diff options
Diffstat (limited to 'scripts/xyz_grid.py')
| -rw-r--r-- | scripts/xyz_grid.py | 587 |
1 files changed, 587 insertions, 0 deletions
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py new file mode 100644 index 00000000..828c2d12 --- /dev/null +++ b/scripts/xyz_grid.py @@ -0,0 +1,587 @@ +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, paths, sd_samplers, processing, sd_models, sd_vae
+from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
+from modules.shared import opts, cmd_opts, state
+import modules.shared as shared
+import modules.sd_samplers
+import modules.sd_models
+import modules.sd_vae
+import glob
+import os
+import re
+
+from modules.ui_components import ToolButton
+
+fill_values_symbol = "\U0001f4d2" # 📒
+
+
+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}")
+ modules.sd_models.reload_model_weights(shared.sd_model, info)
+
+
+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 = x.split(',')
+
+
+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")),
+ 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: list(sd_models.checkpoints_list)),
+ 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("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: list(sd_vae.vae_dict)),
+ AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
+]
+
+
+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):
+ 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]
+
+ # Temporary list of all the images that are generated to be populated into the grid.
+ # Will be filled with empty images for any individual step that fails to process properly
+ image_cache = [None] * (len(xs) * len(ys) * len(zs))
+
+ processed_result = None
+ cell_mode = "P"
+ cell_size = (1, 1)
+
+ state.job_count = len(xs) * len(ys) * len(zs) * p.n_iter
+
+ def process_cell(x, y, z, ix, iy, iz):
+ nonlocal image_cache, processed_result, cell_mode, cell_size
+
+ 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 {len(xs) * len(ys) * len(zs)}"
+
+ processed: Processed = cell(x, y, z)
+
+ try:
+ # this dereference will throw an exception if the image was not processed
+ # (this happens in cases such as if the user stops the process from the UI)
+ processed_image = processed.images[0]
+
+ if processed_result is None:
+ # Use our first valid processed result as a template container to hold our full results
+ processed_result = copy(processed)
+ cell_mode = processed_image.mode
+ cell_size = processed_image.size
+ processed_result.images = [Image.new(cell_mode, cell_size)]
+
+ image_cache[index(ix, iy, iz)] = processed_image
+ if include_lone_images:
+ processed_result.images.append(processed_image)
+ processed_result.all_prompts.append(processed.prompt)
+ processed_result.all_seeds.append(processed.seed)
+ processed_result.infotexts.append(processed.infotexts[0])
+ except:
+ image_cache[index(ix, iy, iz)] = 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:
+ print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
+ return Processed(p, [])
+
+ grids = [None] * len(zs)
+ for i in range(len(zs)):
+ start_index = i * len(xs) * len(ys)
+ end_index = start_index + len(xs) * len(ys)
+ grid = images.image_grid(image_cache[start_index:end_index], rows=len(ys))
+ if draw_legend:
+ grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts)
+
+ grids[i] = grid
+ if include_sub_grids and len(zs) > 1:
+ processed_result.images.insert(i+1, grid)
+
+ original_grid_size = grids[0].size
+ grids = images.image_grid(grids, rows=1)
+ processed_result.images[0] = images.draw_grid_annotations(grids, original_grid_size[0], original_grid_size[1], title_texts, [[images.GridAnnotation()]])
+
+ 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
+
+ def __exit__(self, exc_type, exc_value, tb):
+ opts.data["sd_vae"] = self.vae
+ 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"))
+ 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"))
+ 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"))
+ 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"):
+ draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
+ 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"))
+ no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
+ 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, axis2_type, axis2_values):
+ return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
+
+ xy_swap_args = [x_type, x_values, y_type, y_values]
+ swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
+ yz_swap_args = [y_type, y_values, z_type, z_values]
+ swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
+ xz_swap_args = [x_type, x_values, z_type, z_values]
+ 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 ", ".join(axis.choices()) if axis.choices else gr.update()
+
+ fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
+ fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
+ fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
+
+ def select_axis(x_type):
+ return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
+
+ x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
+ y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
+ z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
+
+ return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds]
+
+ def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds):
+ if not no_fixed_seeds:
+ modules.processing.fix_seed(p)
+
+ if not opts.return_grid:
+ p.batch_size = 1
+
+ def process_axis(opt, vals):
+ if opt.label == 'Nothing':
+ return [0]
+
+ valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
+
+ 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]
+ xs = process_axis(x_opt, x_values)
+
+ y_opt = self.current_axis_options[y_type]
+ ys = process_axis(y_opt, y_values)
+
+ z_opt = self.current_axis_options[z_type]
+ zs = process_axis(z_opt, z_values)
+
+ 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 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)
+
+ grid_infotext = [None]
+
+ # 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 = 'x'
+ 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'
+
+ def cell(x, y, z):
+ if shared.state.interrupted:
+ return Processed(p, [], p.seed, "")
+
+ pc = copy(p)
+ x_opt.apply(pc, x, xs)
+ y_opt.apply(pc, y, ys)
+ z_opt.apply(pc, z, zs)
+
+ res = process_images(pc)
+
+ if grid_infotext[0] is None:
+ pc.extra_generation_params = copy(pc.extra_generation_params)
+
+ 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])
+
+ 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
+ )
+
+ if opts.grid_save:
+ images.save_image(processed.images[0], p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
+
+ return processed
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