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Merge pull request #1755 from AUTOMATIC1111/use-typing-list

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use typing.list in prompt_parser.py for wider python version support
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AUTOMATIC1111 2022-10-06 08:50:06 +03:00 committed by GitHub
commit 0e92c36707
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1 changed files with 4 additions and 5 deletions
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modules/prompt_parser.py
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@ -1,6 +1,6 @@
import re import re
from collections import namedtuple from collections import namedtuple
from typing import List
import lark 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]" # 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]"
@ -175,15 +175,14 @@ def get_multicond_prompt_list(prompts):
class ComposableScheduledPromptConditioning: class ComposableScheduledPromptConditioning:
def __init__(self, schedules, weight=1.0): def __init__(self, schedules, weight=1.0):
self.schedules = schedules # : list[ScheduledPromptConditioning] self.schedules: List[ScheduledPromptConditioning] = schedules
self.weight: float = weight self.weight: float = weight
class MulticondLearnedConditioning: class MulticondLearnedConditioning:
def __init__(self, shape, batch): def __init__(self, shape, batch):
self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
self.batch = batch # : list[list[ComposableScheduledPromptConditioning]] self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning: def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
"""same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt. """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
@ -203,7 +202,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
return MulticondLearnedConditioning(shape=(len(prompts),), batch=res) return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
def reconstruct_cond_batch(c, current_step): # c: list[list[ScheduledPromptConditioning]] def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
param = c[0][0].cond param = c[0][0].cond
res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
for i, cond_schedule in enumerate(c): for i, cond_schedule in enumerate(c):