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use typing.list in prompt_parser.py for wider python version support

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DepFA 2022-10-05 22:11:30 +01:00 committed by GitHub
parent f8e41a96bb
commit 34c358d10d
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1 changed files with 4 additions and 4 deletions
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8
modules/prompt_parser.py
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@ -1,6 +1,6 @@
import re
from collections import namedtuple
from typing import List
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]"
@ -175,14 +175,14 @@ def get_multicond_prompt_list(prompts):
class ComposableScheduledPromptConditioning:
def __init__(self, schedules, weight=1.0):
self.schedules: list[ScheduledPromptConditioning] = schedules
self.schedules: List[ScheduledPromptConditioning] = schedules
self.weight: float = weight
class MulticondLearnedConditioning:
def __init__(self, shape, batch):
self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
@ -203,7 +203,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
param = c[0][0].cond
res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
for i, cond_schedule in enumerate(c):