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AttributeError: 'Namespace' object has no attribute 'log_tokenization' (#2698)

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Could be fixed here or alternatively declared in file globals.py
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Lincoln Stein 2023-02-18 01:08:50 -05:00 committed by GitHub
commit 8c63fac958
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2 changed files with 6 additions and 3 deletions
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6
ldm/invoke/conditioning.py
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@ -93,7 +93,7 @@ def _get_conditioning_for_prompt(parsed_prompt: Union[Blend, FlattenedPrompt], p
Process prompt structure and tokens, and return (conditioning, unconditioning, extra_conditioning_info) Process prompt structure and tokens, and return (conditioning, unconditioning, extra_conditioning_info)
""" """
if log_tokens or Globals.log_tokenization: if log_tokens or getattr(Globals, "log_tokenization", False):
print(f"\n>> [TOKENLOG] Parsed Prompt: {parsed_prompt}") print(f"\n>> [TOKENLOG] Parsed Prompt: {parsed_prompt}")
print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {parsed_negative_prompt}") print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {parsed_negative_prompt}")
@ -236,7 +236,7 @@ def _get_embeddings_and_tokens_for_prompt(model, flattened_prompt: FlattenedProm
fragments = [x.text for x in flattened_prompt.children] fragments = [x.text for x in flattened_prompt.children]
weights = [x.weight 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]) embeddings, tokens = model.get_learned_conditioning([fragments], return_tokens=True, fragment_weights=[weights])
if log_tokens or Globals.log_tokenization: if log_tokens or getattr(Globals, "log_tokenization", False):
text = " ".join(fragments) text = " ".join(fragments)
log_tokenization(text, model, display_label=log_display_label) log_tokenization(text, model, display_label=log_display_label)
@ -296,4 +296,4 @@ def log_tokenization(text, model, display_label=None):
if discarded != "": if discarded != "":
print(f'\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):') print(f'\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):')
print(f'{discarded}\x1b[0m') print(f'{discarded}\x1b[0m')

3
ldm/invoke/globals.py
View File

@ -54,6 +54,9 @@ Globals.full_precision = False
# whether we should convert ckpt files into diffusers models on the fly # whether we should convert ckpt files into diffusers models on the fly
Globals.ckpt_convert = False Globals.ckpt_convert = False
# logging tokenization everywhere
Globals.log_tokenization = False
def global_config_file()->Path: def global_config_file()->Path:
return Path(Globals.root, Globals.config_dir, Globals.models_file) return Path(Globals.root, Globals.config_dir, Globals.models_file)