Fix linting warnings and errors using Ruff

.gitignore

@@ -5,6 +5,7 @@ models
 ia_config.ini
 .eslintrc
 .eslintrc.json
+pyproject.toml
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

fast_sam/fast_sam_wrapper.py

@@ -56,7 +56,7 @@ class FastSamAutomaticMaskGenerator:
         resize_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
 
         backup_nn_dict = {}
-        for key, value in torch.nn.__dict__.copy().items():
+        for key, _ in torch.nn.__dict__.copy().items():
             if not inspect.isclass(torch.nn.__dict__.get(key)) and "Norm" in key:
                 backup_nn_dict[key] = torch.nn.__dict__.pop(key)
 
@@ -80,7 +80,7 @@ class FastSamAutomaticMaskGenerator:
             annotations = np.array(annotations.cpu())
 
         annotations_list = []
-        for i, mask in enumerate(annotations):
+        for mask in annotations:
             mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
             mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((7, 7), np.uint8))
             mask = cv2.resize(mask, (width, height), interpolation=cv2.INTER_AREA)

ia_get_dataset_colormap.py

@@ -27,10 +27,10 @@ defined by the different datasets. Supported colormaps are:
 * PASCAL VOC 2012 (http://host.robots.ox.ac.uk/pascal/VOC/).
 """
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
+
 import numpy as np
+
 # from six.moves import range
 
 # Dataset names.

ia_threading.py

@@ -35,7 +35,7 @@ def pre_offload_model_weights(sem):
         if (shared.sd_model is not None and not is_sdxl_lowvram(shared.sd_model) and
                 getattr(shared.sd_model, "device", devices.cpu) != devices.cpu):
             backup_sd_model = shared.sd_model
-            backup_device = getattr(backup_sd_model, "device")
+            backup_device = backup_sd_model.device
             backup_sd_model.to(devices.cpu)
             clear_cache()
 

inpalib/masklib.py

@@ -86,7 +86,7 @@ def create_mask_image(
 
     canvas_image = np.zeros(mask.shape, dtype=np.uint8)
     mask_region = np.zeros(mask.shape, dtype=np.uint8)
-    for idx, seg_dict in enumerate(sam_masks):
+    for seg_dict in sam_masks:
         seg_mask = np.expand_dims(seg_dict["segmentation"].astype(np.uint8), axis=-1)
         canvas_mask = np.logical_not(canvas_image.astype(bool)).astype(np.uint8)
         if (seg_mask * canvas_mask * mask).astype(bool).any():