diff options
Diffstat (limited to 'modules/prompt_parser.py')
| -rw-r--r-- | modules/prompt_parser.py | 139 |
1 files changed, 90 insertions, 49 deletions
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 5d58c4ed..a3b12421 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -1,10 +1,7 @@ import re
from collections import namedtuple
-import torch
-from lark import Lark, Transformer, Visitor
-import functools
-import modules.shared as shared
+import lark
# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
# will be represented with prompt_schedule like this (assuming steps=100):
@@ -14,25 +11,48 @@ import modules.shared as shared # [75, 'fantasy landscape with a lake and an oak in background masterful']
# [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
+schedule_parser = lark.Lark(r"""
+!start: (prompt | /[][():]/+)*
+prompt: (emphasized | scheduled | plain | WHITESPACE)*
+!emphasized: "(" prompt ")"
+ | "(" prompt ":" prompt ")"
+ | "[" prompt "]"
+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
+WHITESPACE: /\s+/
+plain: /([^\\\[\]():]|\\.)+/
+%import common.SIGNED_NUMBER -> NUMBER
+""")
def get_learned_conditioning_prompt_schedules(prompts, steps):
- grammar = r"""
- start: prompt
- prompt: (emphasized | scheduled | weighted | plain)*
- !emphasized: "(" prompt ")"
- | "(" prompt ":" prompt ")"
- | "[" prompt "]"
- scheduled: "[" (prompt ":")? prompt ":" NUMBER "]"
- !weighted: "{" weighted_item ("|" weighted_item)* "}"
- !weighted_item: prompt (":" prompt)?
- plain: /([^\\\[\](){}:|]|\\.)+/
- %import common.SIGNED_NUMBER -> NUMBER
"""
- parser = Lark(grammar, parser='lalr')
+ >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
+ >>> g("test")
+ [[10, 'test']]
+ >>> g("a [b:3]")
+ [[3, 'a '], [10, 'a b']]
+ >>> g("a [b: 3]")
+ [[3, 'a '], [10, 'a b']]
+ >>> g("a [[[b]]:2]")
+ [[2, 'a '], [10, 'a [[b]]']]
+ >>> g("[(a:2):3]")
+ [[3, ''], [10, '(a:2)']]
+ >>> g("a [b : c : 1] d")
+ [[1, 'a b d'], [10, 'a c d']]
+ >>> g("a[b:[c:d:2]:1]e")
+ [[1, 'abe'], [2, 'ace'], [10, 'ade']]
+ >>> g("a [unbalanced")
+ [[10, 'a [unbalanced']]
+ >>> g("a [b:.5] c")
+ [[5, 'a c'], [10, 'a b c']]
+ >>> g("a [{b|d{:.5] c") # not handling this right now
+ [[5, 'a c'], [10, 'a {b|d{ c']]
+ >>> g("((a][:b:c [d:3]")
+ [[3, '((a][:b:c '], [10, '((a][:b:c d']]
+ """
def collect_steps(steps, tree):
l = [steps]
- class CollectSteps(Visitor):
+ class CollectSteps(lark.Visitor):
def scheduled(self, tree):
tree.children[-1] = float(tree.children[-1])
if tree.children[-1] < 1:
@@ -43,13 +63,10 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): return sorted(set(l))
def at_step(step, tree):
- class AtStep(Transformer):
+ class AtStep(lark.Transformer):
def scheduled(self, args):
- if len(args) == 2:
- before, after, when = (), *args
- else:
- before, after, when = args
- yield before if step <= when else after
+ before, after, _, when = args
+ yield before or () if step <= when else after
def start(self, args):
def flatten(x):
if type(x) == str:
@@ -57,16 +74,22 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): else:
for gen in x:
yield from flatten(gen)
- return ''.join(flatten(args[0]))
+ return ''.join(flatten(args))
def plain(self, args):
yield args[0].value
def __default__(self, data, children, meta):
for child in children:
yield from child
return AtStep().transform(tree)
-
+
def get_schedule(prompt):
- tree = parser.parse(prompt)
+ try:
+ tree = schedule_parser.parse(prompt)
+ except lark.exceptions.LarkError as e:
+ if 0:
+ import traceback
+ traceback.print_exc()
+ return [[steps, prompt]]
return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
@@ -77,8 +100,7 @@ ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
-def get_learned_conditioning(prompts, steps):
-
+def get_learned_conditioning(model, prompts, steps):
res = []
prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
@@ -92,7 +114,7 @@ def get_learned_conditioning(prompts, steps): continue
texts = [x[1] for x in prompt_schedule]
- conds = shared.sd_model.get_learned_conditioning(texts)
+ conds = model.get_learned_conditioning(texts)
cond_schedule = []
for i, (end_at_step, text) in enumerate(prompt_schedule):
@@ -105,12 +127,13 @@ def get_learned_conditioning(prompts, steps): def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
- res = torch.zeros(c.shape, device=shared.device, dtype=next(shared.sd_model.parameters()).dtype)
+ param = c.schedules[0][0].cond
+ res = torch.zeros(c.shape, device=param.device, dtype=param.dtype)
for i, cond_schedule in enumerate(c.schedules):
target_index = 0
- for curret_index, (end_at, cond) in enumerate(cond_schedule):
+ for current, (end_at, cond) in enumerate(cond_schedule):
if current_step <= end_at:
- target_index = curret_index
+ target_index = current
break
res[i] = cond_schedule[target_index].cond
@@ -148,23 +171,26 @@ def parse_prompt_attention(text): \\ - literal character '\'
anything else - just text
- Example:
-
- 'a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).'
-
- produces:
-
- [
- ['a ', 1.0],
- ['house', 1.5730000000000004],
- [' ', 1.1],
- ['on', 1.0],
- [' a ', 1.1],
- ['hill', 0.55],
- [', sun, ', 1.1],
- ['sky', 1.4641000000000006],
- ['.', 1.1]
- ]
+ >>> parse_prompt_attention('normal text')
+ [['normal text', 1.0]]
+ >>> parse_prompt_attention('an (important) word')
+ [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+ >>> parse_prompt_attention('(unbalanced')
+ [['unbalanced', 1.1]]
+ >>> parse_prompt_attention('\(literal\]')
+ [['(literal]', 1.0]]
+ >>> parse_prompt_attention('(unnecessary)(parens)')
+ [['unnecessaryparens', 1.1]]
+ >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+ [['a ', 1.0],
+ ['house', 1.5730000000000004],
+ [' ', 1.1],
+ ['on', 1.0],
+ [' a ', 1.1],
+ ['hill', 0.55],
+ [', sun, ', 1.1],
+ ['sky', 1.4641000000000006],
+ ['.', 1.1]]
"""
res = []
@@ -206,4 +232,19 @@ def parse_prompt_attention(text): if len(res) == 0:
res = [["", 1.0]]
+ # merge runs of identical weights
+ i = 0
+ while i + 1 < len(res):
+ if res[i][1] == res[i + 1][1]:
+ res[i][0] += res[i + 1][0]
+ res.pop(i + 1)
+ else:
+ i += 1
+
return res
+
+if __name__ == "__main__":
+ import doctest
+ doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
+else:
+ import torch # doctest faster
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