@ -683,13 +848,17 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         with torch.inference_mode():
             image = self.decode_latents(result_latents)
-            output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_maps)
+            output = InvokeAIStableDiffusionPipelineOutput(
+                images=image,
+                nsfw_content_detected=[],
+                attention_map_saver=result_attention_maps,
+            )
             return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
     def non_noised_latents_from_image(self, init_image, *, device: torch.device, dtype):
         init_image = init_image.to(device=device, dtype=dtype)
         with torch.inference_mode():
-            if device.type == 'mps':
+            if device.type == "mps":
                 # workaround for torch MPS bug that has been fixed in https://github.com/kulinseth/pytorch/pull/222
                 # TODO remove this workaround once kulinseth#222 is merged to pytorch mainline
                 self.vae.to(CPU_DEVICE)
@@ -697,8 +866,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             else:
                 self._model_group.load(self.vae)
             init_latent_dist = self.vae.encode(init_image).latent_dist
-            init_latents = init_latent_dist.sample().to(dtype=dtype)  # FIXME: uses torch.randn. make reproducible!
-            if device.type == 'mps':
+            init_latents = init_latent_dist.sample().to(
+                dtype=dtype
+            )  # FIXME: uses torch.randn. make reproducible!
+            if device.type == "mps":
                 self.vae.to(device)
                 init_latents = init_latents.to(device)
 
@@ -707,14 +878,18 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
     def check_for_safety(self, output, dtype):
         with torch.inference_mode():
-            screened_images, has_nsfw_concept = self.run_safety_checker(output.images, dtype=dtype)
+            screened_images, has_nsfw_concept = self.run_safety_checker(
+                output.images, dtype=dtype
+            )
         screened_attention_map_saver = None
         if has_nsfw_concept is None or not has_nsfw_concept:
             screened_attention_map_saver = output.attention_map_saver
-        return InvokeAIStableDiffusionPipelineOutput(screened_images,
-                                                     has_nsfw_concept,
-                                                     # block the attention maps if NSFW content is detected
-                                                     attention_map_saver=screened_attention_map_saver)
+        return InvokeAIStableDiffusionPipelineOutput(
+            screened_images,
+            has_nsfw_concept,
+            # block the attention maps if NSFW content is detected
+            attention_map_saver=screened_attention_map_saver,
+        )
 
     def run_safety_checker(self, image, device=None, dtype=None):
         # overriding to use the model group for device info instead of requiring the caller to know.
@@ -723,15 +898,18 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         return super().run_safety_checker(image, device, dtype)
 
     @torch.inference_mode()
-    def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fragment_weights=None):
+    def get_learned_conditioning(
+        self, c: List[List[str]], *, return_tokens=True, fragment_weights=None
+    ):
         """
-        Compatibility function for ldm.models.diffusion.ddpm.LatentDiffusion.
+        Compatibility function for invokeai.models.diffusion.ddpm.LatentDiffusion.
         """
         return self.embeddings_provider.get_embeddings_for_weighted_prompt_fragments(
             text_batch=c,
             fragment_weights_batch=fragment_weights,
             should_return_tokens=return_tokens,
-            device=self._model_group.device_for(self.unet))
+            device=self._model_group.device_for(self.unet),
+        )
 
     @property
     def cond_stage_model(self):
@@ -760,6 +938,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     def debug_latents(self, latents, msg):
         with torch.inference_mode():
             from ldm.util import debug_image
+
             decoded = self.numpy_to_pil(self.decode_latents(latents))
             for i, img in enumerate(decoded):
-                debug_image(img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True)
+                debug_image(
+                    img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True
+                )

invokeai/backend/stable_diffusion/diffusion/__init__.py

@@ -0,0 +1,6 @@
+"""
+Initialization file for invokeai.models.diffusion
+"""
+from .cross_attention_control import InvokeAICrossAttentionMixin
+from .cross_attention_map_saving import AttentionMapSaver
+from .shared_invokeai_diffusion import InvokeAIDiffuserComponent, PostprocessingSettings

invokeai/backend/stable_diffusion/diffusion/classifier.py

@@ -1,22 +1,19 @@
 import os
-import torch
+from copy import deepcopy
+from glob import glob
+
 import pytorch_lightning as pl
+import torch
+from einops import rearrange
+from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
+from ldm.util import default, instantiate_from_config, ismap, log_txt_as_img
+from natsort import natsorted
 from omegaconf import OmegaConf
 from torch.nn import functional as F
 from torch.optim import AdamW
 from torch.optim.lr_scheduler import LambdaLR
-from copy import deepcopy
-from einops import rearrange
-from glob import glob
-from natsort import natsorted
 
-from ldm.modules.diffusionmodules.openaimodel import (
-    EncoderUNetModel,
-    UNetModel,
-)
-from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
-
-__models__ = {'class_label': EncoderUNetModel, 'segmentation': UNetModel}
+__models__ = {"class_label": EncoderUNetModel, "segmentation": UNetModel}
 
 
 def disabled_train(self, mode=True):
@@ -31,13 +28,13 @@ class NoisyLatentImageClassifier(pl.LightningModule):
         diffusion_path,
         num_classes,
         ckpt_path=None,
-        pool='attention',
+        pool="attention",
         label_key=None,
         diffusion_ckpt_path=None,
         scheduler_config=None,
         weight_decay=1.0e-2,
         log_steps=10,
-        monitor='val/loss',
+        monitor="val/loss",
         *args,
         **kwargs,
     ):
@@ -45,30 +42,26 @@ class NoisyLatentImageClassifier(pl.LightningModule):
         self.num_classes = num_classes
         # get latest config of diffusion model
         diffusion_config = natsorted(
-            glob(os.path.join(diffusion_path, 'configs', '*-project.yaml'))
+            glob(os.path.join(diffusion_path, "configs", "*-project.yaml"))
         )[-1]
         self.diffusion_config = OmegaConf.load(diffusion_config).model
         self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
         self.load_diffusion()
 
         self.monitor = monitor
-        self.numd = (
-            self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
-        )
-        self.log_time_interval = (
-            self.diffusion_model.num_timesteps // log_steps
-        )
+        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
+        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
         self.log_steps = log_steps
 
         self.label_key = (
             label_key
-            if not hasattr(self.diffusion_model, 'cond_stage_key')
+            if not hasattr(self.diffusion_model, "cond_stage_key")
             else self.diffusion_model.cond_stage_key
         )
 
         assert (
             self.label_key is not None
-        ), 'label_key neither in diffusion model nor in model.params'
+        ), "label_key neither in diffusion model nor in model.params"
 
         if self.label_key not in __models__:
             raise NotImplementedError()
@@ -80,14 +73,14 @@ class NoisyLatentImageClassifier(pl.LightningModule):
         self.weight_decay = weight_decay
 
     def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location='cpu')
-        if 'state_dict' in list(sd.keys()):
-            sd = sd['state_dict']
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
         keys = list(sd.keys())
         for k in keys:
             for ik in ignore_keys:
                 if k.startswith(ik):
-                    print('Deleting key {} from state_dict.'.format(k))
+                    print("Deleting key {} from state_dict.".format(k))
                     del sd[k]
         missing, unexpected = (
             self.load_state_dict(sd, strict=False)
@@ -95,12 +88,12 @@ class NoisyLatentImageClassifier(pl.LightningModule):
             else self.model.load_state_dict(sd, strict=False)
         )
         print(
-            f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
+            f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
         )
         if len(missing) > 0:
-            print(f'Missing Keys: {missing}')
+            print(f"Missing Keys: {missing}")
         if len(unexpected) > 0:
-            print(f'Unexpected Keys: {unexpected}')
+            print(f"Unexpected Keys: {unexpected}")
 
     def load_diffusion(self):
         model = instantiate_from_config(self.diffusion_config)
@@ -110,24 +103,22 @@ class NoisyLatentImageClassifier(pl.LightningModule):
             param.requires_grad = False
 
     def load_classifier(self, ckpt_path, pool):
-        model_config = deepcopy(
-            self.diffusion_config.params.unet_config.params
-        )
+        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
         model_config.in_channels = (
             self.diffusion_config.params.unet_config.params.out_channels
         )
         model_config.out_channels = self.num_classes
-        if self.label_key == 'class_label':
+        if self.label_key == "class_label":
             model_config.pool = pool
 
         self.model = __models__[self.label_key](**model_config)
         if ckpt_path is not None:
             print(
-                '#####################################################################'
+                "#####################################################################"
             )
             print(f'load from ckpt "{ckpt_path}"')
             print(
-                '#####################################################################'
+                "#####################################################################"
             )
             self.init_from_ckpt(ckpt_path)
 
@@ -137,9 +128,7 @@ class NoisyLatentImageClassifier(pl.LightningModule):
         continuous_sqrt_alpha_cumprod = None
         if self.diffusion_model.use_continuous_noise:
             continuous_sqrt_alpha_cumprod = (
-                self.diffusion_model.sample_continuous_noise_level(
-                    x.shape[0], t + 1
-                )
+                self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
             )
             # todo: make sure t+1 is correct here
 
@@ -158,7 +147,7 @@ class NoisyLatentImageClassifier(pl.LightningModule):
         x = batch[k]
         if len(x.shape) == 3:
             x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
+        x = rearrange(x, "b h w c -> b c h w")
         x = x.to(memory_format=torch.contiguous_format).float()
         return x
 
@@ -166,45 +155,41 @@ class NoisyLatentImageClassifier(pl.LightningModule):
     def get_conditioning(self, batch, k=None):
         if k is None:
             k = self.label_key
-        assert k is not None, 'Needs to provide label key'
+        assert k is not None, "Needs to provide label key"
 
         targets = batch[k].to(self.device)
 
-        if self.label_key == 'segmentation':
-            targets = rearrange(targets, 'b h w c -> b c h w')
+        if self.label_key == "segmentation":
+            targets = rearrange(targets, "b h w c -> b c h w")
             for down in range(self.numd):
                 h, w = targets.shape[-2:]
-                targets = F.interpolate(
-                    targets, size=(h // 2, w // 2), mode='nearest'
-                )
+                targets = F.interpolate(targets, size=(h // 2, w // 2), mode="nearest")
 
             # targets = rearrange(targets,'b c h w -> b h w c')
 
         return targets
 
-    def compute_top_k(self, logits, labels, k, reduction='mean'):
+    def compute_top_k(self, logits, labels, k, reduction="mean"):
         _, top_ks = torch.topk(logits, k, dim=1)
-        if reduction == 'mean':
-            return (
-                (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
-            )
-        elif reduction == 'none':
+        if reduction == "mean":
+            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
+        elif reduction == "none":
             return (top_ks == labels[:, None]).float().sum(dim=-1)
 
     def on_train_epoch_start(self):
         # save some memory
-        self.diffusion_model.model.to('cpu')
+        self.diffusion_model.model.to("cpu")
 
     @torch.no_grad()
     def write_logs(self, loss, logits, targets):
-        log_prefix = 'train' if self.training else 'val'
+        log_prefix = "train" if self.training else "val"
         log = {}
-        log[f'{log_prefix}/loss'] = loss.mean()
-        log[f'{log_prefix}/acc@1'] = self.compute_top_k(
-            logits, targets, k=1, reduction='mean'
+        log[f"{log_prefix}/loss"] = loss.mean()
+        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
+            logits, targets, k=1, reduction="mean"
         )
-        log[f'{log_prefix}/acc@5'] = self.compute_top_k(
-            logits, targets, k=5, reduction='mean'
+        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
+            logits, targets, k=5, reduction="mean"
         )
 
         self.log_dict(
@@ -214,19 +199,17 @@ class NoisyLatentImageClassifier(pl.LightningModule):
             on_step=self.training,
             on_epoch=True,
         )
+        self.log("loss", log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
         self.log(
-            'loss', log[f'{log_prefix}/loss'], prog_bar=True, logger=False
-        )
-        self.log(
-            'global_step',
+            "global_step",
             self.global_step,
             logger=False,
             on_epoch=False,
             prog_bar=True,
         )
-        lr = self.optimizers().param_groups[0]['lr']
+        lr = self.optimizers().param_groups[0]["lr"]
         self.log(
-            'lr_abs',
+            "lr_abs",
             lr,
             on_step=True,
             logger=True,
@@ -249,13 +232,11 @@ class NoisyLatentImageClassifier(pl.LightningModule):
                 device=self.device,
             ).long()
         else:
-            t = torch.full(
-                size=(x.shape[0],), fill_value=t, device=self.device
-            ).long()
+            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
         x_noisy = self.get_x_noisy(x, t)
         logits = self(x_noisy, t)
 
-        loss = F.cross_entropy(logits, targets, reduction='none')
+        loss = F.cross_entropy(logits, targets, reduction="none")
 
         self.write_logs(loss.detach(), logits.detach(), targets.detach())
 
@@ -268,7 +249,7 @@ class NoisyLatentImageClassifier(pl.LightningModule):
 
     def reset_noise_accs(self):
         self.noisy_acc = {
-            t: {'acc@1': [], 'acc@5': []}
+            t: {"acc@1": [], "acc@5": []}
             for t in range(
                 0,
                 self.diffusion_model.num_timesteps,
@@ -285,11 +266,11 @@ class NoisyLatentImageClassifier(pl.LightningModule):
 
         for t in self.noisy_acc:
             _, logits, _, targets = self.shared_step(batch, t)
-            self.noisy_acc[t]['acc@1'].append(
-                self.compute_top_k(logits, targets, k=1, reduction='mean')
+            self.noisy_acc[t]["acc@1"].append(
+                self.compute_top_k(logits, targets, k=1, reduction="mean")
             )
-            self.noisy_acc[t]['acc@5'].append(
-                self.compute_top_k(logits, targets, k=5, reduction='mean')
+            self.noisy_acc[t]["acc@5"].append(
+                self.compute_top_k(logits, targets, k=5, reduction="mean")
             )
 
         return loss
@@ -304,14 +285,12 @@ class NoisyLatentImageClassifier(pl.LightningModule):
         if self.use_scheduler:
             scheduler = instantiate_from_config(self.scheduler_config)
 
-            print('Setting up LambdaLR scheduler...')
+            print("Setting up LambdaLR scheduler...")
             scheduler = [
                 {
-                    'scheduler': LambdaLR(
-                        optimizer, lr_lambda=scheduler.schedule
-                    ),
-                    'interval': 'step',
-                    'frequency': 1,
+                    "scheduler": LambdaLR(optimizer, lr_lambda=scheduler.schedule),
+                    "interval": "step",
+                    "frequency": 1,
                 }
             ]
             return [optimizer], scheduler
@@ -322,32 +301,28 @@ class NoisyLatentImageClassifier(pl.LightningModule):
     def log_images(self, batch, N=8, *args, **kwargs):
         log = dict()
         x = self.get_input(batch, self.diffusion_model.first_stage_key)
-        log['inputs'] = x
+        log["inputs"] = x
 
         y = self.get_conditioning(batch)
 
-        if self.label_key == 'class_label':
-            y = log_txt_as_img((x.shape[2], x.shape[3]), batch['human_label'])
-            log['labels'] = y
+        if self.label_key == "class_label":
+            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+            log["labels"] = y
 
         if ismap(y):
-            log['labels'] = self.diffusion_model.to_rgb(y)
+            log["labels"] = self.diffusion_model.to_rgb(y)
 
             for step in range(self.log_steps):
                 current_time = step * self.log_time_interval
 
                 _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
 
-                log[f'inputs@t{current_time}'] = x_noisy
+                log[f"inputs@t{current_time}"] = x_noisy
 
-                pred = F.one_hot(
-                    logits.argmax(dim=1), num_classes=self.num_classes
-                )
-                pred = rearrange(pred, 'b h w c -> b c h w')
+                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
+                pred = rearrange(pred, "b h w c -> b c h w")
 
-                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(
-                    pred
-                )
+                log[f"pred@t{current_time}"] = self.diffusion_model.to_rgb(pred)
 
         for key in log:
             log[key] = log[key][:N]

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -1,21 +1,20 @@
-
 # adapted from bloc97's CrossAttentionControl colab
 # https://github.com/bloc97/CrossAttentionControl
 
 
 import enum
 import math
-from typing import Optional, Callable
+from typing import Callable, Optional
 
+import diffusers
 import psutil
 import torch
-import diffusers
+from compel.cross_attention_control import Arguments
+from diffusers.models.cross_attention import AttnProcessor
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from torch import nn
 
-from compel.cross_attention_control import Arguments
-from diffusers.models.unet_2d_condition import UNet2DConditionModel
-from diffusers.models.cross_attention import AttnProcessor
-from ldm.invoke.devices import torch_dtype
+from ...util import torch_dtype
 
 
 class CrossAttentionType(enum.Enum):
@@ -24,13 +23,12 @@ class CrossAttentionType(enum.Enum):
 
 
 class Context:
-
     cross_attention_mask: Optional[torch.Tensor]
     cross_attention_index_map: Optional[torch.Tensor]
 
     class Action(enum.Enum):
         NONE = 0
-        SAVE = 1,
+        SAVE = (1,)
         APPLY = 2
 
     def __init__(self, arguments: Arguments, step_count: int):
@@ -53,11 +51,13 @@ class Context:
         self.clear_requests(cleanup=True)
 
     def register_cross_attention_modules(self, model):
-        for name,module in get_cross_attention_modules(model, CrossAttentionType.SELF):
+        for name, module in get_cross_attention_modules(model, CrossAttentionType.SELF):
             if name in self.self_cross_attention_module_identifiers:
                 assert False, f"name {name} cannot appear more than once"
             self.self_cross_attention_module_identifiers.append(name)
-        for name,module in get_cross_attention_modules(model, CrossAttentionType.TOKENS):
+        for name, module in get_cross_attention_modules(
+            model, CrossAttentionType.TOKENS
+        ):
             if name in self.tokens_cross_attention_module_identifiers:
                 assert False, f"name {name} cannot appear more than once"
             self.tokens_cross_attention_module_identifiers.append(name)
@@ -68,7 +68,9 @@ class Context:
         else:
             self.tokens_cross_attention_action = Context.Action.SAVE
 
-    def request_apply_saved_attention_maps(self, cross_attention_type: CrossAttentionType):
+    def request_apply_saved_attention_maps(
+        self, cross_attention_type: CrossAttentionType
+    ):
         if cross_attention_type == CrossAttentionType.SELF:
             self.self_cross_attention_action = Context.Action.APPLY
         else:
@@ -91,8 +93,9 @@ class Context:
             return self.tokens_cross_attention_action == Context.Action.APPLY
         return False
 
-    def get_active_cross_attention_control_types_for_step(self, percent_through:float=None)\
-            -> list[CrossAttentionType]:
+    def get_active_cross_attention_control_types_for_step(
+        self, percent_through: float = None
+    ) -> list[CrossAttentionType]:
         """
         Should cross-attention control be applied on the given step?
         :param percent_through: How far through the step sequence are we (0.0=pure noise, 1.0=completely denoised image). Expected range 0.0..<1.0.
@@ -103,50 +106,73 @@ class Context:
 
         opts = self.arguments.edit_options
         to_control = []
-        if opts['s_start'] <= percent_through < opts['s_end']:
+        if opts["s_start"] <= percent_through < opts["s_end"]:
             to_control.append(CrossAttentionType.SELF)
-        if opts['t_start'] <= percent_through < opts['t_end']:
+        if opts["t_start"] <= percent_through < opts["t_end"]:
             to_control.append(CrossAttentionType.TOKENS)
         return to_control
 
-    def save_slice(self, identifier: str, slice: torch.Tensor, dim: Optional[int], offset: int,
-                   slice_size: Optional[int]):
+    def save_slice(
+        self,
+        identifier: str,
+        slice: torch.Tensor,
+        dim: Optional[int],
+        offset: int,
+        slice_size: Optional[int],
+    ):
         if identifier not in self.saved_cross_attention_maps:
             self.saved_cross_attention_maps[identifier] = {
-                'dim': dim,
-                'slice_size': slice_size,
-                'slices': {offset or 0: slice}
+                "dim": dim,
+                "slice_size": slice_size,
+                "slices": {offset or 0: slice},
             }
         else:
-            self.saved_cross_attention_maps[identifier]['slices'][offset or 0] = slice
+            self.saved_cross_attention_maps[identifier]["slices"][offset or 0] = slice
 
