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https://github.com/invoke-ai/InvokeAI
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fix newlines causing negative prompt to be parsed incorrectly
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@ -52,6 +52,8 @@ def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_l
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textual_inversion_manager=model.textual_inversion_manager,
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textual_inversion_manager=model.textual_inversion_manager,
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dtype_for_device_getter=torch_dtype)
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dtype_for_device_getter=torch_dtype)
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# get rid of any newline characters
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prompt_string = prompt_string.replace("\n", " ")
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positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
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positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
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legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
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legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
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positive_prompt: FlattenedPrompt|Blend
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positive_prompt: FlattenedPrompt|Blend
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@ -113,7 +115,7 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
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return tokens
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return tokens
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def split_prompt_to_positive_and_negative(prompt_string_uncleaned):
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def split_prompt_to_positive_and_negative(prompt_string_uncleaned: str):
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unconditioned_words = ''
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unconditioned_words = ''
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unconditional_regex = r'\[(.*?)\]'
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unconditional_regex = r'\[(.*?)\]'
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unconditionals = re.findall(unconditional_regex, prompt_string_uncleaned)
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unconditionals = re.findall(unconditional_regex, prompt_string_uncleaned)
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