49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
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from ..sage import *
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import folder_paths
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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.cache.data.get(lora_path, {}).get("trainedWords", None) is None:
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pull_metadata(lora_path, True)
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return cache.cache.data.get(lora_path, {}).get("trainedWords", [])
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def get_lora_stack_keywords(lora_stack = None):
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lora_keywords = []
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if lora_stack is None:
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return []
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for lora in lora_stack:
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print(f"Let's get keywords for {lora[0]}")
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try:
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keywords = get_lora_keywords(lora[0])
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if keywords != []:
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lora_keywords.extend(keywords)
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print(keywords)
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except:
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print("Exception getting keywords!")
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continue
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lora_keywords = list(set(lora_keywords))
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lora_keywords = [x for x in lora_keywords if x != '']
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lora_keywords = [x for x in lora_keywords if x != None]
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lora_keywords = [x for x in lora_keywords if x != ' ']
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ret = ", ".join(lora_keywords)
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ret = ' '.join(ret.split('\n'))
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return ret
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def add_lora_to_stack(lora_name, model_weight, clip_weight, lora_stack = None):
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if lora_stack is None:
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lora = (lora_name, model_weight, clip_weight)
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stack = [lora]
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return(stack)
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stack = []
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for the_name, m_weight, c_weight in lora_stack:
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stack.append((the_name, m_weight, c_weight))
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stack.append((lora_name, model_weight, clip_weight))
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return stack |