fix: lint & other minor issues

Author: blessedcoolant

invokeai/backend/image_util/depth_anything/__init__.py

@@ -65,13 +65,11 @@ def load_model(self, model_size=Literal["large", "base", "small"]):
             self.model_size = model_size
 
             if self.model_size == "small":
-                self.model = DPT_DINOv2(encoder="vits", features=64, out_channels=[48, 96, 192, 384], localhub=True)
+                self.model = DPT_DINOv2(encoder="vits", features=64, out_channels=[48, 96, 192, 384])
             if self.model_size == "base":
-                self.model = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768], localhub=True)
+                self.model = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768])
             if self.model_size == "large":
-                self.model = DPT_DINOv2(
-                    encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024], localhub=True
-                )
+                self.model = DPT_DINOv2(encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024])
 
             self.model.load_state_dict(torch.load(DEPTH_ANYTHING_MODEL_PATH.as_posix(), map_location="cpu"))
             self.model.eval()

invokeai/backend/image_util/depth_anything/model/dpt.py

@@ -22,9 +22,7 @@ def _make_fusion_block(features, use_bn, size=None):
 
 
 class DPTHead(nn.Module):
-    def __init__(
-        self, nclass, in_channels, features=256, use_bn=False, out_channels=[256, 512, 1024, 1024], use_clstoken=False
-    ):
+    def __init__(self, nclass, in_channels, features, out_channels, use_bn=False, use_clstoken=False):
         super(DPTHead, self).__init__()
 
         self.nclass = nclass
@@ -138,19 +136,18 @@ def forward(self, out_features, patch_h, patch_w):
 class DPT_DINOv2(nn.Module):
     def __init__(
         self,
+        features,
+        out_channels,
         encoder="vitl",
-        features=256,
-        out_channels=[256, 512, 1024, 1024],
         use_bn=False,
         use_clstoken=False,
-        localhub=True,
     ):
         super(DPT_DINOv2, self).__init__()
 
         assert encoder in ["vits", "vitb", "vitl"]
 
         # # in case the Internet connection is not stable, please load the DINOv2 locally
-        # if localhub:
+        # if use_local:
         #     self.pretrained = torch.hub.load(
         #         torchhub_path / "facebookresearch_dinov2_main",
         #         "dinov2_{:}14".format(encoder),
@@ -170,7 +167,7 @@ def __init__(
 
         dim = self.pretrained.blocks[0].attn.qkv.in_features
 
-        self.depth_head = DPTHead(1, dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken)
+        self.depth_head = DPTHead(1, dim, features, out_channels=out_channels, use_bn=use_bn, use_clstoken=use_clstoken)
 
     def forward(self, x):
         h, w = x.shape[-2:]