# pylint: disable=unused-argument import re import csv import random from collections import namedtuple from copy import copy from itertools import permutations, chain from io import StringIO from PIL import Image import numpy as np import gradio as gr from modules import shared, errors, scripts, images, sd_samplers, processing, sd_models, sd_vae, ipadapter from modules.ui_components import ToolButton import modules.ui_symbols as symbols def apply_field(field): def fun(p, x, xs): setattr(p, field, x) return fun def apply_setting(field): def fun(p, x, xs): shared.opts.data[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: shared.log.warning(f"XYZ grid: prompt S/R did not find {xs[0]} in prompt or negative prompt.") else: 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 = [] for token in x: token_order.append((p.prompt.find(token), token)) token_order.sort(key=lambda t: t[0]) prompt_parts = [] for _, token in token_order: n = p.prompt.find(token) prompt_parts.append(p.prompt[0:n]) p.prompt = p.prompt[n + len(token):] 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: shared.log.warning(f"XYZ grid: unknown sampler: {x}") else: p.sampler_name = sampler_name def apply_hr_sampler_name(p, x, xs): hr_sampler_name = sd_samplers.samplers_map.get(x.lower(), None) if hr_sampler_name is None: shared.log.warning(f"XYZ grid: unknown sampler: {x}") else: p.hr_sampler_name = hr_sampler_name def confirm_samplers(p, xs): for x in xs: if x.lower() not in sd_samplers.samplers_map: shared.log.warning(f"XYZ grid: unknown sampler: {x}") def apply_checkpoint(p, x, xs): if x == shared.opts.sd_model_checkpoint: return info = sd_models.get_closet_checkpoint_match(x) if info is None: shared.log.warning(f"XYZ grid: apply checkpoint unknown checkpoint: {x}") else: sd_models.reload_model_weights(shared.sd_model, info) p.override_settings['sd_model_checkpoint'] = info.name def apply_refiner(p, x, xs): if x == shared.opts.sd_model_refiner: return if x == 'None': return info = sd_models.get_closet_checkpoint_match(x) if info is None: shared.log.warning(f"XYZ grid: apply refiner unknown checkpoint: {x}") else: sd_models.reload_model_weights(shared.sd_refiner, info) p.override_settings['sd_model_refiner'] = info.name def apply_dict(p, x, xs): if x == shared.opts.sd_model_dict: return info_dict = sd_models.get_closet_checkpoint_match(x) info_ckpt = sd_models.get_closet_checkpoint_match(shared.opts.sd_model_checkpoint) if info_dict is None or info_ckpt is None: shared.log.warning(f"XYZ grid: apply dict unknown checkpoint: {x}") else: shared.opts.sd_model_dict = info_dict.name # this will trigger reload_model_weights via onchange handler p.override_settings['sd_model_checkpoint'] = info_ckpt.name p.override_settings['sd_model_dict'] = info_dict.name def apply_clip_skip(p, x, xs): p.clip_skip = x shared.opts.data["clip_skip"] = x def find_vae(name: str): if name.lower() in ['auto', 'automatic']: return sd_vae.unspecified if name.lower() == 'none': return None else: choices = [x for x in sorted(sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()] if len(choices) == 0: shared.log.warning(f"No VAE found for {name}; using automatic") return sd_vae.unspecified else: return sd_vae.vae_dict[choices[0]] def apply_vae(p, x, xs): sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x)) def apply_styles(p: processing.StableDiffusionProcessingTxt2Img, x: str, _): p.styles.extend(x.split(',')) def apply_upscaler(p: processing.StableDiffusionProcessingTxt2Img, opt, x): p.enable_hr = True p.hr_force = True p.denoising_strength = 0.0 p.hr_upscaler = opt 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): def fun(p, x, xs): p.override_settings[field] = x return fun 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 def list_to_csv_string(data_list): with StringIO() as o: csv.writer(o).writerow(data_list) return o.getvalue().strip() class AxisOption: def __init__(self, label, tipe, apply, fmt=format_value_add_label, confirm=None, cost=0.0, choices=None): self.label = label self.type = tipe self.apply = apply self.format_value = fmt 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, fmt=format_nothing), AxisOption("Prompt S/R", str, apply_prompt, fmt=format_value), AxisOption("Model", str, apply_checkpoint, fmt=format_value, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list)), AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)), AxisOption("Styles", str, apply_styles, choices=lambda: [s.name for s in shared.prompt_styles.styles.values()]), AxisOptionTxt2Img("Sampler", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), AxisOptionImg2Img("Sampler", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]), AxisOption("Seed", int, apply_field("seed")), AxisOption("Steps", int, apply_field("steps")), AxisOption("CFG Scale", float, apply_field("cfg_scale")), AxisOption("CFG End", float, apply_field("cfg_end")), AxisOption("Variation seed", int, apply_field("subseed")), AxisOption("Variation strength", float, apply_field("subseed_strength")), AxisOption("Clip skip", float, apply_clip_skip), AxisOption("Denoising strength", float, apply_field("denoising_strength")), AxisOption("Prompt order", str_permutations, apply_order, fmt=format_value_join_list), AxisOption("Model dictionary", str, apply_dict, fmt=format_value, cost=1.0, choices=lambda: ['None'] + list(sd_models.checkpoints_list)), AxisOptionImg2Img("Image mask weight", float, apply_field("inpainting_mask_weight")), AxisOption("[Postprocess] Upscaler", str, apply_upscaler, choices=lambda: [x.name for x in shared.sd_upscalers][1:]), AxisOption("[Postprocess] Face restore", str, apply_face_restore, fmt=format_value), AxisOption("[Sampler] Sigma min", float, apply_field("s_min")), AxisOption("[Sampler] Sigma max", float, apply_field("s_max")), AxisOption("[Sampler] Sigma tmin", float, apply_field("s_tmin")), AxisOption("[Sampler] Sigma tmax", float, apply_field("s_tmax")), AxisOption("[Sampler] Sigma Churn", float, apply_field("s_churn")), AxisOption("[Sampler] Sigma noise", float, apply_field("s_noise")), AxisOption("[Sampler] ETA", float, apply_setting("scheduler_eta")), AxisOption("[Sampler] Solver order", int, apply_setting("schedulers_solver_order")), AxisOption("[Second pass] Upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]), AxisOption("[Second pass] Sampler", str, apply_hr_sampler_name, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), AxisOption("[Second pass] Denoising Strength", float, apply_field("denoising_strength")), AxisOption("[Second pass] Hires steps", int, apply_field("hr_second_pass_steps")), AxisOption("[Second pass] CFG scale", float, apply_field("image_cfg_scale")), AxisOption("[Second pass] Guidance rescale", float, apply_field("diffusers_guidance_rescale")), AxisOption("[Refiner] Model", str, apply_refiner, fmt=format_value, cost=1.0, choices=lambda: ['None'] + sorted(sd_models.checkpoints_list)), AxisOption("[Refiner] Refiner start", float, apply_field("refiner_start")), AxisOption("[Refiner] Refiner steps", float, apply_field("refiner_steps")), AxisOption("[HDR] Mode", int, apply_field("hdr_mode")), AxisOption("[HDR] Brightness", float, apply_field("hdr_brightness")), AxisOption("[HDR] Color", float, apply_field("hdr_color")), AxisOption("[HDR] Sharpen", float, apply_field("hdr_sharpen")), AxisOption("[HDR] Clamp boundary", float, apply_field("hdr_boundary")), AxisOption("[HDR] Clamp threshold", float, apply_field("hdr_threshold")), AxisOption("[HDR] Maximize center shift", float, apply_field("hdr_max_center")), AxisOption("[HDR] Maximize boundary", float, apply_field("hdr_max_boundry")), AxisOption("[HDR] Tint Color Hex", str, apply_field("hdr_color_picker")), AxisOption("[HDR] Tint Ratio", float, apply_field("hdr_tint_ratio")), AxisOption("[ToMe] Token merging ratio (txt2img)", float, apply_override('token_merging_ratio')), AxisOption("[ToMe] Token merging ratio (hires)", float, apply_override('token_merging_ratio_hr')), AxisOption("[FreeU] 1st stage backbone factor", float, apply_setting('freeu_b1')), AxisOption("[FreeU] 2nd stage backbone factor", float, apply_setting('freeu_b2')), AxisOption("[FreeU] 1st stage skip factor", float, apply_setting('freeu_s1')), AxisOption("[FreeU] 2nd stage skip factor", float, apply_setting('freeu_s2')), AxisOption("[IP adapter] Name", str, apply_field('ip_adapter_names'), cost=1.0, choices=lambda: list(ipadapter.ADAPTERS)), AxisOption("[IP adapter] Scale", float, apply_field('ip_adapter_scales')), AxisOption("[IP adapter] Starts", float, apply_field('ip_adapter_starts')), AxisOption("[IP adapter] Ends", float, apply_field('ip_adapter_ends')), ] 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, no_grid): 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 shared.