-    def get_slice(self, identifier: str, requested_dim: Optional[int], requested_offset: int, slice_size: int):
+    def get_slice(
+        self,
+        identifier: str,
+        requested_dim: Optional[int],
+        requested_offset: int,
+        slice_size: int,
+    ):
         saved_attention_dict = self.saved_cross_attention_maps[identifier]
         if requested_dim is None:
-            if saved_attention_dict['dim'] is not None:
-                raise RuntimeError(f"dim mismatch: expected dim=None, have {saved_attention_dict['dim']}")
-            return saved_attention_dict['slices'][0]
-
-        if saved_attention_dict['dim'] == requested_dim:
-            if slice_size != saved_attention_dict['slice_size']:
+            if saved_attention_dict["dim"] is not None:
                 raise RuntimeError(
-                    f"slice_size mismatch: expected slice_size={slice_size}, have {saved_attention_dict['slice_size']}")
-            return saved_attention_dict['slices'][requested_offset]
+                    f"dim mismatch: expected dim=None, have {saved_attention_dict['dim']}"
+                )
+            return saved_attention_dict["slices"][0]
 
-        if saved_attention_dict['dim'] is None:
-            whole_saved_attention = saved_attention_dict['slices'][0]
+        if saved_attention_dict["dim"] == requested_dim:
+            if slice_size != saved_attention_dict["slice_size"]:
+                raise RuntimeError(
+                    f"slice_size mismatch: expected slice_size={slice_size}, have {saved_attention_dict['slice_size']}"
+                )
+            return saved_attention_dict["slices"][requested_offset]
+
+        if saved_attention_dict["dim"] is None:
+            whole_saved_attention = saved_attention_dict["slices"][0]
             if requested_dim == 0:
-                return whole_saved_attention[requested_offset:requested_offset + slice_size]
+                return whole_saved_attention[
+                    requested_offset : requested_offset + slice_size
+                ]
             elif requested_dim == 1:
-                return whole_saved_attention[:, requested_offset:requested_offset + slice_size]
+                return whole_saved_attention[
+                    :, requested_offset : requested_offset + slice_size
+                ]
 
-        raise RuntimeError(f"Cannot convert dim {saved_attention_dict['dim']} to requested dim {requested_dim}")
+        raise RuntimeError(
+            f"Cannot convert dim {saved_attention_dict['dim']} to requested dim {requested_dim}"
+        )
 
-    def get_slicing_strategy(self, identifier: str) -> tuple[Optional[int], Optional[int]]:
+    def get_slicing_strategy(
+        self, identifier: str
+    ) -> tuple[Optional[int], Optional[int]]:
         saved_attention = self.saved_cross_attention_maps.get(identifier, None)
         if saved_attention is None:
             return None, None
-        return saved_attention['dim'], saved_attention['slice_size']
+        return saved_attention["dim"], saved_attention["slice_size"]
 
     def clear_requests(self, cleanup=True):
         self.tokens_cross_attention_action = Context.Action.NONE
@@ -156,9 +182,8 @@ class Context:
 
     def offload_saved_attention_slices_to_cpu(self):
         for key, map_dict in self.saved_cross_attention_maps.items():
-            for offset, slice in map_dict['slices'].items():
-                map_dict[offset] = slice.to('cpu')
-
+            for offset, slice in map_dict["slices"].items():
+                map_dict[offset] = slice.to("cpu")
 
 
 class InvokeAICrossAttentionMixin:
@@ -167,14 +192,20 @@ class InvokeAICrossAttentionMixin:
     through both to an attention_slice_wrangler and a slicing_strategy_getter for custom attention map wrangling
     and dymamic slicing strategy selection.
     """
+
     def __init__(self):
         self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
         self.attention_slice_wrangler = None
         self.slicing_strategy_getter = None
         self.attention_slice_calculated_callback = None
 
-    def set_attention_slice_wrangler(self, wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]]):
-        '''
+    def set_attention_slice_wrangler(
+        self,
+        wrangler: Optional[
+            Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]
+        ],
+    ):
+        """
         Set custom attention calculator to be called when attention is calculated
         :param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
         which returns either the suggested_attention_slice or an adjusted equivalent.
@@ -185,20 +216,30 @@ class InvokeAICrossAttentionMixin:
 
         Pass None to use the default attention calculation.
         :return:
-        '''
+        """
         self.attention_slice_wrangler = wrangler
 
-    def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int,int]]]):
+    def set_slicing_strategy_getter(
+        self, getter: Optional[Callable[[nn.Module], tuple[int, int]]]
+    ):
         self.slicing_strategy_getter = getter
 
-    def set_attention_slice_calculated_callback(self, callback: Optional[Callable[[torch.Tensor], None]]):
+    def set_attention_slice_calculated_callback(
+        self, callback: Optional[Callable[[torch.Tensor], None]]
+    ):
         self.attention_slice_calculated_callback = callback
 
     def einsum_lowest_level(self, query, key, value, dim, offset, slice_size):
         # calculate attention scores
-        #attention_scores = torch.einsum('b i d, b j d -> b i j', q, k)
+        # attention_scores = torch.einsum('b i d, b j d -> b i j', q, k)
         attention_scores = torch.baddbmm(
-            torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
+            torch.empty(
+                query.shape[0],
+                query.shape[1],
+                key.shape[1],
+                dtype=query.dtype,
+                device=query.device,
+            ),
             query,
             key.transpose(-1, -2),
             beta=0,
@@ -206,35 +247,49 @@ class InvokeAICrossAttentionMixin:
         )
 
         # calculate attention slice by taking the best scores for each latent pixel
-        default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
+        default_attention_slice = attention_scores.softmax(
+            dim=-1, dtype=attention_scores.dtype
+        )
         attention_slice_wrangler = self.attention_slice_wrangler
         if attention_slice_wrangler is not None:
-            attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
+            attention_slice = attention_slice_wrangler(
+                self, default_attention_slice, dim, offset, slice_size
+            )
         else:
             attention_slice = default_attention_slice
 
         if self.attention_slice_calculated_callback is not None:
-            self.attention_slice_calculated_callback(attention_slice, dim, offset, slice_size)
+            self.attention_slice_calculated_callback(
+                attention_slice, dim, offset, slice_size
+            )
 
         hidden_states = torch.bmm(attention_slice, value)
         return hidden_states
 
     def einsum_op_slice_dim0(self, q, k, v, slice_size):
-        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+        r = torch.zeros(
+            q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype
+        )
         for i in range(0, q.shape[0], slice_size):
             end = i + slice_size
-            r[i:end] = self.einsum_lowest_level(q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size)
+            r[i:end] = self.einsum_lowest_level(
+                q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size
+            )
         return r
 
     def einsum_op_slice_dim1(self, q, k, v, slice_size):
-        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+        r = torch.zeros(
+            q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype
+        )
         for i in range(0, q.shape[1], slice_size):
             end = i + slice_size
-            r[:, i:end] = self.einsum_lowest_level(q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size)
+            r[:, i:end] = self.einsum_lowest_level(
+                q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size
+            )
         return r
 
     def einsum_op_mps_v1(self, q, k, v):
-        if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
+        if q.shape[1] <= 4096:  # (512x512) max q.shape[1]: 4096
             return self.einsum_lowest_level(q, k, v, None, None, None)
         else:
             slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
@@ -272,13 +327,12 @@ class InvokeAICrossAttentionMixin:
         # Divide factor of safety as there's copying and fragmentation
         return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
 
-
     def get_invokeai_attention_mem_efficient(self, q, k, v):
-        if q.device.type == 'cuda':
-            #print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
+        if q.device.type == "cuda":
+            # print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
             return self.einsum_op_cuda(q, k, v)
 
-        if q.device.type == 'mps' or q.device.type == 'cpu':
+        if q.device.type == "mps" or q.device.type == "cpu":
             if self.mem_total_gb >= 32:
                 return self.einsum_op_mps_v1(q, k, v)
             return self.einsum_op_mps_v2(q, k, v)
@@ -288,8 +342,11 @@ class InvokeAICrossAttentionMixin:
         return self.einsum_op_tensor_mem(q, k, v, 32)
 
 
-
-def restore_default_cross_attention(model, is_running_diffusers: bool, restore_attention_processor: Optional[AttnProcessor]=None):
+def restore_default_cross_attention(
+    model,
+    is_running_diffusers: bool,
+    restore_attention_processor: Optional[AttnProcessor] = None,
+):
     if is_running_diffusers:
         unet = model
         unet.set_attn_processor(restore_attention_processor or CrossAttnProcessor())
@@ -297,7 +354,7 @@ def restore_default_cross_attention(model, is_running_diffusers: bool, restore_a
         remove_attention_function(model)
 
 
-def override_cross_attention(model, context: Context, is_running_diffusers = False):
+def override_cross_attention(model, context: Context, is_running_diffusers=False):
     """
     Inject attention parameters and functions into the passed in model to enable cross attention editing.
 
@@ -316,7 +373,7 @@ def override_cross_attention(model, context: Context, is_running_diffusers = Fal
     indices = torch.arange(max_length, dtype=torch.long)
     for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
         if b0 < max_length:
-            if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
+            if name == "equal":  # or (name == "replace" and a1 - a0 == b1 - b0):
                 # these tokens have not been edited
                 indices[b0:b1] = indices_target[a0:a1]
                 mask[b0:b1] = 1
@@ -332,7 +389,14 @@ def override_cross_attention(model, context: Context, is_running_diffusers = Fal
         else:
             # try to re-use an existing slice size
             default_slice_size = 4
-            slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
+            slice_size = next(
+                (
+                    p.slice_size
+                    for p in old_attn_processors.values()
+                    if type(p) is SlicedAttnProcessor
+                ),
+                default_slice_size,
+            )
             unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
         return old_attn_processors
     else:
@@ -341,65 +405,96 @@ def override_cross_attention(model, context: Context, is_running_diffusers = Fal
         return None
 
 
+def get_cross_attention_modules(
+    model, which: CrossAttentionType
+) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
+    from ldm.modules.attention import CrossAttention  # avoid circular import
 
-
-def get_cross_attention_modules(model, which: CrossAttentionType) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
-    from ldm.modules.attention import CrossAttention # avoid circular import
-    cross_attention_class: type = InvokeAIDiffusersCrossAttention if isinstance(model,UNet2DConditionModel) else CrossAttention
+    cross_attention_class: type = (
+        InvokeAIDiffusersCrossAttention
+        if isinstance(model, UNet2DConditionModel)
+        else CrossAttention
+    )
     which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
-    attention_module_tuples = [(name,module) for name, module in model.named_modules() if
-                isinstance(module, cross_attention_class) and which_attn in name]
+    attention_module_tuples = [
+        (name, module)
+        for name, module in model.named_modules()
+        if isinstance(module, cross_attention_class) and which_attn in name
+    ]
     cross_attention_modules_in_model_count = len(attention_module_tuples)
     expected_count = 16
     if cross_attention_modules_in_model_count != expected_count:
         # non-fatal error but .swap() won't work.
-        print(f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model " +
-              f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed " +
-              f"or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, " +
-              f"and/or update the {expected_count} above to an appropriate number, and/or find and inform someone who knows " +
-              f"what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not " +
-              f"work properly until it is fixed.")
+        print(
+            f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model "
+            + f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed "
+            + f"or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, "
+            + f"and/or update the {expected_count} above to an appropriate number, and/or find and inform someone who knows "
+            + f"what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not "
+            + f"work properly until it is fixed."
+        )
     return attention_module_tuples
 
 
 def inject_attention_function(unet, context: Context):
     # ORIGINAL SOURCE CODE: https://github.com/huggingface/diffusers/blob/91ddd2a25b848df0fa1262d4f1cd98c7ccb87750/src/diffusers/models/attention.py#L276
 
-    def attention_slice_wrangler(module, suggested_attention_slice:torch.Tensor, dim, offset, slice_size):
-
-        #memory_usage = suggested_attention_slice.element_size() * suggested_attention_slice.nelement()
+    def attention_slice_wrangler(
+        module, suggested_attention_slice: torch.Tensor, dim, offset, slice_size
+    ):
+        # memory_usage = suggested_attention_slice.element_size() * suggested_attention_slice.nelement()
 
         attention_slice = suggested_attention_slice
 
         if context.get_should_save_maps(module.identifier):
-            #print(module.identifier, "saving suggested_attention_slice of shape",
+            # print(module.identifier, "saving suggested_attention_slice of shape",
             #      suggested_attention_slice.shape, "dim", dim, "offset", offset)
-            slice_to_save = attention_slice.to('cpu') if dim is not None else attention_slice
-            context.save_slice(module.identifier, slice_to_save, dim=dim, offset=offset, slice_size=slice_size)
+            slice_to_save = (
+                attention_slice.to("cpu") if dim is not None else attention_slice
+            )
+            context.save_slice(
+                module.identifier,
+                slice_to_save,
+                dim=dim,
+                offset=offset,
+                slice_size=slice_size,
+            )
         elif context.get_should_apply_saved_maps(module.identifier):
-            #print(module.identifier, "applying saved attention slice for dim", dim, "offset", offset)
-            saved_attention_slice = context.get_slice(module.identifier, dim, offset, slice_size)
+            # print(module.identifier, "applying saved attention slice for dim", dim, "offset", offset)
+            saved_attention_slice = context.get_slice(
+                module.identifier, dim, offset, slice_size
+            )
 
             # slice may have been offloaded to CPU
-            saved_attention_slice = saved_attention_slice.to(suggested_attention_slice.device)
+            saved_attention_slice = saved_attention_slice.to(
+                suggested_attention_slice.device
+            )
 
             if context.is_tokens_cross_attention(module.identifier):
                 index_map = context.cross_attention_index_map
-                remapped_saved_attention_slice = torch.index_select(saved_attention_slice, -1, index_map)
+                remapped_saved_attention_slice = torch.index_select(
+                    saved_attention_slice, -1, index_map
+                )
                 this_attention_slice = suggested_attention_slice
 
-                mask = context.cross_attention_mask.to(torch_dtype(suggested_attention_slice.device))
+                mask = context.cross_attention_mask.to(
+                    torch_dtype(suggested_attention_slice.device)
+                )
                 saved_mask = mask
                 this_mask = 1 - mask
-                attention_slice = remapped_saved_attention_slice * saved_mask + \
-                                  this_attention_slice * this_mask
+                attention_slice = (
+                    remapped_saved_attention_slice * saved_mask
+                    + this_attention_slice * this_mask
+                )
             else:
                 # just use everything
                 attention_slice = saved_attention_slice
 
         return attention_slice
 
-    cross_attention_modules = get_cross_attention_modules(unet, CrossAttentionType.TOKENS) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
+    cross_attention_modules = get_cross_attention_modules(
+        unet, CrossAttentionType.TOKENS
+    ) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
     for identifier, module in cross_attention_modules:
         module.identifier = identifier
         try:
@@ -408,56 +503,61 @@ def inject_attention_function(unet, context: Context):
                 lambda module: context.get_slicing_strategy(identifier)
             )
         except AttributeError as e:
-            if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
-                print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")  # TODO
+            if is_attribute_error_about(e, "set_attention_slice_wrangler"):
+                print(
+                    f"TODO: implement set_attention_slice_wrangler for {type(module)}"
+                )  # TODO
             else:
                 raise
 
 
 def remove_attention_function(unet):
-    cross_attention_modules = get_cross_attention_modules(unet, CrossAttentionType.TOKENS) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
+    cross_attention_modules = get_cross_attention_modules(
+        unet, CrossAttentionType.TOKENS
+    ) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
     for identifier, module in cross_attention_modules:
         try:
             # clear wrangler callback
             module.set_attention_slice_wrangler(None)
             module.set_slicing_strategy_getter(None)
         except AttributeError as e:
-            if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
-                print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")
+            if is_attribute_error_about(e, "set_attention_slice_wrangler"):
+                print(
+                    f"TODO: implement set_attention_slice_wrangler for {type(module)}"
+                )
             else:
                 raise
 
 
 def is_attribute_error_about(error: AttributeError, attribute: str):
-    if hasattr(error, 'name'):  # Python 3.10
+    if hasattr(error, "name"):  # Python 3.10
         return error.name == attribute
     else:  # Python 3.9
         return attribute in str(error)
 
 
-
 def get_mem_free_total(device):
-    #only on cuda
+    # only on cuda
     if not torch.cuda.is_available():
         return None
     stats = torch.cuda.memory_stats(device)
-    mem_active = stats['active_bytes.all.current']
-    mem_reserved = stats['reserved_bytes.all.current']
+    mem_active = stats["active_bytes.all.current"]
+    mem_reserved = stats["reserved_bytes.all.current"]
     mem_free_cuda, _ = torch.cuda.mem_get_info(device)
     mem_free_torch = mem_reserved - mem_active
     mem_free_total = mem_free_cuda + mem_free_torch
     return mem_free_total
 
 
-
-class InvokeAIDiffusersCrossAttention(diffusers.models.attention.CrossAttention, InvokeAICrossAttentionMixin):
-
+class InvokeAIDiffusersCrossAttention(
+    diffusers.models.attention.CrossAttention, InvokeAICrossAttentionMixin
+):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         InvokeAICrossAttentionMixin.__init__(self)
 
     def _attention(self, query, key, value, attention_mask=None):
-        #default_result = super()._attention(query,  key, value)
+        # default_result = super()._attention(query,  key, value)
         if attention_mask is not None:
             print(f"{type(self).__name__} ignoring passed-in attention_mask")
         attention_result = self.get_invokeai_attention_mem_efficient(query, key, value)
@@ -466,9 +566,6 @@ class InvokeAIDiffusersCrossAttention(diffusers.models.attention.CrossAttention,
         return hidden_states
 
 
-
-
-
 ## 🧨diffusers implementation follows
 
 
@@ -501,25 +598,30 @@ class CrossAttnProcessor:
         return hidden_states
 
 """
-from dataclasses import field, dataclass
+from dataclasses import dataclass, field
 
 import torch
-
-from diffusers.models.cross_attention import CrossAttention, CrossAttnProcessor, SlicedAttnProcessor
+from diffusers.models.cross_attention import (
+    CrossAttention,
+    CrossAttnProcessor,
+    SlicedAttnProcessor,
+)
 
 
 @dataclass
 class SwapCrossAttnContext:
     modified_text_embeddings: torch.Tensor
-    index_map: torch.Tensor # maps from original prompt token indices to the equivalent tokens in the modified prompt
-    mask: torch.Tensor # in the target space of the index_map
+    index_map: torch.Tensor  # maps from original prompt token indices to the equivalent tokens in the modified prompt
+    mask: torch.Tensor  # in the target space of the index_map
     cross_attention_types_to_do: list[CrossAttentionType] = field(default_factory=list)
 
-    def __int__(self,
-                cac_types_to_do: [CrossAttentionType],
-                modified_text_embeddings: torch.Tensor,
-                index_map: torch.Tensor,
-                mask: torch.Tensor):
+    def __int__(
+        self,
+        cac_types_to_do: [CrossAttentionType],
+        modified_text_embeddings: torch.Tensor,
+        index_map: torch.Tensor,
+        mask: torch.Tensor,
+    ):
         self.cross_attention_types_to_do = cac_types_to_do
         self.modified_text_embeddings = modified_text_embeddings
         self.index_map = index_map
@@ -529,9 +631,9 @@ class SwapCrossAttnContext:
         return attn_type in self.cross_attention_types_to_do
 
