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BatchFuncs.py
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# These nodes were made using code from the Deforum extension for A1111 webui
# You can find the project here: https://github.com/deforum-art/sd-webui-deforum
import numexpr
import torch
import numpy as np
import pandas as pd
import re
from .ScheduleFuncs import *
# calculates numexpr expressions from the text input and return a string
def prepare_batch_prompt(prompt_series, max_frames, frame_idx, prompt_weight_1=0, prompt_weight_2=0, prompt_weight_3=0,
prompt_weight_4=0):
max_f = max_frames - 1
pattern = r'`.*?`' # set so the expression will be read between two backticks (``)
regex = re.compile(pattern)
prompt_parsed = str(prompt_series)
for match in regex.finditer(prompt_parsed):
matched_string = match.group(0)
parsed_string = matched_string.replace('t', f'{frame_idx}').replace("pw_a", f"{prompt_weight_1}").replace("pw_b",
f"{prompt_weight_2}").replace("pw_c", f"{prompt_weight_3}").replace("pw_d",
f"{prompt_weight_4}").replace("max_f",
f"{max_f}").replace('`', '') # replace t, max_f and `` respectively
parsed_value = numexpr.evaluate(parsed_string)
prompt_parsed = prompt_parsed.replace(matched_string, str(parsed_value))
return prompt_parsed.strip()
def prepare_batch_promptA(prompt, settings:ScheduleSettings, index):
max_f = settings.max_frames - 1
pattern = r'`.*?`' # set so the expression will be read between two backticks (``)
regex = re.compile(pattern)
prompt_parsed = str(prompt)
for match in regex.finditer(prompt_parsed):
matched_string = match.group(0)
parsed_string = matched_string.replace(
't',
f'{index}').replace("pw_a",
f"{settings.pw_a[index]}").replace("pw_b",
f"{settings.pw_b[index]}").replace("pw_c",
f"{settings.pw_c[index]}").replace("pw_d",
f"{settings.pw_d[index]}").replace("max_f",
f"{max_f}").replace('`', '') # replace t, max_f and `` respectively
parsed_value = numexpr.evaluate(parsed_string)
prompt_parsed = prompt_parsed.replace(matched_string, str(parsed_value))
return prompt_parsed.strip()
#splits the prompt into positive and negative outputs
#denoted with --neg for where the split should be.
def batch_split_weighted_subprompts(text, pre_text, app_text):
pos = {}
neg = {}
pre_text = str(pre_text)
app_text = str(app_text)
if "--neg" in pre_text:
pre_pos, pre_neg = pre_text.split("--neg")
else:
pre_pos, pre_neg = pre_text, ""
if "--neg" in app_text:
app_pos, app_neg = app_text.split("--neg")
else:
app_pos, app_neg = app_text, ""
for frame, prompt in text.items():
negative_prompts = ""
positive_prompts = ""
prompt_split = prompt.split("--neg")
if len(prompt_split) > 1:
positive_prompts, negative_prompts = prompt_split[0], prompt_split[1]
else:
positive_prompts = prompt_split[0]
pos[frame] = ""
neg[frame] = ""
pos[frame] += (str(pre_pos) + " " + positive_prompts + " " + str(app_pos))
neg[frame] += (str(pre_neg) + " " + negative_prompts + " " + str(app_neg))
if pos[frame].endswith('0'):
pos[frame] = pos[frame][:-1]
if neg[frame].endswith('0'):
neg[frame] = neg[frame][:-1]
return pos, neg
# converts the prompt weight variables to tuples. if it is an int variable,
# set all frames to have the same value
def convert_pw_to_tuples(settings):
if isinstance(settings.pw_a, (int, float, np.float64)):
settings.pw_a = tuple([settings.pw_a] * settings.max_frames)
if isinstance(settings.pw_b, (int, float, np.float64)):
settings.pw_b = tuple([settings.pw_b] * settings.max_frames)
if isinstance(settings.pw_c, (int, float, np.float64)):
settings.pw_c = tuple([settings.pw_c] * settings.max_frames)
if isinstance(settings.pw_d, (int, float, np.float64)):
settings.pw_d = tuple([settings.pw_d] * settings.max_frames)
def interpolate_prompt_seriesA(animation_prompts, settings:ScheduleSettings):
max_f = settings.max_frames # needed for numexpr even though it doesn't look like it's in use.
