linting

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

installer.py

@@ -568,7 +568,7 @@ def check_transformers():
     pkg_tokenizers = package_spec('tokenizers')
     # target_commit = '753d61104116eefc8ffc977327b441ee0c8d599f' # transformers commit hash == 4.57.6
     # target_commit = "380e3cc5d59912a48508cb6d4959a31cd460e12e" # transformers commit hash == 5.5.0.dev-0409
-    target_commit = "d9e7791b129797be80ca7b3b0bf2a53cda0d4b8c" # transformers commit hash == 5.13.0.dev0 == 06-29-2026
+    target_commit = "b70d02fc724d04c916832ca4ead03ff05e8fb1ee" # transformers commit hash == 5.13.0.dev0 == 07-03-2026
     if args.use_directml:
         target_transformers = '4.52.4'
         target_tokenizers = '0.21.4'

modules/api/helpers.py

@@ -12,7 +12,7 @@ _upload_store_getter = None
 
 
 def register_upload_store(getter_fn):
-    global _upload_store_getter
+    global _upload_store_getter # pylint: disable=global-statement
     _upload_store_getter = getter_fn
 
 

modules/image/sharpfin.py

@@ -184,14 +184,14 @@ def resize_tensor(tensor: torch.Tensor, target_size: tuple[int, int], *, kernel=
         mode = 'bilinear' if (target_size[0] * target_size[1]) > (tensor.shape[-2] * tensor.shape[-1]) else 'area'
         log.debug(f'Resize tensor: method=torch mode={mode} shape={tensor.shape} target={target_size} fn={fn}')
         inp = tensor if tensor.dim() == 4 else tensor.unsqueeze(0)
-        result = torch.nn.functional.interpolate(inp, size=target_size, mode=mode, antialias=(mode != 'area'))
+        result = torch.nn.functional.interpolate(inp, size=target_size, mode=mode, antialias=mode != 'area')
         return result.squeeze(0) if tensor.dim() == 3 else result
     rk = get_kernel(kernel)
     if rk is None:
         mode = 'bilinear' if (target_size[0] * target_size[1]) > (tensor.shape[-2] * tensor.shape[-1]) else 'area'
         log.debug(f'Resize tensor: method=torch mode={mode} shape={tensor.shape} target={target_size} kernel=None fn={fn}')
         inp = tensor if tensor.dim() == 4 else tensor.unsqueeze(0)
-        result = torch.nn.functional.interpolate(inp, size=target_size, mode=mode, antialias=(mode != 'area'))
+        result = torch.nn.functional.interpolate(inp, size=target_size, mode=mode, antialias=mode != 'area')
         return result.squeeze(0) if tensor.dim() == 3 else result
 
     from modules.sharpfin.functional import scale

modules/lora/extra_networks_lora.py

@@ -202,7 +202,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
         debug_log(f'Network check: type=LoRA key="{key}" requested={requested} loaded={loaded} status="same"')
         return False, "none"
 
-    def activate(self, p, params_list, step=0, include=None, exclude=None):
+    def activate(self, p, params_list, step=0, include=None, exclude=None): # pylint: disable=arguments-differ
         if exclude is None:
             exclude = []
         if include is None:

modules/ltx/ltx_diffusers_patch.py

@@ -18,8 +18,6 @@ import inspect
 import torch
 import torch.nn.functional as F
 
-from modules.logger import log
-
 
 _PATCH_APPLIED = False
 _BROKEN_MARKER = 'torch.flip(hidden_states, dims=[1])'

modules/processing.py

@@ -6,12 +6,12 @@ from PIL import Image, ImageOps
 from modules import shared, devices, errors, images, scripts_manager, memstats, script_callbacks, extra_networks, sd_models, sd_checkpoint, sd_vae, processing_helpers, processing_grading, timer, masking
 from modules.logger import log
 from modules.sd_hijack_hypertile import context_hypertile_vae, context_hypertile_unet
-from modules.processing_class import (
+from modules.processing_class import ( # pylint: disable=unused-import
     StableDiffusionProcessing,
-    StableDiffusionProcessingTxt2Img, # pylint: disable=unused-import
-    StableDiffusionProcessingImg2Img, # pylint: disable=unused-import
-    StableDiffusionProcessingVideo, # pylint: disable=unused-import
-    StableDiffusionProcessingControl, # pylint: disable=unused-import
+    StableDiffusionProcessingTxt2Img,
+    StableDiffusionProcessingImg2Img,
+    StableDiffusionProcessingControl,
+    StableDiffusionProcessingVideo,
 )
 from modules.processing_info import create_infotext
 

modules/video_models/google_veo.py

@@ -6,7 +6,7 @@ import sys
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))
 
 from PIL import Image
-from installer import install, reload
+from installer import install
 from modules.logger import log
 
 

modules/video_models/video_vae.py

@@ -43,7 +43,7 @@ def vae_decode_tiny(latents):
         log.warning(f'Decode: type=Tiny cls={shared.sd_model.__class__.__name__} not supported')
         return None
     from modules.vae import sd_vae_taesd
-    vae, variant = sd_vae_taesd.get_model(variant=variant)
+    vae, variant = sd_vae_taesd.load_model(variant=variant)
     if vae is None:
         return None
     log.debug(f'Decode: type=Tiny cls={vae.__class__.__name__} variant="{variant}" latents={latents.shape}')

pyproject.toml

@@ -104,6 +104,9 @@ main.ignore-paths=[
     ".ruff_cache",
     ".vscode",
     ".*/node_modules/.*",
+    "cli",
+    "tmp",
+    "test",
     "modules/control/proc",
     "modules/schedulers/scheduler_*.py",
     "modules/apg",
@@ -325,6 +328,8 @@ typecheck.generated-members=[
     "logging.*",
     "torch.*",
     "cv2.*",
+    "av.*",
+    "PIL.*",
 ]
 typecheck.ignore-none=true
 typecheck.ignore-on-opaque-inference=true