     @classmethod
-    def make_mask_and_index_map(cls, edit_opcodes: list[tuple[str, int, int, int, int]], max_length: int) \
-            -> tuple[torch.Tensor, torch.Tensor]:
-
+    def make_mask_and_index_map(
+        cls, edit_opcodes: list[tuple[str, int, int, int, int]], max_length: int
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         # mask=1 means use original prompt attention, mask=0 means use modified prompt attention
         mask = torch.zeros(max_length)
         indices_target = torch.arange(max_length, dtype=torch.long)
@@ -547,28 +649,42 @@ class SwapCrossAttnContext:
 
 
 class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
-
     # TODO: dynamically pick slice size based on memory conditions
 
-    def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None,
-                 # kwargs
-                 swap_cross_attn_context: SwapCrossAttnContext=None):
-
-        attention_type = CrossAttentionType.SELF if encoder_hidden_states is None else CrossAttentionType.TOKENS
+    def __call__(
+        self,
+        attn: CrossAttention,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        # kwargs
+        swap_cross_attn_context: SwapCrossAttnContext = None,
+    ):
+        attention_type = (
+            CrossAttentionType.SELF
+            if encoder_hidden_states is None
+            else CrossAttentionType.TOKENS
+        )
 
         # if cross-attention control is not in play, just call through to the base implementation.
-        if attention_type is CrossAttentionType.SELF or \
-            swap_cross_attn_context is None or \
-            not swap_cross_attn_context.wants_cross_attention_control(attention_type):
-            #print(f"SwapCrossAttnContext for {attention_type} not active - passing request to superclass")
-            return super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask)
-        #else:
+        if (
+            attention_type is CrossAttentionType.SELF
+            or swap_cross_attn_context is None
+            or not swap_cross_attn_context.wants_cross_attention_control(attention_type)
+        ):
+            # print(f"SwapCrossAttnContext for {attention_type} not active - passing request to superclass")
+            return super().__call__(
+                attn, hidden_states, encoder_hidden_states, attention_mask
+            )
+        # else:
         #    print(f"SwapCrossAttnContext for {attention_type} active")
 
         batch_size, sequence_length, _ = hidden_states.shape
         attention_mask = attn.prepare_attention_mask(
-            attention_mask=attention_mask, target_length=sequence_length,
-            batch_size=batch_size)
+            attention_mask=attention_mask,
+            target_length=sequence_length,
+            batch_size=batch_size,
+        )
 
         query = attn.to_q(hidden_states)
         dim = query.shape[-1]
@@ -589,41 +705,51 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
         # compute slices and prepare output tensor
         batch_size_attention = query.shape[0]
         hidden_states = torch.zeros(
-            (batch_size_attention, sequence_length, dim // attn.heads), device=query.device, dtype=query.dtype
+            (batch_size_attention, sequence_length, dim // attn.heads),
+            device=query.device,
+            dtype=query.dtype,
         )
 
         # do slices
-        for i in range(max(1,hidden_states.shape[0] // self.slice_size)):
+        for i in range(max(1, hidden_states.shape[0] // self.slice_size)):
             start_idx = i * self.slice_size
             end_idx = (i + 1) * self.slice_size
 
             query_slice = query[start_idx:end_idx]
             original_key_slice = original_text_key[start_idx:end_idx]
             modified_key_slice = modified_text_key[start_idx:end_idx]
-            attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
+            attn_mask_slice = (
+                attention_mask[start_idx:end_idx]
+                if attention_mask is not None
+                else None
+            )
 
-            original_attn_slice = attn.get_attention_scores(query_slice, original_key_slice, attn_mask_slice)
-            modified_attn_slice = attn.get_attention_scores(query_slice, modified_key_slice, attn_mask_slice)
+            original_attn_slice = attn.get_attention_scores(
+                query_slice, original_key_slice, attn_mask_slice
+            )
+            modified_attn_slice = attn.get_attention_scores(
+                query_slice, modified_key_slice, attn_mask_slice
+            )
 
             # because the prompt modifications may result in token sequences shifted forwards or backwards,
             # the original attention probabilities must be remapped to account for token index changes in the
             # modified prompt
-            remapped_original_attn_slice = torch.index_select(original_attn_slice, -1,
-                                                              swap_cross_attn_context.index_map)
+            remapped_original_attn_slice = torch.index_select(
+                original_attn_slice, -1, swap_cross_attn_context.index_map
+            )
 
             # only some tokens taken from the original attention probabilities. this is controlled by the mask.
             mask = swap_cross_attn_context.mask
             inverse_mask = 1 - mask
-            attn_slice = \
-                remapped_original_attn_slice * mask + \
-                modified_attn_slice * inverse_mask
+            attn_slice = (
+                remapped_original_attn_slice * mask + modified_attn_slice * inverse_mask
+            )
 
             del remapped_original_attn_slice, modified_attn_slice
 
             attn_slice = torch.bmm(attn_slice, modified_value[start_idx:end_idx])
             hidden_states[start_idx:end_idx] = attn_slice
 
-
         # done
         hidden_states = attn.batch_to_head_dim(hidden_states)
 
@@ -636,7 +762,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
 
 
 class SwapCrossAttnProcessor(SlicedSwapCrossAttnProcesser):
-
     def __init__(self):
-        super(SwapCrossAttnProcessor, self).__init__(slice_size=int(1e9)) # massive slice size = don't slice
-
+        super(SwapCrossAttnProcessor, self).__init__(
+            slice_size=int(1e9)
+        )  # massive slice size = don't slice

invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py

@@ -2,17 +2,17 @@ import math
 
 import PIL
 import torch
-from torchvision.transforms.functional import resize as tv_resize, InterpolationMode
+from torchvision.transforms.functional import InterpolationMode
+from torchvision.transforms.functional import resize as tv_resize
 
-from ldm.models.diffusion.cross_attention_control import get_cross_attention_modules, CrossAttentionType
+from .cross_attention_control import CrossAttentionType, get_cross_attention_modules
 
 
-class AttentionMapSaver():
-
+class AttentionMapSaver:
     def __init__(self, token_ids: range, latents_shape: torch.Size):
         self.token_ids = token_ids
         self.latents_shape = latents_shape
-        #self.collated_maps = #torch.zeros([len(token_ids), latents_shape[0], latents_shape[1]])
+        # self.collated_maps = #torch.zeros([len(token_ids), latents_shape[0], latents_shape[1]])
         self.collated_maps = {}
 
     def clear_maps(self):
@@ -25,7 +25,7 @@ class AttentionMapSaver():
         :param key: Storage key. If a map already exists for this key it will be summed with the incoming data. In this case the maps sizes (H and W) should match.
         :return: None
         """
-        key_and_size = f'{key}_{maps.shape[1]}'
+        key_and_size = f"{key}_{maps.shape[1]}"
 
         # extract desired tokens
         maps = maps[:, :, self.token_ids]
@@ -35,12 +35,12 @@ class AttentionMapSaver():
 
         # store
         if key_and_size not in self.collated_maps:
-            self.collated_maps[key_and_size] = torch.zeros_like(maps, device='cpu')
+            self.collated_maps[key_and_size] = torch.zeros_like(maps, device="cpu")
         self.collated_maps[key_and_size] += maps.cpu()
 
     def write_maps_to_disk(self, path: str):
         pil_image = self.get_stacked_maps_image()
-        pil_image.save(path, 'PNG')
+        pil_image.save(path, "PNG")
 
     def get_stacked_maps_image(self) -> PIL.Image:
         """
@@ -57,39 +57,50 @@ class AttentionMapSaver():
         merged = None
 
         for key, maps in self.collated_maps.items():
-
             # maps has shape [(H*W), N] for N tokens
             # but we want [N, H, W]
-            this_scale_factor = math.sqrt(maps.shape[0] / (latents_width * latents_height))
+            this_scale_factor = math.sqrt(
+                maps.shape[0] / (latents_width * latents_height)
+            )
             this_maps_height = int(float(latents_height) * this_scale_factor)
             this_maps_width = int(float(latents_width) * this_scale_factor)
             # and we need to do some dimension juggling
-            maps = torch.reshape(torch.swapdims(maps, 0, 1), [num_tokens, this_maps_height, this_maps_width])
+            maps = torch.reshape(
+                torch.swapdims(maps, 0, 1),
+                [num_tokens, this_maps_height, this_maps_width],
+            )
 
             # scale to output size if necessary
             if this_scale_factor != 1:
-                maps = tv_resize(maps, [latents_height, latents_width], InterpolationMode.BICUBIC)
+                maps = tv_resize(
+                    maps, [latents_height, latents_width], InterpolationMode.BICUBIC
+                )
 
             # normalize
             maps_min = torch.min(maps)
             maps_range = torch.max(maps) - maps_min
-            #print(f"map {key} size {[this_maps_width, this_maps_height]} range {[maps_min, maps_min + maps_range]}")
+            # print(f"map {key} size {[this_maps_width, this_maps_height]} range {[maps_min, maps_min + maps_range]}")
             maps_normalized = (maps - maps_min) / maps_range
             # expand to (-0.1, 1.1) and clamp
             maps_normalized_expanded = maps_normalized * 1.1 - 0.05
-            maps_normalized_expanded_clamped = torch.clamp(maps_normalized_expanded, 0, 1)
+            maps_normalized_expanded_clamped = torch.clamp(
+                maps_normalized_expanded, 0, 1
+            )
 
             # merge together, producing a vertical stack
-            maps_stacked = torch.reshape(maps_normalized_expanded_clamped, [num_tokens * latents_height, latents_width])
+            maps_stacked = torch.reshape(
+                maps_normalized_expanded_clamped,
+                [num_tokens * latents_height, latents_width],
+            )
 
             if merged is None:
                 merged = maps_stacked
             else:
                 # screen blend
-                merged = 1 - (1 - maps_stacked)*(1 - merged)
+                merged = 1 - (1 - maps_stacked) * (1 - merged)
 
         if merged is None:
             return None
 
-        merged_bytes = merged.mul(0xff).byte()
-        return PIL.Image.fromarray(merged_bytes.numpy(), mode='L')
+        merged_bytes = merged.mul(0xFF).byte()
+        return PIL.Image.fromarray(merged_bytes.numpy(), mode="L")

invokeai/backend/stable_diffusion/diffusion/ddim.py

@@ -1,77 +1,82 @@
 """SAMPLING ONLY."""
 
 import torch
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-from ldm.models.diffusion.sampler import Sampler
-from ldm.modules.diffusionmodules.util import  noise_like
+
+from ..diffusionmodules.util import noise_like
+from .sampler import Sampler
+from .shared_invokeai_diffusion import InvokeAIDiffuserComponent
+
 
 class DDIMSampler(Sampler):
-    def __init__(self, model, schedule='linear', device=None, **kwargs):
-        super().__init__(model,schedule,model.num_timesteps,device)
+    def __init__(self, model, schedule="linear", device=None, **kwargs):
+        super().__init__(model, schedule, model.num_timesteps, device)
 
-        self.invokeai_diffuser = InvokeAIDiffuserComponent(self.model,
-                                                           model_forward_callback = lambda x, sigma, cond: self.model.apply_model(x, sigma, cond))
+        self.invokeai_diffuser = InvokeAIDiffuserComponent(
+            self.model,
+            model_forward_callback=lambda x, sigma, cond: self.model.apply_model(
+                x, sigma, cond
+            ),
+        )
 
     def prepare_to_sample(self, t_enc, **kwargs):
         super().prepare_to_sample(t_enc, **kwargs)
 
-        extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
-        all_timesteps_count = kwargs.get('all_timesteps_count', t_enc)
+        extra_conditioning_info = kwargs.get("extra_conditioning_info", None)
+        all_timesteps_count = kwargs.get("all_timesteps_count", t_enc)
 
-        if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
-            self.invokeai_diffuser.override_cross_attention(extra_conditioning_info, step_count = all_timesteps_count)
+        if (
+            extra_conditioning_info is not None
+            and extra_conditioning_info.wants_cross_attention_control
+        ):
+            self.invokeai_diffuser.override_cross_attention(
+                extra_conditioning_info, step_count=all_timesteps_count
+            )
         else:
             self.invokeai_diffuser.restore_default_cross_attention()
 
-
     # This is the central routine
     @torch.no_grad()
     def p_sample(
-            self,
-            x,
-            c,
-            t,
-            index,
-            repeat_noise=False,
-            use_original_steps=False,
-            quantize_denoised=False,
-            temperature=1.0,
-            noise_dropout=0.0,
-            score_corrector=None,
-            corrector_kwargs=None,
-            unconditional_guidance_scale=1.0,
-            unconditional_conditioning=None,
-            step_count:int=1000, # total number of steps
-            **kwargs,
+        self,
+        x,
+        c,
+        t,
+        index,
+        repeat_noise=False,
+        use_original_steps=False,
+        quantize_denoised=False,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        step_count: int = 1000,  # total number of steps
+        **kwargs,
     ):
         b, *_, device = *x.shape, x.device
 
-        if (
-            unconditional_conditioning is None
-            or unconditional_guidance_scale == 1.0
-        ):
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.0:
             # damian0815 would like to know when/if this code path is used
             e_t = self.model.apply_model(x, t, c)
         else:
             # step_index counts in the opposite direction to index
-            step_index = step_count-(index+1)
+            step_index = step_count - (index + 1)
             e_t = self.invokeai_diffuser.do_diffusion_step(
-                x, t,
-                unconditional_conditioning, c,
+                x,
+                t,
+                unconditional_conditioning,
+                c,
                 unconditional_guidance_scale,
-                step_index=step_index
+                step_index=step_index,
             )
         if score_corrector is not None:
-            assert self.model.parameterization == 'eps'
+            assert self.model.parameterization == "eps"
             e_t = score_corrector.modify_score(
                 self.model, e_t, x, t, c, **corrector_kwargs
             )
 
-        alphas = (
-            self.model.alphas_cumprod
-            if use_original_steps
-            else self.ddim_alphas
-        )
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
         alphas_prev = (
             self.model.alphas_cumprod_prev
             if use_original_steps
@@ -101,11 +106,8 @@ class DDIMSampler(Sampler):
             pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
         # direction pointing to x_t
         dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
-        noise = (
-            sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        )
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
         if noise_dropout > 0.0:
             noise = torch.nn.functional.dropout(noise, p=noise_dropout)
         x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
         return x_prev, pred_x0, None
-

invokeai/backend/stable_diffusion/diffusion/ksampler.py

@@ -8,12 +8,12 @@ from .cross_attention_map_saving import AttentionMapSaver
 from .sampler import Sampler
 from .shared_invokeai_diffusion import InvokeAIDiffuserComponent
 
-
 # at this threshold, the scheduler will stop using the Karras
 # noise schedule and start using the model's schedule
 STEP_THRESHOLD = 30
 
-def cfg_apply_threshold(result, threshold = 0.0, scale = 0.7):
+
+def cfg_apply_threshold(result, threshold=0.0, scale=0.7):
     if threshold <= 0.0:
         return result
     maxval = 0.0 + torch.max(result).cpu().numpy()
@@ -21,35 +21,43 @@ def cfg_apply_threshold(result, threshold = 0.0, scale = 0.7):
     if maxval < threshold and minval > -threshold:
         return result
     if maxval > threshold:
-        maxval = min(max(1, scale*maxval), threshold)
+        maxval = min(max(1, scale * maxval), threshold)
     if minval < -threshold:
-        minval = max(min(-1, scale*minval), -threshold)
+        minval = max(min(-1, scale * minval), -threshold)
     return torch.clamp(result, min=minval, max=maxval)
 
 
 class CFGDenoiser(nn.Module):
-    def __init__(self, model, threshold = 0, warmup = 0):
+    def __init__(self, model, threshold=0, warmup=0):
         super().__init__()
         self.inner_model = model
         self.threshold = threshold
         self.warmup_max = warmup
         self.warmup = max(warmup / 10, 1)
-        self.invokeai_diffuser = InvokeAIDiffuserComponent(model,
-                                                           model_forward_callback=lambda x, sigma, cond: self.inner_model(x, sigma, cond=cond))
-
+        self.invokeai_diffuser = InvokeAIDiffuserComponent(
+            model,
+            model_forward_callback=lambda x, sigma, cond: self.inner_model(
+                x, sigma, cond=cond
+            ),
+        )
 
     def prepare_to_sample(self, t_enc, **kwargs):
+        extra_conditioning_info = kwargs.get("extra_conditioning_info", None)
 
-        extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
-
-        if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
-            self.invokeai_diffuser.override_cross_attention(extra_conditioning_info, step_count = t_enc)
+        if (
+            extra_conditioning_info is not None
+            and extra_conditioning_info.wants_cross_attention_control
+        ):
+            self.invokeai_diffuser.override_cross_attention(
+                extra_conditioning_info, step_count=t_enc
+            )
         else:
             self.invokeai_diffuser.restore_default_cross_attention()
 
-
     def forward(self, x, sigma, uncond, cond, cond_scale):
-        next_x = self.invokeai_diffuser.do_diffusion_step(x, sigma, uncond, cond, cond_scale)
+        next_x = self.invokeai_diffuser.do_diffusion_step(
+            x, sigma, uncond, cond, cond_scale
+        )
         if self.warmup < self.warmup_max:
             thresh = max(1, 1 + (self.threshold - 1) * (self.warmup / self.warmup_max))
             self.warmup += 1
@@ -59,8 +67,9 @@ class CFGDenoiser(nn.Module):
             thresh = self.threshold
         return cfg_apply_threshold(next_x, thresh)
 
+
 class KSampler(Sampler):
-    def __init__(self, model, schedule='lms', device=None, **kwargs):
+    def __init__(self, model, schedule="lms", device=None, **kwargs):
         denoiser = K.external.CompVisDenoiser(model)
         super().__init__(
             denoiser,
@@ -68,45 +77,49 @@ class KSampler(Sampler):
             steps=model.num_timesteps,
         )
         self.sigmas = None
-        self.ds     = None
-        self.s_in   = None
-        self.karras_max = kwargs.get('karras_max',STEP_THRESHOLD)
+        self.ds = None
+        self.s_in = None
+        self.karras_max = kwargs.get("karras_max", STEP_THRESHOLD)
         if self.karras_max is None:
             self.karras_max = STEP_THRESHOLD
 
     def make_schedule(
-            self,
-            ddim_num_steps,
-            ddim_discretize='uniform',
-            ddim_eta=0.0,
-            verbose=False,
+        self,
+        ddim_num_steps,
+        ddim_discretize="uniform",
+        ddim_eta=0.0,
+        verbose=False,
     ):
         outer_model = self.model
-        self.model  = outer_model.inner_model
+        self.model = outer_model.inner_model
         super().make_schedule(
             ddim_num_steps,
-            ddim_discretize='uniform',
+            ddim_discretize="uniform",
             ddim_eta=0.0,
             verbose=False,
         )
-        self.model          = outer_model
+        self.model = outer_model
         self.ddim_num_steps = ddim_num_steps
         # we don't need both of these sigmas, but storing them here to make
         # comparison easier later on
-        self.model_sigmas  = self.model.get_sigmas(ddim_num_steps)
+        self.model_sigmas = self.model.get_sigmas(ddim_num_steps)
         self.karras_sigmas = K.sampling.get_sigmas_karras(
             n=ddim_num_steps,
             sigma_min=self.model.sigmas[0].item(),
             sigma_max=self.model.sigmas[-1].item(),
-            rho=7.,
+            rho=7.0,
             device=self.device,
         )
 
         if ddim_num_steps >= self.karras_max:
-            print(f'>> Ksampler using model noise schedule (steps >= {self.karras_max})')
+            print(
+                f">> Ksampler using model noise schedule (steps >= {self.karras_max})"
+            )
             self.sigmas = self.model_sigmas
         else:
-            print(f'>> Ksampler using karras noise schedule (steps < {self.karras_max})')
+            print(
+                f">> Ksampler using karras noise schedule (steps < {self.karras_max})"
+            )
             self.sigmas = self.karras_sigmas
 
     # ALERT: We are completely overriding the sample() method in the base class, which
@@ -116,31 +129,31 @@ class KSampler(Sampler):
 
