resolve merge conflicts

.github/workflows/style-checks.yml

@@ -1,6 +1,6 @@
 name: style checks
-# just formatting for now
+# just formatting and flake8 for now
-# TODO: add isort and flake8 later
+# TODO: add isort later
 
 on:
   pull_request:
@@ -20,8 +20,8 @@ jobs:
 
       - name: Install dependencies with pip
         run: |
-          pip install black
+          pip install black flake8 Flake8-pyproject
 
       # - run: isort --check-only .
       - run: black --check .
-      # - run: flake8
+      - run: flake8

.pre-commit-config.yaml

@@ -8,3 +8,10 @@ repos:
     language: system
     entry: black
     types: [python]
+
+  - id: flake8
+    name: flake8
+    stages: [commit]
+    language: system
+    entry: flake8
+    types: [python]

installer/lib/installer.py

@@ -407,7 +407,7 @@ def get_pip_from_venv(venv_path: Path) -> str:
     :rtype: str
     """
 
-    pip = "Scripts\pip.exe" if OS == "Windows" else "bin/pip"
+    pip = "Scripts\\pip.exe" if OS == "Windows" else "bin/pip"
     return str(venv_path.expanduser().resolve() / pip)
 
 

installer/lib/main.py

@@ -49,7 +49,7 @@ if __name__ == "__main__":
 
     try:
         inst.install(**args.__dict__)
-    except KeyboardInterrupt as exc:
+    except KeyboardInterrupt:
         print("\n")
         print("Ctrl-C pressed. Aborting.")
         print("Come back soon!")

installer/lib/messages.py

@@ -70,7 +70,7 @@ def confirm_install(dest: Path) -> bool:
         )
     else:
         print(f"InvokeAI will be installed in {dest}")
-        dest_confirmed = not Confirm.ask(f"Would you like to pick a different location?", default=False)
+        dest_confirmed = not Confirm.ask("Would you like to pick a different location?", default=False)
     console.line()
 
     return dest_confirmed
@@ -90,7 +90,7 @@ def dest_path(dest=None) -> Path:
         dest = Path(dest).expanduser().resolve()
     else:
         dest = Path.cwd().expanduser().resolve()
-    prev_dest = dest.expanduser().resolve()
+    prev_dest = init_path = dest
 
     dest_confirmed = confirm_install(dest)
 
@@ -109,9 +109,9 @@ def dest_path(dest=None) -> Path:
         )
 
         console.line()
-        print(f"[orange3]Please select the destination directory for the installation:[/] \[{browse_start}]: ")
+        console.print(f"[orange3]Please select the destination directory for the installation:[/] \\[{browse_start}]: ")
         selected = prompt(
-            f">>> ",
+            ">>> ",
             complete_in_thread=True,
             completer=path_completer,
             default=str(browse_start) + os.sep,
@@ -134,14 +134,14 @@ def dest_path(dest=None) -> Path:
     try:
         dest.mkdir(exist_ok=True, parents=True)
         return dest
-    except PermissionError as exc:
+    except PermissionError:
-        print(
+        console.print(
             f"Failed to create directory {dest} due to insufficient permissions",
             style=Style(color="red"),
             highlight=True,
         )
-    except OSError as exc:
+    except OSError:
-        console.print_exception(exc)
+        console.print_exception()
 
     if Confirm.ask("Would you like to try again?"):
         dest_path(init_path)

invokeai/app/api/dependencies.py

@@ -1,6 +1,5 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Optional
 from logging import Logger
 from invokeai.app.services.board_image_record_storage import (
     SqliteBoardImageRecordStorage,
@@ -45,7 +44,7 @@ def check_internet() -> bool:
     try:
         urllib.request.urlopen(host, timeout=1)
         return True
-    except:
+    except Exception:
         return False
 
 

invokeai/app/api/routers/board_images.py

@@ -34,7 +34,7 @@ async def add_image_to_board(
             board_id=board_id, image_name=image_name
         )
         return result
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to add image to board")
 
 
@@ -53,7 +53,7 @@ async def remove_image_from_board(
     try:
         result = ApiDependencies.invoker.services.board_images.remove_image_from_board(image_name=image_name)
         return result
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to remove image from board")
 
 
@@ -79,10 +79,10 @@ async def add_images_to_board(
                     board_id=board_id, image_name=image_name
                 )
                 added_image_names.append(image_name)
-            except:
+            except Exception:
                 pass
         return AddImagesToBoardResult(board_id=board_id, added_image_names=added_image_names)
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to add images to board")
 
 
@@ -105,8 +105,8 @@ async def remove_images_from_board(
             try:
                 ApiDependencies.invoker.services.board_images.remove_image_from_board(image_name=image_name)
                 removed_image_names.append(image_name)
-            except:
+            except Exception:
                 pass
         return RemoveImagesFromBoardResult(removed_image_names=removed_image_names)
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to remove images from board")

invokeai/app/api/routers/boards.py

@@ -37,7 +37,7 @@ async def create_board(
     try:
         result = ApiDependencies.invoker.services.boards.create(board_name=board_name)
         return result
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to create board")
 
 
@@ -50,7 +50,7 @@ async def get_board(
     try:
         result = ApiDependencies.invoker.services.boards.get_dto(board_id=board_id)
         return result
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=404, detail="Board not found")
 
 
@@ -73,7 +73,7 @@ async def update_board(
     try:
         result = ApiDependencies.invoker.services.boards.update(board_id=board_id, changes=changes)
         return result
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to update board")
 
 
@@ -105,7 +105,7 @@ async def delete_board(
                 deleted_board_images=deleted_board_images,
                 deleted_images=[],
             )
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to delete board")
 
 

invokeai/app/api/routers/images.py

@@ -55,7 +55,7 @@ async def upload_image(
         if crop_visible:
             bbox = pil_image.getbbox()
             pil_image = pil_image.crop(bbox)
-    except:
+    except Exception:
         # Error opening the image
         raise HTTPException(status_code=415, detail="Failed to read image")
 
@@ -73,7 +73,7 @@ async def upload_image(
         response.headers["Location"] = image_dto.image_url
 
         return image_dto
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to create image")
 
 
@@ -85,7 +85,7 @@ async def delete_image(
 
     try:
         ApiDependencies.invoker.services.images.delete(image_name)
-    except Exception as e:
+    except Exception:
         # TODO: Does this need any exception handling at all?
         pass
 
@@ -97,7 +97,7 @@ async def clear_intermediates() -> int:
     try:
         count_deleted = ApiDependencies.invoker.services.images.delete_intermediates()
         return count_deleted
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to clear intermediates")
         pass
 
@@ -115,7 +115,7 @@ async def update_image(
 
     try:
         return ApiDependencies.invoker.services.images.update(image_name, image_changes)
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=400, detail="Failed to update image")
 
 
@@ -131,7 +131,7 @@ async def get_image_dto(
 
     try:
         return ApiDependencies.invoker.services.images.get_dto(image_name)
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=404)
 
 
@@ -147,7 +147,7 @@ async def get_image_metadata(
 
     try:
         return ApiDependencies.invoker.services.images.get_metadata(image_name)
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=404)
 
 
@@ -183,7 +183,7 @@ async def get_image_full(
         )
         response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
         return response
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=404)
 
 
@@ -212,7 +212,7 @@ async def get_image_thumbnail(
         response = FileResponse(path, media_type="image/webp", content_disposition_type="inline")
         response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
         return response
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=404)
 
 
@@ -234,7 +234,7 @@ async def get_image_urls(
             image_url=image_url,
             thumbnail_url=thumbnail_url,
         )
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=404)
 
 
@@ -282,10 +282,10 @@ async def delete_images_from_list(
             try:
                 ApiDependencies.invoker.services.images.delete(image_name)
                 deleted_images.append(image_name)
-            except:
+            except Exception:
                 pass
         return DeleteImagesFromListResult(deleted_images=deleted_images)
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to delete images")
 
 
@@ -303,10 +303,10 @@ async def star_images_in_list(
             try:
                 ApiDependencies.invoker.services.images.update(image_name, changes=ImageRecordChanges(starred=True))
                 updated_image_names.append(image_name)
-            except:
+            except Exception:
                 pass
         return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to star images")
 
 
@@ -320,8 +320,8 @@ async def unstar_images_in_list(
             try:
                 ApiDependencies.invoker.services.images.update(image_name, changes=ImageRecordChanges(starred=False))
                 updated_image_names.append(image_name)
-            except:
+            except Exception:
                 pass
         return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
-    except Exception as e:
+    except Exception:
         raise HTTPException(status_code=500, detail="Failed to unstar images")

invokeai/app/api/routers/sessions.py

@@ -1,12 +1,13 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Annotated, List, Optional, Union
+from typing import Annotated, Optional, Union
 
 from fastapi import Body, HTTPException, Path, Query, Response
 from fastapi.routing import APIRouter
 from pydantic.fields import Field
 
-from ...invocations import *
+# Importing * is bad karma but needed here for node detection
+from ...invocations import *  # noqa: F401 F403
 from ...invocations.baseinvocation import BaseInvocation
 from ...services.graph import (
     Edge,

invokeai/app/api_app.py

@@ -1,6 +1,5 @@
 # Copyright (c) 2022-2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
 import asyncio
-import sys
 from inspect import signature
 
 import logging
@@ -17,21 +16,11 @@ from fastapi_events.middleware import EventHandlerASGIMiddleware
 from pathlib import Path
 from pydantic.schema import schema
 
-# This should come early so that modules can log their initialization properly
 from .services.config import InvokeAIAppConfig
 from ..backend.util.logging import InvokeAILogger
 
-app_config = InvokeAIAppConfig.get_config()
-app_config.parse_args()
-logger = InvokeAILogger.getLogger(config=app_config)
 from invokeai.version.invokeai_version import __version__
 
-# we call this early so that the message appears before
-# other invokeai initialization messages
-if app_config.version:
-    print(f"InvokeAI version {__version__}")
-    sys.exit(0)
-
 import invokeai.frontend.web as web_dir
 import mimetypes
 