parsed_animation_prompts = {}
for key, value in animation_prompts.items():
if check_is_number(key): # default case 0:(1 + t %5), 30:(5-t%2)
parsed_animation_prompts[key] = value
else: # math on the left hand side case 0:(1 + t %5), maxKeyframes/2:(5-t%2)
parsed_animation_prompts[int(numexpr.evaluate(key))] = value
sorted_prompts = sorted(parsed_animation_prompts.items(), key=lambda item: int(item[0]))
# Automatically set the first keyframe to 0 if it's missing
if sorted_prompts[0][0] != "0":
sorted_prompts.insert(0, ("0", sorted_prompts[0][1]))
# Automatically set the last keyframe to the maximum number of frames
if sorted_prompts[-1][0] != str(settings.max_frames):
sorted_prompts.append((str(settings.max_frames), sorted_prompts[-1][1]))
# Setup containers for interpolated prompts
nan_list = [np.nan for a in range(settings.max_frames)]
cur_prompt_series = pd.Series(nan_list,dtype=object)
nxt_prompt_series = pd.Series(nan_list,dtype=object)
# simple array for strength values
weight_series = [np.nan] * settings.max_frames
# in case there is only one keyed prompt, set all prompts to that prompt
if settings.max_frames == 1:
for i in range(0, len(cur_prompt_series) - 1):
current_prompt = sorted_prompts[0][1]
cur_prompt_series[i] = str(current_prompt)
nxt_prompt_series[i] = str(current_prompt)
#make sure prompt weights are tuples and convert them if not
convert_pw_to_tuples(settings)
# Initialized outside of loop for nan check
current_key = 0
next_key = 0
# For every keyframe prompt except the last
for i in range(0, len(sorted_prompts) - 1):
# Get current and next keyframe
current_key = int(sorted_prompts[i][0])
next_key = int(sorted_prompts[i + 1][0])
# Ensure there's no weird ordering issues or duplication in the animation prompts
# (unlikely because we sort above, and the json parser will strip dupes)
if current_key >= next_key:
print(
f"WARNING: Sequential prompt keyframes {i}:{current_key} and {i + 1}:{next_key} are not monotonously increasing; skipping interpolation.")
continue
# Get current and next keyframes' positive and negative prompts (if any)
current_prompt = sorted_prompts[i][1]
next_prompt = sorted_prompts[i + 1][1]
# Calculate how much to shift the weight from current to next prompt at each frame.
weight_step = 1 / (next_key - current_key)
for f in range(max(current_key, 0), min(next_key, len(cur_prompt_series))):
next_weight = weight_step * (f - current_key)
current_weight = 1 - next_weight
# add the appropriate prompts and weights to their respective containers.
weight_series[f] = 0.0
cur_prompt_series[f] = str(current_prompt)
nxt_prompt_series[f] = str(next_prompt)
weight_series[f] += current_weight
current_key = next_key
next_key = settings.max_frames
current_weight = 0.0
index_offset = 0
# Evaluate the current and next prompt's expressions
for i in range(settings.start_frame, min(settings.end_frame,len(cur_prompt_series))):
cur_prompt_series[i] = prepare_batch_promptA(cur_prompt_series[i], settings, i)
nxt_prompt_series[i] = prepare_batch_promptA(nxt_prompt_series[i], settings, i)
if settings.print_output == True:
# Show the to/from prompts with evaluated expressions for transparency.
if(settings.start_frame >= i):
if(settings.end_frame > 0):
if(settings.end_frame > i):
print("\n", "Max Frames: ", settings.max_frames, "\n", "frame index: ", (settings.start_frame + i),
"\n", "Current Prompt: ",
cur_prompt_series[i], "\n", "Next Prompt: ", nxt_prompt_series[i], "\n", "Strength : ",
weight_series[i], "\n")
else:
print("\n", "Max Frames: ", settings.max_frames, "\n", "frame index: ", (settings.start_frame + i), "\n", "Current Prompt: ",
cur_prompt_series[i], "\n", "Next Prompt: ", nxt_prompt_series[i], "\n", "Strength : ",
weight_series[i], "\n")
index_offset = index_offset + 1
# Output methods depending if the prompts are the same or if the current frame is a keyframe.
# if it is an in-between frame and the prompts differ, composable diffusion will be performed.
return (cur_prompt_series, nxt_prompt_series, weight_series)
def interpolate_prompt_series(animation_prompts, max_frames, start_frame, pre_text, app_text, prompt_weight_1=[],
prompt_weight_2=[], prompt_weight_3=[], prompt_weight_4=[], Is_print = False):
max_f = max_frames # needed for numexpr even though it doesn't look like it's in use.
