Add --log_tokenization to sysargs

invokeai/backend/invoke_ai_web_server.py

@@ -626,9 +626,10 @@ def handle_generate_image_event(
                         printable_parameters["init_mask"][:64] + "..."
                     )
 
-                print(
-                    f">> Image generation requested: {printable_parameters}\nESRGAN parameters: {esrgan_parameters}\nFacetool parameters: {facetool_parameters}"
-                )
+                print(f'\n>> Image Generation Parameters:\n\n{printable_parameters}\n')
+                print(f'>> ESRGAN Parameters: {esrgan_parameters}')
+                print(f'>> Facetool Parameters: {facetool_parameters}')
+
                 self.generate_images(
                     generation_parameters,
                     esrgan_parameters,
@@ -1154,7 +1155,7 @@ def image_done(image, seed, first_seed, attention_maps_image=None):
                     image, os.path.basename(path), self.thumbnail_image_path
                 )
 
-                print(f'>> Image generated: "{path}"')
+                print(f'\n\n>> Image generated: "{path}"\n')
                 self.write_log_message(f'[Generated] "{path}": {command}')
 
                 if progress.total_iterations > progress.current_iteration:
@@ -1193,8 +1194,6 @@ def image_done(image, seed, first_seed, attention_maps_image=None):
 
                 progress.set_current_iteration(progress.current_iteration + 1)
 
-            print(generation_parameters)
-
             def diffusers_step_callback_adapter(*cb_args, **kwargs):
                 if isinstance(cb_args[0], PipelineIntermediateState):
                     progress_state: PipelineIntermediateState = cb_args[0]
@@ -1305,8 +1304,6 @@ def parameters_to_generated_image_metadata(self, parameters):
 
             rfc_dict["variations"] = variations
 
-            print(parameters)
-
             if rfc_dict["type"] == "img2img":
                 rfc_dict["strength"] = parameters["strength"]
                 rfc_dict["fit"] = parameters["fit"]  # TODO: Noncompliant

ldm/generate.py

@@ -574,7 +574,7 @@ def process_image(image,seed):
             print('>> Could not generate image.')
 
         toc = time.time()
-        print('>> Usage stats:')
+        print('\n>> Usage stats:')
         print(
             f'>>   {len(results)} image(s) generated in', '%4.2fs' % (
                 toc - tic)

ldm/invoke/args.py

@@ -196,6 +196,7 @@ def parse_args(self):
             elif os.path.exists(legacyinit):
                 print(f'>> WARNING: Old initialization file found at {legacyinit}. This location is deprecated. Please move it to {Globals.root}/invokeai.init.')
                 sysargs.insert(0,f'@{legacyinit}')
+            Globals.log_tokenization = self._arg_parser.parse_args(sysargs).log_tokenization
 
             self._arg_switches = self._arg_parser.parse_args(sysargs)
             return self._arg_switches
@@ -599,6 +600,12 @@ def _create_arg_parser(self):
             help=f'Set the default sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
             default='k_lms',
         )
+        render_group.add_argument(
+            '--log_tokenization',
+            '-t',
+            action='store_true',
+            help='shows how the prompt is split into tokens'
+        )
         render_group.add_argument(
             '-f',
             '--strength',
@@ -744,7 +751,7 @@ def _create_dream_cmd_parser(self):
                 invoke> !fetch 0000015.8929913.png
                 invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
                 invoke> !fetch /path/to/images/*.png prompts.txt
- 
+
             !replay /path/to/prompts.txt
             Replays all the prompts contained in the file prompts.txt.
 

ldm/invoke/conditioning.py

@@ -17,6 +17,7 @@
 from ..models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from ..modules.encoders.modules import WeightedFrozenCLIPEmbedder
 from ..modules.prompt_to_embeddings_converter import WeightedPromptFragmentsToEmbeddingsConverter
+from ldm.invoke.globals import Globals
 
 
 def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False):
@@ -92,9 +93,9 @@ def _get_conditioning_for_prompt(parsed_prompt: Union[Blend, FlattenedPrompt], p
     Process prompt structure and tokens, and return (conditioning, unconditioning, extra_conditioning_info)
     """
 
-    if log_tokens:
-        print(f">> Parsed prompt to {parsed_prompt}")
-        print(f">> Parsed negative prompt to {parsed_negative_prompt}")
+    if log_tokens or Globals.log_tokenization:
+        print(f"\n>> [TOKENLOG] Parsed Prompt: {parsed_prompt}")
+        print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {parsed_negative_prompt}")
 
     conditioning = None
     cac_args: cross_attention_control.Arguments = None
@@ -235,7 +236,7 @@ def _get_embeddings_and_tokens_for_prompt(model, flattened_prompt: FlattenedProm
     fragments = [x.text for x in flattened_prompt.children]
     weights = [x.weight for x in flattened_prompt.children]
     embeddings, tokens = model.get_learned_conditioning([fragments], return_tokens=True, fragment_weights=[weights])
-    if log_tokens:
+    if log_tokens or Globals.log_tokenization:
         text = " ".join(fragments)
         log_tokenization(text, model, display_label=log_display_label)
 
@@ -273,12 +274,12 @@ def log_tokenization(text, model, display_label=None):
     # usually tokens have '</w>' to indicate end-of-word,
     # but for readability it has been replaced with ' '
     """
-
     tokens = 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
@@ -288,8 +289,11 @@ def log_tokenization(text, model, display_label=None):
             usedTokens += 1
         else:  # over max token length
             discarded = discarded + f"\x1b[0;3{s};40m{token}"
-    print(f"\n>> Tokens {display_label or ''} ({usedTokens}):\n{tokenized}\x1b[0m")
+
+    if usedTokens > 0:
+        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
+        print(f'{tokenized}\x1b[0m')
+
     if discarded != "":
-        print(
-            f">> Tokens Discarded ({totalTokens - usedTokens}):\n{discarded}\x1b[0m"
-        )
+        print(f'\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):')
+        print(f'{discarded}\x1b[0m')

ldm/invoke/generator/diffusers_pipeline.py

@@ -4,7 +4,6 @@
 import inspect
 import secrets
 import sys
-import warnings
 from dataclasses import dataclass, field
 from typing import List, Optional, Union, Callable, Type, TypeVar, Generic, Any
 
@@ -641,7 +640,6 @@ def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fr
 
     @property
     def cond_stage_model(self):
-        warnings.warn("legacy compatibility layer", DeprecationWarning)
         return self.prompt_fragments_to_embeddings_converter
 
     @torch.inference_mode()