     @torch.no_grad()
     def decode(
-            self,
-            z_enc,
-            cond,
-            t_enc,
-            img_callback=None,
-            unconditional_guidance_scale=1.0,
-            unconditional_conditioning=None,
-            use_original_steps=False,
-            init_latent       = None,
-            mask              = None,
-            **kwargs
+        self,
+        z_enc,
+        cond,
+        t_enc,
+        img_callback=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        use_original_steps=False,
+        init_latent=None,
+        mask=None,
+        **kwargs,
     ):
-        samples,_ = self.sample(
-            batch_size = 1,
-            S          = t_enc,
-            x_T        = z_enc,
-            shape      = z_enc.shape[1:],
-            conditioning = cond,
+        samples, _ = self.sample(
+            batch_size=1,
+            S=t_enc,
+            x_T=z_enc,
+            shape=z_enc.shape[1:],
+            conditioning=cond,
             unconditional_guidance_scale=unconditional_guidance_scale,
-            unconditional_conditioning = unconditional_conditioning,
-            img_callback = img_callback,
-            x0           = init_latent,
-            mask         = mask,
-            **kwargs
-            )
+            unconditional_conditioning=unconditional_conditioning,
+            img_callback=img_callback,
+            x0=init_latent,
+            mask=mask,
+            **kwargs,
+        )
         return samples
 
     # this is a no-op, provided here for compatibility with ddim and plms samplers
@@ -174,26 +187,26 @@ class KSampler(Sampler):
         log_every_t=100,
         unconditional_guidance_scale=1.0,
         unconditional_conditioning=None,
-        extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo=None,
-        threshold = 0,
-        perlin = 0,
+        extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
+        threshold=0,
+        perlin=0,
         # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
         **kwargs,
     ):
         def route_callback(k_callback_values):
             if img_callback is not None:
-                img_callback(k_callback_values['x'],k_callback_values['i'])
+                img_callback(k_callback_values["x"], k_callback_values["i"])
 
         # if make_schedule() hasn't been called, we do it now
         if self.sigmas is None:
             self.make_schedule(
                 ddim_num_steps=S,
-                ddim_eta = eta,
-                verbose = False,
+                ddim_eta=eta,
+                verbose=False,
             )
 
         # sigmas are set up in make_schedule - we take the last steps items
-        sigmas = self.sigmas[-S-1:]
+        sigmas = self.sigmas[-S - 1 :]
 
         # x_T is variation noise. When an init image is provided (in x0) we need to add
         # more randomness to the starting image.
@@ -205,27 +218,40 @@ class KSampler(Sampler):
         else:
             x = torch.randn([batch_size, *shape], device=self.device) * sigmas[0]
 
-        model_wrap_cfg = CFGDenoiser(self.model, threshold=threshold, warmup=max(0.8*S,S-10))
-        model_wrap_cfg.prepare_to_sample(S, extra_conditioning_info=extra_conditioning_info)
+        model_wrap_cfg = CFGDenoiser(
+            self.model, threshold=threshold, warmup=max(0.8 * S, S - 10)
+        )
+        model_wrap_cfg.prepare_to_sample(
+            S, extra_conditioning_info=extra_conditioning_info
+        )
 
         # setup attention maps saving. checks for None are because there are multiple code paths to get here.
         attention_map_saver = None
         if attention_maps_callback is not None and extra_conditioning_info is not None:
             eos_token_index = extra_conditioning_info.tokens_count_including_eos_bos - 1
             attention_map_token_ids = range(1, eos_token_index)
-            attention_map_saver = AttentionMapSaver(token_ids = attention_map_token_ids, latents_shape=x.shape[-2:])
-            model_wrap_cfg.invokeai_diffuser.setup_attention_map_saving(attention_map_saver)
+            attention_map_saver = AttentionMapSaver(
+                token_ids=attention_map_token_ids, latents_shape=x.shape[-2:]
+            )
+            model_wrap_cfg.invokeai_diffuser.setup_attention_map_saving(
+                attention_map_saver
+            )
 
         extra_args = {
-            'cond': conditioning,
-            'uncond': unconditional_conditioning,
-            'cond_scale': unconditional_guidance_scale,
+            "cond": conditioning,
+            "uncond": unconditional_conditioning,
+            "cond_scale": unconditional_guidance_scale,
         }
-        print(f'>> Sampling with k_{self.schedule} starting at step {len(self.sigmas)-S-1} of {len(self.sigmas)-1} ({S} new sampling steps)')
+        print(
+            f">> Sampling with k_{self.schedule} starting at step {len(self.sigmas)-S-1} of {len(self.sigmas)-1} ({S} new sampling steps)"
+        )
         sampling_result = (
-            K.sampling.__dict__[f'sample_{self.schedule}'](
-                model_wrap_cfg, x, sigmas, extra_args=extra_args,
-                callback=route_callback
+            K.sampling.__dict__[f"sample_{self.schedule}"](
+                model_wrap_cfg,
+                x,
+                sigmas,
+                extra_args=extra_args,
+                callback=route_callback,
             ),
             None,
         )
@@ -237,25 +263,25 @@ class KSampler(Sampler):
     # a workaround is found.
     @torch.no_grad()
     def p_sample(
-            self,
-            img,
-            cond,
-            ts,
-            index,
-            unconditional_guidance_scale=1.0,
-            unconditional_conditioning=None,
-            extra_conditioning_info=None,
-            **kwargs,
+        self,
+        img,
+        cond,
+        ts,
+        index,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        extra_conditioning_info=None,
+        **kwargs,
     ):
         if self.model_wrap is None:
             self.model_wrap = CFGDenoiser(self.model)
         extra_args = {
-            'cond': cond,
-            'uncond': unconditional_conditioning,
-            'cond_scale': unconditional_guidance_scale,
+            "cond": cond,
+            "uncond": unconditional_conditioning,
+            "cond_scale": unconditional_guidance_scale,
         }
         if self.s_in is None:
-            self.s_in  = img.new_ones([img.shape[0]])
+            self.s_in = img.new_ones([img.shape[0]])
         if self.ds is None:
             self.ds = []
 
@@ -270,14 +296,16 @@ class KSampler(Sampler):
         # so the actual formula for indexing into sigmas:
         # sigma_index = (steps-index)
         s_index = t_enc - index - 1
-        self.model_wrap.prepare_to_sample(s_index, extra_conditioning_info=extra_conditioning_info)
-        img =  K.sampling.__dict__[f'_{self.schedule}'](
+        self.model_wrap.prepare_to_sample(
+            s_index, extra_conditioning_info=extra_conditioning_info
+        )
+        img = K.sampling.__dict__[f"_{self.schedule}"](
             self.model_wrap,
             img,
             self.sigmas,
             s_index,
-            s_in = self.s_in,
-            ds   = self.ds,
+            s_in=self.s_in,
+            ds=self.ds,
             extra_args=extra_args,
         )
 
@@ -287,26 +315,25 @@ class KSampler(Sampler):
     # we should not be multiplying by self.sigmas[0] if we
     # are at an intermediate step in img2img. See similar in
     # sample() which does work.
-    def get_initial_image(self,x_T,shape,steps):
-        print(f'WARNING: ksampler.get_initial_image(): get_initial_image needs testing')
-        x = (torch.randn(shape, device=self.device) * self.sigmas[0])
+    def get_initial_image(self, x_T, shape, steps):
+        print(f"WARNING: ksampler.get_initial_image(): get_initial_image needs testing")
+        x = torch.randn(shape, device=self.device) * self.sigmas[0]
         if x_T is not None:
             return x_T + x
         else:
             return x
 
-    def prepare_to_sample(self,t_enc,**kwargs):
-        self.t_enc      = t_enc
+    def prepare_to_sample(self, t_enc, **kwargs):
+        self.t_enc = t_enc
         self.model_wrap = None
-        self.ds         = None
-        self.s_in       = None
+        self.ds = None
+        self.s_in = None
 
-    def q_sample(self,x0,ts):
-        '''
+    def q_sample(self, x0, ts):
+        """
         Overrides parent method to return the q_sample of the inner model.
-        '''
-        return self.model.inner_model.q_sample(x0,ts)
+        """
+        return self.model.inner_model.q_sample(x0, ts)
 
-    def conditioning_key(self)->str:
+    def conditioning_key(self) -> str:
         return self.model.inner_model.model.conditioning_key
-

invokeai/backend/stable_diffusion/diffusion/plms.py

@@ -1,52 +1,58 @@
 """SAMPLING ONLY."""
 
-import torch
-import numpy as np
-from tqdm import tqdm
 from functools import partial
-from ldm.invoke.devices import choose_torch_device
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-from ldm.models.diffusion.sampler import Sampler
-from ldm.modules.diffusionmodules.util import  noise_like
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from ...util import choose_torch_device
+from ..diffusionmodules.util import noise_like
+from .sampler import Sampler
+from .shared_invokeai_diffusion import InvokeAIDiffuserComponent
 
 
 class PLMSSampler(Sampler):
-    def __init__(self, model, schedule='linear', device=None, **kwargs):
-        super().__init__(model,schedule,model.num_timesteps, device)
+    def __init__(self, model, schedule="linear", device=None, **kwargs):
+        super().__init__(model, schedule, model.num_timesteps, device)
 
     def prepare_to_sample(self, t_enc, **kwargs):
         super().prepare_to_sample(t_enc, **kwargs)
 
-        extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
-        all_timesteps_count = kwargs.get('all_timesteps_count', t_enc)
+        extra_conditioning_info = kwargs.get("extra_conditioning_info", None)
+        all_timesteps_count = kwargs.get("all_timesteps_count", t_enc)
 
-        if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
-            self.invokeai_diffuser.override_cross_attention(extra_conditioning_info, step_count = all_timesteps_count)
+        if (
+            extra_conditioning_info is not None
+            and extra_conditioning_info.wants_cross_attention_control
+        ):
+            self.invokeai_diffuser.override_cross_attention(
+                extra_conditioning_info, step_count=all_timesteps_count
+            )
         else:
             self.invokeai_diffuser.restore_default_cross_attention()
 
-
     # this is the essential routine
     @torch.no_grad()
     def p_sample(
-            self,
-            x,    # image, called 'img' elsewhere
-            c,    # conditioning, called 'cond' elsewhere
-            t,    # timesteps, called 'ts' elsewhere
-            index,
-            repeat_noise=False,
-            use_original_steps=False,
-            quantize_denoised=False,
-            temperature=1.0,
-            noise_dropout=0.0,
-            score_corrector=None,
-            corrector_kwargs=None,
-            unconditional_guidance_scale=1.0,
-            unconditional_conditioning=None,
-            old_eps=[],
-            t_next=None,
-            step_count:int=1000, # total number of steps
-            **kwargs,
+        self,
+        x,  # image, called 'img' elsewhere
+        c,  # conditioning, called 'cond' elsewhere
+        t,  # timesteps, called 'ts' elsewhere
+        index,
+        repeat_noise=False,
+        use_original_steps=False,
+        quantize_denoised=False,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        old_eps=[],
+        t_next=None,
+        step_count: int = 1000,  # total number of steps
+        **kwargs,
     ):
         b, *_, device = *x.shape, x.device
 
@@ -59,24 +65,24 @@ class PLMSSampler(Sampler):
                 e_t = self.model.apply_model(x, t, c)
             else:
                 # step_index counts in the opposite direction to index
-                step_index = step_count-(index+1)
-                e_t = self.invokeai_diffuser.do_diffusion_step(x, t,
-                                                               unconditional_conditioning, c,
-                                                               unconditional_guidance_scale,
-                                                               step_index=step_index)
+                step_index = step_count - (index + 1)
+                e_t = self.invokeai_diffuser.do_diffusion_step(
+                    x,
+                    t,
+                    unconditional_conditioning,
+                    c,
+                    unconditional_guidance_scale,
+                    step_index=step_index,
+                )
             if score_corrector is not None:
-                assert self.model.parameterization == 'eps'
+                assert self.model.parameterization == "eps"
                 e_t = score_corrector.modify_score(
                     self.model, e_t, x, t, c, **corrector_kwargs
                 )
 
             return e_t
 
-        alphas = (
-            self.model.alphas_cumprod
-            if use_original_steps
-            else self.ddim_alphas
-        )
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
         alphas_prev = (
             self.model.alphas_cumprod_prev
             if use_original_steps
@@ -96,9 +102,7 @@ class PLMSSampler(Sampler):
         def get_x_prev_and_pred_x0(e_t, index):
             # select parameters corresponding to the currently considered timestep
             a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-            a_prev = torch.full(
-                (b, 1, 1, 1), alphas_prev[index], device=device
-            )
+            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
             sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
             sqrt_one_minus_at = torch.full(
                 (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
@@ -110,11 +114,7 @@ class PLMSSampler(Sampler):
                 pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
             # direction pointing to x_t
             dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
-            noise = (
-                sigma_t
-                * noise_like(x.shape, device, repeat_noise)
-                * temperature
-            )
+            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
             if noise_dropout > 0.0:
                 noise = torch.nn.functional.dropout(noise, p=noise_dropout)
             x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
@@ -135,10 +135,7 @@ class PLMSSampler(Sampler):
         elif len(old_eps) >= 3:
             # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
             e_t_prime = (
-                55 * e_t
-                - 59 * old_eps[-1]
-                + 37 * old_eps[-2]
-                - 9 * old_eps[-3]
+                55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]
             ) / 24
 
         x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)

invokeai/backend/stable_diffusion/diffusion/sampler.py

@@ -1,31 +1,37 @@
-'''
-ldm.models.diffusion.sampler
+"""
+invokeai.models.diffusion.sampler
 
-Base class for ldm.models.diffusion.ddim, ldm.models.diffusion.ksampler, etc
-'''
-import torch
-import numpy as np
-from tqdm import tqdm
+Base class for invokeai.models.diffusion.ddim, invokeai.models.diffusion.ksampler, etc
+"""
 from functools import partial
-from ldm.invoke.devices import choose_torch_device
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 
-from ldm.modules.diffusionmodules.util import (
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from ...util import choose_torch_device
+from ..diffusionmodules.util import (
+    extract_into_tensor,
     make_ddim_sampling_parameters,
     make_ddim_timesteps,
     noise_like,
-    extract_into_tensor,
 )
+from .shared_invokeai_diffusion import InvokeAIDiffuserComponent
+
 
 class Sampler(object):
-    def __init__(self, model, schedule='linear', steps=None, device=None, **kwargs):
+    def __init__(self, model, schedule="linear", steps=None, device=None, **kwargs):
         self.model = model
         self.ddim_timesteps = None
         self.ddpm_num_timesteps = steps
         self.schedule = schedule
-        self.device   = device or choose_torch_device()
-        self.invokeai_diffuser = InvokeAIDiffuserComponent(self.model,
-                                                           model_forward_callback = lambda x, sigma, cond: self.model.apply_model(x, sigma, cond))
+        self.device = device or choose_torch_device()
+        self.invokeai_diffuser = InvokeAIDiffuserComponent(
+            self.model,
+            model_forward_callback=lambda x, sigma, cond: self.model.apply_model(
+                x, sigma, cond
+            ),
+        )
 
     def register_buffer(self, name, attr):
         if type(attr) == torch.Tensor:
@@ -36,11 +42,11 @@ class Sampler(object):
     # This method was copied over from ddim.py and probably does stuff that is
     # ddim-specific. Disentangle at some point.
     def make_schedule(
-            self,
-            ddim_num_steps,
-            ddim_discretize='uniform',
-            ddim_eta=0.0,
-            verbose=False,
+        self,
+        ddim_num_steps,
+        ddim_discretize="uniform",
+        ddim_eta=0.0,
+        verbose=False,
     ):
         self.total_steps = ddim_num_steps
         self.ddim_timesteps = make_ddim_timesteps(
@@ -52,38 +58,33 @@ class Sampler(object):
         alphas_cumprod = self.model.alphas_cumprod
         assert (
             alphas_cumprod.shape[0] == self.ddpm_num_timesteps
-        ), 'alphas have to be defined for each timestep'
-        to_torch = (
-            lambda x: x.clone()
-            .detach()
-            .to(torch.float32)
-            .to(self.model.device)
-        )
+        ), "alphas have to be defined for each timestep"
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
 
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer("betas", to_torch(self.model.betas))
+        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
         self.register_buffer(
-            'alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)
+            "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
         )
 
         # calculations for diffusion q(x_t | x_{t-1}) and others
         self.register_buffer(
-            'sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))
+            "sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))
         )
         self.register_buffer(
-            'sqrt_one_minus_alphas_cumprod',
+            "sqrt_one_minus_alphas_cumprod",
             to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
         )
         self.register_buffer(
-            'log_one_minus_alphas_cumprod',
+            "log_one_minus_alphas_cumprod",
             to_torch(np.log(1.0 - alphas_cumprod.cpu())),
         )
         self.register_buffer(
-            'sqrt_recip_alphas_cumprod',
+            "sqrt_recip_alphas_cumprod",
             to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())),
         )
         self.register_buffer(
-            'sqrt_recipm1_alphas_cumprod',
+            "sqrt_recipm1_alphas_cumprod",
             to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
         )
 
@@ -98,19 +99,17 @@ class Sampler(object):
             eta=ddim_eta,
             verbose=verbose,
         )
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer(
-            'ddim_sqrt_one_minus_alphas', np.sqrt(1.0 - ddim_alphas)
-        )
+        self.register_buffer("ddim_sigmas", ddim_sigmas)
+        self.register_buffer("ddim_alphas", ddim_alphas)
+        self.register_buffer("ddim_alphas_prev", ddim_alphas_prev)
+        self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas))
         sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
             (1 - self.alphas_cumprod_prev)
             / (1 - self.alphas_cumprod)
             * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
         )
         self.register_buffer(
-            'ddim_sigmas_for_original_num_steps',
+            "ddim_sigmas_for_original_num_steps",
             sigmas_for_original_sampling_steps,
         )
 
@@ -129,20 +128,19 @@ class Sampler(object):
             noise = torch.randn_like(x0)
         return (
             extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
-            + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape)
-            * noise
+            + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise
         )
 
     @torch.no_grad()
     def sample(
         self,
-        S,          # S is steps
+        S,  # S is steps
         batch_size,
         shape,
         conditioning=None,
         callback=None,
         normals_sequence=None,
-        img_callback=None,   # TODO: this is very confusing because it is called "step_callback" elsewhere. Change.
+        img_callback=None,  # TODO: this is very confusing because it is called "step_callback" elsewhere. Change.
         quantize_x0=False,
         eta=0.0,
         mask=None,
@@ -159,7 +157,6 @@ class Sampler(object):
         # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
         **kwargs,
     ):
-
         if conditioning is not None:
             if isinstance(conditioning, dict):
                 ctmp = conditioning[list(conditioning.keys())[0]]
@@ -167,17 +164,21 @@ class Sampler(object):
                     ctmp = ctmp[0]
                 cbs = ctmp.shape[0]
                 if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+                    print(
+                        f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
+                    )
             else:
                 if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+                    print(
+                        f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}"
+                    )
 
         # check to see if make_schedule() has run, and if not, run it
         if self.ddim_timesteps is None:
             self.make_schedule(
                 ddim_num_steps=S,
-                ddim_eta = eta,
-                verbose = False,
+                ddim_eta=eta,
+                verbose=False,
             )
 
         ts = self.get_timesteps(S)
@@ -204,32 +205,32 @@ class Sampler(object):
             unconditional_guidance_scale=unconditional_guidance_scale,
             unconditional_conditioning=unconditional_conditioning,
             steps=S,
-            **kwargs
+            **kwargs,
         )
         return samples, intermediates
 
