xyz_grid.py

from collections import namedtuple
from copy import copy
from itertools import permutations, chain
import random
import csv
from io import StringIO
from PIL import Image
import numpy as np

import modules.scripts as scripts
import gradio as gr

from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion
from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
from modules.shared import opts, state
import modules.shared as shared
import modules.sd_samplers
import modules.sd_models
import modules.sd_vae
import re

from modules.ui_components import ToolButton

fill_values_symbol = "\U0001f4d2"  # 📒

AxisInfo = namedtuple('AxisInfo', ['axis', 'values'])


def apply_field(field):
    def fun(p, x, xs):
        setattr(p, field, x)

    return fun


def apply_prompt(p, x, xs):
    if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
        raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")

    p.prompt = p.prompt.replace(xs[0], x)
    p.negative_prompt = p.negative_prompt.replace(xs[0], x)


def apply_order(p, x, xs):
    token_order = []

    # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
    for token in x:
        token_order.append((p.prompt.find(token), token))

    token_order.sort(key=lambda t: t[0])

    prompt_parts = []

    # Split the prompt up, taking out the tokens
    for _, token in token_order:
        n = p.prompt.find(token)
        prompt_parts.append(p.prompt[0:n])
        p.prompt = p.prompt[n + len(token):]

    # Rebuild the prompt with the tokens in the order we want
    prompt_tmp = ""
    for idx, part in enumerate(prompt_parts):
        prompt_tmp += part
        prompt_tmp += x[idx]
    p.prompt = prompt_tmp + p.prompt


def apply_sampler(p, x, xs):
    sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
    if sampler_name is None:
        raise RuntimeError(f"Unknown sampler: {x}")

    p.sampler_name = sampler_name


def confirm_samplers(p, xs):
    for x in xs:
        if x.lower() not in sd_samplers.samplers_map:
            raise RuntimeError(f"Unknown sampler: {x}")


def apply_checkpoint(p, x, xs):
    info = modules.sd_models.get_closet_checkpoint_match(x)
    if info is None:
        raise RuntimeError(f"Unknown checkpoint: {x}")
    p.override_settings['sd_model_checkpoint'] = info.name


def confirm_checkpoints(p, xs):
    for x in xs:
        if modules.sd_models.get_closet_checkpoint_match(x) is None:
            raise RuntimeError(f"Unknown checkpoint: {x}")


def apply_clip_skip(p, x, xs):
    opts.data["CLIP_stop_at_last_layers"] = x


def apply_upscale_latent_space(p, x, xs):
    if x.lower().strip() != '0':
        opts.data["use_scale_latent_for_hires_fix"] = True
    else:
        opts.data["use_scale_latent_for_hires_fix"] = False


def find_vae(name: str):
    if name.lower() in ['auto', 'automatic']:
        return modules.sd_vae.unspecified
    if name.lower() == 'none':
        return None
    else:
        choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
        if len(choices) == 0:
            print(f"No VAE found for {name}; using automatic")
            return modules.sd_vae.unspecified
        else:
            return modules.sd_vae.vae_dict[choices[0]]


def apply_vae(p, x, xs):
    modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))


def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
    p.styles.extend(x.split(','))


def apply_uni_pc_order(p, x, xs):
    opts.data["uni_pc_order"] = min(x, p.steps - 1)


def apply_face_restore(p, opt, x):
    opt = opt.lower()
    if opt == 'codeformer':
        is_active = True
        p.face_restoration_model = 'CodeFormer'
    elif opt == 'gfpgan':
        is_active = True
        p.face_restoration_model = 'GFPGAN'
    else:
        is_active = opt in ('true', 'yes', 'y', '1')

    p.restore_faces = is_active


def apply_override(field, boolean: bool = False):
    def fun(p, x, xs):
        if boolean:
            x = True if x == "True" else False
        p.override_settings[field] = x
    return fun


def boolean_choice(reverse: bool = False):
    def choice():
        return ["False", "True"] if reverse else ["True", "False"]
    return choice


def format_value_add_label(p, opt, x):
    if type(x) == float:
        x = round(x, 8)

    return f"{opt.label}: {x}"


def format_value(p, opt, x):
    if type(x) == float:
        x = round(x, 8)
    return x


