make style for https://github.com/huggingface/diffusers/pull/10368 (#10370)

* fix bug for torch.uint1-7 not support in torch<2.6

* up

---------
Co-authored-by: baymax591 <cbai@mail.nwpu.edu.cn>

src/diffusers/quantizers/torchao/torchao_quantizer.py

@@ -23,7 +23,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Union
 
 from packaging import version
 
-from ...utils import get_module_from_name, is_torch_available, is_torchao_available, logging
+from ...utils import get_module_from_name, is_torch_available, is_torch_version, is_torchao_available, logging
 from ..base import DiffusersQuantizer
 
 
@@ -35,21 +35,28 @@ if is_torch_available():
     import torch
     import torch.nn as nn
 
-    SUPPORTED_TORCH_DTYPES_FOR_QUANTIZATION = (
-        # At the moment, only int8 is supported for integer quantization dtypes.
-        # In Torch 2.6, int1-int7 will be introduced, so this can be visited in the future
-        # to support more quantization methods, such as intx_weight_only.
-        torch.int8,
-        torch.float8_e4m3fn,
-        torch.float8_e5m2,
-        torch.uint1,
-        torch.uint2,
-        torch.uint3,
-        torch.uint4,
-        torch.uint5,
-        torch.uint6,
-        torch.uint7,
-    )
+    if is_torch_version(">=", "2.5"):
+        SUPPORTED_TORCH_DTYPES_FOR_QUANTIZATION = (
+            # At the moment, only int8 is supported for integer quantization dtypes.
+            # In Torch 2.6, int1-int7 will be introduced, so this can be visited in the future
+            # to support more quantization methods, such as intx_weight_only.
+            torch.int8,
+            torch.float8_e4m3fn,
+            torch.float8_e5m2,
+            torch.uint1,
+            torch.uint2,
+            torch.uint3,
+            torch.uint4,
+            torch.uint5,
+            torch.uint6,
+            torch.uint7,
+        )
+    else:
+        SUPPORTED_TORCH_DTYPES_FOR_QUANTIZATION = (
+            torch.int8,
+            torch.float8_e4m3fn,
+            torch.float8_e5m2,
+        )
 
 if is_torchao_available():
     from torchao.quantization import quantize_