     @torch.no_grad()
     def do_sampling(
-            self,
-            cond,
-            shape,
-            timesteps=None,
-            x_T=None,
-            ddim_use_original_steps=False,
-            callback=None,
-            quantize_denoised=False,
-            mask=None,
-            x0=None,
-            img_callback=None,
-            log_every_t=100,
-            temperature=1.0,
-            noise_dropout=0.0,
-            score_corrector=None,
-            corrector_kwargs=None,
-            unconditional_guidance_scale=1.0,
-            unconditional_conditioning=None,
-            steps=None,
-            **kwargs
+        self,
+        cond,
+        shape,
+        timesteps=None,
+        x_T=None,
+        ddim_use_original_steps=False,
+        callback=None,
+        quantize_denoised=False,
+        mask=None,
+        x0=None,
+        img_callback=None,
+        log_every_t=100,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        steps=None,
+        **kwargs,
     ):
         b = shape[0]
         time_range = (
@@ -238,29 +239,24 @@ class Sampler(object):
             else np.flip(timesteps)
         )
 
-        total_steps=steps
+        total_steps = steps
 
         iterator = tqdm(
             time_range,
-            desc=f'{self.__class__.__name__}',
+            desc=f"{self.__class__.__name__}",
             total=total_steps,
             dynamic_ncols=True,
         )
         old_eps = []
-        self.prepare_to_sample(t_enc=total_steps,all_timesteps_count=steps,**kwargs)
-        img = self.get_initial_image(x_T,shape,total_steps)
+        self.prepare_to_sample(t_enc=total_steps, all_timesteps_count=steps, **kwargs)
+        img = self.get_initial_image(x_T, shape, total_steps)
 
         # probably don't need this at all
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
+        intermediates = {"x_inter": [img], "pred_x0": [img]}
 
         for i, step in enumerate(iterator):
             index = total_steps - i - 1
-            ts = torch.full(
-                (b,),
-                step,
-                device=self.device,
-                dtype=torch.long
-            )
+            ts = torch.full((b,), step, device=self.device, dtype=torch.long)
             ts_next = torch.full(
                 (b,),
                 time_range[min(i + 1, len(time_range) - 1)],
@@ -290,7 +286,7 @@ class Sampler(object):
                 unconditional_conditioning=unconditional_conditioning,
                 old_eps=old_eps,
                 t_next=ts_next,
-                step_count=steps
+                step_count=steps,
             )
             img, pred_x0, e_t = outs
 
@@ -300,11 +296,11 @@ class Sampler(object):
             if callback:
                 callback(i)
             if img_callback:
-                img_callback(img,i)
+                img_callback(img, i)
 
             if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
+                intermediates["x_inter"].append(img)
+                intermediates["pred_x0"].append(pred_x0)
 
         return img, intermediates
 
@@ -312,18 +308,18 @@ class Sampler(object):
     # The variable names are changed in order to be confusing.
     @torch.no_grad()
     def decode(
-            self,
-            x_latent,
-            cond,
-            t_start,
-            img_callback=None,
-            unconditional_guidance_scale=1.0,
-            unconditional_conditioning=None,
-            use_original_steps=False,
-            init_latent       = None,
-            mask              = None,
-            all_timesteps_count = None,
-            **kwargs
+        self,
+        x_latent,
+        cond,
+        t_start,
+        img_callback=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        use_original_steps=False,
+        init_latent=None,
+        mask=None,
+        all_timesteps_count=None,
+        **kwargs,
     ):
         timesteps = (
             np.arange(self.ddpm_num_timesteps)
@@ -334,12 +330,16 @@ class Sampler(object):
 
         time_range = np.flip(timesteps)
         total_steps = timesteps.shape[0]
-        print(f'>> Running {self.__class__.__name__} sampling starting at step {self.total_steps - t_start} of {self.total_steps} ({total_steps} new sampling steps)')
+        print(
+            f">> Running {self.__class__.__name__} sampling starting at step {self.total_steps - t_start} of {self.total_steps} ({total_steps} new sampling steps)"
+        )
 
-        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
-        x_dec    = x_latent
-        x0       = init_latent
-        self.prepare_to_sample(t_enc=total_steps, all_timesteps_count=all_timesteps_count, **kwargs)
+        iterator = tqdm(time_range, desc="Decoding image", total=total_steps)
+        x_dec = x_latent
+        x0 = init_latent
+        self.prepare_to_sample(
+            t_enc=total_steps, all_timesteps_count=all_timesteps_count, **kwargs
+        )
 
         for i, step in enumerate(iterator):
             index = total_steps - i - 1
@@ -370,81 +370,85 @@ class Sampler(object):
                 use_original_steps=use_original_steps,
                 unconditional_guidance_scale=unconditional_guidance_scale,
                 unconditional_conditioning=unconditional_conditioning,
-                t_next = ts_next,
-                step_count=len(self.ddim_timesteps)
+                t_next=ts_next,
+                step_count=len(self.ddim_timesteps),
             )
 
             x_dec, pred_x0, e_t = outs
             if img_callback:
-                img_callback(x_dec,i)
+                img_callback(x_dec, i)
 
         return x_dec
 
-    def get_initial_image(self,x_T,shape,timesteps=None):
+    def get_initial_image(self, x_T, shape, timesteps=None):
         if x_T is None:
             return torch.randn(shape, device=self.device)
         else:
             return x_T
 
     def p_sample(
-            self,
-            img,
-            cond,
-            ts,
-            index,
-            repeat_noise=False,
-            use_original_steps=False,
-            quantize_denoised=False,
-            temperature=1.0,
-            noise_dropout=0.0,
-            score_corrector=None,
-            corrector_kwargs=None,
-            unconditional_guidance_scale=1.0,
-            unconditional_conditioning=None,
-            old_eps=None,
-            t_next=None,
-            steps=None,
+        self,
+        img,
+        cond,
+        ts,
+        index,
+        repeat_noise=False,
+        use_original_steps=False,
+        quantize_denoised=False,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        old_eps=None,
+        t_next=None,
+        steps=None,
     ):
-        raise NotImplementedError("p_sample() must be implemented in a descendent class")
+        raise NotImplementedError(
+            "p_sample() must be implemented in a descendent class"
+        )
 
-    def prepare_to_sample(self,t_enc,**kwargs):
-        '''
+    def prepare_to_sample(self, t_enc, **kwargs):
+        """
         Hook that will be called right before the very first invocation of p_sample()
         to allow subclass to do additional initialization. t_enc corresponds to the actual
         number of steps that will be run, and may be less than total steps if img2img is
         active.
-        '''
+        """
         pass
 
-    def get_timesteps(self,ddim_steps):
-        '''
+    def get_timesteps(self, ddim_steps):
+        """
         The ddim and plms samplers work on timesteps. This method is called after
         ddim_timesteps are created in make_schedule(), and selects the portion of
         timesteps that will be used for sampling, depending on the t_enc in img2img.
-        '''
+        """
         return self.ddim_timesteps[:ddim_steps]
 
-    def q_sample(self,x0,ts):
-        '''
+    def q_sample(self, x0, ts):
+        """
         Returns self.model.q_sample(x0,ts). Is overridden in the k* samplers to
         return self.model.inner_model.q_sample(x0,ts)
-        '''
-        return self.model.q_sample(x0,ts)
+        """
+        return self.model.q_sample(x0, ts)
 
-    def conditioning_key(self)->str:
+    def conditioning_key(self) -> str:
         return self.model.model.conditioning_key
 
-    def uses_inpainting_model(self)->bool:
-        return self.conditioning_key() in ('hybrid','concat')
+    def uses_inpainting_model(self) -> bool:
+        return self.conditioning_key() in ("hybrid", "concat")
 
-    def adjust_settings(self,**kwargs):
-        '''
+    def adjust_settings(self, **kwargs):
+        """
         This is a catch-all method for adjusting any instance variables
         after the sampler is instantiated. No type-checking performed
         here, so use with care!
-        '''
+        """
         for k in kwargs.keys():
             try:
-                setattr(self,k,kwargs[k])
+                setattr(self, k, kwargs[k])
             except AttributeError:
-                print(f'** Warning: attempt to set unknown attribute {k} in sampler of type {type(self)}')
+                print(
+                    f"** Warning: attempt to set unknown attribute {k} in sampler of type {type(self)}"
+                )

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -1,25 +1,36 @@
 from contextlib import contextmanager
 from dataclasses import dataclass
 from math import ceil
-from typing import Callable, Optional, Union, Any, Dict
+from typing import Any, Callable, Dict, Optional, Union
 
 import numpy as np
 import torch
 from diffusers.models.cross_attention import AttnProcessor
 from typing_extensions import TypeAlias
 
-from ldm.invoke.globals import Globals
-from ldm.models.diffusion.cross_attention_control import Arguments, \
-    restore_default_cross_attention, override_cross_attention, Context, get_cross_attention_modules, \
-    CrossAttentionType, SwapCrossAttnContext
-from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
+from invokeai.backend.globals import Globals
+
+from .cross_attention_control import (
+    Arguments,
+    Context,
+    CrossAttentionType,
+    SwapCrossAttnContext,
+    get_cross_attention_modules,
+    override_cross_attention,
+    restore_default_cross_attention,
+)
+from .cross_attention_map_saving import AttentionMapSaver
 
 ModelForwardCallback: TypeAlias = Union[
     # x, t, conditioning, Optional[cross-attention kwargs]
-    Callable[[torch.Tensor, torch.Tensor, torch.Tensor, Optional[dict[str, Any]]], torch.Tensor],
-    Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
+    Callable[
+        [torch.Tensor, torch.Tensor, torch.Tensor, Optional[dict[str, Any]]],
+        torch.Tensor,
+    ],
+    Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],
 ]
 
+
 @dataclass(frozen=True)
 class PostprocessingSettings:
     threshold: float
@@ -29,20 +40,20 @@ class PostprocessingSettings:
 
 
 class InvokeAIDiffuserComponent:
-    '''
+    """
     The aim of this component is to provide a single place for code that can be applied identically to
     all InvokeAI diffusion procedures.
 
     At the moment it includes the following features:
     * Cross attention control ("prompt2prompt")
     * Hybrid conditioning (used for inpainting)
-    '''
+    """
+
     debug_thresholding = False
     sequential_guidance = False
 
     @dataclass
     class ExtraConditioningInfo:
-
         tokens_count_including_eos_bos: int
         cross_attention_control_args: Optional[Arguments] = None
 
@@ -50,10 +61,12 @@ class InvokeAIDiffuserComponent:
         def wants_cross_attention_control(self):
             return self.cross_attention_control_args is not None
 
-
-    def __init__(self, model, model_forward_callback: ModelForwardCallback,
-                 is_running_diffusers: bool=False,
-                 ):
+    def __init__(
+        self,
+        model,
+        model_forward_callback: ModelForwardCallback,
+        is_running_diffusers: bool = False,
+    ):
         """
         :param model: the unet model to pass through to cross attention control
         :param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
@@ -66,23 +79,29 @@ class InvokeAIDiffuserComponent:
         self.sequential_guidance = Globals.sequential_guidance
 
     @contextmanager
-    def custom_attention_context(self,
-                                 extra_conditioning_info: Optional[ExtraConditioningInfo],
-                                 step_count: int):
-        do_swap = extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control
+    def custom_attention_context(
+        self, extra_conditioning_info: Optional[ExtraConditioningInfo], step_count: int
+    ):
+        do_swap = (
+            extra_conditioning_info is not None
+            and extra_conditioning_info.wants_cross_attention_control
+        )
         old_attn_processor = None
         if do_swap:
-            old_attn_processor = self.override_cross_attention(extra_conditioning_info,
-                                                               step_count=step_count)
+            old_attn_processor = self.override_cross_attention(
+                extra_conditioning_info, step_count=step_count
+            )
         try:
             yield None
         finally:
             if old_attn_processor is not None:
                 self.restore_default_cross_attention(old_attn_processor)
             # TODO resuscitate attention map saving
-            #self.remove_attention_map_saving()
+            # self.remove_attention_map_saving()
 
-    def override_cross_attention(self, conditioning: ExtraConditioningInfo, step_count: int) -> Dict[str, AttnProcessor]:
+    def override_cross_attention(
+        self, conditioning: ExtraConditioningInfo, step_count: int
+    ) -> Dict[str, AttnProcessor]:
         """
         setup cross attention .swap control. for diffusers this replaces the attention processor, so
         the previous attention processor is returned so that the caller can restore it later.
@@ -90,18 +109,24 @@ class InvokeAIDiffuserComponent:
         self.conditioning = conditioning
         self.cross_attention_control_context = Context(
             arguments=self.conditioning.cross_attention_control_args,
-            step_count=step_count
+            step_count=step_count,
+        )
+        return override_cross_attention(
+            self.model,
+            self.cross_attention_control_context,
+            is_running_diffusers=self.is_running_diffusers,
         )
-        return override_cross_attention(self.model,
-                                        self.cross_attention_control_context,
-                                        is_running_diffusers=self.is_running_diffusers)
 
-    def restore_default_cross_attention(self, restore_attention_processor: Optional['AttnProcessor']=None):
+    def restore_default_cross_attention(
+        self, restore_attention_processor: Optional["AttnProcessor"] = None
+    ):
         self.conditioning = None
         self.cross_attention_control_context = None
-        restore_default_cross_attention(self.model,
-                                        is_running_diffusers=self.is_running_diffusers,
-                                        restore_attention_processor=restore_attention_processor)
+        restore_default_cross_attention(
+            self.model,
+            is_running_diffusers=self.is_running_diffusers,
+            restore_attention_processor=restore_attention_processor,
+        )
 
     def setup_attention_map_saving(self, saver: AttentionMapSaver):
         def callback(slice, dim, offset, slice_size, key):
@@ -110,26 +135,40 @@ class InvokeAIDiffuserComponent:
                 return
             saver.add_attention_maps(slice, key)
 
-        tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
+        tokens_cross_attention_modules = get_cross_attention_modules(
+            self.model, CrossAttentionType.TOKENS
+        )
         for identifier, module in tokens_cross_attention_modules:
-            key = ('down' if identifier.startswith('down') else
-                   'up' if identifier.startswith('up') else
-                   'mid')
+            key = (
+                "down"
+                if identifier.startswith("down")
+                else "up"
+                if identifier.startswith("up")
+                else "mid"
+            )
             module.set_attention_slice_calculated_callback(
-                lambda slice, dim, offset, slice_size, key=key: callback(slice, dim, offset, slice_size, key))
+                lambda slice, dim, offset, slice_size, key=key: callback(
+                    slice, dim, offset, slice_size, key
+                )
+            )
 
     def remove_attention_map_saving(self):
-        tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
+        tokens_cross_attention_modules = get_cross_attention_modules(
+            self.model, CrossAttentionType.TOKENS
+        )
         for _, module in tokens_cross_attention_modules:
             module.set_attention_slice_calculated_callback(None)
 
-    def do_diffusion_step(self, x: torch.Tensor, sigma: torch.Tensor,
-                                unconditioning: Union[torch.Tensor,dict],
-                                conditioning: Union[torch.Tensor,dict],
-                                unconditional_guidance_scale: float,
-                                step_index: Optional[int]=None,
-                                total_step_count: Optional[int]=None,
-                          ):
+    def do_diffusion_step(
+        self,
+        x: torch.Tensor,
+        sigma: torch.Tensor,
+        unconditioning: Union[torch.Tensor, dict],
+        conditioning: Union[torch.Tensor, dict],
+        unconditional_guidance_scale: float,
+        step_index: Optional[int] = None,
+        total_step_count: Optional[int] = None,
+    ):
         """
         :param x: current latents
         :param sigma: aka t, passed to the internal model to control how much denoising will occur
@@ -140,33 +179,55 @@ class InvokeAIDiffuserComponent:
         :return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning.
         """
 
-
         cross_attention_control_types_to_do = []
         context: Context = self.cross_attention_control_context
         if self.cross_attention_control_context is not None:
-            percent_through = self.calculate_percent_through(sigma, step_index, total_step_count)
-            cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)
+            percent_through = self.calculate_percent_through(
+                sigma, step_index, total_step_count
+            )
+            cross_attention_control_types_to_do = (
+                context.get_active_cross_attention_control_types_for_step(
+                    percent_through
+                )
+            )
 
-        wants_cross_attention_control = (len(cross_attention_control_types_to_do) > 0)
+        wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
         wants_hybrid_conditioning = isinstance(conditioning, dict)
 
         if wants_hybrid_conditioning:
-            unconditioned_next_x, conditioned_next_x = self._apply_hybrid_conditioning(x, sigma, unconditioning,
-                                                                                       conditioning)
+            unconditioned_next_x, conditioned_next_x = self._apply_hybrid_conditioning(
+                x, sigma, unconditioning, conditioning
+            )
         elif wants_cross_attention_control:
-            unconditioned_next_x, conditioned_next_x = self._apply_cross_attention_controlled_conditioning(x, sigma,
-                                                                                                           unconditioning,
-                                                                                                           conditioning,
-                                                                                                           cross_attention_control_types_to_do)
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_cross_attention_controlled_conditioning(
+                x,
+                sigma,
+                unconditioning,
+                conditioning,
+                cross_attention_control_types_to_do,
+            )
         elif self.sequential_guidance:
-            unconditioned_next_x, conditioned_next_x = self._apply_standard_conditioning_sequentially(
-                x, sigma, unconditioning, conditioning)
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_standard_conditioning_sequentially(
+                x, sigma, unconditioning, conditioning
+            )
 
         else:
-            unconditioned_next_x, conditioned_next_x = self._apply_standard_conditioning(
-                x, sigma, unconditioning, conditioning)
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_standard_conditioning(
+                x, sigma, unconditioning, conditioning
+            )
 
-        combined_next_x = self._combine(unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale)
+        combined_next_x = self._combine(
+            unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale
+        )
 
         return combined_next_x
 
@@ -176,24 +237,33 @@ class InvokeAIDiffuserComponent:
         latents: torch.Tensor,
         sigma,
         step_index,
-        total_step_count
+        total_step_count,
     ) -> torch.Tensor:
         if postprocessing_settings is not None:
-            percent_through = self.calculate_percent_through(sigma, step_index, total_step_count)
-            latents = self.apply_threshold(postprocessing_settings, latents, percent_through)
-            latents = self.apply_symmetry(postprocessing_settings, latents, percent_through)
+            percent_through = self.calculate_percent_through(
+                sigma, step_index, total_step_count
+            )
+            latents = self.apply_threshold(
+                postprocessing_settings, latents, percent_through
+            )
+            latents = self.apply_symmetry(
+                postprocessing_settings, latents, percent_through
+            )
         return latents
 
     def calculate_percent_through(self, sigma, step_index, total_step_count):
         if step_index is not None and total_step_count is not None:
             # 🧨diffusers codepath
-            percent_through = step_index / total_step_count  # will never reach 1.0 - this is deliberate
+            percent_through = (
+                step_index / total_step_count
+            )  # will never reach 1.0 - this is deliberate
         else:
             # legacy compvis codepath
             # TODO remove when compvis codepath support is dropped
             if step_index is None and sigma is None:
                 raise ValueError(
-                    f"Either step_index or sigma is required when doing cross attention control, but both are None.")
+                    f"Either step_index or sigma is required when doing cross attention control, but both are None."
+                )
             percent_through = self.estimate_percent_through(step_index, sigma)
         return percent_through
 
@@ -204,24 +274,30 @@ class InvokeAIDiffuserComponent:
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
         both_conditionings = torch.cat([unconditioning, conditioning])
-        both_results = self.model_forward_callback(x_twice, sigma_twice, both_conditionings)
+        both_results = self.model_forward_callback(
+            x_twice, sigma_twice, both_conditionings
+        )
         unconditioned_next_x, conditioned_next_x = both_results.chunk(2)
-        if conditioned_next_x.device.type == 'mps':
+        if conditioned_next_x.device.type == "mps":
             # prevent a result filled with zeros. seems to be a torch bug.
             conditioned_next_x = conditioned_next_x.clone()
         return unconditioned_next_x, conditioned_next_x
 
