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10 Commits
v2.3.1
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v2.3.1.pos
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604acb9d91 | ||
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5beeb1a897 | ||
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de6304b729 | ||
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d0be79c33d | ||
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b4ed8bc47a | ||
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11a29fdc4d | ||
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a7c2333312 | ||
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ad6ea02c9c | ||
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1a6ed85d99 |
@@ -1 +1 @@
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__version__='2.3.1'
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__version__='2.3.1.post1'
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@@ -88,7 +88,7 @@ def get_prompt_structure(prompt_string, skip_normalize_legacy_blend: bool = Fals
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return positive_prompt, negative_prompt
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def get_max_token_count(tokenizer, prompt: FlattenedPrompt|Blend, truncate_if_too_long=True) -> int:
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def get_max_token_count(tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=True) -> int:
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if type(prompt) is Blend:
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blend: Blend = prompt
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return max([get_max_token_count(tokenizer, c, truncate_if_too_long) for c in blend.prompts])
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@@ -129,8 +129,8 @@ def split_prompt_to_positive_and_negative(prompt_string_uncleaned):
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return prompt_string_cleaned, unconditioned_words
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def log_tokenization(positive_prompt: Blend | FlattenedPrompt,
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negative_prompt: Blend | FlattenedPrompt,
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def log_tokenization(positive_prompt: Union[Blend, FlattenedPrompt],
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negative_prompt: Union[Blend, FlattenedPrompt],
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tokenizer):
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print(f"\n>> [TOKENLOG] Parsed Prompt: {positive_prompt}")
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print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
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@@ -139,7 +139,7 @@ def log_tokenization(positive_prompt: Blend | FlattenedPrompt,
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log_tokenization_for_prompt_object(negative_prompt, tokenizer, display_label_prefix="(negative prompt)")
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def log_tokenization_for_prompt_object(p: Blend | FlattenedPrompt, tokenizer, display_label_prefix=None):
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def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None):
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display_label_prefix = display_label_prefix or ""
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if type(p) is Blend:
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blend: Blend = p
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@@ -624,7 +624,7 @@ class ModelManager(object):
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self,
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repo_or_path: Union[str, Path],
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model_name: str = None,
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model_description: str = None,
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description: str = None,
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vae: dict = None,
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commit_to_conf: Path = None,
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) -> bool:
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@@ -640,7 +640,7 @@ class ModelManager(object):
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models.yaml file.
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"""
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model_name = model_name or Path(repo_or_path).stem
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model_description = model_description or f"Imported diffusers model {model_name}"
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model_description = description or f"Imported diffusers model {model_name}"
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new_config = dict(
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description=model_description,
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vae=vae,
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