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| from collections import namedtuple | |
| from copy import copy | |
| from itertools import permutations, chain | |
| import random | |
| import csv | |
| from io import StringIO | |
| from PIL import Image | |
| import numpy as np | |
| import modules.scripts as scripts | |
| import gradio as gr | |
| from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion | |
| from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img | |
| from modules.shared import opts, state | |
| import modules.shared as shared | |
| import modules.sd_samplers | |
| import modules.sd_models | |
| import modules.sd_vae | |
| import re | |
| from modules.ui_components import ToolButton | |
| fill_values_symbol = "\U0001f4d2" # 📒 | |
| AxisInfo = namedtuple('AxisInfo', ['axis', 'values']) | |
| def apply_field(field): | |
| def fun(p, x, xs): | |
| setattr(p, field, x) | |
| return fun | |
| def apply_prompt(p, x, xs): | |
| if xs[0] not in p.prompt and xs[0] not in p.negative_prompt: | |
| raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.") | |
| p.prompt = p.prompt.replace(xs[0], x) | |
| p.negative_prompt = p.negative_prompt.replace(xs[0], x) | |
| def apply_order(p, x, xs): | |
| token_order = [] | |
| # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen | |
| for token in x: | |
| token_order.append((p.prompt.find(token), token)) | |
| token_order.sort(key=lambda t: t[0]) | |
| prompt_parts = [] | |
| # Split the prompt up, taking out the tokens | |
| for _, token in token_order: | |
| n = p.prompt.find(token) | |
| prompt_parts.append(p.prompt[0:n]) | |
| p.prompt = p.prompt[n + len(token):] | |
| # Rebuild the prompt with the tokens in the order we want | |
| prompt_tmp = "" | |
| for idx, part in enumerate(prompt_parts): | |
| prompt_tmp += part | |
| prompt_tmp += x[idx] | |
| p.prompt = prompt_tmp + p.prompt | |
| def apply_sampler(p, x, xs): | |
| sampler_name = sd_samplers.samplers_map.get(x.lower(), None) | |
| if sampler_name is None: | |
| raise RuntimeError(f"Unknown sampler: {x}") | |
| p.sampler_name = sampler_name | |
| def confirm_samplers(p, xs): | |
| for x in xs: | |
| if x.lower() not in sd_samplers.samplers_map: | |
| raise RuntimeError(f"Unknown sampler: {x}") | |
| def apply_checkpoint(p, x, xs): | |
| info = modules.sd_models.get_closet_checkpoint_match(x) | |
| if info is None: | |
| raise RuntimeError(f"Unknown checkpoint: {x}") | |
| p.override_settings['sd_model_checkpoint'] = info.name | |
| def confirm_checkpoints(p, xs): | |
| for x in xs: | |
| if modules.sd_models.get_closet_checkpoint_match(x) is None: | |
| raise RuntimeError(f"Unknown checkpoint: {x}") | |
| def apply_clip_skip(p, x, xs): | |
| opts.data["CLIP_stop_at_last_layers"] = x | |
| def apply_upscale_latent_space(p, x, xs): | |
| if x.lower().strip() != '0': | |
| opts.data["use_scale_latent_for_hires_fix"] = True | |
| else: | |
| opts.data["use_scale_latent_for_hires_fix"] = False | |
| def find_vae(name: str): | |
| if name.lower() in ['auto', 'automatic']: | |
| return modules.sd_vae.unspecified | |
| if name.lower() == 'none': | |
| return None | |
| else: | |
| choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()] | |
| if len(choices) == 0: | |
| print(f"No VAE found for {name}; using automatic") | |
| return modules.sd_vae.unspecified | |
| else: | |
| return modules.sd_vae.vae_dict[choices[0]] | |
| def apply_vae(p, x, xs): | |
| modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x)) | |
| def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _): | |
| p.styles.extend(x.split(',')) | |
| def apply_uni_pc_order(p, x, xs): | |
| opts.data["uni_pc_order"] = min(x, p.steps - 1) | |
| def apply_face_restore(p, opt, x): | |
| opt = opt.lower() | |
| if opt == 'codeformer': | |
| is_active = True | |
| p.face_restoration_model = 'CodeFormer' | |
| elif opt == 'gfpgan': | |
| is_active = True | |
| p.face_restoration_model = 'GFPGAN' | |
| else: | |
| is_active = opt in ('true', 'yes', 'y', '1') | |
| p.restore_faces = is_active | |
| def apply_override(field, boolean: bool = False): | |
| def fun(p, x, xs): | |
| if boolean: | |
| x = True if x.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 str_permutations(x): | |