def format_value_join_list(p, opt, x):
    return ", ".join(x)


def do_nothing(p, x, xs):
    pass


def format_nothing(p, opt, x):
    return ""


def str_permutations(x):
    """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
    return x


class AxisOption:
    def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None):
        self.label = label
        self.type = type
        self.apply = apply
        self.format_value = format_value
        self.confirm = confirm
        self.cost = cost
        self.choices = choices


class AxisOptionImg2Img(AxisOption):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.is_img2img = True

class AxisOptionTxt2Img(AxisOption):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.is_img2img = False


axis_options = [
    AxisOption("Nothing", str, do_nothing, format_value=format_nothing),
    AxisOption("Seed", int, apply_field("seed")),
    AxisOption("Var. seed", int, apply_field("subseed")),
    AxisOption("Var. strength", float, apply_field("subseed_strength")),
    AxisOption("Steps", int, apply_field("steps")),
    AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")),
    AxisOption("CFG Scale", float, apply_field("cfg_scale")),
    AxisOptionImg2Img("Image CFG Scale", float, apply_field("image_cfg_scale")),
    AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),
    AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
    AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
    AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
    AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list, key=str.casefold)),
    AxisOption("Negative Guidance minimum sigma", float, apply_field("s_min_uncond")),
    AxisOption("Sigma Churn", float, apply_field("s_churn")),
    AxisOption("Sigma min", float, apply_field("s_tmin")),
    AxisOption("Sigma max", float, apply_field("s_tmax")),
    AxisOption("Sigma noise", float, apply_field("s_noise")),
    AxisOption("Schedule type", str, apply_override("k_sched_type"), choices=lambda: list(sd_samplers_kdiffusion.k_diffusion_scheduler)),
    AxisOption("Schedule min sigma", float, apply_override("sigma_min")),
    AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
    AxisOption("Schedule rho", float, apply_override("rho")),
    AxisOption("Eta", float, apply_field("eta")),
    AxisOption("Clip skip", int, apply_clip_skip),
    AxisOption("Denoising", float, apply_field("denoising_strength")),
    AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
    AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
    AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
    AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
    AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
    AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
    AxisOption("Token merging ratio", float, apply_override('token_merging_ratio')),
    AxisOption("Token merging ratio high-res", float, apply_override('token_merging_ratio_hr')),
    AxisOption("Always discard next-to-last sigma", str, apply_override('always_discard_next_to_last_sigma', boolean=True), choices=boolean_choice(reverse=True)),
]


def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size):
    hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
    ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
    title_texts = [[images.GridAnnotation(z)] for z in z_labels]

    list_size = (len(xs) * len(ys) * len(zs))

    processed_result = None

    state.job_count = list_size * p.n_iter

    def process_cell(x, y, z, ix, iy, iz):
        nonlocal processed_result

        def index(ix, iy, iz):
            return ix + iy * len(xs) + iz * len(xs) * len(ys)

        state.job = f"{index(ix, iy, iz) + 1} out of {list_size}"

        processed: Processed = cell(x, y, z, ix, iy, iz)

        if processed_result is None:
            # Use our first processed result object as a template container to hold our full results
            processed_result = copy(processed)
            processed_result.images = [None] * list_size
            processed_result.all_prompts = [None] * list_size
            processed_result.all_seeds = [None] * list_size
            processed_result.infotexts = [None] * list_size
            processed_result.index_of_first_image = 1

        idx = index(ix, iy, iz)
        if processed.images:
            # Non-empty list indicates some degree of success.
            processed_result.images[idx] = processed.images[0]
            processed_result.all_prompts[idx] = processed.prompt
            processed_result.all_seeds[idx] = processed.seed
            processed_result.infotexts[idx] = processed.infotexts[0]
        else:
            cell_mode = "P"
            cell_size = (processed_result.width, processed_result.height)
            if processed_result.images[0] is not None:
                cell_mode = processed_result.images[0].mode
                #This corrects size in case of batches:
                cell_size = processed_result.images[0].size
            processed_result.images[idx] = Image.new(cell_mode, cell_size)


