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

1 files changed, 4 insertions, 4 deletions

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 800b12c7..fdfa21ae 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -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):