stable-diffusion-webui/scripts/xyz_grid.py

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from collections import namedtuple
from copy import copy
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from itertools import permutations, chain
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import random
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import csv
import os.path
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from io import StringIO
from PIL import Image
import numpy as np
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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
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from modules.shared import opts, state
import modules.shared as shared
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import modules.sd_samplers
import modules.sd_models
import modules.sd_vae
import re
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from modules.ui_components import ToolButton
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fill_values_symbol = "\U0001f4d2" # 📒
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AxisInfo = namedtuple('AxisInfo', ['axis', 'values'])
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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.")
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p.prompt = p.prompt.replace(xs[0], x)
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
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def apply_order(p, x, xs):
token_order = []
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# Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
for token in x:
token_order.append((p.prompt.find(token), token))
token_order.sort(key=lambda t: t[0])
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prompt_parts = []
# Split the prompt up, taking out the tokens
for _, token in token_order:
n = p.prompt.find(token)
prompt_parts.append(p.prompt[0:n])
p.prompt = p.prompt[n + len(token):]
# Rebuild the prompt with the tokens in the order we want
prompt_tmp = ""
for idx, part in enumerate(prompt_parts):
prompt_tmp += part
prompt_tmp += x[idx]
p.prompt = prompt_tmp + p.prompt
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def apply_sampler(p, x, xs):
sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
if sampler_name is None:
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raise RuntimeError(f"Unknown sampler: {x}")
p.sampler_name = sampler_name
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def confirm_samplers(p, xs):
for x in xs:
if x.lower() not in sd_samplers.samplers_map:
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raise RuntimeError(f"Unknown sampler: {x}")
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def apply_checkpoint(p, x, xs):
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info = modules.sd_models.get_closet_checkpoint_match(x)
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if info is None:
raise RuntimeError(f"Unknown checkpoint: {x}")
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p.override_settings['sd_model_checkpoint'] = info.name
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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}")
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def apply_clip_skip(p, x, xs):
opts.data["CLIP_stop_at_last_layers"] = x
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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(','))
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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):
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def fun(p, x, xs):
if boolean:
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x = True if x.lower() == "true" else False
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p.override_settings[field] = x
return fun
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def boolean_choice(reverse: bool = False):
def choice():
return ["False", "True"] if reverse else ["True", "False"]
return choice
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def format_value_add_label(p, opt, x):
if type(x) == float:
x = round(x, 8)
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return f"{opt.label}: {x}"
def format_value(p, opt, x):
if type(x) == float:
x = round(x, 8)
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return x
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def format_value_join_list(p, opt, x):
return ", ".join(x)
def do_nothing(p, x, xs):
pass
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def format_nothing(p, opt, x):
return ""
def format_remove_path(p, opt, x):
return os.path.basename(x)
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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
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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_remove_path, confirm=confirm_checkpoints, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list, key=str.casefold)),
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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")),
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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)),
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AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
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AxisOption("Token merging ratio", float, apply_override('token_merging_ratio')),
AxisOption("Token merging ratio high-res", float, apply_override('token_merging_ratio_hr')),
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AxisOption("Always discard next-to-last sigma", str, apply_override('always_discard_next_to_last_sigma', boolean=True), choices=boolean_choice(reverse=True)),
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]
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]
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title_texts = [[images.GridAnnotation(z)] for z in z_labels]
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list_size = (len(xs) * len(ys) * len(zs))
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processed_result = None
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state.job_count = list_size * p.n_iter
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def process_cell(x, y, z, ix, iy, iz):
nonlocal processed_result
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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}"
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processed: Processed = cell(x, y, z, ix, iy, iz)
if processed_result is None:
# Use our first processed result object as a template container to hold our full results
processed_result = copy(processed)
processed_result.images = [None] * list_size
processed_result.all_prompts = [None] * list_size
processed_result.all_seeds = [None] * list_size
processed_result.infotexts = [None] * list_size
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processed_result.index_of_first_image = 1
idx = index(ix, iy, iz)
if processed.images:
