BatchFuncs.py

# 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
    cur_prompt_series = pd.Series([np.nan for a in range(settings.max_frames)])
    nxt_prompt_series = pd.Series([np.nan for a in range(settings.max_frames)])

    # 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 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)

    #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, 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.
            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 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 BatchPoolAnimConditioning(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)
            max_size = max(max_size, cond_to.shape[1])
    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)

        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):
    pooled_out = []
    cond_out = []

    for i in range(len(cur_prompt_series)):
        interpolated_conditioning = addWeighted(cur_prompt_series[i],
                                                nxt_prompt_series[i],
                                                weight_series[i])

        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_G, cur_prompt_series_G,
                                                               nxt_prompt_series_G, settings.pre_text_G, settings.app_text_G)
    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(settings.pre_text_G) + " " + str(current_prompt_G) + " " + str(settings.app_text_G))
                nxt_prompt_series_G[f] += (str(settings.pre_text_G) + " " + str(next_prompt_G) + " " + str(settings.app_text_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(settings.pre_text_G) + " " + str(current_prompt_G) + " " + str(settings.app_text_G))
            nxt_prompt_series_G[f] += (str(settings.pre_text_G) + " " + str(next_prompt_G) + " " + str(settings.app_text_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(settings.pre_text_L) + " " + str(current_prompt_L) + " " + str(settings.app_text_L))
                nxt_prompt_series_L[f] += (str(settings.pre_text_L) + " " + str(next_prompt_L) + " " + str(settings.app_text_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(settings.pre_text_L) + " " + str(current_prompt_L) + " " + str(settings.app_text_L))
            nxt_prompt_series_L[f] += (str(settings.pre_text_L) + " " + str(next_prompt_L) + " " + str(settings.app_text_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, "\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"), "\n", "Current weight: ", weight_series[i]

    return current_conds, next_conds, weight_series