linting

Signed-off-by: Vladimir Mandic <[email protected]>

.eslintrc.json

@@ -123,6 +123,7 @@
     "venv",
     "panzoom.js",
     "split.js",
-    "exifr.js"
+    "exifr.js",
+    "iframeResizer.min.js"
   ]
 }

extensions-builtin/Lora/networks.py

@@ -321,7 +321,7 @@ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Li
     timer['restore'] += t1 - t0
 
 
-def maybe_backup_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention, diffusers.models.lora.LoRACompatibleLinear, diffusers.models.lora.LoRACompatibleConv], wanted_names, current_names):
+def maybe_backup_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention, diffusers.models.lora.LoRACompatibleLinear, diffusers.models.lora.LoRACompatibleConv], wanted_names, current_names): # pylint: disable=W0613
     weights_backup = getattr(self, "network_weights_backup", None)
     if weights_backup is None and wanted_names != (): # pylint: disable=C1803
         if isinstance(self, torch.nn.MultiheadAttention):
@@ -362,7 +362,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
     t0 = time.time()
     current_names = getattr(self, "network_current_names", ())
     wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
-    if any([net.modules.get(network_layer_name, None) for net in loaded_networks]):
+    if any([net.modules.get(network_layer_name, None) for net in loaded_networks]): # noqa: C419 # pylint: disable=R1729
         maybe_backup_weights(self, wanted_names, current_names)
     if current_names != wanted_names:
         network_restore_weights_from_backup(self)

modules/linfusion/attention.py

@@ -44,7 +44,7 @@ def add_non_linear_model(self, mid_dim=None, **kwargs):
         self.to_q_ = get_none_linear_projection(query_dim, mid_dim, **kwargs)
         self.to_k_ = get_none_linear_projection(query_dim, mid_dim, **kwargs)
 
-    def forward(
+    def forward( # pylint: disable=unused-argument
         self,
         hidden_states,
         encoder_hidden_states=None,

modules/linfusion/linfusion.py

@@ -86,7 +86,7 @@ def construct_for(
         if load_pretrained:
             # Load from pretrained
             if not pretrained_model_name_or_path:
-                pipe_name_path = pipe_name_path or pipeline._internal_dict._name_or_path
+                pipe_name_path = pipe_name_path or pipeline._internal_dict._name_or_path # pylint: disable=protected-access
                 pretrained_model_name_or_path = model_dict.get(pipe_name_path, None)
                 if pretrained_model_name_or_path:
                     print(

modules/sd_samplers_diffusers.py

@@ -191,7 +191,7 @@ def __init__(self, name, constructor, model, **kwargs):
         if 'lower_order_final' in self.config:
             self.config['lower_order_final'] = shared.opts.schedulers_use_loworder
         if 'solver_order' in self.config and int(shared.opts.schedulers_solver_order) > 0:
-            self.config['solver_order'] = (shared.opts.schedulers_solver_order)
+            self.config['solver_order'] = int(shared.opts.schedulers_solver_order)
         if 'predict_x0' in self.config:
             self.config['solver_type'] = shared.opts.uni_pc_variant
         if 'beta_start' in self.config and shared.opts.schedulers_beta_start > 0: