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add prompt attention normalization

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Vladimir Mandic
2024-06-10 15:48:55 -04:00
parent 38c6dbdd4a
commit a20103a86c
5 changed files with 28 additions and 6 deletions
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modules/prompt_parser_diffusers.py
+20 -2
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@@ -183,12 +183,30 @@ def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, c
return
def normalize_prompt(pairs: list):
num_words = 0
total_weight = 0
for section in pairs:
words = len(section[0].split())
if section[1] == -1: # control tokens
continue
num_words += words
total_weight += section[1] * words
avg_weight = round(100 * total_weight / num_words) / 100 if num_words > 0 else 1
debug(f'Prompt stats: words={num_words} weight={avg_weight}')
for section in pairs:
section[1] = section[1] / avg_weight if section[1] != -1 else -1 # skip control tokens
debug(f'Prompt normalized: {pairs}')
return pairs
def get_prompts_with_weights(prompt: str):
t0 = time.time()
manager = DiffusersTextualInversionManager(shared.sd_model,
shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
manager = DiffusersTextualInversionManager(shared.sd_model, shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
prompt = manager.maybe_convert_prompt(prompt, shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
texts_and_weights = prompt_parser.parse_prompt_attention(prompt)
if shared.opts.prompt_mean_norm:
texts_and_weights = normalize_prompt(texts_and_weights)
texts, text_weights = zip(*texts_and_weights)
debug(f'Prompt: weights={texts_and_weights} time={(time.time() - t0):.3f}')
return texts, text_weights