@@ -40,12 +29,17 @@ from .api.routers import sessions, models, images, boards, board_images, app_inf
 from .api.sockets import SocketIO
 from .invocations.baseinvocation import BaseInvocation, _InputField, _OutputField, UIConfigBase
 
-
 import torch
-import invokeai.backend.util.hotfixes
+import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
 
 if torch.backends.mps.is_available():
-    import invokeai.backend.util.mps_fixes
+    import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+
+
+app_config = InvokeAIAppConfig.get_config()
+app_config.parse_args()
+logger = InvokeAILogger.getLogger(config=app_config)
+
 
 # fix for windows mimetypes registry entries being borked
 # see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
@@ -230,13 +224,16 @@ def invoke_api():
 
     # replace uvicorn's loggers with InvokeAI's for consistent appearance
     for logname in ["uvicorn.access", "uvicorn"]:
-        l = logging.getLogger(logname)
+        log = logging.getLogger(logname)
-        l.handlers.clear()
+        log.handlers.clear()
         for ch in logger.handlers:
-            l.addHandler(ch)
+            log.addHandler(ch)
 
     loop.run_until_complete(server.serve())
 
 
 if __name__ == "__main__":
-    invoke_api()
+    if app_config.version:
+        print(f"InvokeAI version {__version__}")
+    else:
+        invoke_api()

invokeai/app/cli/completer.py

@@ -145,10 +145,10 @@ def set_autocompleter(services: InvocationServices) -> Completer:
     completer = Completer(services.model_manager)
 
     readline.set_completer(completer.complete)
-    # pyreadline3 does not have a set_auto_history() method
     try:
         readline.set_auto_history(True)
-    except:
+    except AttributeError:
+        # pyreadline3 does not have a set_auto_history() method
         pass
     readline.set_pre_input_hook(completer._pre_input_hook)
     readline.set_completer_delims(" ")

invokeai/app/cli_app.py

@@ -13,16 +13,8 @@ from pydantic.fields import Field
 # This should come early so that the logger can pick up its configuration options
 from .services.config import InvokeAIAppConfig
 from invokeai.backend.util.logging import InvokeAILogger
-
-config = InvokeAIAppConfig.get_config()
-config.parse_args()
-logger = InvokeAILogger().getLogger(config=config)
 from invokeai.version.invokeai_version import __version__
 
-# we call this early so that the message appears before other invokeai initialization messages
-if config.version:
-    print(f"InvokeAI version {__version__}")
-    sys.exit(0)
 
 from invokeai.app.services.board_image_record_storage import (
     SqliteBoardImageRecordStorage,
@@ -62,10 +54,15 @@ from .services.processor import DefaultInvocationProcessor
 from .services.sqlite import SqliteItemStorage
 
 import torch
-import invokeai.backend.util.hotfixes
+import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
 
 if torch.backends.mps.is_available():
-    import invokeai.backend.util.mps_fixes
+    import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+
+
+config = InvokeAIAppConfig.get_config()
+config.parse_args()
+logger = InvokeAILogger().getLogger(config=config)
 
 
 class CliCommand(BaseModel):
@@ -482,4 +479,7 @@ def invoke_cli():
 
 
 if __name__ == "__main__":
-    invoke_cli()
+    if config.version:
+        print(f"InvokeAI version {__version__}")
+    else:
+        invoke_cli()

invokeai/app/invocations/collections.py

@@ -5,10 +5,10 @@ from typing import Literal
 import numpy as np
 from pydantic import validator
 
-from invokeai.app.invocations.primitives import ImageCollectionOutput, ImageField, IntegerCollectionOutput
+from invokeai.app.invocations.primitives import IntegerCollectionOutput
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIType, tags, title
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, tags, title
 
 
 @title("Integer Range")

invokeai/app/invocations/compel.py

@@ -12,7 +12,7 @@ from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion impor
     SDXLConditioningInfo,
 )
 
-from ...backend.model_management import ModelPatcher, ModelType
+from ...backend.model_management.models import ModelType
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.model_management.models import ModelNotFoundException
 from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent

invokeai/app/invocations/controlnet_image_processors.py

@@ -29,7 +29,7 @@ from pydantic import BaseModel, Field, validator
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 
 
-from ...backend.model_management import BaseModelType, ModelType
+from ...backend.model_management import BaseModelType
 from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,

invokeai/app/invocations/infill.py

@@ -90,7 +90,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
         return im
 
     # Find all invalid tiles and replace with a random valid tile
-    replace_count = (tiles_mask == False).sum()
+    replace_count = (tiles_mask is 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), :, :, :]
 

invokeai/app/invocations/latent.py

@@ -15,7 +15,7 @@ from diffusers.models.attention_processor import (
 )
 from diffusers.schedulers import DPMSolverSDEScheduler
 from diffusers.schedulers import SchedulerMixin as Scheduler
-from pydantic import BaseModel, Field, validator
+from pydantic import validator
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.metadata import CoreMetadata
@@ -30,7 +30,7 @@ from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 
-from ...backend.model_management import BaseModelType, ModelPatcher
+from ...backend.model_management.models import BaseModelType
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
@@ -45,12 +45,10 @@ from ...backend.util.devices import choose_precision, choose_torch_device
 from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
-    BaseInvocationOutput,
     FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
-    OutputField,
     UIType,
     tags,
     title,

invokeai/app/invocations/model.py

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Literal, Optional, Union
+from typing import List, Literal, Optional
 
 from pydantic import BaseModel, Field
 

invokeai/app/invocations/noise.py

@@ -16,7 +16,6 @@ from .baseinvocation import (
     InputField,
     InvocationContext,
     OutputField,
-    UIType,
     tags,
     title,
 )

invokeai/app/invocations/onnx.py

@@ -2,14 +2,13 @@
 
 import inspect
 import re
-from contextlib import ExitStack
+
+# from contextlib import ExitStack
 from typing import List, Literal, Optional, Union
 
 import numpy as np
 import torch
-from diffusers import ControlNetModel, DPMSolverMultistepScheduler
 from diffusers.image_processor import VaeImageProcessor
-from diffusers.schedulers import SchedulerMixin as Scheduler
 from pydantic import BaseModel, Field, validator
 from tqdm import tqdm
 
@@ -72,7 +71,7 @@ class ONNXPromptInvocation(BaseInvocation):
         text_encoder_info = context.services.model_manager.get_model(
             **self.clip.text_encoder.dict(),
         )
-        with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder, ExitStack() as stack:
+        with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder:  # , ExitStack() as stack:
             loras = [
                 (context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight)
                 for lora in self.clip.loras
@@ -259,7 +258,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
 
         unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
 
-        with unet_info as unet, ExitStack() as stack:
+        with unet_info as unet:  # , ExitStack() as stack:
             # loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
             loras = [
                 (context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight)

invokeai/app/invocations/param_easing.py

@@ -38,13 +38,10 @@ from easing_functions import (
     SineEaseInOut,
     SineEaseOut,
 )
-from matplotlib.figure import Figure
 from matplotlib.ticker import MaxNLocator
-from pydantic import BaseModel, Field
 
 from invokeai.app.invocations.primitives import FloatCollectionOutput
 
-from ...backend.util.logging import InvokeAILogger
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, tags, title
 
 

invokeai/app/invocations/primitives.py

@@ -1,7 +1,6 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Literal, Optional, Tuple, Union
+from typing import Literal, Optional, Tuple
-from anyio import Condition
 
 from pydantic import BaseModel, Field
 import torch

invokeai/app/invocations/upscale.py

@@ -1,6 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
 from pathlib import Path
-from typing import Literal, Union
+from typing import Literal
 
 import cv2 as cv
 import numpy as np

invokeai/app/services/board_images.py

@@ -1,18 +1,14 @@
 from abc import ABC, abstractmethod
 from logging import Logger
-from typing import List, Union, Optional
+from typing import Optional
 from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
 from invokeai.app.services.board_record_storage import (
     BoardRecord,
     BoardRecordStorageBase,
 )
 
-from invokeai.app.services.image_record_storage import (
+from invokeai.app.services.image_record_storage import ImageRecordStorageBase
-    ImageRecordStorageBase,
-    OffsetPaginatedResults,
-)
 from invokeai.app.services.models.board_record import BoardDTO
-from invokeai.app.services.models.image_record import ImageDTO, image_record_to_dto
 from invokeai.app.services.urls import UrlServiceBase
 
 

invokeai/app/services/board_record_storage.py

@@ -1,15 +1,14 @@
-from abc import ABC, abstractmethod
-from typing import Optional, cast
-import sqlite3
 import threading
-from typing import Optional, Union
 import uuid
+from abc import ABC, abstractmethod
+from typing import Optional, Union, cast
+
+import sqlite3
 from invokeai.app.services.image_record_storage import OffsetPaginatedResults
 from invokeai.app.services.models.board_record import (
     BoardRecord,
     deserialize_board_record,
 )
-
 from pydantic import BaseModel, Field, Extra
 
 
@@ -230,7 +229,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
             # Change the name of a board
             if changes.board_name is not None:
                 self._cursor.execute(
-                    f"""--sql
+                    """--sql
                     UPDATE boards
                     SET board_name = ?
                     WHERE board_id = ?;
@@ -241,7 +240,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
             # Change the cover image of a board
             if changes.cover_image_name is not None:
                 self._cursor.execute(
-                    f"""--sql
+                    """--sql
                     UPDATE boards
                     SET cover_image_name = ?
                     WHERE board_id = ?;

invokeai/app/services/config/invokeai_config.py

@@ -213,7 +213,6 @@ class InvokeAIAppConfig(InvokeAISettings):
     patchmatch          : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
     ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
 