parsed_animation_prompts = {}
for key, value in animation_prompts.items():
if check_is_number(key): # default case 0:(1 + t %5), 30:(5-t%2)
parsed_animation_prompts[key] = value
else: # math on the left hand side case 0:(1 + t %5), maxKeyframes/2:(5-t%2)
parsed_animation_prompts[int(numexpr.evaluate(key))] = value
sorted_prompts = sorted(parsed_animation_prompts.items(), key=lambda item: int(item[0]))
# Automatically set the first keyframe to 0 if it's missing
if sorted_prompts[0][0] != "0":
sorted_prompts.insert(0, ("0", sorted_prompts[0][1]))
# Automatically set the last keyframe to the maximum number of frames
if sorted_prompts[-1][0] != str(max_frames):
sorted_prompts.append((str(max_frames), sorted_prompts[-1][1]))
# Setup containers for interpolated prompts
cur_prompt_series = pd.Series([np.nan for a in range(max_frames)])
nxt_prompt_series = pd.Series([np.nan for a in range(max_frames)])
# simple array for strength values
weight_series = [np.nan] * max_frames
# in case there is only one keyed promt, set all prompts to that prompt
if len(sorted_prompts) == 1:
for i in range(0, len(cur_prompt_series) - 1):
current_prompt = sorted_prompts[0][1]
cur_prompt_series[i] = str(current_prompt)
nxt_prompt_series[i] = str(current_prompt)
# Initialized outside of loop for nan check
current_key = 0
next_key = 0
if type(prompt_weight_1) in {int, float, np.float64}:
prompt_weight_1 = tuple([prompt_weight_1] * max_frames)
if type(prompt_weight_2) in {int, float, np.float64}:
prompt_weight_2 = tuple([prompt_weight_2] * max_frames)
if type(prompt_weight_3) in {int, float, np.float64}:
prompt_weight_3 = tuple([prompt_weight_3] * max_frames)
if type(prompt_weight_4) in {int, float, np.float64}:
prompt_weight_4 = tuple([prompt_weight_4] * max_frames)
# For every keyframe prompt except the last
for i in range(0, len(sorted_prompts) - 1):
# Get current and next keyframe
current_key = int(sorted_prompts[i][0])
next_key = int(sorted_prompts[i + 1][0])
# Ensure there's no weird ordering issues or duplication in the animation prompts
# (unlikely because we sort above, and the json parser will strip dupes)
if current_key >= next_key:
print(
f"WARNING: Sequential prompt keyframes {i}:{current_key} and {i + 1}:{next_key} are not monotonously increasing; skipping interpolation.")
continue
# Get current and next keyframes' positive and negative prompts (if any)
current_prompt = sorted_prompts[i][1]
next_prompt = sorted_prompts[i + 1][1]
# Calculate how much to shift the weight from current to next prompt at each frame.
weight_step = 1 / (next_key - current_key)
for f in range(max(current_key, 0), min(next_key, len(cur_prompt_series))):
next_weight = weight_step * (f - current_key)
current_weight = 1 - next_weight
# add the appropriate prompts and weights to their respective containers.
weight_series[f] = 0.0
cur_prompt_series[f] = str(current_prompt)
nxt_prompt_series[f] = str(next_prompt)
weight_series[f] += current_weight
current_key = next_key
next_key = max_frames
current_weight = 0.0
index_offset = 0
# Evaluate the current and next prompt's expressions
for i in range(start_frame, len(cur_prompt_series)):
cur_prompt_series[i] = prepare_batch_prompt(cur_prompt_series[i], max_frames, i, prompt_weight_1[i],
prompt_weight_2[i], prompt_weight_3[i], prompt_weight_4[i])
nxt_prompt_series[i] = prepare_batch_prompt(nxt_prompt_series[i], max_frames, i, prompt_weight_1[i],
prompt_weight_2[i], prompt_weight_3[i], prompt_weight_4[i])
if Is_print == True:
# Show the to/from prompts with evaluated expressions for transparency.
print("\n", "Max Frames: ", max_frames, "\n", "frame index: ", (start_frame + i), "\n", "Current Prompt: ",
cur_prompt_series[i], "\n", "Next Prompt: ", nxt_prompt_series[i], "\n", "Strength : ",
weight_series[i], "\n")