| """dummy function for specifying it in AxisOption's type when you want to get a list of permutations""" | |
| return x | |
| class AxisOption: | |
| def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None): | |
| self.label = label | |
| self.type = type | |
| self.apply = apply | |
| self.format_value = format_value | |
| self.confirm = confirm | |
| self.cost = cost | |
| self.choices = choices | |
| class AxisOptionImg2Img(AxisOption): | |
| def __init__(self, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| self.is_img2img = True | |
| class AxisOptionTxt2Img(AxisOption): | |
| def __init__(self, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| self.is_img2img = False | |
| axis_options = [ | |
| AxisOption("Nothing", str, do_nothing, format_value=format_nothing), | |
| AxisOption("Seed", int, apply_field("seed")), | |
| AxisOption("Var. seed", int, apply_field("subseed")), | |
| AxisOption("Var. strength", float, apply_field("subseed_strength")), | |
| AxisOption("Steps", int, apply_field("steps")), | |
| AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")), | |
| AxisOption("CFG Scale", float, apply_field("cfg_scale")), | |
| AxisOptionImg2Img("Image CFG Scale", float, apply_field("image_cfg_scale")), | |
| AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value), | |
| AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list), | |
| AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), | |
| AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]), | |
| AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list, key=str.casefold)), | |
| AxisOption("Negative Guidance minimum sigma", float, apply_field("s_min_uncond")), | |
| AxisOption("Sigma Churn", float, apply_field("s_churn")), | |
| AxisOption("Sigma min", float, apply_field("s_tmin")), | |
| AxisOption("Sigma max", float, apply_field("s_tmax")), | |
| AxisOption("Sigma noise", float, apply_field("s_noise")), | |
| AxisOption("Schedule type", str, apply_override("k_sched_type"), choices=lambda: list(sd_samplers_kdiffusion.k_diffusion_scheduler)), | |
| AxisOption("Schedule min sigma", float, apply_override("sigma_min")), | |
| AxisOption("Schedule max sigma", float, apply_override("sigma_max")), | |
| AxisOption("Schedule rho", float, apply_override("rho")), | |
| AxisOption("Eta", float, apply_field("eta")), | |
| AxisOption("Clip skip", int, apply_clip_skip), | |
| AxisOption("Denoising", float, apply_field("denoising_strength")), | |
| AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]), | |
| AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")), | |
| AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)), | |
| AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)), | |
| AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5), | |
| AxisOption("Face restore", str, apply_face_restore, format_value=format_value), | |
| AxisOption("Token merging ratio", float, apply_override('token_merging_ratio')), | |
| AxisOption("Token merging ratio high-res", float, apply_override('token_merging_ratio_hr')), | |
| AxisOption("Always discard next-to-last sigma", str, apply_override('always_discard_next_to_last_sigma', boolean=True), choices=boolean_choice(reverse=True)), | |
| ] | |
| def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size): | |
| hor_texts = [[images.GridAnnotation(x)] for x in x_labels] | |
| ver_texts = [[images.GridAnnotation(y)] for y in y_labels] | |
| title_texts = [[images.GridAnnotation(z)] for z in z_labels] | |
| list_size = (len(xs) * len(ys) * len(zs)) | |
| processed_result = None | |
| state.job_count = list_size * p.n_iter | |
| def process_cell(x, y, z, ix, iy, iz): | |
| nonlocal processed_result | |
| def index(ix, iy, iz): | |
| return ix + iy * len(xs) + iz * len(xs) * len(ys) | |
| state.job = f"{index(ix, iy, iz) + 1} out of {list_size}" | |
| processed: Processed = cell(x, y, z, ix, iy, iz) | |
| if processed_result is None: | |
| # Use our first processed result object as a template container to hold our full results | |
| processed_result = copy(processed) | |
| processed_result.images = [None] * list_size | |
| processed_result.all_prompts = [None] * list_size | |
| processed_result.all_seeds = [None] * list_size | |
| processed_result.infotexts = [None] * list_size | |
| processed_result.index_of_first_image = 1 | |
| idx = index(ix, iy, iz) | |
| if processed.images: | |
| # Non-empty list indicates some degree of success. | |
| processed_result.images[idx] = processed.images[0] | |
| processed_result.all_prompts[idx] = processed.prompt | |
| processed_result.all_seeds[idx] = processed.seed | |
| processed_result.infotexts[idx] = processed.infotexts[0] | |
| else: | |
| cell_mode = "P" | |
| cell_size = (processed_result.width, processed_result.height) | |
| if processed_result.images[0] is not None: | |
| cell_mode = processed_result.images[0].mode | |
| #This corrects size in case of batches: | |
| cell_size = processed_result.images[0].size | |
| processed_result.images[idx] = Image.new(cell_mode, cell_size) | |
| if first_axes_processed == 'x': | |
| for ix, x in enumerate(xs): | |
| if second_axes_processed == 'y': | |
| for iy, y in enumerate(ys): | |
| for iz, z in enumerate(zs): | |
| process_cell(x, y, z, ix, iy, iz) | |
| else: | |
| for iz, z in enumerate(zs): | |
| for iy, y in enumerate(ys): | |
| process_cell(x, y, z, ix, iy, iz) | |
| elif first_axes_processed == 'y': | |
| for iy, y in enumerate(ys): | |
| if second_axes_processed == 'x': | |
| for ix, x in enumerate(xs): | |
| for iz, z in enumerate(zs): | |
| process_cell(x, y, z, ix, iy, iz) | |
| else: | |
| for iz, z in enumerate(zs): | |
| for ix, x in enumerate(xs): | |
| process_cell(x, y, z, ix, iy, iz) | |
| elif first_axes_processed == 'z': | |
| for iz, z in enumerate(zs): | |
| if second_axes_processed == 'x': | |
| for ix, x in enumerate(xs): | |
| for iy, y in enumerate(ys): | |
| process_cell(x, y, z, ix, iy, iz) | |
| else: | |
| for iy, y in enumerate(ys): | |
| for ix, x in enumerate(xs): | |
| process_cell(x, y, z, ix, iy, iz) | |
| if not processed_result: | |
| # Should never happen, I've only seen it on one of four open tabs and it needed to refresh. | |
| print("Unexpected error: Processing could not begin, you may need to refresh the tab or restart the service.") | |
| return Processed(p, []) | |
| elif not any(processed_result.images): | |
| print("Unexpected error: draw_xyz_grid failed to return even a single processed image") | |
| return Processed(p, []) | |
| z_count = len(zs) | |
| for i in range(z_count): | |
| start_index = (i * len(xs) * len(ys)) + i | |
| end_index = start_index + len(xs) * len(ys) | |
| grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys)) | |
| if draw_legend: | |
| grid = images.draw_grid_annotations(grid, processed_result.images[start_index].size[0], processed_result.images[start_index].size[1], hor_texts, ver_texts, margin_size) | |
| processed_result.images.insert(i, grid) | |
| processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index]) | |
| processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index]) | |
| processed_result.infotexts.insert(i, processed_result.infotexts[start_index]) | |
| sub_grid_size = processed_result.images[0].size | |
| z_grid = images.image_grid(processed_result.images[:z_count], rows=1) | |
| if draw_legend: | |
| z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]]) | |
| processed_result.images.insert(0, z_grid) | |
| #TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal. | |
| #processed_result.all_prompts.insert(0, processed_result.all_prompts[0]) | |
| #processed_result.all_seeds.insert(0, processed_result.all_seeds[0]) | |
| processed_result.infotexts.insert(0, processed_result.infotexts[0]) | |
| return processed_result | |
| class SharedSettingsStackHelper(object): | |
| def __enter__(self): | |
| self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers | |
| self.vae = opts.sd_vae | |
| self.uni_pc_order = opts.uni_pc_order | |
| def __exit__(self, exc_type, exc_value, tb): | |
| opts.data["sd_vae"] = self.vae | |
| opts.data["uni_pc_order"] = self.uni_pc_order | |
| modules.sd_models.reload_model_weights() | |
| modules.sd_vae.reload_vae_weights() | |
| opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers | |
| re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*") | |
| re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*") | |
| re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*") | |
| re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*") | |
| class Script(scripts.Script): | |
| def title(self): | |
| return "X/Y/Z plot" | |
| def ui(self, is_img2img): | |
| self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img] | |
| with gr.Row(): | |
| with gr.Column(scale=19): | |
| with gr.Row(): | |
| x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type")) | |
| x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values")) | |
| x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True) | |
| fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False) | |
| with gr.Row(): | |
| y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type")) | |
| y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values")) | |
| y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True) | |
| fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False) | |
| with gr.Row(): | |
| z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type")) | |
| z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values")) | |
| z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True) | |
| fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False) | |
| with gr.Row(variant="compact", elem_id="axis_options"): | |
| with gr.Column(): | |
| draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend")) | |
| no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds")) | |
| with gr.Column(): | |
| include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images")) | |
| include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids")) | |
| with gr.Column(): | |
| margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size")) | |
| with gr.Row(variant="compact", elem_id="swap_axes"): | |
| swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button") | |
| swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button") | |
| swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button") | |
| def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown): | |
| return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown | |
| xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown] | |
| swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args) | |
| yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown] | |
| swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args) | |
| xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown] | |
| swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args) | |
| def fill(x_type): | |
| axis = self.current_axis_options[x_type] | |
| return axis.choices() if axis.choices else gr.update() | |
| fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown]) | |
| fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown]) | |
| fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown]) | |
| def select_axis(axis_type,axis_values_dropdown): | |
| choices = self.current_axis_options[axis_type].choices | |
| has_choices = choices is not None | |
| current_values = axis_values_dropdown | |
| if has_choices: | |
| choices = choices() | |
| if isinstance(current_values,str): | |
| current_values = current_values.split(",") | |
| current_values = list(filter(lambda x: x in choices, current_values)) | |
| return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values) | |
| x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown]) | |
| y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown]) | |
| z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown]) | |
| def get_dropdown_update_from_params(axis,params): | |
| val_key = f"{axis} Values" | |
| vals = params.get(val_key,"") | |
| valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x] | |
| return gr.update(value = valslist) | |
| self.infotext_fields = ( | |
| (x_type, "X Type"), | |
| (x_values, "X Values"), | |
| (x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)), | |
| (y_type, "Y Type"), | |
| (y_values, "Y Values"), | |
| (y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)), | |
| (z_type, "Z Type"), | |
| (z_values, "Z Values"), | |
| (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)), | |
| ) | |
| return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size] | |
| def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size): | |
| if not no_fixed_seeds: | |
| modules.processing.fix_seed(p) | |
| if not opts.return_grid: | |
| p.batch_size = 1 | |
| def process_axis(opt, vals, vals_dropdown): | |
| if opt.label == 'Nothing': | |
| return [0] | |
| if opt.choices is not None: | |
| valslist = vals_dropdown | |
| else: | |
| valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x] | |
| if opt.type == int: | |
| valslist_ext = [] | |
| for val in valslist: | |
| m = re_range.fullmatch(val) | |
| mc = re_range_count.fullmatch(val) | |
| if m is not None: | |
| start = int(m.group(1)) | |
| end = int(m.group(2))+1 | |
| step = int(m.group(3)) if m.group(3) is not None else 1 | |
| valslist_ext += list(range(start, end, step)) | |
| elif mc is not None: | |
| start = int(mc.group(1)) | |
| end = int(mc.group(2)) | |
| num = int(mc.group(3)) if mc.group(3) is not None else 1 | |
| valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()] | |
| else: | |
| valslist_ext.append(val) | |
| valslist = valslist_ext | |
| elif opt.type == float: | |
| valslist_ext = [] | |
| for val in valslist: | |
| m = re_range_float.fullmatch(val) | |
| mc = re_range_count_float.fullmatch(val) | |
| if m is not None: | |
| start = float(m.group(1)) | |
| end = float(m.group(2)) | |
| step = float(m.group(3)) if m.group(3) is not None else 1 | |
| valslist_ext += np.arange(start, end + step, step).tolist() | |
| elif mc is not None: | |
| start = float(mc.group(1)) | |
| end = float(mc.group(2)) | |
| num = int(mc.group(3)) if mc.group(3) is not None else 1 | |
| valslist_ext += np.linspace(start=start, stop=end, num=num).tolist() | |
| else: | |
| valslist_ext.append(val) | |
| valslist = valslist_ext | |
| elif opt.type == str_permutations: | |
| valslist = list(permutations(valslist)) | |
| valslist = [opt.type(x) for x in valslist] | |
| # Confirm options are valid before starting | |
| if opt.confirm: | |
| opt.confirm(p, valslist) | |
| return valslist | |
| x_opt = self.current_axis_options[x_type] | |
| if x_opt.choices is not None: | |
| x_values = ",".join(x_values_dropdown) | |
| xs = process_axis(x_opt, x_values, x_values_dropdown) | |
| y_opt = self.current_axis_options[y_type] | |
| if y_opt.choices is not None: | |
| y_values = ",".join(y_values_dropdown) | |
| ys = process_axis(y_opt, y_values, y_values_dropdown) | |
| z_opt = self.current_axis_options[z_type] | |
| if z_opt.choices is not None: | |
| z_values = ",".join(z_values_dropdown) | |
| zs = process_axis(z_opt, z_values, z_values_dropdown) | |
| # this could be moved to common code, but unlikely to be ever triggered anywhere else | |
| Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes | |
| grid_mp = round(len(xs) * len(ys) * len(zs) * p.width * p.height / 1000000) | |
| assert grid_mp < opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {opts.img_max_size_mp} MPixels)' | |
| def fix_axis_seeds(axis_opt, axis_list): | |
| if axis_opt.label in ['Seed', 'Var. seed']: | |
| return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list] | |
| else: | |
| return axis_list | |
| if not no_fixed_seeds: | |
| xs = fix_axis_seeds(x_opt, xs) | |
| ys = fix_axis_seeds(y_opt, ys) | |
| zs = fix_axis_seeds(z_opt, zs) | |
| if x_opt.label == 'Steps': | |
| total_steps = sum(xs) * len(ys) * len(zs) | |
| elif y_opt.label == 'Steps': | |
| total_steps = sum(ys) * len(xs) * len(zs) | |
| elif z_opt.label == 'Steps': | |
| total_steps = sum(zs) * len(xs) * len(ys) | |
| else: | |
| total_steps = p.steps * len(xs) * len(ys) * len(zs) | |
| if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr: | |
| if x_opt.label == "Hires steps": | |
| total_steps += sum(xs) * len(ys) * len(zs) | |
| elif y_opt.label == "Hires steps": | |
| total_steps += sum(ys) * len(xs) * len(zs) | |
| elif z_opt.label == "Hires steps": | |
| total_steps += sum(zs) * len(xs) * len(ys) | |
| elif p.hr_second_pass_steps: | |
| total_steps += p.hr_second_pass_steps * len(xs) * len(ys) * len(zs) | |
| else: | |
| total_steps *= 2 | |
| total_steps *= p.n_iter | |
| image_cell_count = p.n_iter * p.batch_size | |
| cell_console_text = f"; {image_cell_count} images per cell" if image_cell_count > 1 else "" | |
| plural_s = 's' if len(zs) > 1 else '' | |
| print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})") | |
| shared.total_tqdm.updateTotal(total_steps) | |
| state.xyz_plot_x = AxisInfo(x_opt, xs) | |
| state.xyz_plot_y = AxisInfo(y_opt, ys) | |
| state.xyz_plot_z = AxisInfo(z_opt, zs) | |
| # If one of the axes is very slow to change between (like SD model | |
| # checkpoint), then make sure it is in the outer iteration of the nested | |
| # `for` loop. | |
| first_axes_processed = 'z' | |
| second_axes_processed = 'y' | |
| if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost: | |
| first_axes_processed = 'x' | |
| if y_opt.cost > z_opt.cost: | |
| second_axes_processed = 'y' | |
| else: | |
| second_axes_processed = 'z' | |
| elif y_opt.cost > x_opt.cost and y_opt.cost > z_opt.cost: | |
| first_axes_processed = 'y' | |
| if x_opt.cost > z_opt.cost: | |
| second_axes_processed = 'x' | |
| else: | |
| second_axes_processed = 'z' | |
| elif z_opt.cost > x_opt.cost and z_opt.cost > y_opt.cost: | |
| first_axes_processed = 'z' | |
| if x_opt.cost > y_opt.cost: | |
| second_axes_processed = 'x' | |
| else: | |
| second_axes_processed = 'y' | |
| grid_infotext = [None] * (1 + len(zs)) | |
| def cell(x, y, z, ix, iy, iz): | |
| if shared.state.interrupted: | |
| return Processed(p, [], p.seed, "") | |
| pc = copy(p) | |
| pc.styles = pc.styles[:] | |
| x_opt.apply(pc, x, xs) | |
| y_opt.apply(pc, y, ys) | |
| z_opt.apply(pc, z, zs) | |
| res = process_images(pc) | |
| # Sets subgrid infotexts | |
| subgrid_index = 1 + iz | |
| if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0: | |
| pc.extra_generation_params = copy(pc.extra_generation_params) | |
| pc.extra_generation_params['Script'] = self.title() | |
| if x_opt.label != 'Nothing': | |
| pc.extra_generation_params["X Type"] = x_opt.label | |
| pc.extra_generation_params["X Values"] = x_values | |
| if x_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds: | |
| pc.extra_generation_params["Fixed X Values"] = ", ".join([str(x) for x in xs]) | |
| if y_opt.label != 'Nothing': | |
| pc.extra_generation_params["Y Type"] = y_opt.label | |
| pc.extra_generation_params["Y Values"] = y_values | |
| if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds: | |
| pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys]) | |
| grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds) | |
| # Sets main grid infotext | |
| if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0: | |
| pc.extra_generation_params = copy(pc.extra_generation_params) | |
| if z_opt.label != 'Nothing': | |
| pc.extra_generation_params["Z Type"] = z_opt.label | |
| pc.extra_generation_params["Z Values"] = z_values | |
| if z_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds: | |
| pc.extra_generation_params["Fixed Z Values"] = ", ".join([str(z) for z in zs]) | |
| grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds) | |
| return res | |
| with SharedSettingsStackHelper(): | |
| processed = draw_xyz_grid( | |
| p, | |
| xs=xs, | |
| ys=ys, | |
| zs=zs, | |
| x_labels=[x_opt.format_value(p, x_opt, x) for x in xs], | |
| y_labels=[y_opt.format_value(p, y_opt, y) for y in ys], | |
| z_labels=[z_opt.format_value(p, z_opt, z) for z in zs], | |
| cell=cell, | |
| draw_legend=draw_legend, | |
| include_lone_images=include_lone_images, | |
| include_sub_grids=include_sub_grids, | |
| first_axes_processed=first_axes_processed, | |
| second_axes_processed=second_axes_processed, | |
| margin_size=margin_size | |
| ) | |
| if not processed.images: | |
| # It broke, no further handling needed. | |
| return processed | |
| z_count = len(zs) | |
| # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids) | |
| processed.infotexts[:1+z_count] = grid_infotext[:1+z_count] | |
| if not include_lone_images: | |
| # Don't need sub-images anymore, drop from list: | |
| processed.images = processed.images[:z_count+1] | |
| if opts.grid_save: | |
| # Auto-save main and sub-grids: | |
| grid_count = z_count + 1 if z_count > 1 else 1 | |
| for g in range(grid_count): | |
| #TODO: See previous comment about intentional data misalignment. | |
| adj_g = g-1 if g > 0 else g | |
| images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed) | |
| if not include_sub_grids: | |
| # Done with sub-grids, drop all related information: | |
| for _ in range(z_count): | |
| del processed.images[1] | |
| del processed.all_prompts[1] | |
| del processed.all_seeds[1] | |
| del processed.infotexts[1] | |
| return processed | |