-
     # PATHS
     root                : Path = Field(default=None, description='InvokeAI runtime root directory', category='Paths')
     autoimport_dir      : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
@@ -231,8 +230,8 @@ class InvokeAIAppConfig(InvokeAISettings):
     # LOGGING
     log_handlers        : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
     # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
-    log_format          : Literal[tuple(['plain','color','syslog','legacy'])] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
+    log_format          : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
-    log_level           : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="info", description="Emit logging messages at this level or  higher", category="Logging")
+    log_level           : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or  higher", category="Logging")
 
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
 
@@ -279,7 +278,7 @@ class InvokeAIAppConfig(InvokeAISettings):
         if conf is None:
             try:
                 conf = OmegaConf.load(self.root_dir / INIT_FILE)
-            except:
+            except Exception:
                 pass
         InvokeAISettings.initconf = conf
 
@@ -298,7 +297,7 @@ class InvokeAIAppConfig(InvokeAISettings):
         """
         if (
             cls.singleton_config is None
-            or type(cls.singleton_config) != cls
+            or type(cls.singleton_config) is not cls
             or (kwargs and cls.singleton_init != kwargs)
         ):
             cls.singleton_config = cls(**kwargs)

invokeai/app/services/graph.py

@@ -9,7 +9,8 @@ import networkx as nx
 from pydantic import BaseModel, root_validator, validator
 from pydantic.fields import Field
 
-from ..invocations import *
+# Importing * is bad karma but needed here for node detection
+from ..invocations import *  # noqa: F401 F403
 from ..invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -445,7 +446,7 @@ class Graph(BaseModel):
         node = graph.nodes[node_id]
 
         # Ensure the node type matches the new node
-        if type(node) != type(new_node):
+        if type(node) is not type(new_node):
             raise TypeError(f"Node {node_path} is type {type(node)} but new node is type {type(new_node)}")
 
         # Ensure the new id is either the same or is not in the graph
@@ -632,7 +633,7 @@ class Graph(BaseModel):
             [
                 t
                 for input_field in input_fields
-                for t in ([input_field] if get_origin(input_field) == None else get_args(input_field))
+                for t in ([input_field] if get_origin(input_field) is None else get_args(input_field))
                 if t != NoneType
             ]
         )  # Get unique types
@@ -923,7 +924,7 @@ class GraphExecutionState(BaseModel):
             None,
         )
 
-        if next_node_id == None:
+        if next_node_id is None:
             return None
 
         # Get all parents of the next node

invokeai/app/services/image_file_storage.py

@@ -179,7 +179,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
         return None if image_name not in self.__cache else self.__cache[image_name]
 
     def __set_cache(self, image_name: Path, image: PILImageType):
-        if not image_name in self.__cache:
+        if image_name not in self.__cache:
             self.__cache[image_name] = image
             self.__cache_ids.put(image_name)  # TODO: this should refresh position for LRU cache
             if len(self.__cache) > self.__max_cache_size:

invokeai/app/services/image_record_storage.py

@@ -282,7 +282,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             self._lock.acquire()
 
             self._cursor.execute(
-                f"""--sql
+                """--sql
                 SELECT images.metadata FROM images
                 WHERE image_name = ?;
                 """,
@@ -309,7 +309,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             # Change the category of the image
             if changes.image_category is not None:
                 self._cursor.execute(
-                    f"""--sql
+                    """--sql
                     UPDATE images
                     SET image_category = ?
                     WHERE image_name = ?;
@@ -320,7 +320,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             # Change the session associated with the image
             if changes.session_id is not None:
                 self._cursor.execute(
-                    f"""--sql
+                    """--sql
                     UPDATE images
                     SET session_id = ?
                     WHERE image_name = ?;
@@ -331,7 +331,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             # Change the image's `is_intermediate`` flag
             if changes.is_intermediate is not None:
                 self._cursor.execute(
-                    f"""--sql
+                    """--sql
                     UPDATE images
                     SET is_intermediate = ?
                     WHERE image_name = ?;
@@ -342,7 +342,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             # Change the image's `starred`` state
             if changes.starred is not None:
                 self._cursor.execute(
-                    f"""--sql
+                    """--sql
                     UPDATE images
                     SET starred = ?
                     WHERE image_name = ?;

invokeai/app/services/images.py

@@ -1,4 +1,3 @@
-import json
 from abc import ABC, abstractmethod
 from logging import Logger
 from typing import TYPE_CHECKING, Optional
@@ -379,10 +378,10 @@ class ImageService(ImageServiceABC):
             self._services.image_files.delete(image_name)
             self._services.image_records.delete(image_name)
         except ImageRecordDeleteException:
-            self._services.logger.error(f"Failed to delete image record")
+            self._services.logger.error("Failed to delete image record")
             raise
         except ImageFileDeleteException:
-            self._services.logger.error(f"Failed to delete image file")
+            self._services.logger.error("Failed to delete image file")
             raise
         except Exception as e:
             self._services.logger.error("Problem deleting image record and file")
@@ -395,10 +394,10 @@ class ImageService(ImageServiceABC):
                 self._services.image_files.delete(image_name)
             self._services.image_records.delete_many(image_names)
         except ImageRecordDeleteException:
-            self._services.logger.error(f"Failed to delete image records")
+            self._services.logger.error("Failed to delete image records")
             raise
         except ImageFileDeleteException:
-            self._services.logger.error(f"Failed to delete image files")
+            self._services.logger.error("Failed to delete image files")
             raise
         except Exception as e:
             self._services.logger.error("Problem deleting image records and files")
@@ -412,10 +411,10 @@ class ImageService(ImageServiceABC):
                 self._services.image_files.delete(image_name)
             return count
         except ImageRecordDeleteException:
-            self._services.logger.error(f"Failed to delete image records")
+            self._services.logger.error("Failed to delete image records")
             raise
         except ImageFileDeleteException:
-            self._services.logger.error(f"Failed to delete image files")
+            self._services.logger.error("Failed to delete image files")
             raise
         except Exception as e:
             self._services.logger.error("Problem deleting image records and files")

invokeai/app/services/invocation_services.py

@@ -7,6 +7,7 @@ if TYPE_CHECKING:
     from invokeai.app.services.board_images import BoardImagesServiceABC
     from invokeai.app.services.boards import BoardServiceABC
     from invokeai.app.services.images import ImageServiceABC
+    from invokeai.app.services.invocation_stats import InvocationStatsServiceBase
     from invokeai.app.services.model_manager_service import ModelManagerServiceBase
     from invokeai.app.services.events import EventServiceBase
     from invokeai.app.services.latent_storage import LatentsStorageBase

invokeai/app/services/invocation_stats.py

@@ -1,7 +1,6 @@
 # Copyright 2023 Lincoln D. Stein <lincoln.stein@gmail.com>
-"""Utility to collect execution time and GPU usage stats on invocations in flight"""
+"""Utility to collect execution time and GPU usage stats on invocations in flight
 
-"""
 Usage:
 
 statistics = InvocationStatsService(graph_execution_manager)

invokeai/app/services/latent_storage.py

@@ -60,7 +60,7 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
         return None if name not in self.__cache else self.__cache[name]
 
     def __set_cache(self, name: str, data: torch.Tensor):
-        if not name in self.__cache:
+        if name not in self.__cache:
             self.__cache[name] = data
             self.__cache_ids.put(name)
             if self.__cache_ids.qsize() > self.__max_cache_size:

invokeai/app/util/controlnet_utils.py

@@ -1,3 +1,4 @@
+from typing import Union
 import torch
 import numpy as np
 import cv2
@@ -5,7 +6,7 @@ from PIL import Image
 from diffusers.utils import PIL_INTERPOLATION
 
 from einops import rearrange
-from controlnet_aux.util import HWC3, resize_image
+from controlnet_aux.util import HWC3
 
 ###################################################################
 # Copy of scripts/lvminthin.py from Mikubill/sd-webui-controlnet
@@ -232,7 +233,8 @@ def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device:
     k0 = float(h) / old_h
     k1 = float(w) / old_w
 
-    safeint = lambda x: int(np.round(x))
+    def safeint(x: Union[int, float]) -> int:
+        return int(np.round(x))
 
     # if resize_mode == external_code.ResizeMode.OUTER_FIT:
     if resize_mode == "fill_resize":  # OUTER_FIT

invokeai/app/util/step_callback.py

@@ -5,7 +5,6 @@ from invokeai.app.models.image import ProgressImage
 from ..invocations.baseinvocation import InvocationContext
 from ...backend.util.util import image_to_dataURL
 from ...backend.stable_diffusion import PipelineIntermediateState
-from invokeai.app.services.config import InvokeAIAppConfig
 from ...backend.model_management.models import BaseModelType
 