    if first_axes_processed == 'x':
        for ix, x in enumerate(xs):
            if second_axes_processed == 'y':
                for iy, y in enumerate(ys):
                    for iz, z in enumerate(zs):
                        process_cell(x, y, z, ix, iy, iz)
            else:
                for iz, z in enumerate(zs):
                    for iy, y in enumerate(ys):
                        process_cell(x, y, z, ix, iy, iz)
    elif first_axes_processed == 'y':
        for iy, y in enumerate(ys):
            if second_axes_processed == 'x':
                for ix, x in enumerate(xs):
                    for iz, z in enumerate(zs):
                        process_cell(x, y, z, ix, iy, iz)
            else:
                for iz, z in enumerate(zs):
                    for ix, x in enumerate(xs):
                        process_cell(x, y, z, ix, iy, iz)
    elif first_axes_processed == 'z':
        for iz, z in enumerate(zs):
            if second_axes_processed == 'x':
                for ix, x in enumerate(xs):
                    for iy, y in enumerate(ys):
                        process_cell(x, y, z, ix, iy, iz)
            else:
                for iy, y in enumerate(ys):
                    for ix, x in enumerate(xs):
                        process_cell(x, y, z, ix, iy, iz)

    if not processed_result:
        # Should never happen, I've only seen it on one of four open tabs and it needed to refresh.
        print("Unexpected error: Processing could not begin, you may need to refresh the tab or restart the service.")
        return Processed(p, [])
    elif not any(processed_result.images):
        print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
        return Processed(p, [])

    z_count = len(zs)

    for i in range(z_count):
        start_index = (i * len(xs) * len(ys)) + i
        end_index = start_index + len(xs) * len(ys)
        grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
        if draw_legend:
            grid = images.draw_grid_annotations(grid, processed_result.images[start_index].size[0], processed_result.images[start_index].size[1], hor_texts, ver_texts, margin_size)
        processed_result.images.insert(i, grid)
        processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
        processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
        processed_result.infotexts.insert(i, processed_result.infotexts[start_index])

    sub_grid_size = processed_result.images[0].size
    z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
    if draw_legend:
        z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
    processed_result.images.insert(0, z_grid)
    #TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
    #processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
    #processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
    processed_result.infotexts.insert(0, processed_result.infotexts[0])

    return processed_result


class SharedSettingsStackHelper(object):
    def __enter__(self):
        self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
        self.vae = opts.sd_vae
        self.uni_pc_order = opts.uni_pc_order

    def __exit__(self, exc_type, exc_value, tb):
        opts.data["sd_vae"] = self.vae
        opts.data["uni_pc_order"] = self.uni_pc_order
        modules.sd_models.reload_model_weights()
        modules.sd_vae.reload_vae_weights()

        opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers


re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")

re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")


class Script(scripts.Script):
    def title(self):
        return "X/Y/Z plot"

    def ui(self, is_img2img):
        self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]

        with gr.Row():
            with gr.Column(scale=19):
                with gr.Row():
                    x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
                    x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
                    x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True)
                    fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)

                with gr.Row():
                    y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
                    y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
                    y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True)
                    fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)

                with gr.Row():
                    z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
                    z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
                    z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
                    fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)

        with gr.Row(variant="compact", elem_id="axis_options"):
            with gr.Column():
                draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
                no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
            with gr.Column():
                include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images"))
                include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
            with gr.Column():
                margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))

        with gr.Row(variant="compact", elem_id="swap_axes"):
            swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
            swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
            swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")

        def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown):
            return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown

        xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
        swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
        yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
        swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
        xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
        swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)

        def fill(x_type):
            axis = self.current_axis_options[x_type]
            return axis.choices() if axis.choices else gr.update()

        fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown])
        fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown])
        fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown])

        def select_axis(axis_type,axis_values_dropdown):
            choices = self.current_axis_options[axis_type].choices
            has_choices = choices is not None
            current_values = axis_values_dropdown
            if has_choices:
                choices = choices()
                if isinstance(current_values,str):
                    current_values = current_values.split(",")
                current_values = list(filter(lambda x: x in choices, current_values))
            return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values)

        x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown])
        y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown])
        z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])

        def get_dropdown_update_from_params(axis,params):
            val_key = f"{axis} Values"
            vals = params.get(val_key,"")
            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
            return gr.update(value = valslist)

        self.infotext_fields = (
            (x_type, "X Type"),
            (x_values, "X Values"),
            (x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
            (y_type, "Y Type"),
            (y_values, "Y Values"),
            (y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
            (z_type, "Z Type"),
            (z_values, "Z Values"),
            (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
        )

