tokenization logging (take 2)

This adds an option -t argument that will print out color-coded tokenization, SD has a maximum of 77 tokens, it silently discards tokens over the limit if your prompt is too long.
By using -t you can see how your prompt is being tokenized which helps prompt crafting.

ldm/simplet2i.py

@@ -213,6 +213,7 @@ class T2I:
         upscale=None,
         variants=None,
         sampler_name=None,
+        log_tokenization=False,
         **args,
     ):   # eat up additional cruft
         """
@@ -253,6 +254,7 @@ class T2I:
         batch_size = batch_size or self.batch_size
         iterations = iterations or self.iterations
         strength = strength or self.strength
+        self.log_tokenization = log_tokenization
 
         model = (
             self.load_model()
@@ -489,6 +491,7 @@ class T2I:
                 weight = weights[i]
                 if not skip_normalize:
                     weight = weight / totalWeight
+                self._log_tokenization(subprompts[i])
                 c = torch.add(
                     c,
                     self.model.get_learned_conditioning(
@@ -497,6 +500,7 @@ class T2I:
                     alpha=weight,
                 )
         else:   # just standard 1 prompt
+            self._log_tokenization(prompt)
             c = self.model.get_learned_conditioning(batch_size * [prompt])
         return (uc, c)
 
@@ -657,3 +661,27 @@ class T2I:
                     weights.append(1.0)
                 remaining = 0
         return prompts, weights
+        
+    # shows how the prompt is tokenized 
+    # usually tokens have '</w>' to indicate end-of-word, 
+    # but for readability it has been replaced with ' '
+    def _log_tokenization(self, text):
+        if not self.log_tokenization:
+            return
+        tokens = self.model.cond_stage_model.tokenizer._tokenize(text)
+        tokenized = ""
+        discarded = ""
+        usedTokens = 0
+        totalTokens = len(tokens)
+        for i in range(0,totalTokens):                
+            token = tokens[i].replace('</w>',' ')
+            # alternate color
+            s = (usedTokens % 6) + 1
+            if i < self.model.cond_stage_model.max_length:
+                tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
+                usedTokens += 1
+            else: # over max token length
+                discarded = discarded + f"\x1b[0;3{s};40m{token}"
+        print(f"\nTokens ({usedTokens}):\n{tokenized}\x1b[0m")
+        if discarded != "":
+            print(f"Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m")

scripts/dream.py

@@ -462,6 +462,12 @@ def create_cmd_parser():
         metavar='SAMPLER_NAME',
         help=f'Switch to a different sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
     )
+    parser.add_argument(
+        '-t',
+        '--log_tokenization',
+        action='store_true',
+        help='shows how the prompt is split into tokens'
+    )
     return parser