 

invokeai/backend/__init__.py

@@ -1,5 +1,5 @@
 """
 Initialization file for invokeai.backend
 """
-from .model_management import ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo
+from .model_management import ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo  # noqa: F401
-from .model_management.models import SilenceWarnings
+from .model_management.models import SilenceWarnings  # noqa: F401

invokeai/backend/image_util/__init__.py

@@ -1,14 +1,16 @@
 """
 Initialization file for invokeai.backend.image_util methods.
 """
-from .patchmatch import PatchMatch
+from .patchmatch import PatchMatch  # noqa: F401
-from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata
+from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata  # noqa: F401
-from .seamless import configure_model_padding
+from .seamless import configure_model_padding  # noqa: F401
-from .txt2mask import Txt2Mask
+from .txt2mask import Txt2Mask  # noqa: F401
-from .util import InitImageResizer, make_grid
+from .util import InitImageResizer, make_grid  # noqa: F401
 
 
 def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False):
+    from PIL import ImageDraw
+
     if not debug_status:
         return
 

invokeai/backend/image_util/pngwriter.py

@@ -26,7 +26,7 @@ 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)),
+            (f for f in dirlist if re.match(r"^(\d+)\..*\.png", f)),
             "0000000.0.png",
         )
         basecount = int(existing_name.split(".", 1)[0]) + 1
@@ -98,11 +98,11 @@ class PromptFormatter:
         # 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("--seamless")
         if opt.init_img:
             switches.append(f"-I{opt.init_img}")
         if opt.fit:
-            switches.append(f"--fit")
+            switches.append("--fit")
         if opt.strength and opt.init_img is not None:
             switches.append(f"-f{opt.strength or t2i.strength}")
         if opt.gfpgan_strength:

invokeai/backend/install/invokeai_configure.py

@@ -54,7 +54,6 @@ from invokeai.frontend.install.widgets import (
     MultiSelectColumns,
     CenteredButtonPress,
     FileBox,
-    IntTitleSlider,
     set_min_terminal_size,
     CyclingForm,
     MIN_COLS,

invokeai/backend/install/migrate_to_3.py

@@ -525,7 +525,7 @@ def do_migrate(src_directory: Path, dest_directory: Path):
     if version_3:  # write into the dest directory
         try:
             shutil.copy(dest_directory / "configs" / "models.yaml", config_file)
-        except:
+        except Exception:
             MigrateTo3.initialize_yaml(config_file)
         mgr = ModelManager(config_file)  # important to initialize BEFORE moving the models directory
         (dest_directory / "models").replace(dest_models)

invokeai/backend/install/model_install_backend.py

@@ -12,7 +12,6 @@ from typing import Optional, List, Dict, Callable, Union, Set
 import requests
 from diffusers import DiffusionPipeline
 from diffusers import logging as dlogging
-import onnx
 import torch
 from huggingface_hub import hf_hub_url, HfFolder, HfApi
 from omegaconf import OmegaConf

invokeai/backend/model_management/__init__.py

@@ -1,10 +1,10 @@
 """
 Initialization file for invokeai.backend.model_management
 """
-from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType
+from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType  # noqa: F401
-from .model_cache import ModelCache
+from .model_cache import ModelCache  # noqa: F401
-from .lora import ModelPatcher, ONNXModelPatcher
+from .lora import ModelPatcher, ONNXModelPatcher  # noqa: F401
-from .models import (
+from .models import (  # noqa: F401
     BaseModelType,
     ModelType,
     SubModelType,
@@ -12,5 +12,4 @@ from .models import (
     ModelNotFoundException,
     DuplicateModelException,
 )
-from .model_merge import ModelMerger, MergeInterpolationMethod
+from .model_merge import ModelMerger, MergeInterpolationMethod  # noqa: F401
-from .lora import ModelPatcher

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -20,11 +20,36 @@
 import re
 from contextlib import nullcontext
 from io import BytesIO
-from typing import Optional, Union
 from pathlib import Path
+from typing import Optional, Union
 
 import requests
 import torch
+from diffusers.models import (
+    AutoencoderKL,
+    ControlNetModel,
+    PriorTransformer,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
+from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DDPMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    HeunDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UnCLIPScheduler,
+)
+from diffusers.utils import is_accelerate_available, is_omegaconf_available
+from diffusers.utils.import_utils import BACKENDS_MAPPING
+from picklescan.scanner import scan_file_path
 from transformers import (
     AutoFeatureExtractor,
     BertTokenizerFast,
@@ -37,35 +62,8 @@ from transformers import (
     CLIPVisionModelWithProjection,
 )
 
-from diffusers.models import (
-    AutoencoderKL,
-    ControlNetModel,
-    PriorTransformer,
-    UNet2DConditionModel,
-)
-from diffusers.schedulers import (
-    DDIMScheduler,
-    DDPMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    HeunDiscreteScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-    UnCLIPScheduler,
-)
-from diffusers.utils import is_accelerate_available, is_omegaconf_available, is_safetensors_available
-from diffusers.utils.import_utils import BACKENDS_MAPPING
-from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
-from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
-
-from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.app.services.config import InvokeAIAppConfig
-
+from invokeai.backend.util.logging import InvokeAILogger
-from picklescan.scanner import scan_file_path
 from .models import BaseModelType, ModelVariantType
 
 try:
@@ -1221,9 +1219,6 @@ def download_from_original_stable_diffusion_ckpt(
         raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
 
     if from_safetensors:
-        if not is_safetensors_available():
-            raise ValueError(BACKENDS_MAPPING["safetensors"][1])
-
         from safetensors.torch import load_file as safe_load
 
         checkpoint = safe_load(checkpoint_path, device="cpu")
@@ -1662,9 +1657,6 @@ def download_controlnet_from_original_ckpt(
     from omegaconf import OmegaConf
 
     if from_safetensors:
-        if not is_safetensors_available():
-            raise ValueError(BACKENDS_MAPPING["safetensors"][1])
-
         from safetensors import safe_open
 
         checkpoint = {}
@@ -1741,7 +1733,7 @@ def convert_ckpt_to_diffusers(
 
     pipe.save_pretrained(
         dump_path,
-        safe_serialization=use_safetensors and is_safetensors_available(),
+        safe_serialization=use_safetensors,
     )
 
 
@@ -1757,7 +1749,4 @@ def convert_controlnet_to_diffusers(
     """
     pipe = download_controlnet_from_original_ckpt(checkpoint_path, **kwargs)
 
-    pipe.save_pretrained(
+    pipe.save_pretrained(dump_path, safe_serialization=True)
-        dump_path,
-        safe_serialization=is_safetensors_available(),
-    )

invokeai/backend/model_management/lora.py

@@ -5,21 +5,16 @@ from contextlib import contextmanager
 from typing import Optional, Dict, Tuple, Any, Union, List
 from pathlib import Path
 
-import torch
-from safetensors.torch import load_file
-from torch.utils.hooks import RemovableHandle
-
-from diffusers.models import UNet2DConditionModel
-from transformers import CLIPTextModel
-from onnx import numpy_helper
-from onnxruntime import OrtValue
 import numpy as np
-
+import torch
 from compel.embeddings_provider import BaseTextualInversionManager
 from diffusers.models import UNet2DConditionModel
 from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
+from .models.lora import LoRAModel
+
+
 """
 loras = [
     (lora_model1, 0.7),
@@ -52,7 +47,7 @@ class ModelPatcher:
                 module = module.get_submodule(submodule_name)
                 module_key += "." + submodule_name
                 submodule_name = key_parts.pop(0)
-            except:
+            except Exception:
                 submodule_name += "_" + key_parts.pop(0)
 
         module = module.get_submodule(submodule_name)
@@ -312,7 +307,8 @@ class TextualInversionManager(BaseTextualInversionManager):
 
 
 class ONNXModelPatcher:
-    from .models.base import IAIOnnxRuntimeModel, OnnxRuntimeModel
+    from .models.base import IAIOnnxRuntimeModel
+    from diffusers import OnnxRuntimeModel
 
     @classmethod
     @contextmanager
@@ -341,7 +337,7 @@ class ONNXModelPatcher:
     def apply_lora(
         cls,
         model: IAIOnnxRuntimeModel,
-        loras: List[Tuple[LoraModel, float]],
+        loras: List[Tuple[LoRAModel, float]],
         prefix: str,
     ):
         from .models.base import IAIOnnxRuntimeModel

invokeai/backend/model_management/model_cache.py

@@ -273,7 +273,7 @@ class ModelCache(object):
                     self.cache.logger.debug(f"Locking {self.key} in {self.cache.execution_device}")
                     self.cache._print_cuda_stats()
 
-                except:
+                except Exception:
                     self.cache_entry.unlock()
                     raise
 

invokeai/backend/model_management/model_manager.py

@@ -420,12 +420,12 @@ class ModelManager(object):
         base_model_str, model_type_str, model_name = model_key.split("/", 2)
         try:
             model_type = ModelType(model_type_str)
-        except:
+        except Exception:
             raise Exception(f"Unknown model type: {model_type_str}")
 
         try:
             base_model = BaseModelType(base_model_str)
-        except:
+        except Exception:
             raise Exception(f"Unknown base model: {base_model_str}")
 
         return (model_name, base_model, model_type)
@@ -856,7 +856,7 @@ class ModelManager(object):
             info.pop("config")
 
             result = self.add_model(model_name, base_model, model_type, model_attributes=info, clobber=True)
-        except:
+        except Exception:
             # something went wrong, so don't leave dangling diffusers model in directory or it will cause a duplicate model error!
             rmtree(new_diffusers_path)
             raise
@@ -1043,7 +1043,7 @@ class ModelManager(object):
         # Patch in the SD VAE from core so that it is available for use by the UI
         try:
             self.heuristic_import({str(self.resolve_model_path("core/convert/sd-vae-ft-mse"))})
-        except:
+        except Exception:
             pass
 
         installer = ModelInstall(

invokeai/backend/model_management/model_probe.py

@@ -217,9 +217,9 @@ class ModelProbe(object):
             raise "The model {model_name} is potentially infected by malware. Aborting import."
 