# Non-empty list indicates some degree of success.
processed_result.images[idx] = processed.images[0]
processed_result.all_prompts[idx] = processed.prompt
processed_result.all_seeds[idx] = processed.seed
processed_result.infotexts[idx] = processed.infotexts[0]
else:
cell_mode = "P"
cell_size = (processed_result.width, processed_result.height)
if processed_result.images[0] is not None:
cell_mode = processed_result.images[0].mode
#This corrects size in case of batches:
cell_size = processed_result.images[0].size
processed_result.images[idx] = Image.new(cell_mode, cell_size)
if first_axes_processed == 'x':
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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:
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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)
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if not processed_result:
# Should never happen, I've only seen it on one of four open tabs and it needed to refresh.
print("Unexpected error: Processing could not begin, you may need to refresh the tab or restart the service.")
return Processed(p, [])
elif not any(processed_result.images):
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print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
return Processed(p, [])
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z_count = len(zs)
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for i in range(z_count):
start_index = (i * len(xs) * len(ys)) + i
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end_index = start_index + len(xs) * len(ys)
grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
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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)
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#TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
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#processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
#processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
processed_result.infotexts.insert(0, processed_result.infotexts[0])
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return processed_result
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class SharedSettingsStackHelper(object):
def __enter__(self):
self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
self.vae = opts.sd_vae
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self.uni_pc_order = opts.uni_pc_order
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def __exit__(self, exc_type, exc_value, tb):
opts.data["sd_vae"] = self.vae
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opts.data["uni_pc_order"] = self.uni_pc_order
modules.sd_models.reload_model_weights()
modules.sd_vae.reload_vae_weights()
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opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
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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*")
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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*")
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class Script(scripts.Script):
def title(self):
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return "X/Y/Z plot"
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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]
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with gr.Row():
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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"))
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x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
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x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True)
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fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
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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"))
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y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
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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)
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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"))
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z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
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fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
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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"))
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with gr.Row(variant="compact", elem_id="swap_axes"):
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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")
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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
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xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
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swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
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yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
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swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
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xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
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swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
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def fill(x_type):
axis = self.current_axis_options[x_type]
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return axis.choices() if axis.choices else gr.update()
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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])
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def select_axis(axis_type,axis_values_dropdown):
choices = self.current_axis_options[axis_type].choices
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has_choices = choices is not None
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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"
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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"),
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(x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
(y_type, "Y Type"),
(y_values, "Y Values"),
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(y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
(z_type, "Z Type"),
(z_values, "Z Values"),
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(z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
)
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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]
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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
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def process_axis(opt, vals, vals_dropdown):
if opt.label == 'Nothing':
return [0]
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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]
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if opt.type == int:
valslist_ext = []
for val in valslist:
m = re_range.fullmatch(val)
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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
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valslist_ext += list(range(start, end, step))
