| 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 modules.scripts as scripts |
| import gradio as gr |
|
|
| from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion, 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 = [] |
|
|
| |
| 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 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 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 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))))) |
|
|
|
|
| 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_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_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")), |
| 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: ['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"]), |
| ] |
|
|
|
|
| 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: |
| |
| 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: |
| |
| 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: |
| |
| 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) |
| |
| |
| |
| 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.Column(): |
| csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode")) |
|
|
| 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): |
| 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, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size, csv_mode] |
|
|
| 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, csv_mode): |
| 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 and not csv_mode: |
| valslist = vals_dropdown |
| else: |
| valslist = csv_string_to_list_strip(vals) |
|
|
| if opt.type == int: |
| valslist_ext = [] |
|
|
| for val in valslist: |
| m = re_range.fullmatch(val) |
| mc = re_range_count.fullmatch(val) |
| if m is not None: |
| start = int(m.group(1)) |
| end = int(m.group(2))+1 |
| step = int(m.group(3)) if m.group(3) is not None else 1 |
|
|
| valslist_ext += list(range(start, end, step)) |
| elif mc is not None: |
| start = int(mc.group(1)) |
| end = int(mc.group(2)) |
| num = int(mc.group(3)) if mc.group(3) is not None else 1 |
|
|
| valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()] |
| else: |
| valslist_ext.append(val) |
|
|
| valslist = valslist_ext |
| elif opt.type == float: |
| valslist_ext = [] |
|
|
| for val in valslist: |
| m = re_range_float.fullmatch(val) |
| mc = re_range_count_float.fullmatch(val) |
| if m is not None: |
| start = float(m.group(1)) |
| end = float(m.group(2)) |
| step = float(m.group(3)) if m.group(3) is not None else 1 |
|
|
| valslist_ext += np.arange(start, end + step, step).tolist() |
| elif mc is not None: |
| start = float(mc.group(1)) |
| end = float(mc.group(2)) |
| num = int(mc.group(3)) if mc.group(3) is not None else 1 |
|
|
| valslist_ext += np.linspace(start=start, stop=end, num=num).tolist() |
| else: |
| valslist_ext.append(val) |
|
|
| valslist = valslist_ext |
| elif opt.type == str_permutations: |
| valslist = list(permutations(valslist)) |
|
|
| valslist = [opt.type(x) for x in valslist] |
|
|
| |
| 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 |
| 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) |
|
|
| |
| |
| |
| 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) |
|
|
| try: |
| res = process_images(pc) |
| except Exception as e: |
| errors.display(e, "generating image for xyz plot") |
|
|
| res = Processed(p, [], p.seed, "") |
|
|
| |
| 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(): |
| 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: |
| |
| return processed |
|
|
| z_count = len(zs) |
|
|
| |
| processed.infotexts[:1+z_count] = grid_infotext[:1+z_count] |
|
|
| if not include_lone_images: |
| |
| processed.images = processed.images[:z_count+1] |
|
|
| if opts.grid_save: |
| |
| grid_count = z_count + 1 if z_count > 1 else 1 |
| 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=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed) |
|
|
| if not include_sub_grids: |
| |
| 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 |
|
|