-
-    def _apply_standard_conditioning_sequentially(self, x: torch.Tensor, sigma, unconditioning: torch.Tensor, conditioning: torch.Tensor):
+    def _apply_standard_conditioning_sequentially(
+        self,
+        x: torch.Tensor,
+        sigma,
+        unconditioning: torch.Tensor,
+        conditioning: torch.Tensor,
+    ):
         # low-memory sequential path
         unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
         conditioned_next_x = self.model_forward_callback(x, sigma, conditioning)
-        if conditioned_next_x.device.type == 'mps':
+        if conditioned_next_x.device.type == "mps":
             # prevent a result filled with zeros. seems to be a torch bug.
             conditioned_next_x = conditioned_next_x.clone()
         return unconditioned_next_x, conditioned_next_x
 
-
     def _apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning):
         assert isinstance(conditioning, dict)
         assert isinstance(unconditioning, dict)
@@ -236,48 +312,80 @@ class InvokeAIDiffuserComponent:
                 ]
             else:
                 both_conditionings[k] = torch.cat([unconditioning[k], conditioning[k]])
-        unconditioned_next_x, conditioned_next_x = self.model_forward_callback(x_twice, sigma_twice, both_conditionings).chunk(2)
+        unconditioned_next_x, conditioned_next_x = self.model_forward_callback(
+            x_twice, sigma_twice, both_conditionings
+        ).chunk(2)
         return unconditioned_next_x, conditioned_next_x
 
-
-    def _apply_cross_attention_controlled_conditioning(self,
-                                                     x: torch.Tensor,
-                                                     sigma,
-                                                     unconditioning,
-                                                     conditioning,
-                                                     cross_attention_control_types_to_do):
+    def _apply_cross_attention_controlled_conditioning(
+        self,
+        x: torch.Tensor,
+        sigma,
+        unconditioning,
+        conditioning,
+        cross_attention_control_types_to_do,
+    ):
         if self.is_running_diffusers:
-            return self._apply_cross_attention_controlled_conditioning__diffusers(x, sigma, unconditioning,
-                                                                                  conditioning,
-                                                                                  cross_attention_control_types_to_do)
+            return self._apply_cross_attention_controlled_conditioning__diffusers(
+                x,
+                sigma,
+                unconditioning,
+                conditioning,
+                cross_attention_control_types_to_do,
+            )
         else:
-            return self._apply_cross_attention_controlled_conditioning__compvis(x, sigma, unconditioning, conditioning,
-                                                                                cross_attention_control_types_to_do)
+            return self._apply_cross_attention_controlled_conditioning__compvis(
+                x,
+                sigma,
+                unconditioning,
+                conditioning,
+                cross_attention_control_types_to_do,
+            )
 
-    def _apply_cross_attention_controlled_conditioning__diffusers(self,
-                                                                 x: torch.Tensor,
-                                                                 sigma,
-                                                                 unconditioning,
-                                                                 conditioning,
-                                                                 cross_attention_control_types_to_do):
+    def _apply_cross_attention_controlled_conditioning__diffusers(
+        self,
+        x: torch.Tensor,
+        sigma,
+        unconditioning,
+        conditioning,
+        cross_attention_control_types_to_do,
+    ):
         context: Context = self.cross_attention_control_context
 
-        cross_attn_processor_context = SwapCrossAttnContext(modified_text_embeddings=context.arguments.edited_conditioning,
-                                                            index_map=context.cross_attention_index_map,
-                                                            mask=context.cross_attention_mask,
-                                                            cross_attention_types_to_do=[])
+        cross_attn_processor_context = SwapCrossAttnContext(
+            modified_text_embeddings=context.arguments.edited_conditioning,
+            index_map=context.cross_attention_index_map,
+            mask=context.cross_attention_mask,
+            cross_attention_types_to_do=[],
+        )
         # no cross attention for unconditioning (negative prompt)
-        unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning,
-                                                           {"swap_cross_attn_context": cross_attn_processor_context})
+        unconditioned_next_x = self.model_forward_callback(
+            x,
+            sigma,
+            unconditioning,
+            {"swap_cross_attn_context": cross_attn_processor_context},
+        )
 
         # do requested cross attention types for conditioning (positive prompt)
-        cross_attn_processor_context.cross_attention_types_to_do = cross_attention_control_types_to_do
-        conditioned_next_x = self.model_forward_callback(x, sigma, conditioning,
-                                                         {"swap_cross_attn_context": cross_attn_processor_context})
+        cross_attn_processor_context.cross_attention_types_to_do = (
+            cross_attention_control_types_to_do
+        )
+        conditioned_next_x = self.model_forward_callback(
+            x,
+            sigma,
+            conditioning,
+            {"swap_cross_attn_context": cross_attn_processor_context},
+        )
         return unconditioned_next_x, conditioned_next_x
 
-
-    def _apply_cross_attention_controlled_conditioning__compvis(self, x:torch.Tensor, sigma, unconditioning, conditioning, cross_attention_control_types_to_do):
+    def _apply_cross_attention_controlled_conditioning__compvis(
+        self,
+        x: torch.Tensor,
+        sigma,
+        unconditioning,
+        conditioning,
+        cross_attention_control_types_to_do,
+    ):
         # print('pct', percent_through, ': doing cross attention control on', cross_attention_control_types_to_do)
         # slower non-batched path (20% slower on mac MPS)
         # We are only interested in using attention maps for conditioned_next_x, but batching them with generation of
@@ -287,24 +395,28 @@ class InvokeAIDiffuserComponent:
         # representing batched uncond + cond, but then when it comes to applying the saved attention, the
         # wrangler gets an attention tensor which only has shape[0]=8, representing just self.edited_conditionings.)
         # todo: give CrossAttentionControl's `wrangler` function more info so it can work with a batched call as well.
-        context:Context = self.cross_attention_control_context
+        context: Context = self.cross_attention_control_context
 
         try:
             unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
 
             # process x using the original prompt, saving the attention maps
-            #print("saving attention maps for", cross_attention_control_types_to_do)
+            # print("saving attention maps for", cross_attention_control_types_to_do)
             for ca_type in cross_attention_control_types_to_do:
                 context.request_save_attention_maps(ca_type)
             _ = self.model_forward_callback(x, sigma, conditioning)
             context.clear_requests(cleanup=False)
 
             # process x again, using the saved attention maps to control where self.edited_conditioning will be applied
-            #print("applying saved attention maps for", cross_attention_control_types_to_do)
+            # print("applying saved attention maps for", cross_attention_control_types_to_do)
             for ca_type in cross_attention_control_types_to_do:
                 context.request_apply_saved_attention_maps(ca_type)
-            edited_conditioning = self.conditioning.cross_attention_control_args.edited_conditioning
-            conditioned_next_x = self.model_forward_callback(x, sigma, edited_conditioning)
+            edited_conditioning = (
+                self.conditioning.cross_attention_control_args.edited_conditioning
+            )
+            conditioned_next_x = self.model_forward_callback(
+                x, sigma, edited_conditioning
+            )
             context.clear_requests(cleanup=True)
 
         except:
@@ -323,17 +435,21 @@ class InvokeAIDiffuserComponent:
         self,
         postprocessing_settings: PostprocessingSettings,
         latents: torch.Tensor,
-        percent_through: float
+        percent_through: float,
     ) -> torch.Tensor:
-
-        if postprocessing_settings.threshold is None or postprocessing_settings.threshold == 0.0:
+        if (
+            postprocessing_settings.threshold is None
+            or postprocessing_settings.threshold == 0.0
+        ):
             return latents
 
         threshold = postprocessing_settings.threshold
         warmup = postprocessing_settings.warmup
 
         if percent_through < warmup:
-            current_threshold = threshold + threshold * 5 * (1 - (percent_through / warmup))
+            current_threshold = threshold + threshold * 5 * (
+                1 - (percent_through / warmup)
+            )
         else:
             current_threshold = threshold
 
@@ -347,10 +463,14 @@ class InvokeAIDiffuserComponent:
 
         if self.debug_thresholding:
             std, mean = [i.item() for i in torch.std_mean(latents)]
-            outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold))
-            print(f"\nThreshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
-                  f"  | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
-                  f"  | {outside / latents.numel() * 100:.2f}% values outside threshold")
+            outside = torch.count_nonzero(
+                (latents < -current_threshold) | (latents > current_threshold)
+            )
+            print(
+                f"\nThreshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
+                f"  | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
+                f"  | {outside / latents.numel() * 100:.2f}% values outside threshold"
+            )
 
         if maxval < current_threshold and minval > -current_threshold:
             return latents
@@ -363,17 +483,23 @@ class InvokeAIDiffuserComponent:
             latents = torch.clone(latents)
             maxval = np.clip(maxval * scale, 1, current_threshold)
             num_altered += torch.count_nonzero(latents > maxval)
-            latents[latents > maxval] = torch.rand_like(latents[latents > maxval]) * maxval
+            latents[latents > maxval] = (
+                torch.rand_like(latents[latents > maxval]) * maxval
+            )
 
         if minval < -current_threshold:
             latents = torch.clone(latents)
             minval = np.clip(minval * scale, -current_threshold, -1)
             num_altered += torch.count_nonzero(latents < minval)
-            latents[latents < minval] = torch.rand_like(latents[latents < minval]) * minval
+            latents[latents < minval] = (
+                torch.rand_like(latents[latents < minval]) * minval
+            )
 
         if self.debug_thresholding:
-            print(f"  | min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})\n"
-                  f"  | {num_altered / latents.numel() * 100:.2f}% values altered")
+            print(
+                f"  | min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})\n"
+                f"  | {num_altered / latents.numel() * 100:.2f}% values altered"
+            )
 
         return latents
 
@@ -381,9 +507,8 @@ class InvokeAIDiffuserComponent:
         self,
         postprocessing_settings: PostprocessingSettings,
         latents: torch.Tensor,
-        percent_through: float
+        percent_through: float,
     ) -> torch.Tensor:
-
         # Reset our last percent through if this is our first step.
         if percent_through == 0.0:
             self.last_percent_through = 0.0
@@ -393,36 +518,52 @@ class InvokeAIDiffuserComponent:
 
         # Check for out of bounds
         h_symmetry_time_pct = postprocessing_settings.h_symmetry_time_pct
-        if (h_symmetry_time_pct is not None and (h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0)):
+        if h_symmetry_time_pct is not None and (
+            h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0
+        ):
             h_symmetry_time_pct = None
 
         v_symmetry_time_pct = postprocessing_settings.v_symmetry_time_pct
-        if (v_symmetry_time_pct is not None and (v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0)):
+        if v_symmetry_time_pct is not None and (
+            v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0
+        ):
             v_symmetry_time_pct = None
 
         dev = latents.device.type
 
-        latents.to(device='cpu')
+        latents.to(device="cpu")
 
         if (
-            h_symmetry_time_pct != None and
-            self.last_percent_through < h_symmetry_time_pct and
-            percent_through >= h_symmetry_time_pct
+            h_symmetry_time_pct != None
+            and self.last_percent_through < h_symmetry_time_pct
+            and percent_through >= h_symmetry_time_pct
         ):
             # Horizontal symmetry occurs on the 3rd dimension of the latent
             width = latents.shape[3]
             x_flipped = torch.flip(latents, dims=[3])
-            latents = torch.cat([latents[:, :, :, 0:int(width/2)], x_flipped[:, :, :, int(width/2):int(width)]], dim=3)
+            latents = torch.cat(
+                [
+                    latents[:, :, :, 0 : int(width / 2)],
+                    x_flipped[:, :, :, int(width / 2) : int(width)],
+                ],
+                dim=3,
+            )
 
         if (
-            v_symmetry_time_pct != None and
-            self.last_percent_through < v_symmetry_time_pct and
-            percent_through >= v_symmetry_time_pct
+            v_symmetry_time_pct != None
+            and self.last_percent_through < v_symmetry_time_pct
+            and percent_through >= v_symmetry_time_pct
         ):
             # Vertical symmetry occurs on the 2nd dimension of the latent
             height = latents.shape[2]
             y_flipped = torch.flip(latents, dims=[2])
-            latents = torch.cat([latents[:, :, 0:int(height / 2)], y_flipped[:, :, int(height / 2):int(height)]], dim=2)
+            latents = torch.cat(
+                [
+                    latents[:, :, 0 : int(height / 2)],
+                    y_flipped[:, :, int(height / 2) : int(height)],
+                ],
+                dim=2,
+            )
 
         self.last_percent_through = percent_through
         return latents.to(device=dev)
@@ -430,7 +571,9 @@ class InvokeAIDiffuserComponent:
     def estimate_percent_through(self, step_index, sigma):
         if step_index is not None and self.cross_attention_control_context is not None:
             # percent_through will never reach 1.0 (but this is intended)
-            return float(step_index) / float(self.cross_attention_control_context.step_count)
+            return float(step_index) / float(
+                self.cross_attention_control_context.step_count
+            )
         # find the best possible index of the current sigma in the sigma sequence
         smaller_sigmas = torch.nonzero(self.model.sigmas <= sigma)
         sigma_index = smaller_sigmas[-1].item() if smaller_sigmas.shape[0] > 0 else 0
@@ -439,33 +582,38 @@ class InvokeAIDiffuserComponent:
         return 1.0 - float(sigma_index + 1) / float(self.model.sigmas.shape[0])
         # print('estimated percent_through', percent_through, 'from sigma', sigma.item())
 
-
     # todo: make this work
     @classmethod
-    def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):
+    def apply_conjunction(
+        cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale
+    ):
         x_in = torch.cat([x] * 2)
-        t_in = torch.cat([t] * 2) # aka sigmas
+        t_in = torch.cat([t] * 2)  # aka sigmas
 
         deltas = None
         uncond_latents = None
-        weighted_cond_list = c_or_weighted_c_list if type(c_or_weighted_c_list) is list else [(c_or_weighted_c_list, 1)]
+        weighted_cond_list = (
+            c_or_weighted_c_list
+            if type(c_or_weighted_c_list) is list
+            else [(c_or_weighted_c_list, 1)]
+        )
 
         # below is fugly omg
         num_actual_conditionings = len(c_or_weighted_c_list)
-        conditionings = [uc] + [c for c,weight in weighted_cond_list]
-        weights = [1] + [weight for c,weight in weighted_cond_list]
-        chunk_count = ceil(len(conditionings)/2)
+        conditionings = [uc] + [c for c, weight in weighted_cond_list]
+        weights = [1] + [weight for c, weight in weighted_cond_list]
+        chunk_count = ceil(len(conditionings) / 2)
         deltas = None
         for chunk_index in range(chunk_count):
-            offset = chunk_index*2
-            chunk_size = min(2, len(conditionings)-offset)
+            offset = chunk_index * 2
+            chunk_size = min(2, len(conditionings) - offset)
 
             if chunk_size == 1:
                 c_in = conditionings[offset]
                 latents_a = forward_func(x_in[:-1], t_in[:-1], c_in)
                 latents_b = None
             else:
-                c_in = torch.cat(conditionings[offset:offset+2])
+                c_in = torch.cat(conditionings[offset : offset + 2])
                 latents_a, latents_b = forward_func(x_in, t_in, c_in).chunk(2)
 
             # first chunk is guaranteed to be 2 entries: uncond_latents + first conditioining
@@ -478,11 +626,15 @@ class InvokeAIDiffuserComponent:
                     deltas = torch.cat((deltas, latents_b - uncond_latents))
 
         # merge the weighted deltas together into a single merged delta
-        per_delta_weights = torch.tensor(weights[1:], dtype=deltas.dtype, device=deltas.device)
+        per_delta_weights = torch.tensor(
+            weights[1:], dtype=deltas.dtype, device=deltas.device
+        )
         normalize = False
         if normalize:
             per_delta_weights /= torch.sum(per_delta_weights)
-        reshaped_weights = per_delta_weights.reshape(per_delta_weights.shape + (1, 1, 1))
+        reshaped_weights = per_delta_weights.reshape(
+            per_delta_weights.shape + (1, 1, 1)
+        )
         deltas_merged = torch.sum(deltas * reshaped_weights, dim=0, keepdim=True)
 
         # old_return_value = super().forward(x, sigma, uncond, cond, cond_scale)

invokeai/backend/stable_diffusion/diffusionmodules/openaimodel.py

@@ -1,23 +1,22 @@
+import math
 from abc import abstractmethod
 from functools import partial
-import math
 from typing import Iterable
 
 import numpy as np
 import torch as th
 import torch.nn as nn
 import torch.nn.functional as F
-
+from ldm.modules.attention import SpatialTransformer
 from ldm.modules.diffusionmodules.util import (
+    avg_pool_nd,
     checkpoint,
     conv_nd,
     linear,
-    avg_pool_nd,
-    zero_module,
     normalization,
     timestep_embedding,
+    zero_module,
 )
-from ldm.modules.attention import SpatialTransformer
 
 
 # dummy replace
@@ -100,9 +99,7 @@ class Upsample(nn.Module):
                  upsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(
-        self, channels, use_conv, dims=2, out_channels=None, padding=1
-    ):
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
@@ -117,10 +114,10 @@ class Upsample(nn.Module):
         assert x.shape[1] == self.channels
         if self.dims == 3:
             x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest'
+                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
             )
         else:
-            x = F.interpolate(x, scale_factor=2, mode='nearest')
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
         if self.use_conv:
             x = self.conv(x)
         return x
@@ -151,9 +148,7 @@ class Downsample(nn.Module):
                  downsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(
-        self, channels, use_conv, dims=2, out_channels=None, padding=1
-    ):
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
@@ -237,9 +232,7 @@ class ResBlock(TimestepBlock):
             nn.SiLU(),
             linear(
                 emb_channels,
-                2 * self.out_channels
-                if use_scale_shift_norm
-                else self.out_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
             ),
         )
         self.out_layers = nn.Sequential(
@@ -247,9 +240,7 @@ class ResBlock(TimestepBlock):
             nn.SiLU(),
             nn.Dropout(p=dropout),
             zero_module(
-                conv_nd(
-                    dims, self.out_channels, self.out_channels, 3, padding=1
-                )
+                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
             ),
         )
 
@@ -260,9 +251,7 @@ class ResBlock(TimestepBlock):
                 dims, channels, self.out_channels, 3, padding=1
             )
         else:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 1
-            )
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
 
     def forward(self, x, emb):
         """
@@ -320,7 +309,7 @@ class AttentionBlock(nn.Module):
         else:
             assert (
                 channels % num_head_channels == 0
-            ), f'q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}'
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
             self.num_heads = channels // num_head_channels
         self.use_checkpoint = use_checkpoint
         self.norm = normalization(channels)
@@ -337,7 +326,7 @@ class AttentionBlock(nn.Module):
     def forward(self, x):
         return checkpoint(
             self._forward, (x,), self.parameters(), True
-        )   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+        )  # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
         # return pt_checkpoint(self._forward, x)  # pytorch
 
     def _forward(self, x):
@@ -387,15 +376,13 @@ class QKVAttentionLegacy(nn.Module):
         bs, width, length = qkv.shape
         assert width % (3 * self.n_heads) == 0
         ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(
-            ch, dim=1
-        )
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
         scale = 1 / math.sqrt(math.sqrt(ch))
         weight = th.einsum(
-            'bct,bcs->bts', q * scale, k * scale
+            "bct,bcs->bts", q * scale, k * scale
         )  # More stable with f16 than dividing afterwards
         weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum('bts,bcs->bct', weight, v)
+        a = th.einsum("bts,bcs->bct", weight, v)
         return a.reshape(bs, -1, length)
 
     @staticmethod
@@ -424,14 +411,12 @@ class QKVAttention(nn.Module):
         q, k, v = qkv.chunk(3, dim=1)
         scale = 1 / math.sqrt(math.sqrt(ch))
         weight = th.einsum(
-            'bct,bcs->bts',
+            "bct,bcs->bts",
             (q * scale).view(bs * self.n_heads, ch, length),
             (k * scale).view(bs * self.n_heads, ch, length),
         )  # More stable with f16 than dividing afterwards
         weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum(
-            'bts,bcs->bct', weight, v.reshape(bs * self.n_heads, ch, length)
-        )
+        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
         return a.reshape(bs, -1, length)
 