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) shared.state.job = 'grid' processed: processing.Processed = cell(x, y, z, ix, iy, iz) if processed_result is None: processed_result = copy(processed) if processed_result is None: shared.log.error('XYZ grid: no processing results') return processing.Processed(p, []) 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 is not None and processed.images: 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 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: shared.log.error("XYZ grid: Failed to initialize processing") return processing.Processed(p, []) elif not any(processed_result.images): shared.log.error("XYZ grid: Failed to return processed image") return processing.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) if (not no_grid or include_sub_grids) and images.check_grid_size(processed_result.images[start_index:end_index]): 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, title=title_texts[i]) 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 if not no_grid and images.check_grid_size(processed_result.images[:z_count]): 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], [[images.GridAnnotation()] for _ in z_labels], [[images.GridAnnotation()]]) processed_result.images.insert(0, z_grid) #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): vae = None schedulers_solver_order = None token_merging_ratio_hr = None token_merging_ratio = None sd_model_checkpoint = None sd_model_dict = None sd_vae_checkpoint = None def __enter__(self): #Save overridden settings so they can be restored later. self.vae = shared.opts.sd_vae self.schedulers_solver_order = shared.opts.schedulers_solver_order self.token_merging_ratio_hr = shared.opts.token_merging_ratio_hr self.token_merging_ratio = shared.opts.token_merging_ratio self.sd_model_checkpoint = shared.opts.sd_model_checkpoint self.sd_model_dict = shared.opts.sd_model_dict self.sd_vae_checkpoint = shared.opts.sd_vae def __exit__(self, exc_type, exc_value, tb): #Restore overriden settings after plot generation. shared.opts.data["sd_vae"] = self.vae shared.opts.data["schedulers_solver_order"] = self.schedulers_solver_order shared.opts.data["token_merging_ratio_hr"] = self.token_merging_ratio_hr shared.opts.data["token_merging_ratio"] = self.token_merging_ratio if self.sd_model_dict != shared.opts.sd_model_dict: shared.opts.data["sd_model_dict"] = self.sd_model_dict if self.sd_model_checkpoint != shared.opts.sd_model_checkpoint: shared.opts.data["sd_model_checkpoint"] = self.sd_model_checkpoint sd_models.reload_model_weights() if self.sd_vae_checkpoint != shared.opts.sd_vae: shared.opts.data["sd_vae"] = self.sd_vae_checkpoint sd_vae.reload_vae_weights() re_range = re.compile(r'([-+]?[0-9]*\.?[0-9]+)-([-+]?[0-9]*\.?[0-9]+):?([0-9]+)?') class Script(scripts.Script): current_axis_options = [] def title(self): return "X/Y/Z Grid" 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(): gr.HTML('  X/Y/Z Grid
') with gr.Row(): with gr.Column(): with gr.Row(variant='compact'): x_type = gr.Dropdown(label="X type", container=True, choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("x_type")) x_values = gr.Textbox(label="X values", container=True, lines=1, elem_id=self.elem_id("x_values")) x_values_dropdown = gr.Dropdown(label="X values", container=True, visible=False, multiselect=True, interactive=True) fill_x_button = ToolButton(value=symbols.fill, elem_id="xyz_grid_fill_x_tool_button", visible=False) with gr.Row(variant='compact'): y_type = gr.Dropdown(label="Y type", container=True, 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", container=True, lines=1, elem_id=self.elem_id("y_values")) y_values_dropdown = gr.Dropdown(label="Y values", container=True, visible=False, multiselect=True, interactive=True) fill_y_button = ToolButton(value=symbols.fill, elem_id="xyz_grid_fill_y_tool_button", visible=False) with gr.Row(variant='compact'): z_type = gr.Dropdown(label="Z type", container=True, 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", container=True, lines=1, elem_id=self.elem_id("z_values")) z_values_dropdown = gr.Dropdown(label="Z values", container=True, visible=False, multiselect=True, interactive=True) fill_z_button = ToolButton(value=symbols.fill, elem_id="xyz_grid_fill_z_tool_button", visible=False) with gr.Row(): with gr.Column(): csv_mode = gr.Checkbox(label='Text inputs', value=False, elem_id=self.elem_id("csv_mode"), container=False) draw_legend = gr.Checkbox(label='Legend', value=True, elem_id=self.elem_id("draw_legend"), container=False) no_fixed_seeds = gr.Checkbox(label='Random seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"), container=False) with gr.Column(): no_grid = gr.Checkbox(label='Skip grid', value=False, elem_id=self.elem_id("no_xyz_grid"), container=False) include_lone_images = gr.Checkbox(label='Sub-images', value=False, elem_id=self.elem_id("include_lone_images"), container=False) include_sub_grids = gr.Checkbox(label='Sub-grids', value=False, elem_id=self.elem_id("include_sub_grids"), container=False) with gr.Row(): margin_size = gr.Slider(label="Grid margins", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size")) with gr.Row(): swap_xy_axes_button = gr.Button(value="Swap X/Y", elem_id="xy_grid_swap_axes_button", variant="secondary") swap_yz_axes_button = gr.Button(value="Swap Y/Z", elem_id="yz_grid_swap_axes_button", variant="secondary") swap_xz_axes_button = gr.Button(value="Swap X/Z", elem_id="xz_grid_swap_axes_button", variant="secondary") 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(axis_type, csv_mode): axis = self.current_axis_options[axis_type] if axis.choices: if csv_mode: return list_to_csv_string(axis.choices()), gr.update() else: return gr.update(), axis.choices() else: return gr.update(), gr.update() fill_x_button.click(fn=fill, inputs=[x_type, csv_mode], outputs=[x_values, x_values_dropdown]) fill_y_button.click(fn=fill, inputs=[y_type, csv_mode], outputs=[y_values, y_values_dropdown]) fill_z_button.click(fn=fill, inputs=[z_type, csv_mode], outputs=[z_values, z_values_dropdown]) def select_axis(axis_type, axis_values, axis_values_dropdown, csv_mode): choices = self.current_axis_options[axis_type].choices has_choices = choices is not None current_values = axis_values current_dropdown_values = axis_values_dropdown if has_choices: choices = choices() if csv_mode: current_dropdown_values = list(filter(lambda x: x in choices, current_dropdown_values)) current_values = list_to_csv_string(current_dropdown_values) else: current_dropdown_values = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(axis_values)))] current_dropdown_values = list(filter(lambda x: x in choices, current_dropdown_values)) return (gr.Button.update(visible=has_choices), gr.Textbox.update(visible=not has_choices or csv_mode, value=current_values), gr.update(choices=choices if has_choices else None, visible=has_choices and not csv_mode, value=current_dropdown_values)) x_type.change(fn=select_axis, inputs=[x_type, x_values, x_values_dropdown, csv_mode], outputs=[fill_x_button, x_values, x_values_dropdown]) y_type.change(fn=select_axis, inputs=[y_type, y_values, y_values_dropdown, csv_mode], outputs=[fill_y_button, y_values, y_values_dropdown]) z_type.change(fn=select_axis, inputs=[z_type, z_values, z_values_dropdown, csv_mode], outputs=[fill_z_button, z_values, z_values_dropdown]) def change_choice_mode(csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown): _fill_x_button, _x_values, _x_values_dropdown = select_axis(x_type, x_values, x_values_dropdown, csv_mode) _fill_y_button, _y_values, _y_values_dropdown = select_axis(y_type, y_values, y_values_dropdown, csv_mode) _fill_z_button, _z_values, _z_values_dropdown = select_axis(z_type, z_values, z_values_dropdown, csv_mode) return _fill_x_button, _x_values, _x_values_dropdown, _fill_y_button, _y_values, _y_values_dropdown, _fill_z_button, _z_values, _z_values_dropdown csv_mode.change(fn=change_choice_mode, inputs=[csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown], outputs=[fill_x_button, x_values, x_values_dropdown, fill_y_button, y_values, y_values_dropdown, 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, csv_mode, draw_legend, no_fixed_seeds, no_grid, include_lone_images, include_sub_grids, 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, csv_mode, draw_legend, no_fixed_seeds, no_grid, include_lone_images, include_sub_grids, margin_size): # pylint: disable=W0221 shared.log.debug(f'xyzgrid: x_type={x_type}|x_values={x_values}|x_values_dropdown={x_values_dropdown}|y_type={y_type}|{y_values}={y_values}|{y_values_dropdown}={y_values_dropdown}|z_type={z_type}|z_values={z_values}|z_values_dropdown={z_values_dropdown}|draw_legend={draw_legend}|include_lone_images={include_lone_images}|include_sub_grids={include_sub_grids}|no_grid={no_grid}|margin_size={margin_size}') if not no_fixed_seeds: processing.fix_seed(p) if not shared.