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fix attention maps crash by properly restricting token sequence length #1999

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merged 1 commit into from
Dec 14, 2022
Merged

fix attention maps crash by properly restricting token sequence length #1999

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13 changes: 10 additions & 3 deletions ldm/invoke/conditioning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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


Expand Down Expand Up @@ -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 (
Expand Down