     @staticmethod
@@ -500,12 +485,12 @@ class UNetModel(nn.Module):
         if use_spatial_transformer:
             assert (
                 context_dim is not None
-            ), 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+            ), "Fool!! You forgot to include the dimension of your cross-attention conditioning..."
 
         if context_dim is not None:
             assert (
                 use_spatial_transformer
-            ), 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            ), "Fool!! You forgot to use the spatial transformer for your cross-attention conditioning..."
             from omegaconf.listconfig import ListConfig
 
             if type(context_dim) == ListConfig:
@@ -517,12 +502,12 @@ class UNetModel(nn.Module):
         if num_heads == -1:
             assert (
                 num_head_channels != -1
-            ), 'Either num_heads or num_head_channels has to be set'
+            ), "Either num_heads or num_head_channels has to be set"
 
         if num_head_channels == -1:
             assert (
                 num_heads != -1
-            ), 'Either num_heads or num_head_channels has to be set'
+            ), "Either num_heads or num_head_channels has to be set"
 
         self.image_size = image_size
         self.in_channels = in_channels
@@ -641,11 +626,7 @@ class UNetModel(nn.Module):
             dim_head = num_head_channels
         if legacy:
             # num_heads = 1
-            dim_head = (
-                ch // num_heads
-                if use_spatial_transformer
-                else num_head_channels
-            )
+            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
         self.middle_block = TimestepEmbedSequential(
             ResBlock(
                 ch,
@@ -741,9 +722,7 @@ class UNetModel(nn.Module):
                             up=True,
                         )
                         if resblock_updown
-                        else Upsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
                     )
                     ds //= 2
                 self.output_blocks.append(TimestepEmbedSequential(*layers))
@@ -752,9 +731,7 @@ class UNetModel(nn.Module):
         self.out = nn.Sequential(
             normalization(ch),
             nn.SiLU(),
-            zero_module(
-                conv_nd(dims, model_channels, out_channels, 3, padding=1)
-            ),
+            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
         )
         if self.predict_codebook_ids:
             self.id_predictor = nn.Sequential(
@@ -790,11 +767,9 @@ class UNetModel(nn.Module):
         """
         assert (y is not None) == (
             self.num_classes is not None
-        ), 'must specify y if and only if the model is class-conditional'
+        ), "must specify y if and only if the model is class-conditional"
         hs = []
-        t_emb = timestep_embedding(
-            timesteps, self.model_channels, repeat_only=False
-        )
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
         emb = self.time_embed(t_emb)
 
         if self.num_classes is not None:
@@ -842,7 +817,7 @@ class EncoderUNetModel(nn.Module):
         use_scale_shift_norm=False,
         resblock_updown=False,
         use_new_attention_order=False,
-        pool='adaptive',
+        pool="adaptive",
         *args,
         **kwargs,
     ):
@@ -962,7 +937,7 @@ class EncoderUNetModel(nn.Module):
         )
         self._feature_size += ch
         self.pool = pool
-        if pool == 'adaptive':
+        if pool == "adaptive":
             self.out = nn.Sequential(
                 normalization(ch),
                 nn.SiLU(),
@@ -970,7 +945,7 @@ class EncoderUNetModel(nn.Module):
                 zero_module(conv_nd(dims, ch, out_channels, 1)),
                 nn.Flatten(),
             )
-        elif pool == 'attention':
+        elif pool == "attention":
             assert num_head_channels != -1
             self.out = nn.Sequential(
                 normalization(ch),
@@ -979,13 +954,13 @@ class EncoderUNetModel(nn.Module):
                     (image_size // ds), ch, num_head_channels, out_channels
                 ),
             )
-        elif pool == 'spatial':
+        elif pool == "spatial":
             self.out = nn.Sequential(
                 nn.Linear(self._feature_size, 2048),
                 nn.ReLU(),
                 nn.Linear(2048, self.out_channels),
             )
-        elif pool == 'spatial_v2':
+        elif pool == "spatial_v2":
             self.out = nn.Sequential(
                 nn.Linear(self._feature_size, 2048),
                 normalization(2048),
@@ -993,7 +968,7 @@ class EncoderUNetModel(nn.Module):
                 nn.Linear(2048, self.out_channels),
             )
         else:
-            raise NotImplementedError(f'Unexpected {pool} pooling')
+            raise NotImplementedError(f"Unexpected {pool} pooling")
 
     def convert_to_fp16(self):
         """
@@ -1016,18 +991,16 @@ class EncoderUNetModel(nn.Module):
         :param timesteps: a 1-D batch of timesteps.
         :return: an [N x K] Tensor of outputs.
         """
-        emb = self.time_embed(
-            timestep_embedding(timesteps, self.model_channels)
-        )
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
 
         results = []
         h = x.type(self.dtype)
         for module in self.input_blocks:
             h = module(h, emb)
-            if self.pool.startswith('spatial'):
+            if self.pool.startswith("spatial"):
                 results.append(h.type(x.dtype).mean(dim=(2, 3)))
         h = self.middle_block(h, emb)
-        if self.pool.startswith('spatial'):
+        if self.pool.startswith("spatial"):
             results.append(h.type(x.dtype).mean(dim=(2, 3)))
             h = th.cat(results, axis=-1)
             return self.out(h)

invokeai/backend/stable_diffusion/diffusionmodules/util.py

@@ -8,20 +8,21 @@
 # thanks!
 
 
-import os
 import math
+import os
+
+import numpy as np
 import torch
 import torch.nn as nn
-import numpy as np
 from einops import repeat
 
-from ldm.util import instantiate_from_config
+from ...util.util import instantiate_from_config
 
 
 def make_beta_schedule(
     schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
 ):
-    if schedule == 'linear':
+    if schedule == "linear":
         betas = (
             torch.linspace(
                 linear_start**0.5,
@@ -32,10 +33,9 @@ def make_beta_schedule(
             ** 2
         )
 
-    elif schedule == 'cosine':
+    elif schedule == "cosine":
         timesteps = (
-            torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep
-            + cosine_s
+            torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
         )
         alphas = timesteps / (1 + cosine_s) * np.pi / 2
         alphas = torch.cos(alphas).pow(2)
@@ -43,15 +43,13 @@ def make_beta_schedule(
         betas = 1 - alphas[1:] / alphas[:-1]
         betas = np.clip(betas, a_min=0, a_max=0.999)
 
-    elif schedule == 'sqrt_linear':
+    elif schedule == "sqrt_linear":
         betas = torch.linspace(
             linear_start, linear_end, n_timestep, dtype=torch.float64
         )
-    elif schedule == 'sqrt':
+    elif schedule == "sqrt":
         betas = (
-            torch.linspace(
-                linear_start, linear_end, n_timestep, dtype=torch.float64
-            )
+            torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
             ** 0.5
         )
     else:
@@ -62,19 +60,14 @@ def make_beta_schedule(
 def make_ddim_timesteps(
     ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True
 ):
-    if ddim_discr_method == 'uniform':
+    if ddim_discr_method == "uniform":
         c = num_ddpm_timesteps // num_ddim_timesteps
         if c < 1:
-          c = 1
+            c = 1
         ddim_timesteps = (np.arange(0, num_ddim_timesteps) * c).astype(int)
-    elif ddim_discr_method == 'quad':
+    elif ddim_discr_method == "quad":
         ddim_timesteps = (
-            (
-                np.linspace(
-                    0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps
-                )
-            )
-            ** 2
+            (np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2
         ).astype(int)
     else:
         raise NotImplementedError(
@@ -87,18 +80,14 @@ def make_ddim_timesteps(
     # steps_out = ddim_timesteps
 
     if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
+        print(f"Selected timesteps for ddim sampler: {steps_out}")
     return steps_out
 
 
-def make_ddim_sampling_parameters(
-    alphacums, ddim_timesteps, eta, verbose=True
-):
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
     # select alphas for computing the variance schedule
     alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray(
-        [alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()
-    )
+    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
 
     # according the the formula provided in https://arxiv.org/abs/2010.02502
     sigmas = eta * np.sqrt(
@@ -106,11 +95,11 @@ def make_ddim_sampling_parameters(
     )
     if verbose:
         print(
-            f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}'
+            f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}"
         )
         print(
-            f'For the chosen value of eta, which is {eta}, '
-            f'this results in the following sigma_t schedule for ddim sampler {sigmas}'
+            f"For the chosen value of eta, which is {eta}, "
+            f"this results in the following sigma_t schedule for ddim sampler {sigmas}"
         )
     return sigmas, alphas, alphas_prev
 
@@ -150,9 +139,7 @@ def checkpoint(func, inputs, params, flag):
                    explicitly take as arguments.
     :param flag: if False, disable gradient checkpointing.
     """
-    if (
-        False
-    ):   # disabled checkpointing to allow requires_grad = False for main model
+    if False:  # disabled checkpointing to allow requires_grad = False for main model
         args = tuple(inputs) + tuple(params)
         return CheckpointFunction.apply(func, len(inputs), *args)
     else:
@@ -172,9 +159,7 @@ class CheckpointFunction(torch.autograd.Function):
 
     @staticmethod
     def backward(ctx, *output_grads):
-        ctx.input_tensors = [
-            x.detach().requires_grad_(True) for x in ctx.input_tensors
-        ]
+        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
         with torch.enable_grad():
             # Fixes a bug where the first op in run_function modifies the
             # Tensor storage in place, which is not allowed for detach()'d
@@ -216,7 +201,7 @@ def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
                 [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
             )
     else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
+        embedding = repeat(timesteps, "b -> b d", d=dim)
     return embedding
 
 
@@ -269,7 +254,7 @@ def conv_nd(dims, *args, **kwargs):
         return nn.Conv2d(*args, **kwargs)
     elif dims == 3:
         return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f'unsupported dimensions: {dims}')
+    raise ValueError(f"unsupported dimensions: {dims}")
 
 
 def linear(*args, **kwargs):
@@ -289,21 +274,19 @@ def avg_pool_nd(dims, *args, **kwargs):
         return nn.AvgPool2d(*args, **kwargs)
     elif dims == 3:
         return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f'unsupported dimensions: {dims}')
+    raise ValueError(f"unsupported dimensions: {dims}")
 
 
 class HybridConditioner(nn.Module):
     def __init__(self, c_concat_config, c_crossattn_config):
         super().__init__()
         self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(
-            c_crossattn_config
-        )
+        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
 
     def forward(self, c_concat, c_crossattn):
         c_concat = self.concat_conditioner(c_concat)
         c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
+        return {"c_concat": [c_concat], "c_crossattn": [c_crossattn]}
 
 
 def noise_like(shape, device, repeat=False):

invokeai/backend/stable_diffusion/distributions/distributions.py

@@ -1,5 +1,5 @@
-import torch
 import numpy as np
+import torch
 
 
 class AbstractDistribution:
@@ -64,9 +64,7 @@ class DiagonalGaussianDistribution(object):
             return torch.Tensor([0.0])
         logtwopi = np.log(2.0 * np.pi)
         return 0.5 * torch.sum(
-            logtwopi
-            + self.logvar
-            + torch.pow(sample - self.mean, 2) / self.var,
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
             dim=dims,
         )
 
@@ -86,7 +84,7 @@ def normal_kl(mean1, logvar1, mean2, logvar2):
         if isinstance(obj, torch.Tensor):
             tensor = obj
             break
-    assert tensor is not None, 'at least one argument must be a Tensor'
+    assert tensor is not None, "at least one argument must be a Tensor"
 
     # Force variances to be Tensors. Broadcasting helps convert scalars to
     # Tensors, but it does not work for torch.exp().

invokeai/backend/stable_diffusion/ema.py

@@ -6,12 +6,12 @@ class LitEma(nn.Module):
     def __init__(self, model, decay=0.9999, use_num_upates=True):
         super().__init__()
         if decay < 0.0 or decay > 1.0:
-            raise ValueError('Decay must be between 0 and 1')
+            raise ValueError("Decay must be between 0 and 1")
 
         self.m_name2s_name = {}
-        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+        self.register_buffer("decay", torch.tensor(decay, dtype=torch.float32))
         self.register_buffer(
-            'num_updates',
+            "num_updates",
             torch.tensor(0, dtype=torch.int)
             if use_num_upates
             else torch.tensor(-1, dtype=torch.int),
@@ -20,7 +20,7 @@ class LitEma(nn.Module):
         for name, p in model.named_parameters():
             if p.requires_grad:
                 # remove as '.'-character is not allowed in buffers
-                s_name = name.replace('.', '')
+                s_name = name.replace(".", "")
                 self.m_name2s_name.update({name: s_name})
                 self.register_buffer(s_name, p.clone().detach().data)
 
@@ -31,9 +31,7 @@ class LitEma(nn.Module):
 
         if self.num_updates >= 0:
             self.num_updates += 1
-            decay = min(
-                self.decay, (1 + self.num_updates) / (10 + self.num_updates)
-            )
+            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
 
         one_minus_decay = 1.0 - decay
 
@@ -44,9 +42,7 @@ class LitEma(nn.Module):
             for key in m_param:
                 if m_param[key].requires_grad:
                     sname = self.m_name2s_name[key]
-                    shadow_params[sname] = shadow_params[sname].type_as(
-                        m_param[key]
-                    )
+                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
                     shadow_params[sname].sub_(
                         one_minus_decay * (shadow_params[sname] - m_param[key])
                     )
@@ -58,9 +54,7 @@ class LitEma(nn.Module):
         shadow_params = dict(self.named_buffers())
         for key in m_param:
             if m_param[key].requires_grad:
-                m_param[key].data.copy_(
-                    shadow_params[self.m_name2s_name[key]].data
-                )
+                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
             else:
                 assert not key in self.m_name2s_name
 

invokeai/backend/stable_diffusion/encoders/modules.py

@@ -7,14 +7,14 @@ import kornia
 import torch
 import torch.nn as nn
 from einops import repeat
-from transformers import CLIPTokenizer, CLIPTextModel
+from transformers import CLIPTextModel, CLIPTokenizer
 
-from ldm.invoke.devices import choose_torch_device
-from ldm.invoke.globals import global_cache_dir
-from ldm.modules.x_transformer import (
+from ...util import choose_torch_device
+from ..globals import global_cache_dir
+from ..x_transformer import (  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
     Encoder,
     TransformerWrapper,
-)  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
+)
 
 
 def _expand_mask(mask, dtype, tgt_len=None):
@@ -24,9 +24,7 @@ def _expand_mask(mask, dtype, tgt_len=None):
     bsz, src_len = mask.size()
     tgt_len = tgt_len if tgt_len is not None else src_len
 
-    expanded_mask = (
-        mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-    )
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
 
     inverted_mask = 1.0 - expanded_mask
 
@@ -54,7 +52,7 @@ class AbstractEncoder(nn.Module):
 
 
 class ClassEmbedder(nn.Module):
-    def __init__(self, embed_dim, n_classes=1000, key='class'):
+    def __init__(self, embed_dim, n_classes=1000, key="class"):
         super().__init__()
         self.key = key
         self.embedding = nn.Embedding(n_classes, embed_dim)
@@ -99,20 +97,14 @@ class TransformerEmbedder(AbstractEncoder):
 class BERTTokenizer(AbstractEncoder):
     """Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
 
-    def __init__(
-            self, device=choose_torch_device(), vq_interface=True, max_length=77
-    ):
+    def __init__(self, device=choose_torch_device(), vq_interface=True, max_length=77):
         super().__init__()
-        from transformers import (
-            BertTokenizerFast,
-        )
+        from transformers import BertTokenizerFast
 
-        cache = global_cache_dir('hub')
+        cache = global_cache_dir("hub")
         try:
             self.tokenizer = BertTokenizerFast.from_pretrained(
-                'bert-base-uncased',
-                cache_dir=cache,
-                local_files_only=True
+                "bert-base-uncased", cache_dir=cache, local_files_only=True
             )
         except OSError:
             raise SystemExit(
@@ -129,10 +121,10 @@ class BERTTokenizer(AbstractEncoder):
             max_length=self.max_length,
             return_length=True,
             return_overflowing_tokens=False,
-            padding='max_length',
-            return_tensors='pt',
+            padding="max_length",
+            return_tensors="pt",
         )
-        tokens = batch_encoding['input_ids'].to(self.device)
+        tokens = batch_encoding["input_ids"].to(self.device)
         return tokens
 
     @torch.no_grad()
@@ -150,21 +142,19 @@ class BERTEmbedder(AbstractEncoder):
     """Uses the BERT tokenizr model and add some transformer encoder layers"""
 
     def __init__(
-            self,
-            n_embed,
-            n_layer,
-            vocab_size=30522,
-            max_seq_len=77,
-            device=choose_torch_device(),
-            use_tokenizer=True,
-            embedding_dropout=0.0,
+        self,
+        n_embed,
+        n_layer,
+        vocab_size=30522,
+        max_seq_len=77,
+        device=choose_torch_device(),
+        use_tokenizer=True,
+        embedding_dropout=0.0,
     ):
         super().__init__()
         self.use_tknz_fn = use_tokenizer
         if self.use_tknz_fn:
-            self.tknz_fn = BERTTokenizer(
-                vq_interface=False, max_length=max_seq_len
-            )
+            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
         self.device = device
         self.transformer = TransformerWrapper(
             num_tokens=vocab_size,
@@ -192,7 +182,7 @@ class SpatialRescaler(nn.Module):
     def __init__(
         self,
         n_stages=1,
-        method='bilinear',
+        method="bilinear",
         multiplier=0.5,
         in_channels=3,
         out_channels=None,
@@ -202,25 +192,21 @@ class SpatialRescaler(nn.Module):
         self.n_stages = n_stages
         assert self.n_stages >= 0
         assert method in [
-            'nearest',
-            'linear',
-            'bilinear',
-            'trilinear',
-            'bicubic',
-            'area',
+            "nearest",
+            "linear",
+            "bilinear",
+            "trilinear",
+            "bicubic",
+            "area",
         ]
         self.multiplier = multiplier
-        self.interpolator = partial(
-            torch.nn.functional.interpolate, mode=method
-        )
+        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
         self.remap_output = out_channels is not None
         if self.remap_output:
             print(
-                f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.'
-            )
-            self.channel_mapper = nn.Conv2d(
-                in_channels, out_channels, 1, bias=bias
+                f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing."
             )
+            self.channel_mapper = nn.Conv2d(in_channels, out_channels, 1, bias=bias)
 
     def forward(self, x):
         for stage in range(self.n_stages):
@@ -236,27 +222,24 @@ class SpatialRescaler(nn.Module):
 
 class FrozenCLIPEmbedder(AbstractEncoder):
     """Uses the CLIP transformer encoder for text (from Hugging Face)"""
+
     tokenizer: CLIPTokenizer
     transformer: CLIPTextModel
 
     def __init__(
         self,
-        version:str='openai/clip-vit-large-patch14',
-        max_length:int=77,
-        tokenizer:Optional[CLIPTokenizer]=None,
-        transformer:Optional[CLIPTextModel]=None,
+        version: str = "openai/clip-vit-large-patch14",
+        max_length: int = 77,
+        tokenizer: Optional[CLIPTokenizer] = None,
+        transformer: Optional[CLIPTextModel] = None,
     ):
         super().__init__()
-        cache = global_cache_dir('hub')
+        cache = global_cache_dir("hub")
         self.tokenizer = tokenizer or CLIPTokenizer.from_pretrained(
-            version,
-            cache_dir=cache,
-            local_files_only=True
+            version, cache_dir=cache, local_files_only=True
         )
         self.transformer = transformer or CLIPTextModel.from_pretrained(
-            version,
-            cache_dir=cache,
-            local_files_only=True
+            version, cache_dir=cache, local_files_only=True
         )
         self.max_length = max_length
         self.freeze()
@@ -268,7 +251,6 @@ class FrozenCLIPEmbedder(AbstractEncoder):
             inputs_embeds=None,
             embedding_manager=None,
         ) -> torch.Tensor:
-
             seq_length = (
                 input_ids.shape[-1]
                 if input_ids is not None
@@ -289,8 +271,8 @@ class FrozenCLIPEmbedder(AbstractEncoder):
 
             return embeddings
 
-        self.transformer.text_model.embeddings.forward = (
-            embedding_forward.__get__(self.transformer.text_model.embeddings)
+        self.transformer.text_model.embeddings.forward = embedding_forward.__get__(
+            self.transformer.text_model.embeddings
         )
 
         def encoder_forward(
@@ -313,9 +295,7 @@ class FrozenCLIPEmbedder(AbstractEncoder):
                 else self.config.output_hidden_states
             )
             return_dict = (
-                return_dict
-                if return_dict is not None
-                else self.config.use_return_dict
+                return_dict if return_dict is not None else self.config.use_return_dict
             )
 
             encoder_states = () if output_hidden_states else None
@@ -368,13 +348,11 @@ class FrozenCLIPEmbedder(AbstractEncoder):
                 else self.config.output_hidden_states
             )
             return_dict = (
-                return_dict
-                if return_dict is not None
-                else self.config.use_return_dict
+                return_dict if return_dict is not None else self.config.use_return_dict
             )
 
             if input_ids is None:
-                raise ValueError('You have to specify either input_ids')
+                raise ValueError("You have to specify either input_ids")
 
             input_shape = input_ids.size()
             input_ids = input_ids.view(-1, input_shape[-1])
@@ -395,9 +373,7 @@ class FrozenCLIPEmbedder(AbstractEncoder):
             # expand attention_mask
             if attention_mask is not None:
                 # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-                attention_mask = _expand_mask(
-                    attention_mask, hidden_states.dtype
-                )
+                attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
 
             last_hidden_state = self.encoder(
                 inputs_embeds=hidden_states,
@@ -436,9 +412,7 @@ class FrozenCLIPEmbedder(AbstractEncoder):
                 embedding_manager=embedding_manager,
             )
 
