Autofix Ruff W (not W605) (mostly whitespace)

modules/sd_hijack.py

@@ -34,7 +34,7 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
-    
+
     optimization_method = None
 
     can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention) # not everyone has torch 2.x to use sdp
@@ -92,12 +92,12 @@ def fix_checkpoint():
 def weighted_loss(sd_model, pred, target, mean=True):
     #Calculate the weight normally, but ignore the mean
     loss = sd_model._old_get_loss(pred, target, mean=False)
-    
+
     #Check if we have weights available
     weight = getattr(sd_model, '_custom_loss_weight', None)
     if weight is not None:
         loss *= weight
-    
+
     #Return the loss, as mean if specified
     return loss.mean() if mean else loss
 
@@ -105,7 +105,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
     try:
         #Temporarily append weights to a place accessible during loss calc
         sd_model._custom_loss_weight = w
-        
+
         #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
         #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
         if not hasattr(sd_model, '_old_get_loss'):
@@ -120,7 +120,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
             del sd_model._custom_loss_weight
         except AttributeError:
             pass
-            
+
         #If we have an old loss function, reset the loss function to the original one
         if hasattr(sd_model, '_old_get_loss'):
             sd_model.get_loss = sd_model._old_get_loss
@@ -184,7 +184,7 @@ class StableDiffusionModelHijack:
 
     def undo_hijack(self, m):
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
-            m.cond_stage_model = m.cond_stage_model.wrapped 
+            m.cond_stage_model = m.cond_stage_model.wrapped
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped