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feat: simplify and enhance prompt weight splitting (#258)

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* feat: simplify and enhance prompt weight splitting

* fix: don't shadow the prompt variable

* feat: enable backslash-escaped colons in prompts
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_nderscore 2022-08-31 18:00:10 +00:00 committed by GitHub
parent d022d0dd11
commit c52ba1b022
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1 changed files with 32 additions and 51 deletions
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83
ldm/simplet2i.py
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@ -487,22 +487,19 @@ class T2I:
uc = self.model.get_learned_conditioning([''])
# weighted sub-prompts
subprompts, weights = T2I._split_weighted_subprompts(prompt)
if len(subprompts) > 1:
# get weighted sub-prompts
weighted_subprompts = T2I._split_weighted_subprompts(prompt, skip_normalize)
if len(weighted_subprompts) > 1:
# i dont know if this is correct.. but it works
c = torch.zeros_like(uc)
# get total weight for normalizing
totalWeight = sum(weights)
# normalize each "sub prompt" and add it
for i in range(0, len(subprompts)):
weight = weights[i]
if not skip_normalize:
weight = weight / totalWeight
self._log_tokenization(subprompts[i])
for i in range(0, len(weighted_subprompts)):
subprompt, weight = weighted_subprompts[i]
self._log_tokenization(subprompt)
c = torch.add(
c,
self.model.get_learned_conditioning([subprompts[i]]),
self.model.get_learned_conditioning([subprompt]),
alpha=weight,
)
else: # just standard 1 prompt
@ -616,52 +613,36 @@ class T2I:
image = torch.from_numpy(image)
return 2.0 * image - 1.0
def _split_weighted_subprompts(text):
def _split_weighted_subprompts(text, skip_normalize=False):
"""
grabs all text up to the first occurrence of ':'
uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
if ':' has no value defined, defaults to 1.0
repeats until no text remaining
"""
remaining = len(text)
prompts = []
weights = []
while remaining > 0:
if ':' in text:
idx = text.index(':') # first occurrence from start
# grab up to index as sub-prompt
prompt = text[:idx]
remaining -= idx
# remove from main text
text = text[idx + 1 :]
# find value for weight
if ' ' in text:
idx = text.index(' ') # first occurence
else: # no space, read to end
idx = len(text)
if idx != 0:
try:
weight = float(text[:idx])
except: # couldn't treat as float
print(
f"Warning: '{text[:idx]}' is not a value, are you missing a space?"
)
weight = 1.0
else: # no value found
weight = 1.0
# remove from main text
remaining -= idx
text = text[idx + 1 :]
# append the sub-prompt and its weight
prompts.append(prompt)
weights.append(weight)
else: # no : found
if len(text) > 0: # there is still text though
# take remainder as weight 1
prompts.append(text)
weights.append(1.0)
remaining = 0
return prompts, weights
prompt_parser = re.compile("""
(?P<prompt> # capture group for 'prompt'
(?:\\\:|[^:])+ # match one or more non ':' characters or escaped colons '\:'
) # end 'prompt'
(?: # non-capture group
:+ # match one or more ':' characters
(?P<weight> # capture group for 'weight'
-?\d+(?:\.\d+)? # match positive or negative integer or decimal number
)? # end weight capture group, make optional
\s* # strip spaces after weight
| # OR
$ # else, if no ':' then match end of line
) # end non-capture group
""", re.VERBOSE)
parsed_prompts = [(match.group("prompt").replace("\\:", ":"), float(match.group("weight") or 1)) for match in re.finditer(prompt_parser, text)]
if skip_normalize:
return parsed_prompts
weight_sum = sum(map(lambda x: x[1], parsed_prompts))
if weight_sum == 0:
print("Warning: Subprompt weights add up to zero. Discarding and using even weights instead.")
equal_weight = 1 / len(parsed_prompts)
return [(x[0], equal_weight) for x in parsed_prompts]
return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
# shows how the prompt is tokenized
# usually tokens have '</w>' to indicate end-of-word,