ruff fixes and restore default map location of object serializer load

invokeai/app/invocations/compel.py

@@ -1,7 +1,6 @@
 from typing import Iterator, List, Optional, Tuple, Union, cast
 
 import torch
-import threading
 from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
@@ -140,7 +139,6 @@ class SDXLPromptInvocationBase:
         lora_prefix: str,
         zero_on_empty: bool,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
-        tid = threading.current_thread().ident
         tokenizer_info = context.models.load(clip_field.tokenizer)
         text_encoder_info = context.models.load(clip_field.text_encoder)
 

invokeai/app/invocations/denoise_latents.py

@@ -1,7 +1,6 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 import copy
 import inspect
-import threading
 from contextlib import ExitStack
 from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
@@ -194,7 +193,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         """Get the text embeddings and masks from the input conditioning fields."""
         text_embeddings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]] = []
         text_embeddings_masks: list[Optional[torch.Tensor]] = []
-        tid = threading.current_thread().ident
         for cond in cond_list:
             cond_data = copy.deepcopy(context.conditioning.load(cond.conditioning_name))
             text_embeddings.append(cond_data.conditionings[0].to(device=device, dtype=dtype))
@@ -319,7 +317,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         if not isinstance(uncond_list, list):
             uncond_list = [uncond_list]
 
-        tid = threading.current_thread().ident
         cond_text_embeddings, cond_text_embedding_masks = self._get_text_embeddings_and_masks(
             cond_list, context, unet.device, unet.dtype
         )

invokeai/app/services/object_serializer/object_serializer_disk.py

@@ -47,7 +47,7 @@ class ObjectSerializerDisk(ObjectSerializerBase[T]):
     def load(self, name: str) -> T:
         file_path = self._get_path(name)
         try:
-            return torch.load(file_path, map_location=TorchDevice.choose_torch_device())  # pyright: ignore [reportUnknownMemberType]
+            return torch.load(file_path)  # pyright: ignore [reportUnknownMemberType]
         except FileNotFoundError as e:
             raise ObjectNotFoundError(name) from e
 

invokeai/app/services/shared/graph.py

@@ -652,7 +652,7 @@ class Graph(BaseModel):
         output_fields = [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
 
         # Input type must be a list
-        if get_origin(input_field) != list:
+        if get_origin(input_field) is not list:
             return False
 
         # Validate that all outputs match the input type

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -20,25 +20,21 @@ context. Use like this:
 
 import copy
 import gc
-import math
 import sys
 import threading
-import time
 from contextlib import contextmanager, suppress
 from logging import Logger
 from threading import BoundedSemaphore
 from typing import Dict, Generator, List, Optional, Set
 
 import torch
-from diffusers.configuration_utils import ConfigMixin
 
 from invokeai.backend.model_manager import AnyModel, SubModelType
-from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
+from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
-from ..optimizations import skip_torch_weight_init
 from .model_cache_base import CacheRecord, CacheStats, ModelCacheBase, ModelLockerBase
 from .model_locker import ModelLocker
 
@@ -222,7 +218,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
             size = calc_model_size_by_data(model)
             self.make_room(size)
 
-            tid = threading.current_thread().ident
             cache_record = CacheRecord(key=key, model=model, size=size)
             self._cached_models[key] = cache_record
             self._cache_stack.append(key)

invokeai/backend/model_manager/load/model_cache/model_locker.py

@@ -57,4 +57,3 @@ class ModelLocker(ModelLockerBase):
     def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
         """Return the state dict (if any) for the cached model."""
         return None
-

invokeai/backend/model_patcher.py

@@ -2,8 +2,9 @@
 """These classes implement model patching with LoRAs and Textual Inversions."""
 
 from __future__ import annotations
-import threading
+
 import pickle
+import threading
 from contextlib import contextmanager
 from typing import Any, Dict, Generator, Iterator, List, Optional, Tuple, Union
 
@@ -34,7 +35,6 @@ with LoRAHelper.apply_lora_unet(unet, loras):
 
 # TODO: rename smth like ModelPatcher and add TI method?
 class ModelPatcher:
-
     _thread_lock = threading.Lock()
 
     @staticmethod
@@ -109,10 +109,7 @@ class ModelPatcher:
         """
         original_weights = {}
         try:
-            with (
-                    torch.no_grad(),
-                    cls._thread_lock
-            ):
+            with torch.no_grad(), cls._thread_lock:
                 for lora, lora_weight in loras:
                     # assert lora.device.type == "cpu"
                     for layer_key, layer in lora.layers.items():

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -1,5 +1,4 @@
 import math
-import threading
 from dataclasses import dataclass
 from typing import List, Optional, Union
 
@@ -32,7 +31,6 @@ class SDXLConditioningInfo(BasicConditioningInfo):
     add_time_ids: torch.Tensor
 
     def to(self, device, dtype=None):
-        tid = threading.current_thread().ident
         self.pooled_embeds = self.pooled_embeds.to(device=device, dtype=dtype)
         assert self.pooled_embeds.device == device
         self.add_time_ids = self.add_time_ids.to(device=device, dtype=dtype)

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -296,8 +296,8 @@ class InvokeAIDiffuserComponent:
         added_cond_kwargs = None
         try:
             if conditioning_data.is_sdxl():
-                #tid = threading.current_thread().ident
-                #print(f'DEBUG {tid} {conditioning_data.uncond_text.pooled_embeds.device=} {conditioning_data.cond_text.pooled_embeds.device=}', flush=True),                
+                # tid = threading.current_thread().ident
+                # print(f'DEBUG {tid} {conditioning_data.uncond_text.pooled_embeds.device=} {conditioning_data.cond_text.pooled_embeds.device=}', flush=True),
                 added_cond_kwargs = {
                     "text_embeds": torch.cat(
                         [
@@ -317,7 +317,7 @@ class InvokeAIDiffuserComponent:
                 }
         except Exception as e:
             tid = threading.current_thread().ident
-            print(f'DEBUG: {tid} {str(e)}')
+            print(f"DEBUG: {tid} {str(e)}")
             raise e
 
         if conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None: