Merge remote-tracking branch 'origin/dev' into soft-inpainting

# Conflicts:
#	modules/processing.py

1 files changed, 4 insertions, 5 deletions

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 334efeef..cba13455 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -2,10 +2,9 @@ from __future__ import annotations
 

 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]"

+# 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):

 # [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']

 # [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']

@@ -240,14 +239,14 @@ def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
 

 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, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:

@@ -278,7 +277,7 @@ class DictWithShape(dict):
         return self["crossattn"].shape

 

 

-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

     is_dict = isinstance(param, dict)