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" # 📒 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}") 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.extend(x.split(',')) def apply_uni_pc_order(p, x, xs): opts.data["uni_pc_order"] = min(x, p.steps - 1) 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: 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)), AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5), ] 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) 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) sub_grids = [None] * z_count 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")) 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"): 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, 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]) self.infotext_fields = ( (x_type, "X Type"), (x_values, "X Values"), (y_type, "Y Type"), (y_values, "Y Values"), (z_type, "Z Type"), (z_values, "Z Values"), ) return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size] 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, 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): if opt.label == 'Nothing': return [0] 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] 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) # this could be moved to common code, but unlikely to be ever triggered anywhere else Image.MAX_IMAGE_PIXELS = opts.img_max_size_mp * 1.1 # allow 10% overhead for margins and legend 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) grid_infotext = [None] 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' def cell(x, y, z): 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) if grid_infotext[0] is None: 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]) 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) 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 sg in range(z_count): del processed.images[1] del processed.all_prompts[1] del processed.all_seeds[1] del processed.infotexts[1] return processed