Remove some trailing white space.

comfy_extras/nodes_mask.py

@@ -305,7 +305,7 @@ class FeatherMask:
             output[:, -y, :] *= feather_rate
 
         return (output,)
-    
+
 class GrowMask:
     @classmethod
     def INPUT_TYPES(cls):
@@ -316,7 +316,7 @@ class GrowMask:
                 "tapered_corners": ("BOOLEAN", {"default": True}),
             },
         }
-    
+
     CATEGORY = "mask"
 
     RETURN_TYPES = ("MASK",)

comfy_extras/nodes_perpneg.py

@@ -64,7 +64,7 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider):
     def predict_noise(self, x, timestep, model_options={}, seed=None):
         # in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg
         # but we'd rather do a single batch of sampling pos, neg, and empty, so we call calc_cond_batch([pos,neg,empty]) directly
-        
+
         positive_cond = self.conds.get("positive", None)
         negative_cond = self.conds.get("negative", None)
         empty_cond = self.conds.get("empty_negative_prompt", None)

comfy_extras/nodes_rebatch.py

@@ -40,7 +40,7 @@ class LatentRebatch:
             return slices, indexable[num * batch_size:]
         else:
             return slices, None
-    
+
     @staticmethod
     def slice_batch(batch, num, batch_size):
         result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch]
@@ -81,7 +81,7 @@ class LatentRebatch:
             if current_batch[0].shape[0] > batch_size:
                 num = current_batch[0].shape[0] // batch_size
                 sliced, remainder = self.slice_batch(current_batch, num, batch_size)
-                
+
                 for i in range(num):
                     output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]})
 

comfy_extras/nodes_tomesd.py

@@ -40,9 +40,8 @@ def bipartite_soft_matching_random2d(metric: torch.Tensor,
         return do_nothing, do_nothing
 
     gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather
-    
+
     with torch.no_grad():
-        
         hsy, wsx = h // sy, w // sx
 
         # For each sy by sx kernel, randomly assign one token to be dst and the rest src
@@ -50,7 +49,7 @@ def bipartite_soft_matching_random2d(metric: torch.Tensor,
             rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64)
         else:
             rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device)
-        
+
         # The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead
         idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64)
         idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype))
@@ -99,7 +98,7 @@ def bipartite_soft_matching_random2d(metric: torch.Tensor,
     def merge(x: torch.Tensor, mode="mean") -> torch.Tensor:
         src, dst = split(x)
         n, t1, c = src.shape
-        
+
         unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c))
         src = gather(src, dim=-2, index=src_idx.expand(n, r, c))
         dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode)