from collections import namedtuple from copy import copy from itertools import permutations, chain import random import csv import os.path from io import StringIO from PIL import Image import numpy as np import gc import modules.scripts as scripts import gradio as gr from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_schedulers, errors 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 = [] # Initially 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 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 confirm_checkpoints_or_none(p, xs): for x in xs: if x in (None, "", "None", "none"): continue if modules.sd_models.get_closet_checkpoint_match(x) is None: raise RuntimeError(f"Unknown checkpoint: {x}") def confirm_range(min_val, max_val, axis_label): """Generates a AxisOption.confirm() function that checks all values are within the specified range.""" def confirm_range_fun(p, xs): for x in xs: if not (max_val >= x >= min_val): raise ValueError(f'{axis_label} value "{x}" out of range [{min_val}, {max_val}]') return confirm_range_fun def apply_size(p, x: str, xs) -> None: try: width, _, height = x.partition('x') width = int(width.strip()) height = int(height.strip()) p.width = width p.height = height except ValueError: print(f"Invalid size in XYZ plot: {x}") def find_vae(name: str): if (name := name.strip().lower()) in ('auto', 'automatic'): return 'Automatic' elif name == 'none': return 'None' return next((k for k in modules.sd_vae.vae_dict if k.lower() == name), print(f'No VAE found for {name}; using Automatic') or 'Automatic') def apply_vae(p, x, xs): p.override_settings['sd_vae'] = find_vae(x) def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _): p.styles.extend(x.split(',')) def apply_uni_pc_order(p, x, xs): p.override_settings['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.lower() == "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 format_remove_path(p, opt, x): return os.path.basename(x) 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() def csv_string_to_list_strip(data_str): return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str), skipinitialspace=True)))) class AxisOption: def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None, prepare=None): self.label = label self.type = type self.apply = apply self.format_value = format_value self.confirm = confirm self.cost = cost self.prepare = prepare 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_field("sampler_name"), format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers if x.name not in opts.hide_samplers]), AxisOptionTxt2Img("Hires sampler", str, apply_field("hr_sampler_name"), confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img if x.name not in opts.hide_samplers]), AxisOptionImg2Img("Sampler", str, apply_field("sampler_name"), format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img if x.name not in opts.hide_samplers]), AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_remove_path, 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_field("scheduler"), choices=lambda: [x.label for x in sd_schedulers.schedulers]), 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("Skip Early CFG", float, apply_override('skip_early_cond')), AxisOption("Beta schedule alpha", float, apply_override("beta_dist_alpha")), AxisOption("Beta schedule beta", float, apply_override("beta_dist_beta")), AxisOption("Eta", float, apply_field("eta")), AxisOption("Clip skip", int, apply_override('CLIP_stop_at_last_layers')), AxisOption("Denoising", float, apply_field("denoising_strength")), AxisOption("Initial noise multiplier", float, apply_field("initial_noise_multiplier")), AxisOption("Extra noise", float, apply_override("img2img_extra_noise")), 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: ['Automatic', '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)), AxisOption("SGM noise multiplier", str, apply_override('sgm_noise_multiplier', boolean=True), choices=boolean_choice(reverse=True)), AxisOption("Refiner checkpoint", str, apply_field('refiner_checkpoint'), format_value=format_remove_path, confirm=confirm_checkpoints_or_none, cost=1.0, choices=lambda: ['None'] + sorted(sd_models.checkpoints_list, key=str.casefold)), AxisOption("Refiner switch at", float, apply_field('refiner_switch_at')), AxisOption("RNG source", str, apply_override("randn_source"), choices=lambda: ["GPU", "CPU", "NV"]), AxisOption("FP8 mode", str, apply_override("fp8_storage"), cost=0.9, choices=lambda: ["Disable", "Enable for SDXL", "Enable"]), AxisOption("Size", str, apply_size), ] def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, draw_individual_labels, 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 @staticmethod def draw_label_on_image(image, text): from PIL import ImageDraw, ImageFont draw = ImageDraw.Draw(image) try: font = ImageFont.truetype("arial.ttf", 20) except: font = ImageFont.load_default() margin = 10 # Split text into lines and calculate maximum width lines = text.split('\n') max_width = 0 total_height = 0 # Calculate total size needed for all lines for line in lines: try: left, top, right, bottom = draw.textbbox((margin, margin), line, font=font) width = right - left height = bottom - top except AttributeError: width = len(line) * 10 height = 20 max_width = max(max_width, width) total_height += height # Draw background rectangle for all lines draw.rectangle([(margin, margin), (margin + max_width, margin + total_height)], fill='black') # Draw each line of text current_height = margin for line in lines: draw.text((margin, current_height), line, fill='white', font=font) try: left, top, right, bottom = draw.textbbox((margin, margin), line, font=font) height = bottom - top except AttributeError: height = 20 current_height += height 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. process_image = processed.images[0] # Store reference to image if draw_individual_labels: # Add labels to a copy of the image process_image = process_image.copy() # Make a copy before drawing label = f"X: {x_labels[ix]}\nY: {y_labels[iy]}\nZ: {z_labels[iz]}" draw_label_on_image(process_image, label) processed_result.images[idx] = process_image 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: 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_max_w, grid_max_h = map(max, zip(*(img.size for img in processed_result.images[start_index:end_index]))) grid = images.draw_grid_annotations(grid, grid_max_w, grid_max_h, 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]) z_grid = images.image_grid(processed_result.images[:z_count], rows=1) z_sub_grid_max_w, z_sub_grid_max_h = map(max, zip(*(img.size for img in processed_result.images[:z_count]))) if draw_legend: z_grid = images.draw_grid_annotations(z_grid, z_sub_grid_max_w, z_sub_grid_max_h, title_texts, [[images.GridAnnotation()]]) processed_result.images.insert(0, z_grid) processed_result.infotexts.insert(0, processed_result.infotexts[0]) return processed_result class SharedSettingsStackHelper(object): def __enter__(self): pass def __exit__(self, exc_type, exc_value, tb): modules.sd_models.reload_model_weights() modules.sd_vae.reload_vae_weights() 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")) draw_individual_labels = gr.Checkbox(label='Draw individual labels', value=False, elem_id=self.elem_id("draw_individual_labels")) skip_grid = gr.Checkbox(label='Skip final grid generation', value=False, elem_id=self.elem_id("skip_grid")) items_per_grid = gr.Slider(label='Items per grid (0 = default), for sequential grid generation.', value=0, minimum=0, maximum=200, step=1, elem_id=self.elem_id("items_per_grid")) no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds")) with gr.Row(): vary_seeds_x = gr.Checkbox(label='Vary seeds for X', value=False, min_width=80, elem_id=self.elem_id("vary_seeds_x"), tooltip="Use different seeds for images along X axis.") vary_seeds_y = gr.Checkbox(label='Vary seeds for Y', value=False, min_width=80, elem_id=self.elem_id("vary_seeds_y"), tooltip="Use different seeds for images along Y axis.") vary_seeds_z = gr.Checkbox(label='Vary seeds for Z', value=False, min_width=80, elem_id=self.elem_id("vary_seeds_z"), tooltip="Use different seeds for images along Z axis.") 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")) csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode")) 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")) # Add dependency for skip_grid to force include_lone_images def update_include_lone_images(skip_grid): return gr.update(value=True if skip_grid else include_lone_images.value, interactive=not skip_grid) skip_grid.change( fn=update_include_lone_images, inputs=[skip_grid], outputs=[include_lone_images] ) 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(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): axis_type = axis_type or 0 # if axle type is None set to 0 choices = self.current_axis_options[axis_type].choices has_choices = choices is not None if has_choices: choices = choices() if csv_mode: if axis_values_dropdown: axis_values = list_to_csv_string(list(filter(lambda x: x in choices, axis_values_dropdown))) axis_values_dropdown = [] else: if axis_values: axis_values_dropdown = list(filter(lambda x: x in choices, csv_string_to_list_strip(axis_values))) axis_values = "" return (gr.Button.update(visible=has_choices), gr.Textbox.update(visible=not has_choices or csv_mode, value=axis_values), gr.update(choices=choices if has_choices else None, visible=has_choices and not csv_mode, value=axis_values_dropdown)) 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 = csv_string_to_list_strip(vals) 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, draw_individual_labels, skip_grid, items_per_grid, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode] def draw_label_on_image(image, text): from PIL import ImageDraw, ImageFont draw = ImageDraw.Draw(image) # You might want to adjust font size and position try: font = ImageFont.truetype("arial.ttf", 20) except: font = ImageFont.load_default() # Draw text with background for better visibility margin = 10 text_width, text_height = draw.textsize(text, font=font) draw.rectangle([(margin, margin), (margin + text_width, margin + text_height)], fill='black') draw.text((margin, margin), text, fill='white', font=font) 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, draw_individual_labels, skip_grid, items_per_grid, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode): x_type, y_type, z_type = x_type or 0, y_type or 0, z_type or 0 # if axle type is None set to 0 if not no_fixed_seeds: modules.processing.fix_seed(p) if not opts.return_grid: p.batch_size = 1 if skip_grid: include_lone_images = True include_sub_grids = False 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 elif opt.prepare is not None: valslist = opt.prepare(vals) else: valslist = csv_string_to_list_strip(vals) if opt.type == int: valslist_ext = [] for val in valslist: if val.strip() == '': continue 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: if val.strip() == '': continue 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 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) # 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 or state.stopping_generation: 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) xdim = len(xs) if vary_seeds_x else 1 ydim = len(ys) if vary_seeds_y else 1 if vary_seeds_x: pc.seed += ix if vary_seeds_y: pc.seed += iy * xdim if vary_seeds_z: pc.seed += iz * xdim * ydim try: res = process_images(pc) # If draw_individual_labels is enabled, save the labeled image immediately if draw_individual_labels and res.images: # Create a copy of the image and add labels labeled_image = res.images[0].copy() label = f"X: {x_opt.format_value(p, x_opt, x)}\nY: {y_opt.format_value(p, y_opt, y)}\nZ: {z_opt.format_value(p, z_opt, z)}" # Draw label directly here instead of using a separate method from PIL import ImageDraw, ImageFont draw = ImageDraw.Draw(labeled_image) try: font = ImageFont.truetype("arial.ttf", 20) except: font = ImageFont.load_default() margin = 10 lines = label.split('\n') max_width = 0 total_height = 0 # Calculate total size needed for all lines for line in lines: try: left, top, right, bottom = draw.textbbox((margin, margin), line, font=font) width = right - left height = bottom - top except AttributeError: width = len(line) * 10 height = 20 max_width = max(max_width, width) total_height += height # Draw background rectangle for all