| | 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 = []
|
| |
|
| |
|
| | for token in x:
|
| | token_order.append((p.prompt.find(token), token))
|
| |
|
| | token_order.sort(key=lambda t: t[0])
|
| |
|
| | prompt_parts = []
|
| |
|
| |
|
| | for _, token in token_order:
|
| | n = p.prompt.find(token)
|
| | prompt_parts.append(p.prompt[0:n])
|
| | p.prompt = p.prompt[n + len(token):]
|
| |
|
| |
|
| | prompt_tmp = ""
|
| | for idx, part in enumerate(prompt_parts):
|
| | prompt_tmp += part
|
| | prompt_tmp += x[idx]
|
| | p.prompt = prompt_tmp + p.prompt
|
| |
|
| |
|
| | def apply_sampler(p, x, xs):
|
| | sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
|
| | if sampler_name is None:
|
| | 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):
|
| | def fun(p, x, xs):
|
| | p.override_settings[field] = x
|
| | return fun
|
| |
|
| | def format_value_add_label(p, opt, x):
|
| | if type(x) == float:
|
| | x = round(x, 8)
|
| |
|
| | return f"{opt.label}: {x}"
|
| |
|
| |
|
| | def format_value(p, opt, x):
|
| | if type(x) == float:
|
| | x = round(x, 8)
|
| | return x
|
| |
|
| |
|
| | def format_value_join_list(p, opt, x):
|
| | return ", ".join(x)
|
| |
|
| |
|
| | def do_nothing(p, x, xs):
|
| | pass
|
| |
|
| |
|
| | def format_nothing(p, opt, x):
|
| | return ""
|
| |
|
| |
|
| | def str_permutations(x):
|
| | """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
|
| | return x
|
| |
|
| |
|
| | 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')),
|
| | ]
|
| |
|
| |
|
| | 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.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]
|
| |
|
| |
|
| | 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)
|
| |
|
| |
|
| | 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)
|
| |
|
| | res = process_images(pc)
|
| |
|
| |
|
| | 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
|
| |
|