 
-###################################################3
+# ##################################################3
 # Checkpoint probing
-###################################################3
+# ##################################################3
 class ProbeBase(object):
     def get_base_type(self) -> BaseModelType:
         pass
@@ -431,7 +431,7 @@ class PipelineFolderProbe(FolderProbeBase):
                 return ModelVariantType.Depth
             elif in_channels == 4:
                 return ModelVariantType.Normal
-        except:
+        except Exception:
             pass
         return ModelVariantType.Normal
 

invokeai/backend/model_management/models/__init__.py

@@ -2,7 +2,7 @@ import inspect
 from enum import Enum
 from pydantic import BaseModel
 from typing import Literal, get_origin
-from .base import (
+from .base import (  # noqa: F401
     BaseModelType,
     ModelType,
     SubModelType,
@@ -118,7 +118,7 @@ def get_model_config_enums():
             fields = model_config.__annotations__
         try:
             field = fields["model_format"]
-        except:
+        except Exception:
             raise Exception("format field not found")
 
         # model_format: None

invokeai/backend/model_management/models/base.py

@@ -3,27 +3,28 @@ import os
 import sys
 import typing
 import inspect
-from enum import Enum
+import warnings
 from abc import ABCMeta, abstractmethod
+from contextlib import suppress
+from enum import Enum
 from pathlib import Path
 from picklescan.scanner import scan_file_path
+
 import torch
 import numpy as np
-import safetensors.torch
-from pathlib import Path
-from diffusers import DiffusionPipeline, ConfigMixin, OnnxRuntimeModel
-
-from contextlib import suppress
-from pydantic import BaseModel, Field
-from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
-
 import onnx
+import safetensors.torch
+from diffusers import DiffusionPipeline, ConfigMixin
 from onnx import numpy_helper
 from onnxruntime import (
     InferenceSession,
     SessionOptions,
     get_available_providers,
 )
+from pydantic import BaseModel, Field
+from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
+from diffusers import logging as diffusers_logging
+from transformers import logging as transformers_logging
 
 
 class DuplicateModelException(Exception):
@@ -171,7 +172,7 @@ class ModelBase(metaclass=ABCMeta):
                 fields = value.__annotations__
             try:
                 field = fields["model_format"]
-            except:
+            except Exception:
                 raise Exception(f"Invalid config definition - format field not found({cls.__qualname__})")
 
             if isinstance(field, type) and issubclass(field, str) and issubclass(field, Enum):
@@ -244,7 +245,7 @@ class DiffusersModel(ModelBase):
         try:
             config_data = DiffusionPipeline.load_config(self.model_path)
             # config_data = json.loads(os.path.join(self.model_path, "model_index.json"))
-        except:
+        except Exception:
             raise Exception("Invalid diffusers model! (model_index.json not found or invalid)")
 
         config_data.pop("_ignore_files", None)
@@ -343,7 +344,7 @@ def calc_model_size_by_fs(model_path: str, subfolder: Optional[str] = None, vari
             with open(os.path.join(model_path, file), "r") as f:
                 index_data = json.loads(f.read())
             return int(index_data["metadata"]["total_size"])
-        except:
+        except Exception:
             pass
 
     # calculate files size if there is no index file
@@ -440,7 +441,7 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
     if str(path).endswith(".safetensors"):
         try:
             checkpoint = _fast_safetensors_reader(path)
-        except:
+        except Exception:
             # TODO: create issue for support "meta"?
             checkpoint = safetensors.torch.load_file(path, device="cpu")
     else:
@@ -452,11 +453,6 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
     return checkpoint
 
 
-import warnings
-from diffusers import logging as diffusers_logging
-from transformers import logging as transformers_logging
-
-
 class SilenceWarnings(object):
     def __init__(self):
         self.transformers_verbosity = transformers_logging.get_verbosity()
@@ -639,7 +635,7 @@ class IAIOnnxRuntimeModel:
             raise Exception("You should call create_session before running model")
 
         inputs = {k: np.array(v) for k, v in kwargs.items()}
-        output_names = self.session.get_outputs()
+        # output_names = self.session.get_outputs()
         # for k in inputs:
         #     self.io_binding.bind_cpu_input(k, inputs[k])
         # for name in output_names:

invokeai/backend/model_management/models/controlnet.py

@@ -43,7 +43,7 @@ class ControlNetModel(ModelBase):
         try:
             config = EmptyConfigLoader.load_config(self.model_path, config_name="config.json")
             # config = json.loads(os.path.join(self.model_path, "config.json"))
-        except:
+        except Exception:
             raise Exception("Invalid controlnet model! (config.json not found or invalid)")
 
         model_class_name = config.get("_class_name", None)
@@ -53,7 +53,7 @@ class ControlNetModel(ModelBase):
         try:
             self.model_class = self._hf_definition_to_type(["diffusers", model_class_name])
             self.model_size = calc_model_size_by_fs(self.model_path)
-        except:
+        except Exception:
             raise Exception("Invalid ControlNet model!")
 
     def get_size(self, child_type: Optional[SubModelType] = None):
@@ -78,7 +78,7 @@ class ControlNetModel(ModelBase):
                     variant=variant,
                 )
                 break
-            except:
+            except Exception:
                 pass
         if not model:
             raise ModelNotFoundException()

invokeai/backend/model_management/models/stable_diffusion.py

@@ -330,5 +330,5 @@ def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
             config_path = config_path.relative_to(app_config.root_path)
         return str(config_path)
 
-    except:
+    except Exception:
         return None

invokeai/backend/model_management/models/stable_diffusion_onnx.py

@@ -1,25 +1,17 @@
-import os
-import json
 from enum import Enum
-from pydantic import Field
+from typing import Literal
-from pathlib import Path
+
-from typing import Literal, Optional, Union
+from diffusers import OnnxRuntimeModel
 from .base import (
-    ModelBase,
     ModelConfigBase,
     BaseModelType,
     ModelType,
-    SubModelType,
     ModelVariantType,
     DiffusersModel,
     SchedulerPredictionType,
-    SilenceWarnings,
-    read_checkpoint_meta,
     classproperty,
-    OnnxRuntimeModel,
     IAIOnnxRuntimeModel,
 )
-from invokeai.app.services.config import InvokeAIAppConfig
 
 
 class StableDiffusionOnnxModelFormat(str, Enum):

invokeai/backend/model_management/models/vae.py

@@ -5,7 +5,6 @@ from typing import Optional
 
 import safetensors
 import torch
-from diffusers.utils import is_safetensors_available
 from omegaconf import OmegaConf
 
 from invokeai.app.services.config import InvokeAIAppConfig
@@ -44,14 +43,14 @@ class VaeModel(ModelBase):
         try:
             config = EmptyConfigLoader.load_config(self.model_path, config_name="config.json")
             # config = json.loads(os.path.join(self.model_path, "config.json"))
-        except:
+        except Exception:
             raise Exception("Invalid vae model! (config.json not found or invalid)")
 
         try:
             vae_class_name = config.get("_class_name", "AutoencoderKL")
             self.vae_class = self._hf_definition_to_type(["diffusers", vae_class_name])
             self.model_size = calc_model_size_by_fs(self.model_path)
-        except:
+        except Exception:
             raise Exception("Invalid vae model! (Unkown vae type)")
 
     def get_size(self, child_type: Optional[SubModelType] = None):
@@ -175,5 +174,5 @@ def _convert_vae_ckpt_and_cache(
         vae_config=config,
         image_size=image_size,
     )
-    vae_model.save_pretrained(output_path, safe_serialization=is_safetensors_available())
+    vae_model.save_pretrained(output_path, safe_serialization=True)
     return output_path

invokeai/backend/stable_diffusion/__init__.py

@@ -1,11 +1,15 @@
 """
 Initialization file for the invokeai.backend.stable_diffusion package
 """
-from .diffusers_pipeline import (
+from .diffusers_pipeline import (  # noqa: F401
     ConditioningData,
     PipelineIntermediateState,
     StableDiffusionGeneratorPipeline,
 )
-from .diffusion import InvokeAIDiffuserComponent
+from .diffusion import InvokeAIDiffuserComponent  # noqa: F401
-from .diffusion.cross_attention_map_saving import AttentionMapSaver
+from .diffusion.cross_attention_map_saving import AttentionMapSaver  # noqa: F401
-from .diffusion.shared_invokeai_diffusion import PostprocessingSettings, BasicConditioningInfo, SDXLConditioningInfo
+from .diffusion.shared_invokeai_diffusion import (  # noqa: F401
+    PostprocessingSettings,
+    BasicConditioningInfo,
+    SDXLConditioningInfo,
+)

invokeai/backend/stable_diffusion/diffusion/__init__.py

@@ -1,9 +1,9 @@
 """
 Initialization file for invokeai.models.diffusion
 """
-from .cross_attention_control import InvokeAICrossAttentionMixin
+from .cross_attention_control import InvokeAICrossAttentionMixin  # noqa: F401
-from .cross_attention_map_saving import AttentionMapSaver
+from .cross_attention_map_saving import AttentionMapSaver  # noqa: F401
-from .shared_invokeai_diffusion import (
+from .shared_invokeai_diffusion import (  # noqa: F401
     InvokeAIDiffuserComponent,
     PostprocessingSettings,
     BasicConditioningInfo,

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -4,6 +4,7 @@
 
 import enum
 import math
+from dataclasses import dataclass, field
 from typing import Callable, Optional
 
 import diffusers
@@ -12,6 +13,11 @@ import torch
 from compel.cross_attention_control import Arguments
 from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.attention_processor import (
+    Attention,
+    AttnProcessor,
+    SlicedAttnProcessor,
+)
 from torch import nn
 
 import invokeai.backend.util.logging as logger
@@ -522,14 +528,6 @@ class AttnProcessor:
         return hidden_states
 
 """
-from dataclasses import dataclass, field
-
-import torch
-from diffusers.models.attention_processor import (
-    Attention,
-    AttnProcessor,
-    SlicedAttnProcessor,
-)
 
 
 @dataclass

invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py

@@ -5,8 +5,6 @@ import torch
 from torchvision.transforms.functional import InterpolationMode
 from torchvision.transforms.functional import resize as tv_resize
 
-from .cross_attention_control import CrossAttentionType, get_cross_attention_modules
-
 
 class AttentionMapSaver:
     def __init__(self, token_ids: range, latents_shape: torch.Size):

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -3,15 +3,12 @@ from __future__ import annotations
 from contextlib import contextmanager
 from dataclasses import dataclass
 import math
-from typing import Any, Callable, Dict, Optional, Union, List
+from typing import Any, Callable, Optional, Union
 
-import numpy as np
 import torch
 from diffusers import UNet2DConditionModel
-from diffusers.models.attention_processor import AttentionProcessor
 from typing_extensions import TypeAlias
 
