Resolving merge conflicts for flake8

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 +1,16 @@
-import os
-import json
 from enum import Enum
-from pydantic import Field
-from pathlib import Path
-from typing import Literal, Optional, Union
+from typing import Literal
 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

@@ -44,14 +44,14 @@ 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):