659 lines
28 KiB
Python
659 lines
28 KiB
Python
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, paths, sd_samplers, processing, sd_models, sd_vae
|
|
from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
|
|
from modules.shared import opts, cmd_opts, state
|
|
import modules.shared as shared
|
|
import modules.sd_samplers
|
|
import modules.sd_models
|
|
import modules.sd_vae
|
|
import glob
|
|
import os
|
|
import re
|
|
|
|
from modules.ui_components import ToolButton
|
|
|
|
fill_values_symbol = "\U0001f4d2" # 📒
|
|
|
|
AxisInfo = namedtuple('AxisInfo', ['axis', 'values'])
|
|
|
|
|
|
def apply_field(field):
|
|
def fun(p, x, xs):
|
|
setattr(p, field, x)
|
|
|
|
return fun
|
|
|
|
|
|
def apply_prompt(p, x, xs):
|
|
if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
|
|
raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
|
|
|
|
p.prompt = p.prompt.replace(xs[0], x)
|
|
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
|
|
|
|
|
|
def apply_order(p, x, xs):
|
|
token_order = []
|
|
|
|
# Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
|
|
for token in x:
|
|
token_order.append((p.prompt.find(token), token))
|
|
|
|
token_order.sort(key=lambda t: t[0])
|
|
|
|
prompt_parts = []
|
|
|
|
# Split the prompt up, taking out the tokens
|
|
for _, token in token_order:
|
|
n = p.prompt.find(token)
|
|
prompt_parts.append(p.prompt[0:n])
|
|
p.prompt = p.prompt[n + len(token):]
|
|
|
|
# Rebuild the prompt with the tokens in the order we want
|
|
prompt_tmp = ""
|
|
for idx, part in enumerate(prompt_parts):
|
|
prompt_tmp += part
|
|
prompt_tmp += x[idx]
|
|
p.prompt = prompt_tmp + p.prompt
|
|
|
|
|
|
def apply_sampler(p, x, xs):
|
|
sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
|
|
if sampler_name is None:
|
|
raise RuntimeError(f"Unknown sampler: {x}")
|
|
|
|
p.sampler_name = sampler_name
|
|
|
|
|
|
def confirm_samplers(p, xs):
|
|
for x in xs:
|
|
if x.lower() not in sd_samplers.samplers_map:
|
|
raise RuntimeError(f"Unknown sampler: {x}")
|
|
|
|
|
|
def apply_checkpoint(p, x, xs):
|
|
info = modules.sd_models.get_closet_checkpoint_match(x)
|
|
if info is None:
|
|
raise RuntimeError(f"Unknown checkpoint: {x}")
|
|
modules.sd_models.reload_model_weights(shared.sd_model, info)
|
|
|
|
|
|
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 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: list(sd_models.checkpoints_list)),
|
|
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("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: 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),
|
|
]
|
|
|
|
|
|
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)
|
|
|
|
if processed_result is None:
|
|
# Use our first processed result object as a template container to hold our full results
|
|
processed_result = copy(processed)
|
|
processed_result.images = [None] * list_size
|
|
processed_result.all_prompts = [None] * list_size
|
|
processed_result.all_seeds = [None] * list_size
|
|
processed_result.infotexts = [None] * list_size
|
|
processed_result.index_of_first_image = 1
|
|
|
|
idx = index(ix, iy, iz)
|
|
if processed.images:
|
|
# Non-empty list indicates some degree of success.
|
|
processed_result.images[idx] = processed.images[0]
|
|
processed_result.all_prompts[idx] = processed.prompt
|
|
processed_result.all_seeds[idx] = processed.seed
|
|
processed_result.infotexts[idx] = processed.infotexts[0]
|
|
else:
|
|
cell_mode = "P"
|
|
cell_size = (processed_result.width, processed_result.height)
|
|
if processed_result.images[0] is not None:
|
|
cell_mode = processed_result.images[0].mode
|
|
#This corrects size in case of batches:
|
|
cell_size = processed_result.images[0].size
|
|
processed_result.images[idx] = Image.new(cell_mode, cell_size)
|
|
|
|
|
|
if first_axes_processed == 'x':
|
|
for ix, x in enumerate(xs):
|
|
if second_axes_processed == 'y':
|
|
for iy, y in enumerate(ys):
|
|
for iz, z in enumerate(zs):
|
|
process_cell(x, y, z, ix, iy, iz)
|
|
else:
|
|
for iz, z in enumerate(zs):
|
|
for iy, y in enumerate(ys):
|
|
process_cell(x, y, z, ix, iy, iz)
|
|
elif first_axes_processed == 'y':
|
|
for iy, y in enumerate(ys):
|
|
if second_axes_processed == 'x':
|
|
for ix, x in enumerate(xs):
|
|
for iz, z in enumerate(zs):
|
|
process_cell(x, y, z, ix, iy, iz)
|
|
else:
|
|
for iz, z in enumerate(zs):
|
|
for ix, x in enumerate(xs):
|
|
process_cell(x, y, z, ix, iy, iz)
|
|
elif first_axes_processed == 'z':
|
|
for iz, z in enumerate(zs):
|
|
if second_axes_processed == 'x':
|
|
for ix, x in enumerate(xs):
|
|
for iy, y in enumerate(ys):
|
|
process_cell(x, y, z, ix, iy, iz)
|
|
else:
|
|
for iy, y in enumerate(ys):
|
|
for ix, x in enumerate(xs):
|
|
process_cell(x, y, z, ix, iy, iz)
|
|
|
|
if not processed_result:
|
|
# Should never happen, I've only seen it on one of four open tabs and it needed to refresh.
|
|
print("Unexpected error: Processing could not begin, you may need to refresh the tab or restart the service.")