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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
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valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
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else:
valslist_ext.append(val)
valslist = valslist_ext
elif opt.type == float:
valslist_ext = []
for val in valslist:
m = re_range_float.fullmatch(val)
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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()
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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
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valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
else:
valslist_ext.append(val)
valslist = valslist_ext
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elif opt.type == str_permutations:
valslist = list(permutations(valslist))
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valslist = [opt.type(x) for x in valslist]
# Confirm options are valid before starting
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if opt.confirm:
opt.confirm(p, valslist)
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return valslist
x_opt = self.current_axis_options[x_type]
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if x_opt.choices is not None:
x_values = ",".join(x_values_dropdown)
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xs = process_axis(x_opt, x_values, x_values_dropdown)
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y_opt = self.current_axis_options[y_type]
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if y_opt.choices is not None:
y_values = ",".join(y_values_dropdown)
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ys = process_axis(y_opt, y_values, y_values_dropdown)
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z_opt = self.current_axis_options[z_type]
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if z_opt.choices is not None:
z_values = ",".join(z_values_dropdown)
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zs = process_axis(z_opt, z_values, z_values_dropdown)
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# this could be moved to common code, but unlikely to be ever triggered anywhere else
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Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
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grid_mp = round(len(xs) * len(ys) * len(zs) * p.width * p.height / 1000000)
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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)'
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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)
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zs = fix_axis_seeds(z_opt, zs)
if x_opt.label == 'Steps':
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total_steps = sum(xs) * len(ys) * len(zs)
elif y_opt.label == 'Steps':
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total_steps = sum(ys) * len(xs) * len(zs)
elif z_opt.label == 'Steps':
total_steps = sum(zs) * len(xs) * len(ys)
else:
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total_steps = p.steps * len(xs) * len(ys) * len(zs)
if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
if x_opt.label == "Hires steps":
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total_steps += sum(xs) * len(ys) * len(zs)
elif y_opt.label == "Hires steps":
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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:
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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 ""
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plural_s = 's' if len(zs) > 1 else ''
print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
shared.total_tqdm.updateTotal(total_steps)
state.xyz_plot_x = AxisInfo(x_opt, xs)
state.xyz_plot_y = AxisInfo(y_opt, ys)
state.xyz_plot_z = AxisInfo(z_opt, zs)
# If one of the axes is very slow to change between (like SD model
# checkpoint), then make sure it is in the outer iteration of the nested
# `for` loop.
first_axes_processed = 'z'
second_axes_processed = 'y'
if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost:
first_axes_processed = 'x'
if y_opt.cost > z_opt.cost:
second_axes_processed = 'y'
else:
second_axes_processed = 'z'
elif y_opt.cost > x_opt.cost and y_opt.cost > z_opt.cost:
first_axes_processed = 'y'
if x_opt.cost > z_opt.cost:
second_axes_processed = 'x'
else:
second_axes_processed = 'z'
elif z_opt.cost > x_opt.cost and z_opt.cost > y_opt.cost:
first_axes_processed = 'z'
if x_opt.cost > y_opt.cost:
second_axes_processed = 'x'
else:
second_axes_processed = 'y'
grid_infotext = [None] * (1 + len(zs))
def cell(x, y, z, ix, iy, iz):
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if shared.state.interrupted:
return Processed(p, [], p.seed, "")
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pc = copy(p)
pc.styles = pc.styles[:]
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x_opt.apply(pc, x, xs)
y_opt.apply(pc, y, ys)
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z_opt.apply(pc, z, zs)
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try:
res = process_images(pc)
except Exception as e:
errors.display(e, "generating image for xyz plot")
res = Processed(p, [], p.seed, "")
# Sets subgrid infotexts
subgrid_index = 1 + iz
if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0:
pc.extra_generation_params = copy(pc.extra_generation_params)
pc.extra_generation_params['Script'] = self.title()
if x_opt.label != 'Nothing':
pc.extra_generation_params["X Type"] = x_opt.label
pc.extra_generation_params["X Values"] = x_values
if x_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
pc.extra_generation_params["Fixed X Values"] = ", ".join([str(x) for x in xs])
if y_opt.label != 'Nothing':
pc.extra_generation_params["Y Type"] = y_opt.label
pc.extra_generation_params["Y Values"] = y_values
if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])
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grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
# Sets main grid infotext
if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0:
pc.extra_generation_params = copy(pc.extra_generation_params)
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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,
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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
)
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if not processed.images:
# It broke, no further handling needed.
return processed
z_count = len(zs)
# Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
if not include_lone_images:
# Don't need sub-images anymore, drop from list:
processed.images = processed.images[:z_count+1]
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if opts.grid_save:
# Auto-save main and sub-grids:
grid_count = z_count + 1 if z_count > 1 else 1
for g in range(grid_count):
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#TODO: See previous comment about intentional data misalignment.
adj_g = g-1 if g > 0 else g
images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
if not include_sub_grids:
# Done with sub-grids, drop all related information:
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for _ in range(z_count):
del processed.images[1]
del processed.all_prompts[1]
del processed.all_seeds[1]
del processed.infotexts[1]
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return processed