Satisfy ruff linting

comfy/controlnet.py

@@ -72,7 +72,7 @@ class ControlIsolation:
 
     def __enter__(self):
         self.control.previous_controlnet = None
-    
+
     def __exit__(self, *args):
         self.control.previous_controlnet = self.orig_previous_controlnet
 
@@ -151,7 +151,7 @@ class ControlBase:
     def deepclone_multigpu(self, load_device, autoregister=False):
         '''
         Create deep clone of Control object where model(s) is set to other devices.
-        
+
         When autoregister is set to True, the deep clone is also added to multigpu_clones dict.
         '''
         raise NotImplementedError("Classes inheriting from ControlBase should define their own deepclone_multigpu funtion.")
@@ -846,7 +846,7 @@ class T2IAdapter(ControlBase):
         c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm)
         self.copy_to(c)
         return c
-    
+
     def deepclone_multigpu(self, load_device, autoregister=False):
         c = self.copy()
         c.t2i_model = copy.deepcopy(c.t2i_model)

comfy/model_management.py

@@ -30,7 +30,6 @@ import gc
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from comfy.model_patcher import ModelPatcher
-    from comfy.model_base import BaseModel
 
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram

comfy/multigpu.py

@@ -18,7 +18,7 @@ class GPUOptions:
 
     def clone(self):
         return GPUOptions(self.device_index, self.relative_speed)
-    
+
     def create_dict(self):
         return {
             "relative_speed": self.relative_speed
@@ -86,7 +86,7 @@ def create_multigpu_deepclones(model: ModelPatcher, max_gpus: int, gpu_options:
                         device_patcher = lm.clone()
                         logging.info(f"Reusing loaded deepclone of {device_patcher.model.__class__.__name__} for {device}")
                         break
-            if device_patcher is None:        
+            if device_patcher is None:
                 device_patcher = model.deepclone_multigpu(new_load_device=device)
                 device_patcher.is_multigpu_base_clone = True
             multigpu_models = model.get_additional_models_with_key("multigpu")
@@ -138,7 +138,7 @@ def load_balance_devices(model_options: dict[str], total_work: int, return_idle_
     # if need to compare work idle time, need to normalize to a common total work
     if work_normalized:
         idle_time *= (work_normalized/total_work)
-    
+
     return LoadBalance(dict_work_per_device, idle_time)
 
 def round_preserved(values: list[float]):

comfy/samplers.py

@@ -384,7 +384,7 @@ def _calc_cond_batch_multigpu(model: BaseModel, conds: list[list[dict]], x_in: t
 
     devices = [dev_m for dev_m in model_options['multigpu_clones'].keys()]
     device_batched_hooked_to_run: dict[torch.device, list[tuple[comfy.hooks.HookGroup, tuple]]] = {}
-    
+
     total_conds = 0
     for to_run in hooked_to_run.values():
         total_conds += len(to_run)
@@ -504,7 +504,7 @@ def _calc_cond_batch_multigpu(model: BaseModel, conds: list[list[dict]], x_in: t
         new_thread = threading.Thread(target=_handle_batch, args=(device, batch_tuple, results))
         threads.append(new_thread)
         new_thread.start()
-    
+
     for thread in threads:
         thread.join()
 

comfy_extras/nodes_multigpu.py

@@ -1,5 +1,4 @@
 from __future__ import annotations
-import logging
 from inspect import cleandoc
 
 from typing import TYPE_CHECKING
@@ -11,7 +10,7 @@ import comfy.multigpu
 class MultiGPUWorkUnitsNode:
     """
     Prepares model to have sampling accelerated via splitting work units.
-    
+
     Should be placed after nodes that modify the model object itself, such as compile or attention-switch nodes.
 
     Other than those exceptions, this node can be placed in any order.
@@ -30,7 +29,7 @@ class MultiGPUWorkUnitsNode:
                 "gpu_options": ("GPU_OPTIONS",)
             }
         }
-    
+
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "init_multigpu"
     CATEGORY = "advanced/multigpu"