ruff

comfy/memory_management.py

@@ -1,10 +1,7 @@
-import torch
 from comfy.quant_ops import QuantizedTensor
 
 import comfy_aimdo.torch
 
-import logging
-
 def vram_aligned_size(tensor):
     if isinstance(tensor, list):
         return sum([vram_aligned_size(t) for t in tensor])

comfy/model_patcher.py

@@ -24,7 +24,6 @@ import inspect
 import logging
 import math
 import uuid
-import types
 from typing import Callable, Optional
 
 import torch
@@ -1381,7 +1380,7 @@ class ModelPatcher:
         unet_state_dict = self.model.diffusion_model.state_dict()
         for k, v in unet_state_dict.items():
             op_keys = k.rsplit('.', 1)
-            if (len(op_keys) < 2) or not op_keys[1] in ["weight", "bias"]:
+            if (len(op_keys) < 2) or op_keys[1] not in ["weight", "bias"]:
                 continue
             try:
                 op = comfy.utils.get_attr(self.model.diffusion_model, op_keys[0])
@@ -1467,7 +1466,7 @@ class ModelPatcherDynamic(ModelPatcher):
 
         #Full load doesn't make sense as we dont actually have any loader capability here and
         #now.
-        assert not full_load;
+        assert not full_load
 
         assert device_to == self.load_device
 

comfy/ops.py

@@ -128,11 +128,9 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
 
     def post_cast(s, param_key, x, dtype, resident, update_weight):
         lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
-        hook_fn = getattr(s, param_key + "_hooks", None)
         fns = getattr(s, param_key + "_function", [])
 
         orig = x
-        q_layout = None
 
         def to_dequant(tensor, dtype):
             tensor = tensor.to(dtype=dtype)
@@ -218,7 +216,7 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
         if cast_buffer is None:
             offload_stream = comfy.model_management.get_offload_stream(device)
             cast_buffer = comfy.model_management.get_cast_buffer(offload_stream, device, cast_buffer_size, s)
-        params = interpret_gathered_like([ s.weight, s.bias ], cast_buffer)
+        params = comfy.memory_management.interpret_gathered_like([ s.weight, s.bias ], cast_buffer)
         weight = params[0]
         bias = params[1]
 

comfy/pinned_memory.py

@@ -1,5 +1,4 @@
 import torch
-import logging
 import comfy.model_management
 import comfy.memory_management
 

comfy/samplers.py

@@ -9,7 +9,6 @@ if TYPE_CHECKING:
 import torch
 from functools import partial
 import collections
-from comfy import model_management
 import math
 import logging
 import comfy.sampler_helpers