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be less verbose when assembling prompt

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Damian at mba
2022-10-16 01:53:44 +02:00
parent c6ae9f1176
commit 61357e4e6e
2 changed files with 17 additions and 15 deletions
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13
ldm/invoke/conditioning.py
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@@ -35,9 +35,10 @@ def get_uc_and_c(prompt_string_uncleaned, model, log_tokens=False, skip_normaliz
pp = PromptParser()
def build_conditioning_list(prompt_string:str):
def build_conditioning_list(prompt_string:str, verbose:bool = False):
parsed_conjunction: Conjunction = pp.parse(prompt_string)
print(f"parsed '{prompt_string}' to {parsed_conjunction}")
if verbose:
print(f"parsed '{prompt_string}' to {parsed_conjunction}")
assert (type(parsed_conjunction) is Conjunction)
conditioning_list = []
@@ -46,7 +47,7 @@ def get_uc_and_c(prompt_string_uncleaned, model, log_tokens=False, skip_normaliz
raise f"embeddings can only be made from FlattenedPrompts, got {type(flattened_prompt)} instead"
fragments = [x[0] for x in flattened_prompt.children]
attention_weights = [x[1] for x in flattened_prompt.children]
print(fragments, attention_weights)
#print(fragments, attention_weights)
return model.get_learned_conditioning([fragments], attention_weights=[attention_weights])
for part,weight in zip(parsed_conjunction.prompts, parsed_conjunction.weights):
@@ -65,14 +66,14 @@ def get_uc_and_c(prompt_string_uncleaned, model, log_tokens=False, skip_normaliz
return conditioning_list
positive_conditioning_list = build_conditioning_list(prompt_string_cleaned)
negative_conditioning_list = build_conditioning_list(unconditioned_words)
positive_conditioning_list = build_conditioning_list(prompt_string_cleaned, verbose=True)
negative_conditioning_list = build_conditioning_list(unconditioned_words, verbose=(len(unconditioned_words)>0) )
if len(negative_conditioning_list) == 0:
negative_conditioning = model.get_learned_conditioning([['']], attention_weights=[[1]])
else:
if len(negative_conditioning_list)>1:
print("cannot do conjunctions on unconditioning for now")
print("cannot do conjunctions on unconditioning for now, everything except the first prompt will be ignored")
negative_conditioning = negative_conditioning_list[0][0]
#positive_conditioning_list.append((get_blend_prompts_and_weights(prompt), this_weight))