| 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_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, InputAccordion
|
|
|
| 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 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, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size, draw_grid):
|
| hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
|
| ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
|
| title_texts = [[images.GridAnnotation(z)] for z in z_labels]
|
|
|
| list_size = (len(xs) * len(ys) * len(zs))
|
|
|
| processed_result = None
|
|
|
| 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, [])
|
|
|
| if draw_grid:
|
| 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():
|
| 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"))
|
| csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))
|
|
|
| with InputAccordion(True, label='Draw grid', elem_id=self.elem_id('draw_grid')) as draw_grid:
|
| with gr.Row():
|
| include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
|
| draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
|
| 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(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
|
|
|
| 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, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode, draw_grid]
|
|
|
| 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, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode, draw_grid):
|
| x_type, y_type, z_type = x_type or 0, y_type or 0, z_type or 0
|
|
|
| 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
|
| 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]
|
|
|
|
|
| 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 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)
|
| 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,
|
| draw_grid=draw_grid,
|
| )
|
|
|
| if not processed.images:
|
|
|
| return processed
|
|
|
| z_count = len(zs)
|
|
|
| if draw_grid:
|
|
|
| processed.infotexts[:1 + z_count] = grid_infotext[:1 + z_count]
|
|
|
| if not include_lone_images:
|
|
|
| processed.images = processed.images[:z_count + 1] if draw_grid else []
|
|
|
| if draw_grid and 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:
|
| break
|
|
|
| if draw_grid and 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
|
|
|