index_offset = index_offset + 1
# Output methods depend on if the prompts are the same or if the current frame is a keyframe.
# if it is an in-between frame and the prompts differ, composable diffusion will be performed.
return (cur_prompt_series, nxt_prompt_series, weight_series)
def BatchPoolAnimConditioning(cur_prompt_series, nxt_prompt_series, weight_series, clip, settings:ScheduleSettings):
pooled_out = []
cond_out = []
max_size = 0
if settings.end_frame == 0:
settings.end_frame = settings.max_frames
print("end_frame at 0, using max_frames instead!")
if settings.start_frame >= settings.end_frame:
settings.start_frame = 0
print("start_frame larger than or equal to end_frame, using max_frames instead!")
if max_size == 0:
for i in range(0, settings.end_frame):
tokens = clip.tokenize(str(cur_prompt_series[i]))
cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
max_size = max(max_size, cond_to.shape[1])
for i in range(settings.start_frame, settings.end_frame):
tokens = clip.tokenize(str(cur_prompt_series[i]))
cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
if i < len(nxt_prompt_series):
tokens = clip.tokenize(str(nxt_prompt_series[i]))
cond_from, pooled_from = clip.encode_from_tokens(tokens, return_pooled=True)
else:
cond_from, pooled_from = torch.zeros_like(cond_to), torch.zeros_like(pooled_to)
interpolated_conditioning = addWeighted([[cond_to, {"pooled_output": pooled_to}]],
[[cond_from, {"pooled_output": pooled_from}]],
weight_series[i],max_size)
interpolated_cond = interpolated_conditioning[0][0]
interpolated_pooled = interpolated_conditioning[0][1].get("pooled_output", pooled_from)
cond_out.append(interpolated_cond)
pooled_out.append(interpolated_pooled)
final_pooled_output = torch.cat(pooled_out, dim=0)
final_conditioning = torch.cat(cond_out, dim=0)
return [[final_conditioning, {"pooled_output": final_pooled_output}]]
def BatchGLIGENConditioning(cur_prompt_series, nxt_prompt_series, weight_series, clip):
pooled_out = []
cond_out = []
max_size = 0
if max_size == 0:
for i in range(len(cur_prompt_series)):
tokens = clip.tokenize(str(cur_prompt_series[i]))
cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
tensor_size = cond_to.shape[1]
max_size = max(max_size, tensor_size)
for i in range(len(cur_prompt_series)):
tokens = clip.tokenize(str(cur_prompt_series[i]))
cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
tokens = clip.tokenize(str(nxt_prompt_series[i]))
cond_from, pooled_from = clip.encode_from_tokens(tokens, return_pooled=True)
interpolated_conditioning = addWeighted([[cond_to, {"pooled_output": pooled_to}]],
[[cond_from, {"pooled_output": pooled_from}]],
weight_series[i], max_size)
interpolated_cond = interpolated_conditioning[0][0]
interpolated_pooled = interpolated_conditioning[0][1].get("pooled_output", pooled_from)
pooled_out.append(interpolated_pooled)
cond_out.append(interpolated_cond)
final_pooled_output = torch.cat(pooled_out, dim=0)
final_conditioning = torch.cat(cond_out, dim=0)
return cond_out, pooled_out
def BatchPoolAnimConditioningSDXL(cur_prompt_series, nxt_prompt_series, weight_series, clip, settings:ScheduleSettings):
pooled_out = []
cond_out = []
max_size = 0
if settings.end_frame == 0:
settings.end_frame = settings.max_frames
print("end_frame at 0, using max_frames instead!")
if settings.start_frame >= settings.end_frame:
settings.start_frame = 0
print("start_frame larger than or equal to end_frame, using max_frames instead!")
