35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
import folder_paths
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from .model_cache import cache
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from .helpers import pull_metadata, clean_keywords
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def get_lora_keywords(lora_name):
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lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
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if cache.by_path(lora_path).get("trainedWords") is None:
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pull_metadata(lora_path, timestamp=True)
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return cache.by_path(lora_path).get("trainedWords", [])
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def get_lora_stack_keywords(lora_stack=None):
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if not lora_stack:
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return []
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# Collect unique lora names
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lora_names = {lora[0] for lora in lora_stack}
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lora_paths = [folder_paths.get_full_path_or_raise("loras", name) for name in lora_names]
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pull_metadata(lora_paths, timestamp=True)
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# Gather all keywords into a set for uniqueness
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all_keywords = set()
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for name in lora_names:
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try:
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keywords = cache.by_path(folder_paths.get_full_path_or_raise("loras", name)).get("trainedWords", [])
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if keywords:
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all_keywords.update(map(str.strip, keywords))
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except Exception as e:
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print(f"Exception getting keywords for {name}: {e}")
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continue
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return clean_keywords(all_keywords)
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def add_lora_to_stack(lora_name, model_weight, clip_weight, lora_stack=None):
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lora = (lora_name, model_weight, clip_weight)
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if lora_stack is None:
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return [lora]
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return [*lora_stack, lora]
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