-import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 
 from .cross_attention_control import (
@@ -579,7 +576,7 @@ class InvokeAIDiffuserComponent:
         latents.to(device="cpu")
 
         if (
-            h_symmetry_time_pct != None
+            h_symmetry_time_pct is not None
             and self.last_percent_through < h_symmetry_time_pct
             and percent_through >= h_symmetry_time_pct
         ):
@@ -595,7 +592,7 @@ class InvokeAIDiffuserComponent:
             )
 
         if (
-            v_symmetry_time_pct != None
+            v_symmetry_time_pct is not None
             and self.last_percent_through < v_symmetry_time_pct
             and percent_through >= v_symmetry_time_pct
         ):

invokeai/backend/stable_diffusion/image_degradation/__init__.py

@@ -1,6 +1,6 @@
-from ldm.modules.image_degradation.bsrgan import (
+from ldm.modules.image_degradation.bsrgan import (  # noqa: F401
     degradation_bsrgan_variant as degradation_fn_bsr,
 )
-from ldm.modules.image_degradation.bsrgan_light import (
+from ldm.modules.image_degradation.bsrgan_light import (  # noqa: F401
     degradation_bsrgan_variant as degradation_fn_bsr_light,
 )

invokeai/backend/stable_diffusion/image_degradation/bsrgan.py

@@ -573,14 +573,15 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
     hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
     """
     image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+    jpeg_prob, scale2_prob = 0.9, 0.25
-    sf_ori = sf
+    # isp_prob = 0.25  # uncomment with `if i== 6` block below
+    # sf_ori = sf  # uncomment with `if i== 6` block below
 
     h1, w1 = image.shape[:2]
     image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
     h, w = image.shape[:2]
 
-    hq = image.copy()
+    # hq = image.copy()  # uncomment with `if i== 6` block below
 
     if sf == 4 and random.random() < scale2_prob:  # downsample1
         if np.random.rand() < 0.5:
@@ -777,7 +778,7 @@ if __name__ == "__main__":
         img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
         print(img_lq.shape)
         print("bicubic", img_lq_bicubic.shape)
-        print(img_hq.shape)
+        # print(img_hq.shape)
         lq_nearest = cv2.resize(
             util.single2uint(img_lq),
             (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
@@ -788,5 +789,6 @@ if __name__ == "__main__":
             (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
             interpolation=0,
         )
-        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+        # img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest], axis=1)
         util.imsave(img_concat, str(i) + ".png")

invokeai/backend/stable_diffusion/image_degradation/bsrgan_light.py

@@ -577,14 +577,15 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
     hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
     """
     image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+    jpeg_prob, scale2_prob = 0.9, 0.25
-    sf_ori = sf
+    # isp_prob = 0.25  # uncomment with `if i== 6` block below
+    # sf_ori = sf  # uncomment with `if i== 6` block below
 
     h1, w1 = image.shape[:2]
     image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
     h, w = image.shape[:2]
 
-    hq = image.copy()
+    # hq = image.copy()  # uncomment with `if i== 6` block below
 
     if sf == 4 and random.random() < scale2_prob:  # downsample1
         if np.random.rand() < 0.5:

invokeai/backend/stable_diffusion/image_degradation/utils_image.py

@@ -8,8 +8,6 @@ import numpy as np
 import torch
 from torchvision.utils import make_grid
 
-# import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-
 import invokeai.backend.util.logging as logger
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
@@ -50,6 +48,8 @@ def get_timestamp():
 
 
 def imshow(x, title=None, cbar=False, figsize=None):
+    import matplotlib.pyplot as plt
+
     plt.figure(figsize=figsize)
     plt.imshow(np.squeeze(x), interpolation="nearest", cmap="gray")
     if title:
@@ -60,6 +60,8 @@ def imshow(x, title=None, cbar=False, figsize=None):
 
 
 def surf(Z, cmap="rainbow", figsize=None):
+    import matplotlib.pyplot as plt
+
     plt.figure(figsize=figsize)
     ax3 = plt.axes(projection="3d")
 

invokeai/backend/stable_diffusion/schedulers/__init__.py

@@ -1 +1 @@
-from .schedulers import SCHEDULER_MAP
+from .schedulers import SCHEDULER_MAP  # noqa: F401

invokeai/backend/training/__init__.py

@@ -1,4 +1,4 @@
 """
 Initialization file for invokeai.backend.training
 """
-from .textual_inversion_training import do_textual_inversion_training, parse_args
+from .textual_inversion_training import do_textual_inversion_training, parse_args  # noqa: F401

invokeai/backend/util/__init__.py

@@ -1,7 +1,7 @@
 """
 Initialization file for invokeai.backend.util
 """
-from .devices import (
+from .devices import (  # noqa: F401
     CPU_DEVICE,
     CUDA_DEVICE,
     MPS_DEVICE,
@@ -10,6 +10,11 @@ from .devices import (
     normalize_device,
     torch_dtype,
 )
-from .log import write_log
+from .log import write_log  # noqa: F401
-from .util import ask_user, download_with_resume, instantiate_from_config, url_attachment_name, Chdir
+from .util import (ask_user,
+                   download_with_resume,
+                   instantiate_from_config,
+                   url_attachment_name,
+                   Chdir,
+                   )
 from .attention import auto_detect_slice_size

invokeai/backend/util/hotfixes.py

@@ -25,10 +25,15 @@ from diffusers.models.unet_2d_condition import UNet2DConditionModel
 import diffusers
 from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
 
+from invokeai.backend.util.logging import InvokeAILogger
+
 # TODO: create PR to diffusers
 # Modified ControlNetModel with encoder_attention_mask argument added
 
 
+logger = InvokeAILogger.getLogger(__name__)
+
+
 class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
     """
     A ControlNet model.
@@ -111,7 +116,7 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
             "DownBlock2D",
         ),
         only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
         layers_per_block: int = 2,
         downsample_padding: int = 1,
         mid_block_scale_factor: float = 1,

invokeai/backend/util/log.py

@@ -27,8 +27,8 @@ def write_log_message(results, output_cntr):
     log_lines = [f"{path}: {prompt}\n" for path, prompt in results]
     if len(log_lines) > 1:
         subcntr = 1
-        for l in log_lines:
+        for ll in log_lines:
-            print(f"[{output_cntr}.{subcntr}] {l}", end="")
+            print(f"[{output_cntr}.{subcntr}] {ll}", end="")
             subcntr += 1
     else:
         print(f"[{output_cntr}] {log_lines[0]}", end="")

invokeai/backend/util/logging.py

@@ -182,13 +182,13 @@ import urllib.parse
 from abc import abstractmethod
 from pathlib import Path
 
-from invokeai.app.services.config import InvokeAIAppConfig, get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 try:
     import syslog
 
     SYSLOG_AVAILABLE = True
-except:
+except ImportError:
     SYSLOG_AVAILABLE = False
 
 
@@ -417,7 +417,7 @@ class InvokeAILogger(object):
                     syslog_args["socktype"] = _SOCK_MAP[arg_value[0]]
                 else:
                     syslog_args["address"] = arg_name
-        except:
+        except Exception:
             raise ValueError(f"{args} is not a value argument list for syslog logging")
         return logging.handlers.SysLogHandler(**syslog_args)
 

invokeai/backend/util/mps_fixes.py

@@ -191,7 +191,7 @@ class ChunkedSlicedAttnProcessor:
         assert value.shape[0] == 1
         assert hidden_states.shape[0] == 1
 
-        dtype = query.dtype
+        # dtype = query.dtype
         if attn.upcast_attention:
             query = query.float()
             key = key.float()

invokeai/backend/util/util.py

@@ -84,7 +84,7 @@ def count_params(model, verbose=False):
 
 
 def instantiate_from_config(config, **kwargs):
-    if not "target" in config:
+    if "target" not in config:
         if config == "__is_first_stage__":
             return None
         elif config == "__is_unconditional__":
@@ -234,16 +234,17 @@ def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10
         .repeat_interleave(d[1], 1)
     )
 
-    dot = lambda grad, shift: (
+    def dot(grad, shift):
-        torch.stack(
+        return (
-            (
+            torch.stack(
-                grid[: shape[0], : shape[1], 0] + shift[0],
+                (
-                grid[: shape[0], : shape[1], 1] + shift[1],
+                    grid[: shape[0], : shape[1], 0] + shift[0],
-            ),
+                    grid[: shape[0], : shape[1], 1] + shift[1],
-            dim=-1,
+                ),
-        )
+                dim=-1,
-        * grad[: shape[0], : shape[1]]
+            )
-    ).sum(dim=-1)
+            * grad[: shape[0], : shape[1]]
+        ).sum(dim=-1)
 
     n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0]).to(device)
     n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0]).to(device)
@@ -287,7 +288,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
     if dest.is_dir():
         try:
             file_name = re.search('filename="(.+)"', resp.headers.get("Content-Disposition")).group(1)
-        except:
+        except AttributeError:
             file_name = os.path.basename(url)
         dest = dest / file_name
     else:
@@ -342,7 +343,7 @@ def url_attachment_name(url: str) -> dict:
         resp = requests.get(url, stream=True)
         match = re.search('filename="(.+)"', resp.headers.get("Content-Disposition"))
         return match.group(1)
-    except:
+    except Exception:
         return None
 
 

invokeai/frontend/CLI/__init__.py

@@ -1,4 +1,4 @@
 """
 Initialization file for invokeai.frontend.CLI
 """
-from .CLI import main as invokeai_command_line_interface
+from .CLI import main as invokeai_command_line_interface  # noqa: F401

invokeai/frontend/install/invokeai_configure.py

@@ -1,4 +1,4 @@
 """
 Wrapper for invokeai.backend.configure.invokeai_configure
 """
-from ...backend.install.invokeai_configure import main as invokeai_configure
+from ...backend.install.invokeai_configure import main as invokeai_configure  # noqa: F401

invokeai/frontend/install/invokeai_update.py

@@ -80,7 +80,7 @@ def welcome(versions: dict):
 def get_extras():
     extras = ""
     try:
-        dist = pkg_resources.get_distribution("xformers")
+        _ = pkg_resources.get_distribution("xformers")
         extras = "[xformers]"
     except pkg_resources.DistributionNotFound:
         pass
@@ -90,7 +90,7 @@ def get_extras():
 def main():
     versions = get_versions()
     if invokeai_is_running():
-        print(f":exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]")
+        print(":exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]")
         input("Press any key to continue...")
         return
 