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 and not csv_mode: 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) if m is not None: start_val = int(m.group(1)) if m.group(1) is not None else val end_val = int(m.group(2)) if m.group(2) is not None else val num = int(m.group(3)) if m.group(3) is not None else int(end_val-start_val) valslist_ext += [int(x) for x in np.linspace(start=start_val, stop=end_val, num=max(2, num)).tolist()] shared.log.debug(f'XYZ grid range: start={start_val} end={end_val} num={max(2, num)} list={valslist}') else: valslist_ext.append(int(val)) valslist.clear() valslist = [x for x in valslist_ext if x not in valslist] elif opt.type == float: valslist_ext = [] for val in valslist: m = re_range.fullmatch(val) if m is not None: start_val = float(m.group(1)) if m.group(1) is not None else val end_val = float(m.group(2)) if m.group(2) is not None else val num = int(m.group(3)) if m.group(3) is not None else int(end_val-start_val) valslist_ext += [round(float(x), 2) for x in np.linspace(start=start_val, stop=end_val, num=max(2, num)).tolist()] shared.log.debug(f'XYZ grid range: start={start_val} end={end_val} num={max(2, num)} list={valslist}') else: valslist_ext.append(float(val)) valslist.clear() valslist = [x for x in valslist_ext if x not in valslist] elif opt.type == str_permutations: # pylint: disable=comparison-with-callable 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 and not csv_mode: x_values = list_to_csv_string(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 and not csv_mode: y_values = list_to_csv_string(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 and not csv_mode: z_values = list_to_csv_string(z_values_dropdown) zs = process_axis(z_opt, z_values, z_values_dropdown) Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes 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, processing.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 shared.log.info(f"XYZ grid: images={len(xs)*len(ys)*len(zs)*image_cell_count} grid={len(zs)} {len(xs)}x{len(ys)} cells={len(zs)} steps={total_steps}") AxisInfo = namedtuple('AxisInfo', ['axis', 'values']) shared.state.xyz_plot_x = AxisInfo(x_opt, xs) shared.state.xyz_plot_y = AxisInfo(y_opt, ys) shared.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 processing.Processed(p, [], p.seed, "") pc = copy(p) pc.override_settings_restore_afterwards = False pc.styles = pc.styles[:] x_opt.apply(pc, x, xs) y_opt.apply(pc, y, ys) z_opt.apply(pc, z, zs) try: res = processing.process_images(pc) except Exception as e: shared.log.error(f"XYZ grid: Failed to process image: {e}") errors.display(e, 'XYZ grid') res = None subgrid_index = 1 + iz # Sets subgrid infotexts 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) if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0: # Sets main grid infotext 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, no_grid=no_grid, ) if not processed.images: return processed # It broke, no further handling needed. z_count = len(zs) processed.infotexts[:1+z_count] = grid_infotext[:1+z_count] # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids) if not include_lone_images: # Don't need sub-images anymore, drop from list: if no_grid and include_sub_grids: processed.images = processed.images[:z_count] # we don't have the main grid image, and need zero additional sub-images else: processed.images = processed.images[:z_count+1] # we either have the main grid image, or need one sub-images if shared.opts.grid_save: # Auto-save main and sub-grids: grid_count = z_count + ( 1 if not no_grid and z_count > 1 else 0 ) for g in range(grid_count): 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=shared.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] elif no_grid: # del processed.images[0] # del processed.all_prompts[0] # del processed.all_seeds[0] del processed.infotexts[0] return processed