fixed improper padding

comfy/sd1_clip.py

@@ -247,6 +247,11 @@ class SD1Tokenizer:
         Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
         Returned list has the dimensions NxM where M is the input size of CLIP
         '''
+        if self.pad_with_end:
+            pad_token = self.end_token
+        else:
+            pad_token = 0
+
         text = escape_important(text)
         parsed_weights = token_weights(text, 1.0)
 
@@ -277,30 +282,33 @@ class SD1Tokenizer:
         
         #reshape token array to CLIP input size
         batched_tokens = []
-        batch = []
+        batch = [(self.start_token, 1.0, 0)]
         batched_tokens.append(batch)
         for i, t_group in enumerate(tokens):
             #determine if we're going to try and keep the tokens in a single batch
             is_large = len(t_group) >= self.max_word_length
+
             while len(t_group) > 0:
-                if len(t_group) + len(batch) > self.max_tokens_per_section:
-                    remaining_length = self.max_tokens_per_section - len(batch)
+                if len(t_group) + len(batch) > self.max_length - 1:
+                    remaining_length = self.max_length - len(batch) - 1
+                    #break word in two and add end token
                     if is_large:
                         batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
+                        batch.append((self.end_token, 1.0, 0))
                         t_group = t_group[remaining_length:]
+                    #add end token and pad
                     else:
-                        batch.extend([(self.end_token, 1.0, 0)] * remaining_length)
-                    batch = []
+                        batch.append((self.end_token, 1.0, 0))
+                        batch.extend([(pad_token, 1.0, 0)] * (remaining_length))
+                    #start new batch
+                    batch = [(self.start_token, 1.0, 0)]
                     batched_tokens.append(batch)    
                 else:
                     batch.extend([(t,w,i+1) for t,w in t_group])
                     t_group = []
         
         #fill last batch
-        batch.extend([(self.end_token, 1.0, 0)] * (self.max_tokens_per_section - len(batch)))
-        
-        #add start and end tokens
-        batched_tokens = [[(self.start_token, 1.0, 0)] + x + [(self.end_token, 1.0, 0)] for x in batched_tokens]
+        batch.extend([(self.end_token, 1.0, 0)] + [(pad_token, 1.0, 0)] * (self.max_length - len(batch) - 1))
 
         if not return_word_ids:
             batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]