-        self.transformer.forward = transformer_forward.__get__(
-            self.transformer
-        )
+        self.transformer.forward = transformer_forward.__get__(self.transformer)
 
     def freeze(self):
         self.transformer = self.transformer.eval()
@@ -452,10 +426,10 @@ class FrozenCLIPEmbedder(AbstractEncoder):
             max_length=self.max_length,
             return_length=True,
             return_overflowing_tokens=False,
-            padding='max_length',
-            return_tensors='pt',
+            padding="max_length",
+            return_tensors="pt",
         )
-        tokens = batch_encoding['input_ids'].to(self.device)
+        tokens = batch_encoding["input_ids"].to(self.device)
         z = self.transformer(input_ids=tokens, **kwargs)
 
         return z
@@ -471,25 +445,25 @@ class FrozenCLIPEmbedder(AbstractEncoder):
     def device(self, device):
         self.transformer.to(device=device)
 
-class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
 
+class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
     fragment_weights_key = "fragment_weights"
     return_tokens_key = "return_tokens"
 
-    def set_textual_inversion_manager(self, manager): #TextualInversionManager):
+    def set_textual_inversion_manager(self, manager):  # TextualInversionManager):
         # TODO all of the weighting and expanding stuff needs be moved out of this class
         self.textual_inversion_manager = manager
 
     def forward(self, text: list, **kwargs):
         # TODO all of the weighting and expanding stuff needs be moved out of this class
-        '''
+        """
 
         :param text: A batch of prompt strings, or, a batch of lists of fragments of prompt strings to which different
         weights shall be applied.
         :param kwargs: If the keyword arg "fragment_weights" is passed, it shall contain a batch of lists of weights
         for the prompt fragments. In this case text must contain batches of lists of prompt fragments.
         :return: A tensor of shape (B, 77, 768) containing weighted embeddings
-        '''
+        """
         if self.fragment_weights_key not in kwargs:
             # fallback to base class implementation
             return super().forward(text, **kwargs)
@@ -507,7 +481,6 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
         batch_z = None
         batch_tokens = None
         for fragments, weights in zip(text, fragment_weights):
-
             # First, weight tokens in individual fragments by scaling the feature vectors as requested (effectively
             # applying a multiplier to the CFG scale on a per-token basis).
             # For tokens weighted<1, intuitively we want SD to become not merely *less* interested in the concept
@@ -520,7 +493,9 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
 
             # handle weights >=1
             tokens, per_token_weights = self.get_tokens_and_weights(fragments, weights)
-            base_embedding = self.build_weighted_embedding_tensor(tokens, per_token_weights, **kwargs)
+            base_embedding = self.build_weighted_embedding_tensor(
+                tokens, per_token_weights, **kwargs
+            )
 
             # this is our starting point
             embeddings = base_embedding.unsqueeze(0)
@@ -536,12 +511,18 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
             # such that the resulting lerped embedding is exactly half-way between "mountain man" and "mountain".
             for index, fragment_weight in enumerate(weights):
                 if fragment_weight < 1:
-                    fragments_without_this = fragments[:index] + fragments[index+1:]
-                    weights_without_this = weights[:index] + weights[index+1:]
-                    tokens, per_token_weights = self.get_tokens_and_weights(fragments_without_this, weights_without_this)
-                    embedding_without_this = self.build_weighted_embedding_tensor(tokens, per_token_weights, **kwargs)
+                    fragments_without_this = fragments[:index] + fragments[index + 1 :]
+                    weights_without_this = weights[:index] + weights[index + 1 :]
+                    tokens, per_token_weights = self.get_tokens_and_weights(
+                        fragments_without_this, weights_without_this
+                    )
+                    embedding_without_this = self.build_weighted_embedding_tensor(
+                        tokens, per_token_weights, **kwargs
+                    )
 
-                    embeddings = torch.cat((embeddings, embedding_without_this.unsqueeze(0)), dim=1)
+                    embeddings = torch.cat(
+                        (embeddings, embedding_without_this.unsqueeze(0)), dim=1
+                    )
                     # weight of the embedding *without* this fragment gets *stronger* as its weight approaches 0
                     # if fragment_weight = 0, basically we want embedding_without_this to completely overwhelm base_embedding
                     # therefore:
@@ -554,29 +535,43 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
                     #        inf at PI/2
                     # -> tan((1-weight)*PI/2) should give us ideal lerp weights
                     epsilon = 1e-9
-                    fragment_weight = max(epsilon, fragment_weight) # inf is bad
-                    embedding_lerp_weight = math.tan((1.0 - fragment_weight) * math.pi / 2)
+                    fragment_weight = max(epsilon, fragment_weight)  # inf is bad
+                    embedding_lerp_weight = math.tan(
+                        (1.0 - fragment_weight) * math.pi / 2
+                    )
                     # todo handle negative weight?
 
                     per_embedding_weights.append(embedding_lerp_weight)
 
-            lerped_embeddings = self.apply_embedding_weights(embeddings, per_embedding_weights, normalize=True).squeeze(0)
+            lerped_embeddings = self.apply_embedding_weights(
+                embeddings, per_embedding_weights, normalize=True
+            ).squeeze(0)
 
-            #print(f"assembled tokens for '{fragments}' into tensor of shape {lerped_embeddings.shape}")
+            # print(f"assembled tokens for '{fragments}' into tensor of shape {lerped_embeddings.shape}")
 
             # append to batch
-            batch_z = lerped_embeddings.unsqueeze(0) if batch_z is None else torch.cat([batch_z, lerped_embeddings.unsqueeze(0)], dim=1)
-            batch_tokens = tokens.unsqueeze(0) if batch_tokens is None else torch.cat([batch_tokens, tokens.unsqueeze(0)], dim=1)
+            batch_z = (
+                lerped_embeddings.unsqueeze(0)
+                if batch_z is None
+                else torch.cat([batch_z, lerped_embeddings.unsqueeze(0)], dim=1)
+            )
+            batch_tokens = (
+                tokens.unsqueeze(0)
+                if batch_tokens is None
+                else torch.cat([batch_tokens, tokens.unsqueeze(0)], dim=1)
+            )
 
         # should have shape (B, 77, 768)
-        #print(f"assembled all tokens into tensor of shape {batch_z.shape}")
+        # print(f"assembled all tokens into tensor of shape {batch_z.shape}")
 
         if should_return_tokens:
             return batch_z, batch_tokens
         else:
             return batch_z
 
-    def get_token_ids(self, fragments: list[str], include_start_and_end_markers: bool = True) -> list[list[int]]:
+    def get_token_ids(
+        self, fragments: list[str], include_start_and_end_markers: bool = True
+    ) -> list[list[int]]:
         """
         Convert a list of strings like `["a cat", "sitting", "on a mat"]` into a list of lists of token ids like
         `[[bos, 0, 1, eos], [bos, 2, eos], [bos, 3, 0, 4, eos]]`. bos/eos markers are skipped if
@@ -594,58 +589,81 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
             truncation=True,
             max_length=self.max_length,
             return_overflowing_tokens=False,
-            padding='do_not_pad',
+            padding="do_not_pad",
             return_tensors=None,  # just give me lists of ints
-        )['input_ids']
+        )["input_ids"]
 
         result = []
         for token_ids in token_ids_list:
             # trim eos/bos
             token_ids = token_ids[1:-1]
             # pad for textual inversions with vector length >1
-            token_ids = self.textual_inversion_manager.expand_textual_inversion_token_ids_if_necessary(token_ids)
+            token_ids = self.textual_inversion_manager.expand_textual_inversion_token_ids_if_necessary(
+                token_ids
+            )
             # restrict length to max_length-2 (leaving room for bos/eos)
-            token_ids = token_ids[0:self.max_length - 2]
+            token_ids = token_ids[0 : self.max_length - 2]
             # add back eos/bos if requested
             if include_start_and_end_markers:
-                token_ids = [self.tokenizer.bos_token_id] + token_ids + [self.tokenizer.eos_token_id]
+                token_ids = (
+                    [self.tokenizer.bos_token_id]
+                    + token_ids
+                    + [self.tokenizer.eos_token_id]
+                )
 
             result.append(token_ids)
 
         return result
 
-
     @classmethod
-    def apply_embedding_weights(self, embeddings: torch.Tensor, per_embedding_weights: list[float], normalize:bool) -> torch.Tensor:
-        per_embedding_weights = torch.tensor(per_embedding_weights, dtype=embeddings.dtype, device=embeddings.device)
+    def apply_embedding_weights(
+        self,
+        embeddings: torch.Tensor,
+        per_embedding_weights: list[float],
+        normalize: bool,
+    ) -> torch.Tensor:
+        per_embedding_weights = torch.tensor(
+            per_embedding_weights, dtype=embeddings.dtype, device=embeddings.device
+        )
         if normalize:
-            per_embedding_weights = per_embedding_weights / torch.sum(per_embedding_weights)
-        reshaped_weights = per_embedding_weights.reshape(per_embedding_weights.shape + (1, 1,))
-        #reshaped_weights = per_embedding_weights.reshape(per_embedding_weights.shape + (1,1,)).expand(embeddings.shape)
+            per_embedding_weights = per_embedding_weights / torch.sum(
+                per_embedding_weights
+            )
+        reshaped_weights = per_embedding_weights.reshape(
+            per_embedding_weights.shape
+            + (
+                1,
+                1,
+            )
+        )
+        # reshaped_weights = per_embedding_weights.reshape(per_embedding_weights.shape + (1,1,)).expand(embeddings.shape)
         return torch.sum(embeddings * reshaped_weights, dim=1)
         # lerped embeddings has shape (77, 768)
 
-
-    def get_tokens_and_weights(self, fragments: list[str], weights: list[float]) -> (torch.Tensor, torch.Tensor):
-        '''
+    def get_tokens_and_weights(
+        self, fragments: list[str], weights: list[float]
+    ) -> (torch.Tensor, torch.Tensor):
+        """
 
         :param fragments:
         :param weights: Per-fragment weights (CFG scaling). No need for these to be normalized. They will not be normalized here and that's fine.
         :return:
-        '''
+        """
         # empty is meaningful
         if len(fragments) == 0 and len(weights) == 0:
-            fragments = ['']
+            fragments = [""]
             weights = [1]
-        per_fragment_token_ids = self.get_token_ids(fragments, include_start_and_end_markers=False)
+        per_fragment_token_ids = self.get_token_ids(
+            fragments, include_start_and_end_markers=False
+        )
         all_token_ids = []
         per_token_weights = []
-        #print("all fragments:", fragments, weights)
+        # print("all fragments:", fragments, weights)
         for index, fragment in enumerate(per_fragment_token_ids):
             weight = float(weights[index])
-            #print("processing fragment", fragment, weight)
+            # print("processing fragment", fragment, weight)
             this_fragment_token_ids = per_fragment_token_ids[index]
-            #print("fragment", fragment, "processed to", this_fragment_token_ids)
+            # print("fragment", fragment, "processed to", this_fragment_token_ids)
             # append
             all_token_ids += this_fragment_token_ids
             # fill out weights tensor with one float per token
@@ -654,60 +672,85 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
         # leave room for bos/eos
         max_token_count_without_bos_eos_markers = self.max_length - 2
         if len(all_token_ids) > max_token_count_without_bos_eos_markers:
-            excess_token_count = len(all_token_ids) - max_token_count_without_bos_eos_markers
+            excess_token_count = (
+                len(all_token_ids) - max_token_count_without_bos_eos_markers
+            )
             # TODO build nice description string of how the truncation was applied
             # this should be done by calling self.tokenizer.convert_ids_to_tokens() then passing the result to
             # self.tokenizer.convert_tokens_to_string() for the token_ids on each side of the truncation limit.
-            print(f">> Prompt is {excess_token_count} token(s) too long and has been truncated")
+            print(
+                f">> Prompt is {excess_token_count} token(s) too long and has been truncated"
+            )
             all_token_ids = all_token_ids[0:max_token_count_without_bos_eos_markers]
-            per_token_weights = per_token_weights[0:max_token_count_without_bos_eos_markers]
+            per_token_weights = per_token_weights[
+                0:max_token_count_without_bos_eos_markers
+            ]
 
         # pad out to a 77-entry array: [bos_token, <prompt tokens>, eos_token, pad_token…]
         # (77 = self.max_length)
-        all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id]
+        all_token_ids = (
+            [self.tokenizer.bos_token_id]
+            + all_token_ids
+            + [self.tokenizer.eos_token_id]
+        )
         per_token_weights = [1.0] + per_token_weights + [1.0]
         pad_length = self.max_length - len(all_token_ids)
         all_token_ids += [self.tokenizer.pad_token_id] * pad_length
         per_token_weights += [1.0] * pad_length
 
-        all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long).to(self.device)
-        per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch.float32).to(self.device)
-        #print(f"assembled all_token_ids_tensor with shape {all_token_ids_tensor.shape}")
+        all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long).to(
+            self.device
+        )
+        per_token_weights_tensor = torch.tensor(
+            per_token_weights, dtype=torch.float32
+        ).to(self.device)
+        # print(f"assembled all_token_ids_tensor with shape {all_token_ids_tensor.shape}")
         return all_token_ids_tensor, per_token_weights_tensor
 
-    def build_weighted_embedding_tensor(self, token_ids: torch.Tensor, per_token_weights: torch.Tensor, weight_delta_from_empty=True, **kwargs) -> torch.Tensor:
-        '''
+    def build_weighted_embedding_tensor(
+        self,
+        token_ids: torch.Tensor,
+        per_token_weights: torch.Tensor,
+        weight_delta_from_empty=True,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
         Build a tensor representing the passed-in tokens, each of which has a weight.
         :param token_ids: A tensor of shape (77) containing token ids (integers)
         :param per_token_weights: A tensor of shape (77) containing weights (floats)
         :param method: Whether to multiply the whole feature vector for each token or just its distance from an "empty" feature vector
         :param kwargs: passed on to self.transformer()
         :return: A tensor of shape (1, 77, 768) representing the requested weighted embeddings.
-        '''
-        #print(f"building weighted embedding tensor for {tokens} with weights {per_token_weights}")
+        """
+        # print(f"building weighted embedding tensor for {tokens} with weights {per_token_weights}")
         if token_ids.shape != torch.Size([self.max_length]):
-            raise ValueError(f"token_ids has shape {token_ids.shape} - expected [{self.max_length}]")
+            raise ValueError(
+                f"token_ids has shape {token_ids.shape} - expected [{self.max_length}]"
+            )
 
         z = self.transformer(input_ids=token_ids.unsqueeze(0), **kwargs)
 
-        batch_weights_expanded = per_token_weights.reshape(per_token_weights.shape + (1,)).expand(z.shape)
+        batch_weights_expanded = per_token_weights.reshape(
+            per_token_weights.shape + (1,)
+        ).expand(z.shape)
 
         if weight_delta_from_empty:
-            empty_tokens = self.tokenizer([''] * z.shape[0],
-                                         truncation=True,
-                                         max_length=self.max_length,
-                                         padding='max_length',
-                                         return_tensors='pt'
-                                         )['input_ids'].to(self.device)
+            empty_tokens = self.tokenizer(
+                [""] * z.shape[0],
+                truncation=True,
+                max_length=self.max_length,
+                padding="max_length",
+                return_tensors="pt",
+            )["input_ids"].to(self.device)
             empty_z = self.transformer(input_ids=empty_tokens, **kwargs)
             z_delta_from_empty = z - empty_z
             weighted_z = empty_z + (z_delta_from_empty * batch_weights_expanded)
 
-            #weighted_z_delta_from_empty = (weighted_z-empty_z)
-            #print("weighted z has delta from empty with sum", weighted_z_delta_from_empty.sum().item(), "mean", weighted_z_delta_from_empty.mean().item() )
+            # weighted_z_delta_from_empty = (weighted_z-empty_z)
+            # print("weighted z has delta from empty with sum", weighted_z_delta_from_empty.sum().item(), "mean", weighted_z_delta_from_empty.mean().item() )
 
-            #print("using empty-delta method, first 5 rows:")
-            #print(weighted_z[:5])
+            # print("using empty-delta method, first 5 rows:")
+            # print(weighted_z[:5])
 
             return weighted_z
 
@@ -716,7 +759,7 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
             z *= batch_weights_expanded
             after_weighting_mean = z.mean()
             # correct the mean. not sure if this is right but it's what the automatic1111 fork of SD does
-            mean_correction_factor = original_mean/after_weighting_mean
+            mean_correction_factor = original_mean / after_weighting_mean
             z *= mean_correction_factor
             return z
 
@@ -728,7 +771,7 @@ class FrozenCLIPTextEmbedder(nn.Module):
 
     def __init__(
         self,
-        version='ViT-L/14',
+        version="ViT-L/14",
         device=choose_torch_device(),
         max_length=77,
         n_repeat=1,
@@ -757,7 +800,7 @@ class FrozenCLIPTextEmbedder(nn.Module):
         z = self(text)
         if z.ndim == 2:
             z = z[:, None, :]
-        z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
+        z = repeat(z, "b 1 d -> b k d", k=self.n_repeat)
         return z
 
 
@@ -779,12 +822,12 @@ class FrozenClipImageEmbedder(nn.Module):
         self.antialias = antialias
 
         self.register_buffer(
-            'mean',
+            "mean",
             torch.Tensor([0.48145466, 0.4578275, 0.40821073]),
             persistent=False,
         )
         self.register_buffer(
-            'std',
+            "std",
             torch.Tensor([0.26862954, 0.26130258, 0.27577711]),
             persistent=False,
         )
@@ -794,7 +837,7 @@ class FrozenClipImageEmbedder(nn.Module):
         x = kornia.geometry.resize(
             x,
             (224, 224),
-            interpolation='bicubic',
+            interpolation="bicubic",
             align_corners=True,
             antialias=self.antialias,
         )
@@ -808,8 +851,8 @@ class FrozenClipImageEmbedder(nn.Module):
         return self.model.encode_image(self.preprocess(x))
 
 
-if __name__ == '__main__':
-    from ldm.util import count_params
+if __name__ == "__main__":
+    from ...util.util import count_params
 
     model = FrozenCLIPEmbedder()
     count_params(model, verbose=True)