restrict to 75 tokens and correctly handle blends

Author: damian0815

ldm/invoke/conditioning.py

@@ -36,13 +36,16 @@ def get_prompt_structure(prompt_string, skip_normalize_legacy_blend: bool = Fals
     return prompt, negative_prompt
 
 
-def get_tokens_for_prompt(model, parsed_prompt: FlattenedPrompt) -> [str]:
+def get_tokens_for_prompt(model, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True) -> [str]:
     text_fragments = [x.text if type(x) is Fragment else
                       (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else
                        str(x))
                       for x in parsed_prompt.children]
     text = " ".join(text_fragments)
     tokens = model.cond_stage_model.tokenizer.tokenize(text)
+    if truncate_if_too_long:
+        max_tokens_length = model.cond_stage_model.max_length - 2 # typically 75
+        tokens = tokens[0:max_tokens_length]
     return tokens
 
 
@@ -116,8 +119,12 @@ def _get_conditioning_for_prompt(parsed_prompt: Union[Blend, FlattenedPrompt], p
                 ">> Hybrid conditioning cannot currently be combined with cross attention control. Cross attention control will be ignored.")
             cac_args = None
 
-    eos_token_index = 1
-    if type(parsed_prompt) is not Blend:
+    if type(parsed_prompt) is Blend:
+        blend: Blend = parsed_prompt
+        all_token_sequences = [get_tokens_for_prompt(model, p) for p in blend.prompts]
+        longest_token_sequence = max(all_token_sequences, key=lambda t: len(t))
+        eos_token_index = len(longest_token_sequence)+1
+    else:
         tokens = get_tokens_for_prompt(model, parsed_prompt)
         eos_token_index = len(tokens)+1
     return (