        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]

    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
        if not no_fixed_seeds:
            modules.processing.fix_seed(p)

        if not opts.return_grid:
            p.batch_size = 1

        def process_axis(opt, vals, vals_dropdown):
            if opt.label == 'Nothing':
                return [0]

            if opt.choices is not None:
                valslist = vals_dropdown
            else:
                valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]

            if opt.type == int:
                valslist_ext = []

                for val in valslist:
                    m = re_range.fullmatch(val)
                    mc = re_range_count.fullmatch(val)
                    if m is not None:
                        start = int(m.group(1))
                        end = int(m.group(2))+1
                        step = int(m.group(3)) if m.group(3) is not None else 1

                        valslist_ext += list(range(start, end, step))
                    elif mc is not None:
                        start = int(mc.group(1))
                        end   = int(mc.group(2))
                        num   = int(mc.group(3)) if mc.group(3) is not None else 1

                        valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
                    else:
                        valslist_ext.append(val)

                valslist = valslist_ext
            elif opt.type == float:
                valslist_ext = []

                for val in valslist:
                    m = re_range_float.fullmatch(val)
                    mc = re_range_count_float.fullmatch(val)
                    if m is not None:
                        start = float(m.group(1))
                        end = float(m.group(2))
                        step = float(m.group(3)) if m.group(3) is not None else 1

                        valslist_ext += np.arange(start, end + step, step).tolist()
                    elif mc is not None:
                        start = float(mc.group(1))
                        end   = float(mc.group(2))
                        num   = int(mc.group(3)) if mc.group(3) is not None else 1

                        valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
                    else:
                        valslist_ext.append(val)

                valslist = valslist_ext
            elif opt.type == str_permutations:
                valslist = list(permutations(valslist))

            valslist = [opt.type(x) for x in valslist]

            # Confirm options are valid before starting
            if opt.confirm:
                opt.confirm(p, valslist)

            return valslist

        x_opt = self.current_axis_options[x_type]
        if x_opt.choices is not None:
            x_values = ",".join(x_values_dropdown)
        xs = process_axis(x_opt, x_values, x_values_dropdown)

        y_opt = self.current_axis_options[y_type]
        if y_opt.choices is not None:
            y_values = ",".join(y_values_dropdown)
        ys = process_axis(y_opt, y_values, y_values_dropdown)

        z_opt = self.current_axis_options[z_type]
        if z_opt.choices is not None:
            z_values = ",".join(z_values_dropdown)
        zs = process_axis(z_opt, z_values, z_values_dropdown)

        # this could be moved to common code, but unlikely to be ever triggered anywhere else
        Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
        grid_mp = round(len(xs) * len(ys) * len(zs) * p.width * p.height / 1000000)
        assert grid_mp < opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {opts.img_max_size_mp} MPixels)'

        def fix_axis_seeds(axis_opt, axis_list):
            if axis_opt.label in ['Seed', 'Var. seed']:
                return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
            else:
                return axis_list

        if not no_fixed_seeds:
            xs = fix_axis_seeds(x_opt, xs)
            ys = fix_axis_seeds(y_opt, ys)
            zs = fix_axis_seeds(z_opt, zs)

        if x_opt.label == 'Steps':
            total_steps = sum(xs) * len(ys) * len(zs)
        elif y_opt.label == 'Steps':
            total_steps = sum(ys) * len(xs) * len(zs)
        elif z_opt.label == 'Steps':
            total_steps = sum(zs) * len(xs) * len(ys)
        else:
            total_steps = p.steps * len(xs) * len(ys) * len(zs)

        if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
            if x_opt.label == "Hires steps":
                total_steps += sum(xs) * len(ys) * len(zs)
            elif y_opt.label == "Hires steps":
                total_steps += sum(ys) * len(xs) * len(zs)
            elif z_opt.label == "Hires steps":
                total_steps += sum(zs) * len(xs) * len(ys)
            elif p.hr_second_pass_steps:
                total_steps += p.hr_second_pass_steps * len(xs) * len(ys) * len(zs)
            else:
                total_steps *= 2

        total_steps *= p.n_iter

        image_cell_count = p.n_iter * p.batch_size
        cell_console_text = f"; {image_cell_count} images per cell" if image_cell_count > 1 else ""
        plural_s = 's' if len(zs) > 1 else ''
        print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
        shared.total_tqdm.updateTotal(total_steps)

        state.xyz_plot_x = AxisInfo(x_opt, xs)
        state.xyz_plot_y = AxisInfo(y_opt, ys)
        state.xyz_plot_z = AxisInfo(z_opt, zs)