if max_size == 0:
for i in range(0, settings.end_frame):
tokens = clip.tokenize(str(cur_prompt_series[i]))
cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
max_size = max(max_size, cond_to.shape[1])
for i in range(settings.start_frame,settings.end_frame):
interpolated_conditioning = addWeighted(cur_prompt_series[i],
nxt_prompt_series[i],
weight_series[i], max_size)
interpolated_cond = interpolated_conditioning[0][0]
interpolated_pooled = interpolated_conditioning[0][1].get("pooled_output")
pooled_out.append(interpolated_pooled)
cond_out.append(interpolated_cond)
final_pooled_output = torch.cat(pooled_out, dim=0)
final_conditioning = torch.cat(cond_out, dim=0)
return [[final_conditioning, {"pooled_output": final_pooled_output}]]
def BatchInterpolatePromptsSDXL(animation_promptsG, animation_promptsL, clip, settings: ScheduleSettings):
convert_pw_to_tuples(settings)
# parse the conditioning strength and determine in-betweens.
# Get prompts sorted by keyframe
max_f = settings.max_frames # needed for numexpr even though it doesn't look like it's in use.
parsed_animation_promptsG = {}
parsed_animation_promptsL = {}
for key, value in animation_promptsG.items():
if check_is_number(key): # default case 0:(1 + t %5), 30:(5-t%2)
parsed_animation_promptsG[key] = value
else: # math on the left hand side case 0:(1 + t %5), maxKeyframes/2:(5-t%2)
parsed_animation_promptsG[int(numexpr.evaluate(key))] = value
sorted_prompts_G = sorted(parsed_animation_promptsG.items(), key=lambda item: int(item[0]))
for key, value in animation_promptsL.items():
if check_is_number(key): # default case 0:(1 + t %5), 30:(5-t%2)
parsed_animation_promptsL[key] = value
else: # math on the left hand side case 0:(1 + t %5), maxKeyframes/2:(5-t%2)
parsed_animation_promptsL[int(numexpr.evaluate(key))] = value
sorted_prompts_L = sorted(parsed_animation_promptsL.items(), key=lambda item: int(item[0]))
# Setup containers for interpolated prompts
cur_prompt_series_G = pd.Series([np.nan for a in range(settings.max_frames)])
nxt_prompt_series_G = pd.Series([np.nan for a in range(settings.max_frames)])
cur_prompt_series_L = pd.Series([np.nan for a in range(settings.max_frames)])
nxt_prompt_series_L = pd.Series([np.nan for a in range(settings.max_frames)])
# simple array for strength values
weight_series = [np.nan] * settings.max_frames
def constructPrompt(sorted_prompts, cur_prompt, nxt_prompt, pre_text, app_text):
if len(sorted_prompts) - 1 == 0:
for i in range(0, len(sorted_prompts) - 1):
current_prompt = sorted_prompts[0][1]
cur_prompt[i] = str(pre_text) + " " + str(current_prompt) + " " + str(app_text)
nxt_prompt[i] = str(pre_text) + " " + str(current_prompt) + " " + str(app_text)
return cur_prompt, nxt_prompt
# in case there is only one keyed promt, set all prompts to that prompt
cur_prompt_series_G, nxt_prompt_series_G = constructPrompt(sorted_prompts_G, cur_prompt_series_G,
nxt_prompt_series_G, settings.pre_text_G,
settings.app_text_G)
cur_prompt_series_L, nxt_prompt_series_L = constructPrompt(sorted_prompts_L, cur_prompt_series_L,
nxt_prompt_series_L, settings.pre_text_L,
settings.app_text_L)
if len(sorted_prompts_L) - 1 == 0:
for i in range(0, len(cur_prompt_series_L) - 1):
current_prompt_L = sorted_prompts_L[0][1]
cur_prompt_series_L[i] = str(pre_text_L) + " " + str(current_prompt_L) + " " + str(app_text_L)
nxt_prompt_series_L[i] = str(pre_text_L) + " " + str(current_prompt_L) + " " + str(app_text_L)
# Initialized outside of loop for nan check
current_key = 0
next_key = 0
# For every keyframe prompt except the last
for i in range(0, len(sorted_prompts_G) - 1):
# Get current and next keyframe
current_key = int(sorted_prompts_G[i][0])
next_key = int(sorted_prompts_G[i + 1][0])
# Ensure there's no weird ordering issues or duplication in the animation prompts
# (unlikely because we sort above, and the json parser will strip dupes)
if current_key >= next_key:
print(
f"WARNING: Sequential prompt keyframes {i}:{current_key} and {i + 1}:{next_key} are not monotonously increasing; skipping interpolation.")