@@ -122,9 +122,9 @@ def main():
     print("")
     print("")
     if os.system(cmd) == 0:
-        print(f":heavy_check_mark: Upgrade successful")
+        print(":heavy_check_mark: Upgrade successful")
     else:
-        print(f":exclamation: [bold red]Upgrade failed[/red bold]")
+        print(":exclamation: [bold red]Upgrade failed[/red bold]")
 
 
 if __name__ == "__main__":

invokeai/frontend/install/model_install.py

@@ -251,7 +251,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
     ) -> dict[str, npyscreen.widget]:
         """Generic code to create model selection widgets"""
         widgets = dict()
-        model_list = [x for x in self.all_models if self.all_models[x].model_type == model_type and not x in exclude]
+        model_list = [x for x in self.all_models if self.all_models[x].model_type == model_type and x not in exclude]
         model_labels = [self.model_labels[x] for x in model_list]
 
         show_recommended = len(self.installed_models) == 0
@@ -357,14 +357,14 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                 try:
                     v.hidden = True
                     v.editable = False
-                except:
+                except Exception:
                     pass
         for k, v in widgets[selected_tab].items():
             try:
                 v.hidden = False
                 if not isinstance(v, (npyscreen.FixedText, npyscreen.TitleFixedText, CenteredTitleText)):
                     v.editable = True
-            except:
+            except Exception:
                 pass
         self.__class__.current_tab = selected_tab  # for persistence
         self.display()
@@ -541,7 +541,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             self.ti_models,
         ]
         for section in ui_sections:
-            if not "models_selected" in section:
+            if "models_selected" not in section:
                 continue
             selected = set([section["models"][x] for x in section["models_selected"].value])
             models_to_install = [x for x in selected if not self.all_models[x].installed]
@@ -637,7 +637,7 @@ def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection) -> SchedulerPre
             return None
         else:
             return response
-    except:
+    except Exception:
         return None
 
 
@@ -673,8 +673,7 @@ def process_and_execute(
 def select_and_download_models(opt: Namespace):
     precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
     config.precision = precision
-    helper = lambda x: ask_user_for_prediction_type(x)
+    installer = ModelInstall(config, prediction_type_helper=ask_user_for_prediction_type)
-    installer = ModelInstall(config, prediction_type_helper=helper)
     if opt.list_models:
         installer.list_models(opt.list_models)
     elif opt.add or opt.delete:

invokeai/frontend/install/widgets.py

@@ -102,8 +102,8 @@ def set_min_terminal_size(min_cols: int, min_lines: int) -> bool:
 class IntSlider(npyscreen.Slider):
     def translate_value(self):
         stri = "%2d / %2d" % (self.value, self.out_of)
-        l = (len(str(self.out_of))) * 2 + 4
+        length = (len(str(self.out_of))) * 2 + 4
-        stri = stri.rjust(l)
+        stri = stri.rjust(length)
         return stri
 
 
@@ -167,8 +167,8 @@ class FloatSlider(npyscreen.Slider):
     # this is supposed to adjust display precision, but doesn't
     def translate_value(self):
         stri = "%3.2f / %3.2f" % (self.value, self.out_of)
-        l = (len(str(self.out_of))) * 2 + 4
+        length = (len(str(self.out_of))) * 2 + 4
-        stri = stri.rjust(l)
+        stri = stri.rjust(length)
         return stri
 
 

invokeai/frontend/legacy_launch_invokeai.py

@@ -1,4 +1,3 @@
-import os
 import sys
 import argparse
 

invokeai/frontend/merge/__init__.py

@@ -1,4 +1,4 @@
 """
 Initialization file for invokeai.frontend.merge
 """
-from .merge_diffusers import main as invokeai_merge_diffusers
+from .merge_diffusers import main as invokeai_merge_diffusers  # noqa: F401

invokeai/frontend/merge/merge_diffusers.py

@@ -9,19 +9,15 @@ import curses
 import sys
 from argparse import Namespace
 from pathlib import Path
-from typing import List, Union
+from typing import List, Optional
 
 import npyscreen
-from diffusers import DiffusionPipeline
-from diffusers import logging as dlogging
 from npyscreen import widget
-from omegaconf import OmegaConf
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_management import (
     ModelMerger,
-    MergeInterpolationMethod,
     ModelManager,
     ModelType,
     BaseModelType,
@@ -318,7 +314,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
         else:
             return True
 
-    def get_model_names(self, base_model: BaseModelType = None) -> List[str]:
+    def get_model_names(self, base_model: Optional[BaseModelType] = None) -> List[str]:
         model_names = [
             info["model_name"]
             for info in self.model_manager.list_models(model_type=ModelType.Main, base_model=base_model)

invokeai/frontend/training/__init__.py

@@ -1,4 +1,4 @@
 """
 Initialization file for invokeai.frontend.training
 """
-from .textual_inversion import main as invokeai_textual_inversion
+from .textual_inversion import main as invokeai_textual_inversion  # noqa: F401

invokeai/frontend/training/textual_inversion.py

@@ -59,7 +59,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
 
         try:
             default = self.model_names.index(saved_args["model"])
-        except:
+        except Exception:
             pass
 
         self.add_widget_intelligent(
@@ -377,7 +377,7 @@ def previous_args() -> dict:
     try:
         conf = OmegaConf.load(conf_file)
         conf["placeholder_token"] = conf["placeholder_token"].strip("<>")
-    except:
+    except Exception:
         conf = None
 
     return conf

invokeai/frontend/web/src/features/system/store/systemSlice.ts

@@ -4,7 +4,7 @@ import { InvokeLogLevel } from 'app/logging/logger';
 import { userInvoked } from 'app/store/actions';
 import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
 import { t } from 'i18next';
-import { startCase } from 'lodash-es';
+import { startCase, upperFirst } from 'lodash-es';
 import { LogLevelName } from 'roarr';
 import {
   isAnySessionRejected,
@@ -26,6 +26,7 @@ import {
 import { ProgressImage } from 'services/events/types';
 import { makeToast } from '../util/makeToast';
 import { LANGUAGES } from './constants';
+import { zPydanticValidationError } from './zodSchemas';
 
 export type CancelStrategy = 'immediate' | 'scheduled';
 
@@ -361,9 +362,24 @@ export const systemSlice = createSlice({
       state.progressImage = null;
 
       let errorDescription = undefined;
+      const duration = 5000;
 
       if (action.payload?.status === 422) {
-        errorDescription = 'Validation Error';
+        const result = zPydanticValidationError.safeParse(action.payload);
+        if (result.success) {
+          result.data.error.detail.map((e) => {
+            state.toastQueue.push(
+              makeToast({
+                title: upperFirst(e.msg),
+                status: 'error',
+                description: `Path:
+                ${e.loc.slice(3).join('.')}`,
+                duration,
+              })
+            );
+          });
+          return;
+        }
       } else if (action.payload?.error) {
         errorDescription = action.payload?.error as string;
       }
@@ -373,6 +389,7 @@ export const systemSlice = createSlice({
           title: t('toast.serverError'),
           status: 'error',
           description: errorDescription,
+          duration,
         })
       );
     });

invokeai/frontend/web/src/features/system/store/zodSchemas.ts

@@ -0,0 +1,14 @@
+import { z } from 'zod';
+
+export const zPydanticValidationError = z.object({
+  status: z.literal(422),
+  error: z.object({
+    detail: z.array(
+      z.object({
+        loc: z.array(z.string()),
+        msg: z.string(),
+        type: z.string(),
+      })
+    ),
+  }),
+});

invokeai/version/__init__.py

@@ -1,7 +1,7 @@
 """
 initialization file for invokeai
 """
-from .invokeai_version import __version__
+from .invokeai_version import __version__  # noqa: F401
 
 __app_id__ = "invoke-ai/InvokeAI"
 __app_name__ = "InvokeAI"

notebooks/notebook_helpers.py

@@ -8,9 +8,8 @@ from google.colab import files
 from IPython.display import Image as ipyimg
 import ipywidgets as widgets
 from PIL import Image
-from numpy import asarray
 from einops import rearrange, repeat
-import torch, torchvision
+import torchvision
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import ismap
 import time
@@ -68,14 +67,14 @@ def get_custom_cond(mode):
 
     elif mode == "text_conditional":
         w = widgets.Text(value="A cake with cream!", disabled=True)
-        display(w)
+        display(w)  # noqa: F821
 
         with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", "w") as f:
             f.write(w.value)
 
     elif mode == "class_conditional":
         w = widgets.IntSlider(min=0, max=1000)
-        display(w)
+        display(w)  # noqa: F821
         with open(f"{dest}/{mode}/custom.txt", "w") as f:
             f.write(w.value)
 
@@ -96,7 +95,7 @@ def select_cond_path(mode):
     onlyfiles = [f for f in sorted(os.listdir(path))]
 
     selected = widgets.RadioButtons(options=onlyfiles, description="Select conditioning:", disabled=False)
-    display(selected)
+    display(selected)  # noqa: F821
     selected_path = os.path.join(path, selected.value)
     return selected_path
 
