update ruff and lint

Signed-off-by: Vladimir Mandic <mandic00@live.com>

modules/control/proc/marigold/marigold_pipeline.py

@@ -55,7 +55,7 @@ class MarigoldDepthOutput(BaseOutput):
 
     depth_np: np.ndarray
     depth_colored: Image.Image
-    uncertainty: Union[None, np.ndarray]
+    uncertainty: Union[np.ndarray, None]
 
 
 class MarigoldPipeline(DiffusionPipeline):

modules/lora/lora_apply.py

@@ -139,7 +139,7 @@ def network_calc_weights(self: torch.nn.Conv2d | torch.nn.Linear | torch.nn.Grou
     return batch_updown, batch_ex_bias
 
 
-def network_add_weights(self: torch.nn.Conv2d | torch.nn.Linear | torch.nn.GroupNorm | torch.nn.LayerNorm | diffusers.models.lora.LoRACompatibleLinear | diffusers.models.lora.LoRACompatibleConv, model_weights: None | torch.Tensor = None, lora_weights: torch.Tensor = None, deactivate: bool = False, device: torch.device = None, bias: bool = False):
+def network_add_weights(self: torch.nn.Conv2d | torch.nn.Linear | torch.nn.GroupNorm | torch.nn.LayerNorm | diffusers.models.lora.LoRACompatibleLinear | diffusers.models.lora.LoRACompatibleConv, model_weights: torch.Tensor | None = None, lora_weights: torch.Tensor = None, deactivate: bool = False, device: torch.device = None, bias: bool = False):
     if lora_weights is None:
         return
     if deactivate:

modules/sdnq/quantizer.py

@@ -276,7 +276,7 @@ def sdnq_quantize_layer_weight_dynamic(
     param_name: str | None = None,
     torch_dtype: torch.dtype | None = None,
     quantization_config: "SDNQConfig" = None,
-) -> None | tuple[SDNQDequantizer, dict[str, torch.Tensor]]:
+) -> tuple[SDNQDequantizer, dict[str, torch.Tensor]] | None:
     if torch_dtype is None:
         torch_dtype = weight.dtype
     if dynamic_loss_threshold is None or dynamic_loss_threshold < 0:

modules/seedvr/src/common/distributed/ops.py

@@ -357,7 +357,7 @@ def _broadcast_data(data, shape, dtype, src, group, async_op):
     return comms
 
 
-def _traverse(data: Any, op: Callable) -> Union[None, List, Dict, Any]:
+def _traverse(data: Any, op: Callable) -> Union[List, Dict, Any, None]:
     if isinstance(data, (list, tuple)):
         return [_traverse(sub_data, op) for sub_data in data]
     elif isinstance(data, dict):

modules/sharpfin/sparse_backend.py

@@ -462,7 +462,7 @@ def triton_dds(
         fuse_srgb: bool = False,
         clamp_output: bool = False,
         output_mt: bool = False,
-        output_slice: None | Tuple[int,int] = None
+        output_slice: Tuple[int, int] | None = None
     ):
     assert isinstance(lhs, torch.Tensor)
     assert isinstance(rhs, Matrix)
@@ -616,7 +616,7 @@ def triton_dds_sbsc(
         fuse_srgb: bool = False,
         clamp_output: bool = False,
         output_mt: bool = False,
-        output_slice: None | Tuple[int,int] = None
+        output_slice: Tuple[int, int] | None = None
     ):
     assert isinstance(lhs, torch.Tensor)
     assert isinstance(rhs, SBSCMatrix)
@@ -776,7 +776,7 @@ def triton_dds_zerorhs_sbsc(
         gamma_correction: str = 'fast',
         clamp_output: bool = False,
         output_mt: bool = False,
-        output_slice: None | Tuple[int,int] = None
+        output_slice: Tuple[int, int] | None = None
     ):
     assert isinstance(lhs, torch.Tensor)
 

pipelines/bria/bria_pipeline.py

@@ -255,7 +255,7 @@ class BriaPipeline(FluxPipeline):
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 128,
-        clip_value:Union[None,float] = None,
+        clip_value:Union[float, None] = None,
         normalize:bool = False
     ):
         r"""

requirements.txt

@@ -30,8 +30,8 @@ requests==2.34.2
 tqdm==4.68.3
 accelerate==1.14.0
 einops==0.8.2
-huggingface_hub==1.23.0
-hf_xet==1.5.1
+huggingface_hub==1.24.0
+hf_xet==1.5.2
 numpy==2.1.2
 pandas==2.3.1
 protobuf==7.35.1