continue
# Get current and next keyframes' positive and negative prompts (if any)
current_prompt_G = sorted_prompts_G[i][1]
next_prompt_G = sorted_prompts_G[i + 1][1]
# Calculate how much to shift the weight from current to next prompt at each frame.
weight_step = 1 / (next_key - current_key)
for f in range(current_key, next_key):
next_weight = weight_step * (f - current_key)
current_weight = 1 - next_weight
# add the appropriate prompts and weights to their respective containers.
if f < settings.max_frames:
cur_prompt_series_G[f] = ''
nxt_prompt_series_G[f] = ''
weight_series[f] = 0.0
cur_prompt_series_G[f] += (str(current_prompt_G))
nxt_prompt_series_G[f] += (str(next_prompt_G))
weight_series[f] += current_weight
current_key = next_key
next_key = settings.max_frames
current_weight = 0.0
# second loop to catch any nan runoff
for f in range(current_key, next_key):
next_weight = weight_step * (f - current_key)
# add the appropriate prompts and weights to their respective containers.
cur_prompt_series_G[f] = ''
nxt_prompt_series_G[f] = ''
weight_series[f] = current_weight
cur_prompt_series_G[f] = (str(current_prompt_G))
nxt_prompt_series_G[f] = (str(next_prompt_G))
# Reset outside of loop for nan check
current_key = 0
next_key = 0
for i in range(0, len(sorted_prompts_L) - 1):
current_key = int(sorted_prompts_L[i][0])
next_key = int(sorted_prompts_L[i + 1][0])
# Ensure there's no weird ordering issues or duplication in the animation prompts
# (unlikely because we sort above, and the json parser will strip dupes)
if current_key >= next_key:
print(
f"WARNING: Sequential prompt keyframes {i}:{current_key} and {i + 1}:{next_key} are not monotonously increasing; skipping interpolation.")
continue
# Get current and next keyframes' positive and negative prompts (if any)
current_prompt_L = sorted_prompts_L[i][1]
next_prompt_L = sorted_prompts_L[i + 1][1]
# Calculate how much to shift the weight from current to next prompt at each frame.
weight_step = 1 / (next_key - current_key)
for f in range(current_key, next_key):
next_weight = weight_step * (f - current_key)
current_weight = 1 - next_weight
# add the appropriate prompts and weights to their respective containers.
if f < settings.max_frames:
cur_prompt_series_L[f] = ''
nxt_prompt_series_L[f] = ''
weight_series[f] = 0.0
cur_prompt_series_L[f] += (str(current_prompt_L))
nxt_prompt_series_L[f] += (str(next_prompt_L))
weight_series[f] += current_weight
current_key = next_key
next_key = settings.max_frames
current_weight = 0.0
# second loop to catch any nan runoff
for f in range(current_key, next_key):
next_weight = weight_step * (f - current_key)
# add the appropriate prompts and weights to their respective containers.
cur_prompt_series_L[f] = ''
nxt_prompt_series_L[f] = ''
weight_series[f] = current_weight
cur_prompt_series_L[f] += (str(current_prompt_L))
nxt_prompt_series_L[f] += (str(next_prompt_L))
# Evaluate the current and next prompt's expressions
for i in range(0, settings.max_frames):
cur_prompt_series_G[i] = prepare_batch_promptA(cur_prompt_series_G[i], settings, i)
nxt_prompt_series_G[i] = prepare_batch_promptA(nxt_prompt_series_G[i], settings, i)
cur_prompt_series_L[i] = prepare_batch_promptA(cur_prompt_series_L[i], settings, i)
nxt_prompt_series_L[i] = prepare_batch_promptA(nxt_prompt_series_L[i], settings, i)
current_conds = []
next_conds = []
for i in range(0, settings.max_frames):
current_conds.append(SDXLencode(cur_prompt_series_G[i], cur_prompt_series_L[i], settings, clip))
next_conds.append(SDXLencode(nxt_prompt_series_L[i], nxt_prompt_series_L[i], settings, clip))
if settings.print_output == True:
# Show the to/from prompts with evaluated expressions for transparency.
for i in range(0, settings.max_frames):
print("\n", "Max Frames: ", settings.max_frames, "Curr Frame: ", i, "\n", "Current Prompt G: ",
cur_prompt_series_G[i],
"\n", "Current Prompt L: ", cur_prompt_series_L[i], "\n", "Next Prompt G: ", nxt_prompt_series_G[i],
"\n", "Next Prompt L : ", nxt_prompt_series_L[i], "\n", "Current weight: ", weight_series[i])
return current_conds, next_conds, weight_series