@@ -123,7 +122,7 @@ def get_cond(mode, selected_path):
 
 
 def visualize_cond_img(path):
-    display(ipyimg(filename=path))
+    display(ipyimg(filename=path))  # noqa: F821
 
 
 def run(model, selected_path, task, custom_steps, resize_enabled=False, classifier_ckpt=None, global_step=None):
@@ -331,7 +330,7 @@ def make_convolutional_sample(
         x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
         log["sample_noquant"] = x_sample_noquant
         log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
-    except:
+    except Exception:
         pass
 
     log["sample"] = x_sample

pyproject.toml

@@ -40,7 +40,7 @@ dependencies = [
   "controlnet-aux>=0.0.6",
   "timm==0.6.13",   # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
   "datasets",
-  "diffusers[torch]~=0.19.3",
+  "diffusers[torch]~=0.20.0",
   "dnspython~=2.4.0",
   "dynamicprompts",
   "easing-functions",
@@ -49,7 +49,7 @@ dependencies = [
   "fastapi==0.88.0",
   "fastapi-events==0.8.0",
   "fastapi-socketio==0.0.10",
-  "huggingface-hub>=0.11.1",
+  "huggingface-hub~=0.16.4",
   "invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
   "matplotlib",                 # needed for plotting of Penner easing functions
   "mediapipe",                  # needed for "mediapipeface" controlnet model
@@ -95,7 +95,14 @@ dependencies = [
 "dev" = [
   "pudb",
 ]
-"test" = ["pytest>6.0.0", "pytest-cov", "pytest-datadir", "black"]
+"test" = [
+  "black",
+  "flake8",
+  "Flake8-pyproject",
+  "pytest>6.0.0",
+  "pytest-cov",
+  "pytest-datadir",
+]
 "xformers" = [
 	   "xformers~=0.0.19; sys_platform!='darwin'",
 	   "triton; sys_platform=='linux'",
@@ -185,6 +192,8 @@ output = "coverage/index.xml"
 
 [tool.flake8]
 max-line-length = 120
+ignore = ["E203", "E266", "E501", "W503"]
+select = ["B", "C", "E", "F", "W", "T4"]
 
 [tool.black]
 line-length = 120

scripts/create_checkpoint_template.py

@@ -4,7 +4,6 @@ Read a checkpoint/safetensors file and write out a template .json file containin
 its metadata for use in fast model probing.
 """
 
-import sys
 import argparse
 import json
 

scripts/dream.py

@@ -3,11 +3,12 @@
 
 import warnings
 
+from invokeai.app.cli_app import invoke_cli
+
 warnings.warn(
     "dream.py is being deprecated, please run invoke.py for the " "new UI/API or legacy_api.py for the old API",
     DeprecationWarning,
 )
 
-from invokeai.app.cli_app import invoke_cli
 
 invoke_cli()

scripts/images2prompt.py

@@ -2,7 +2,7 @@
 """This script reads the "Invoke" Stable Diffusion prompt embedded in files generated by invoke.py"""
 
 import sys
-from PIL import Image, PngImagePlugin
+from PIL import Image
 
 if len(sys.argv) < 2:
     print("Usage: file2prompt.py <file1.png> <file2.png> <file3.png>...")

scripts/invokeai-cli.py

@@ -2,13 +2,11 @@
 
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+import os
 import logging
 
 logging.getLogger("xformers").addFilter(lambda record: "A matching Triton is not available" not in record.getMessage())
 
-import os
-import sys
-
 
 def main():
     # Change working directory to the repo root

scripts/invokeai-web.py

@@ -2,13 +2,11 @@
 
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+import os
 import logging
 
 logging.getLogger("xformers").addFilter(lambda record: "A matching Triton is not available" not in record.getMessage())
 
-import os
-import sys
-
 
 def main():
     # Change working directory to the repo root

scripts/orig_scripts/img2img.py

@@ -1,6 +1,7 @@
 """make variations of input image"""
 
-import argparse, os, sys, glob
+import argparse
+import os
 import PIL
 import torch
 import numpy as np
@@ -12,7 +13,6 @@ from einops import rearrange, repeat
 from torchvision.utils import make_grid
 from torch import autocast
 from contextlib import nullcontext
-import time
 from pytorch_lightning import seed_everything
 
 from ldm.util import instantiate_from_config
@@ -234,7 +234,6 @@ def main():
     with torch.no_grad():
         with precision_scope(device.type):
             with model.ema_scope():
-                tic = time.time()
                 all_samples = list()
                 for n in trange(opt.n_iter, desc="Sampling"):
                     for prompts in tqdm(data, desc="data"):
@@ -279,8 +278,6 @@ def main():
                     Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f"grid-{grid_count:04}.png"))
                     grid_count += 1
 
-                toc = time.time()
-
     print(f"Your samples are ready and waiting for you here: \n{outpath} \n" f" \nEnjoy.")
 
 

scripts/orig_scripts/inpaint.py

@@ -1,4 +1,6 @@
-import argparse, os, sys, glob
+import argparse
+import glob
+import os
 from omegaconf import OmegaConf
 from PIL import Image
 from tqdm import tqdm

scripts/orig_scripts/knn2img.py

@@ -1,13 +1,13 @@
-import argparse, os, sys, glob
+import argparse
-import clip
+import glob
+import os
 import torch
-import torch.nn as nn
 import numpy as np
 from omegaconf import OmegaConf
 from PIL import Image
 from tqdm import tqdm, trange
 from itertools import islice
-from einops import rearrange, repeat
+from einops import rearrange
 from torchvision.utils import make_grid
 import scann
 import time
@@ -390,8 +390,8 @@ if __name__ == "__main__":
                     grid = make_grid(grid, nrow=n_rows)
 
                     # to image
-                    grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
+                    grid_np = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
-                    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f"grid-{grid_count:04}.png"))
+                    Image.fromarray(grid_np.astype(np.uint8)).save(os.path.join(outpath, f"grid-{grid_count:04}.png"))
                     grid_count += 1
 
     print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")

scripts/orig_scripts/main.py

@@ -1,24 +1,24 @@
-import argparse, os, sys, datetime, glob, importlib, csv
+import argparse
+import datetime
+import glob
+import os
+import sys
+
 import numpy as np
 import time
 import torch
-
 import torchvision
 import pytorch_lightning as pl
 
 from packaging import version
 from omegaconf import OmegaConf
-from torch.utils.data import random_split, DataLoader, Dataset, Subset
+from torch.utils.data import DataLoader, Dataset
 from functools import partial
 from PIL import Image
 
 from pytorch_lightning import seed_everything
 from pytorch_lightning.trainer import Trainer
-from pytorch_lightning.callbacks import (
+from pytorch_lightning.callbacks import Callback
-    ModelCheckpoint,
-    Callback,
-    LearningRateMonitor,
-)
 from pytorch_lightning.utilities.distributed import rank_zero_only
 from pytorch_lightning.utilities import rank_zero_info
 
@@ -651,7 +651,7 @@ if __name__ == "__main__":
         trainer_config["accelerator"] = "auto"
         for k in nondefault_trainer_args(opt):
             trainer_config[k] = getattr(opt, k)
-        if not "gpus" in trainer_config:
+        if "gpus" not in trainer_config:
             del trainer_config["accelerator"]
             cpu = True
         else:
@@ -803,7 +803,7 @@ if __name__ == "__main__":
             trainer_opt.detect_anomaly = False
 
         trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
-        trainer.logdir = logdir  ###
+        trainer.logdir = logdir
 
         # data
         config.data.params.train.params.data_root = opt.data_root

scripts/orig_scripts/merge_embeddings.py

@@ -2,7 +2,7 @@ from ldm.modules.encoders.modules import FrozenCLIPEmbedder, BERTEmbedder
 from ldm.modules.embedding_manager import EmbeddingManager
 from ldm.invoke.globals import Globals
 
-import argparse, os
+import argparse
 from functools import partial
 
 import torch
@@ -108,7 +108,7 @@ if __name__ == "__main__":
         manager.load(manager_ckpt)
 
         for placeholder_string in manager.string_to_token_dict:
-            if not placeholder_string in string_to_token_dict:
+            if placeholder_string not in string_to_token_dict:
                 string_to_token_dict[placeholder_string] = manager.string_to_token_dict[placeholder_string]
                 string_to_param_dict[placeholder_string] = manager.string_to_param_dict[placeholder_string]
 

scripts/orig_scripts/sample_diffusion.py

@@ -1,6 +1,12 @@
-import argparse, os, sys, glob, datetime, yaml
+import argparse
-import torch
+import datetime
+import glob
+import os
+import sys
 import time
+import yaml
+
+import torch
 import numpy as np
 from tqdm import trange
 
@@ -10,7 +16,9 @@ from PIL import Image
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import instantiate_from_config
 
-rescale = lambda x: (x + 1.0) / 2.0
+
+def rescale(x: float) -> float:
+    return (x + 1.0) / 2.0
 
 
 def custom_to_pil(x):
@@ -45,7 +53,7 @@ def logs2pil(logs, keys=["sample"]):
             else:
                 print(f"Unknown format for key {k}. ")
                 img = None
-        except:
+        except Exception:
             img = None
         imgs[k] = img
     return imgs

scripts/orig_scripts/train_searcher.py

@@ -1,4 +1,5 @@
-import os, sys
+import os
+import sys
 import numpy as np
 import scann
 import argparse

scripts/orig_scripts/txt2img.py

@@ -1,4 +1,5 @@
-import argparse, os, sys, glob
+import argparse
+import os
 import torch
 import numpy as np
 from omegaconf import OmegaConf
@@ -7,10 +8,9 @@ from tqdm import tqdm, trange
 from itertools import islice
 from einops import rearrange
 from torchvision.utils import make_grid
-import time
 from pytorch_lightning import seed_everything
 from torch import autocast
-from contextlib import contextmanager, nullcontext
+from contextlib import nullcontext
 
 import k_diffusion as K
 import torch.nn as nn
@@ -251,7 +251,6 @@ def main():
     with torch.no_grad():
         with precision_scope(device.type):
             with model.ema_scope():
-                tic = time.time()
                 all_samples = list()
                 for n in trange(opt.n_iter, desc="Sampling"):
                     for prompts in tqdm(data, desc="data"):
@@ -310,8 +309,6 @@ def main():
                     Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f"grid-{grid_count:04}.png"))
                     grid_count += 1
 
-                toc = time.time()
-
     print(f"Your samples are ready and waiting for you here: \n{outpath} \n" f" \nEnjoy.")
 
 

scripts/probe-model.py

@@ -1,7 +1,6 @@
 #!/bin/env python
 
 import argparse
-import sys
 from pathlib import Path
 from invokeai.backend.model_management.model_probe import ModelProbe