        # If one of the axes is very slow to change between (like SD model
        # checkpoint), then make sure it is in the outer iteration of the nested
        # `for` loop.
        first_axes_processed = 'z'
        second_axes_processed = 'y'
        if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost:
            first_axes_processed = 'x'
            if y_opt.cost > z_opt.cost:
                second_axes_processed = 'y'
            else:
                second_axes_processed = 'z'
        elif y_opt.cost > x_opt.cost and y_opt.cost > z_opt.cost:
            first_axes_processed = 'y'
            if x_opt.cost > z_opt.cost:
                second_axes_processed = 'x'
            else:
                second_axes_processed = 'z'
        elif z_opt.cost > x_opt.cost and z_opt.cost > y_opt.cost:
            first_axes_processed = 'z'
            if x_opt.cost > y_opt.cost:
                second_axes_processed = 'x'
            else:
                second_axes_processed = 'y'

        grid_infotext = [None] * (1 + len(zs))

        def cell(x, y, z, ix, iy, iz):
            if shared.state.interrupted:
                return Processed(p, [], p.seed, "")

            pc = copy(p)
            pc.styles = pc.styles[:]
            x_opt.apply(pc, x, xs)
            y_opt.apply(pc, y, ys)
            z_opt.apply(pc, z, zs)

            res = process_images(pc)

            # Sets subgrid infotexts
            subgrid_index = 1 + iz
            if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0:
                pc.extra_generation_params = copy(pc.extra_generation_params)
                pc.extra_generation_params['Script'] = self.title()

                if x_opt.label != 'Nothing':
                    pc.extra_generation_params["X Type"] = x_opt.label
                    pc.extra_generation_params["X Values"] = x_values
                    if x_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
                        pc.extra_generation_params["Fixed X Values"] = ", ".join([str(x) for x in xs])

                if y_opt.label != 'Nothing':
                    pc.extra_generation_params["Y Type"] = y_opt.label
                    pc.extra_generation_params["Y Values"] = y_values
                    if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
                        pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])

                grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)

            # Sets main grid infotext
            if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0:
                pc.extra_generation_params = copy(pc.extra_generation_params)

                if z_opt.label != 'Nothing':
                    pc.extra_generation_params["Z Type"] = z_opt.label
                    pc.extra_generation_params["Z Values"] = z_values
                    if z_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
                        pc.extra_generation_params["Fixed Z Values"] = ", ".join([str(z) for z in zs])

                grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)

            return res

        with SharedSettingsStackHelper():
            processed = draw_xyz_grid(
                p,
                xs=xs,
                ys=ys,
                zs=zs,
                x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
                y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
                z_labels=[z_opt.format_value(p, z_opt, z) for z in zs],
                cell=cell,
                draw_legend=draw_legend,
                include_lone_images=include_lone_images,
                include_sub_grids=include_sub_grids,
                first_axes_processed=first_axes_processed,
                second_axes_processed=second_axes_processed,
                margin_size=margin_size
            )

        if not processed.images:
            # It broke, no further handling needed.
            return processed

        z_count = len(zs)

        # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
        processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]

        if not include_lone_images:
            # Don't need sub-images anymore, drop from list:
            processed.images = processed.images[:z_count+1]

        if opts.grid_save:
            # Auto-save main and sub-grids:
            grid_count = z_count + 1 if z_count > 1 else 1
            for g in range(grid_count):
                #TODO: See previous comment about intentional data misalignment.
                adj_g = g-1 if g > 0 else g
                images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)

        if not include_sub_grids:
            # Done with sub-grids, drop all related information:
            for _ in range(z_count):
                del processed.images[1]
                del processed.all_prompts[1]
                del processed.all_seeds[1]
                del processed.infotexts[1]

        return processed