lines draw.rectangle([(margin, margin), (margin + max_width, margin + total_height)], fill='black') # Draw each line of text current_height = margin for line in lines: draw.text((margin, current_height), line, fill='white', font=font) try: left, top, right, bottom = draw.textbbox((margin, margin), line, font=font) height = bottom - top except AttributeError: height = 20 current_height += height # Generate a unique filename based on coordinates filename = f"xyz_grid_x{ix}_y{iy}_z{iz}" # Save the labeled image if opts.grid_save: images.save_image( labeled_image, p.outpath_grids, filename, info=res.infotexts[0], extension=opts.grid_format, prompt=res.all_prompts[0], seed=res.all_seeds[0], grid=False, p=res ) # Use the labeled image for the grid res.images[0] = labeled_image except Exception as e: errors.display(e, "generating image for xyz plot") res = Processed(p, [], p.seed, "") # Rest of the original cell function code... 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) 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(): if items_per_grid > 0 and not skip_grid: items_per_grid = max(1, int(items_per_grid)) # Determine which axis has the most values axis_lengths = { 'x': (len(xs), xs, x_opt, 'X'), 'y': (len(ys), ys, y_opt, 'Y'), 'z': (len(zs), zs, z_opt, 'Z') } # Find the axis with the most values main_axis = max(axis_lengths.items(), key=lambda x: x[1][0])[0] length, values, opt, axis_name = axis_lengths[main_axis] if length > 1: # Only process if we have more than one value chunks = [values[i:i + items_per_grid] for i in range(0, length, items_per_grid)] all_processed = [] for chunk_idx, chunk in enumerate(chunks): print(f"Processing grid {chunk_idx + 1}/{len(chunks)}") grid_args = { 'p': p, 'xs': chunk if main_axis == 'x' else xs, 'ys': chunk if main_axis == 'y' else ys, 'zs': chunk if main_axis == 'z' else zs, 'x_labels': [x_opt.format_value(p, x_opt, x) for x in (chunk if main_axis == 'x' else xs)], 'y_labels': [y_opt.format_value(p, y_opt, y) for y in (chunk if main_axis == 'y' else ys)], 'z_labels': [z_opt.format_value(p, z_opt, z) for z in (chunk if main_axis == 'z' else zs)], 'cell': cell, 'draw_legend': draw_legend, 'draw_individual_labels': draw_individual_labels, '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 } chunk_processed = draw_xyz_grid(**grid_args) # Keep only necessary data if include_lone_images: z_count = len(grid_args['zs']) main_grid = chunk_processed.images[0] individual_images = chunk_processed.images[z_count + 1:] chunk_processed.images = [main_grid] + individual_images main_info = chunk_processed.infotexts[0] individual_infos = chunk_processed.infotexts[z_count + 1:] chunk_processed.infotexts = [main_info] + individual_infos main_prompt = chunk_processed.all_prompts[0] individual_prompts = chunk_processed.all_prompts[z_count + 1:] chunk_processed.all_prompts = [main_prompt] + individual_prompts main_seed = chunk_processed.all_seeds[0] individual_seeds = chunk_processed.all_seeds[z_count + 1:] chunk_processed.all_seeds = [main_seed] + individual_seeds else: chunk_processed.images = [chunk_processed.images[0]] chunk_processed.all_prompts = [chunk_processed.all_prompts[0]] chunk_processed.all_seeds = [chunk_processed.all_seeds[0]] chunk_processed.infotexts = [chunk_processed.infotexts[0]] # Save images immediately if opts.grid_save: for i, image in enumerate(chunk_processed.images): suffix = "" if i == 0 else f"_{i}" images.save_image( image, p.outpath_grids, f"xyz_grid_{chunk_idx+1}{suffix}", info=chunk_processed.infotexts[i], extension=opts.grid_format, prompt=chunk_processed.all_prompts[i], seed=chunk_processed.all_seeds[i], grid=True if i == 0 else False, p=chunk_processed ) # Store only essential information for final results if chunk_idx == 0: final_processed = chunk_processed else: final_processed.images.extend(chunk_processed.images) final_processed.all_prompts.extend(chunk_processed.all_prompts) final_processed.all_seeds.extend(chunk_processed.all_seeds) final_processed.infotexts.extend(chunk_processed.infotexts) # Clear unnecessary references and force garbage collection chunk_processed.images = [] chunk_processed.all_prompts = [] chunk_processed.all_seeds = [] chunk_processed.infotexts = [] del chunk_processed gc.collect() return final_processed # Handle either skip_grid or normal processing without items_per_grid if skip_grid: # When skipping grid, process all images individually processed = Processed(p, [], p.seed, "") processed.images = [] processed.infotexts = [] processed.all_prompts = [] processed.all_seeds = [] total = len(xs) * len(ys) * len(zs) done = 0 for iz, z in enumerate(zs): for iy, y in enumerate(ys): for ix, x in enumerate(xs): if state.interrupted: break proc = cell(x, y, z, ix, iy, iz) if proc.images: processed.images.extend(proc.images) processed.infotexts.extend(proc.infotexts) processed.all_prompts.extend(proc.all_prompts) processed.all_seeds.extend(proc.all_seeds) done += 1 print(f"Processing image {done}/{total}") if opts.grid_save: # Save individual images for i, image in enumerate(processed.images): images.save_image( image, p.outpath_grids, f"xyz_image_{i+1}", info=processed.infotexts[i], extension=opts.grid_format, prompt=processed.all_prompts[i], seed=processed.all_seeds[i], grid=False, p=processed ) return processed else: # Original grid processing without items_per_grid 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, draw_individual_labels=draw_individual_labels, 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 processed.infotexts[:1 + z_count] = grid_infotext[:1 + z_count] if opts.grid_save: # Save the main xyz grid images.save_image( processed.images[0], p.outpath_grids, "xyz_grid", info=processed.infotexts[0], extension=opts.grid_format, prompt=processed.all_prompts[0], seed=processed.all_seeds[0], grid=True, p=processed ) # Save sub-grids if enabled if include_sub_grids: for idx in range(1, z_count + 1): images.save_image( processed.images[idx], p.outpath_grids, f"xyz_grid_z_{idx}", info=processed.infotexts[idx], extension=opts.grid_format, prompt=processed.all_prompts[idx], seed=processed.all_seeds[idx], grid=True, p=processed ) # Save individual images if enabled if include_lone_images: individual_images = processed.images[z_count + 1:] individual_infos = processed.infotexts[z_count + 1:] individual_prompts = processed.all_prompts[z_count + 1:] individual_seeds = processed.all_seeds[z_count + 1:] for idx, (image, info, prompt, seed) in enumerate(zip( individual_images, individual_infos, individual_prompts, individual_seeds)): images.save_image( image, p.outpath_grids, f"xyz_grid_image_{idx + 1}", info=info, extension=opts.grid_format, prompt=prompt, seed=seed, grid=False, p=processed ) # Organize the final image list if include_lone_images: # Keep main grid, sub-grids (if enabled), and individual images main_grid = processed.images[0] sub_grids = processed.images[1:z_count + 1] if include_sub_grids else [] individual_images = processed.images[z_count + 1:] processed.images = [main_grid] + sub_grids + individual_images # Adjust other lists accordingly main_info = processed.infotexts[0] sub_infos = processed.infotexts[1:z_count + 1] if include_sub_grids else [] individual_infos = processed.infotexts[z_count + 1:] processed.infotexts = [main_info] + sub_infos + individual_infos main_prompt = processed.all_prompts[0] sub_prompts = processed.all_prompts[1:z_count + 1] if include_sub_grids else [] individual_prompts = processed.all_prompts[z_count + 1:] processed.all_prompts = [main_prompt] + sub_prompts + individual_prompts main_seed = processed.all_seeds[0] sub_seeds = processed.all_seeds[1:z_count + 1] if include_sub_grids else [] individual_seeds = processed.all_seeds[z_count + 1:] processed.all_seeds = [main_seed] + sub_seeds + individual_seeds elif include_sub_grids: # Keep only the main grid and sub-grids processed.images = processed.images[:z_count + 1] processed.infotexts = processed.infotexts[:z_count + 1] processed.all_prompts = processed.all_prompts[:z_count + 1] processed.all_seeds = processed.all_seeds[:z_count + 1] else: # Keep only the main grid processed.images = [processed.images[0]] processed.infotexts = [processed.infotexts[0]] processed.all_prompts = [processed.all_prompts[0]] processed.all_seeds = [processed.all_seeds[0]] return processed