diff --git a/.pylintrc b/.pylintrc
index 5dc22b99c..e483254dc 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -2,6 +2,7 @@
 analyse-fallback-blocks=no
 clear-cache-post-run=no
 extension-pkg-allow-list=
+prefer-stubs=yes
 extension-pkg-whitelist=
 fail-on=
 fail-under=10
@@ -69,11 +70,11 @@ ignore-patterns=.*test*.py$,
                 .*_model_arch.py*,
                 .*_model_arch_v2.py$,
 ignored-modules=
-jobs=0
+jobs=8
 limit-inference-results=100
 load-plugins=
-persistent=yes
-py-version=3.9
+persistent=no
+py-version=3.10
 recursive=no
 source-roots=
 unsafe-load-any-extension=no
@@ -207,6 +208,8 @@ disable=abstract-method,
         unnecessary-lambda-assigment,
         unnecessary-lambda,
         unused-wildcard-import,
+        unpacking-non-sequence,
+        unsubscriptable-object,
         useless-return,
         use-dict-literal,
         use-symbolic-message-instead,
diff --git a/modules/masking.py b/modules/masking.py
index 5b3319df0..4fc54adaa 100644
--- a/modules/masking.py
+++ b/modules/masking.py
@@ -339,7 +339,7 @@ def outpaint(input_image: Image.Image, outpaint_type: str = 'Edge'):
     debug(f'Run outpaint: fn={fn}') # pylint: disable=protected-access
     image = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)
     h0, w0 = image.shape[:2]
-    empty = (image == 0).all(axis=2)
+    empty = (image == 0).all(axis=2) # pylint: disable=no-member
     y0, x0 = np.where(~empty) # non empty
     x1, x2 = min(x0), max(x0)
     y1, y2 = min(y0), max(y0)
diff --git a/modules/postprocess/pixelart.py b/modules/postprocess/pixelart.py
index d9ce620ee..90b9cb1ac 100644
--- a/modules/postprocess/pixelart.py
+++ b/modules/postprocess/pixelart.py
@@ -55,7 +55,7 @@ def edge_detect_for_pixelart(image: PipelineImageInput, image_weight: float = 1.
     block_height = height // block_size
     block_width = width // block_size
 
-    min_pool = -torch.nn.functional.max_pool2d(-new_image, block_size, 1, block_size//2, 1, False, False)
+    min_pool = 0 - torch.nn.functional.max_pool2d(-new_image, block_size, 1, block_size//2, 1, False, False)
     min_pool = min_pool[:, :, :height, :width]
 
     greyscale = (new_image[:,0,:,:] * 0.299).add_(new_image[:,1,:,:], alpha=0.587).add_(new_image[:,2,:,:], alpha=0.114)
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index aa539af02..dec88e3a9 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -482,7 +482,7 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         param_value: "torch.Tensor",
         param_name: str,
         return_true: bool = True,
-        *args, **kwargs, # pylint: disable=unused-argument
+        *args, **kwargs, # pylint: disable=unused-argument,keyword-arg-before-vararg
     ):
         if return_true:
             return True
diff --git a/pipelines/model_hyimage.py b/pipelines/model_hyimage.py
index 86620d326..501dd9699 100644
--- a/pipelines/model_hyimage.py
+++ b/pipelines/model_hyimage.py
@@ -42,7 +42,7 @@ def load_hyimage3(checkpoint_info, diffusers_load_config={}): # pylint: disable=
     sd_models.hf_auth_check(checkpoint_info)
     shared.log.debug(f'Load model: type=HunyuanImage3 repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype}')
 
-    from sdnq import SDNQConfig # import sdnq to register it into transformers
+    from sdnq import SDNQConfig # pylint: disable=unused-import
     pipe = transformers.AutoModelForCausalLM.from_pretrained(
         repo_id,
         attn_implementation="sdpa",
diff --git a/scripts/nudenet/nudenet.py b/scripts/nudenet/nudenet.py
index 06ab7db25..008ccaa97 100755
--- a/scripts/nudenet/nudenet.py
+++ b/scripts/nudenet/nudenet.py
@@ -150,8 +150,8 @@ class NudeDetector:
             return background
         if fg_channels < 4: # make sure that overlay is rgba
             log.warning('NudeNet overlay image does not have alpha channel')
-            foreground = cv2.cvtColor(foreground, cv2.COLOR_RGB2RGBA)
-            foreground[:, :, 3] = cv2.cvtColor(foreground, cv2.COLOR_BGR2GRAY)
+            foreground_color = cv2.cvtColor(foreground, cv2.COLOR_RGB2RGBA)
+            foreground[:, :, 3] = cv2.cvtColor(foreground_color, cv2.COLOR_BGR2GRAY)
             fg_h, fg_w, fg_channels = foreground.shape
         if x_offset is None: # center by default
             x_offset = (bg_w - fg_w) // 2