|
|
return Processed(p, [])
|
|
elif not any(processed_result.images):
|
|
print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
|
|
return Processed(p, [])
|
|
|
|
z_count = len(zs)
|
|
sub_grids = [None] * z_count
|
|
for i in range(z_count):
|
|
start_index = (i * len(xs) * len(ys)) + i
|
|
end_index = start_index + len(xs) * len(ys)
|
|
grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
|
|
if draw_legend:
|
|
grid = images.draw_grid_annotations(grid, processed_result.images[start_index].size[0], processed_result.images[start_index].size[1], hor_texts, ver_texts, margin_size)
|
|
processed_result.images.insert(i, grid)
|
|
processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
|
|
processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
|
|
processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
|
|
|
|
sub_grid_size = processed_result.images[0].size
|
|
z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
|
|
if draw_legend:
|
|
z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
|
|
processed_result.images.insert(0, z_grid)
|
|
#TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
|
|
#processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
|
|
#processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
|
|
processed_result.infotexts.insert(0, processed_result.infotexts[0])
|
|
|
|
return processed_result
|
|
|
|
|
|
class SharedSettingsStackHelper(object):
|
|
def __enter__(self):
|
|
self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
|
|
self.vae = opts.sd_vae
|
|
self.uni_pc_order = opts.uni_pc_order
|
|
|
|
def __exit__(self, exc_type, exc_value, tb):
|
|
opts.data["sd_vae"] = self.vae
|
|
opts.data["uni_pc_order"] = self.uni_pc_order
|
|
modules.sd_models.reload_model_weights()
|
|
modules.sd_vae.reload_vae_weights()
|
|
|
|
opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
|
|
|
|
|
|
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
|
|
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
|
|
|
|
re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
|
|
re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
|
|
|
|
|
|
class Script(scripts.Script):
|
|
def title(self):
|
|
return "X/Y/Z plot"
|
|
|
|
def ui(self, is_img2img):
|
|
self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=19):
|
|
with gr.Row():
|
|
x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
|
|
x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
|
|
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"))
|
|
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"))
|
|
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, axis2_type, axis2_values):
|
|
return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
|
|
|
|
xy_swap_args = [x_type, x_values, y_type, y_values]
|
|
swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
|
|
yz_swap_args = [y_type, y_values, z_type, z_values]
|
|
swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
|
|
xz_swap_args = [x_type, x_values, z_type, z_values]
|
|
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 ", ".join(axis.choices()) if axis.choices else gr.update()
|
|
|
|
fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
|
|
fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
|
|
fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
|
|
|
|
def select_axis(x_type):
|
|
return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
|
|
|
|
x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
|
|
y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
|
|
z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
|
|
|
|
self.infotext_fields = (
|
|
(x_type, "X Type"),
|
|
(x_values, "X Values"),
|
|
(y_type, "Y Type"),
|
|
(y_values, "Y Values"),
|
|
(z_type, "Z Type"),
|
|
(z_values, "Z Values"),
|
|
)
|
|
|
|
return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
|
|
|
|
def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, 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):
|
|
if opt.label == 'Nothing':
|
|
return [0]
|
|
|
|
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
|
|
|
|
if opt.type == int:
|
|
valslist_ext = []
|
|
|
|
for val in valslist:
|
|
m = re_range.fullmatch(val)
|
|
mc = re_range_count.fullmatch(val)
|
|
if m is not None:
|
|
start = int(m.group(1))
|
|
end = int(m.group(2))+1
|
|
step = int(m.group(3)) if m.group(3) is not None else 1
|
|
|
|
valslist_ext += list(range(start, end, step))
|
|
elif mc is not None:
|
|
start = int(mc.group(1))
|
|
end = int(mc.group(2))
|
|
num = int(mc.group(3)) if mc.group(3) is not None else 1
|
|
|
|
valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
|
|
else:
|
|
valslist_ext.append(val)
|
|
|
|
valslist = valslist_ext
|
|
elif opt.type == float:
|
|
valslist_ext = []
|
|
|
|
for val in valslist:
|
|
m = re_range_float.fullmatch(val)
|
|
mc = re_range_count_float.fullmatch(val)
|
|
if m is not None:
|
|
start = float(m.group(1))
|
|
end = float(m.group(2))
|
|
step = float(m.group(3)) if m.group(3) is not None else 1
|
|
|
|
valslist_ext += np.arange(start, end + step, step).tolist()
|
|
elif mc is not None:
|
|
start = float(mc.group(1))
|
|
end = float(mc.group(2))
|
|
num = int(mc.group(3)) if mc.group(3) is not None else 1
|
|
|
|
valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
|
|
else:
|
|
valslist_ext.append(val)
|
|
|
|
valslist = valslist_ext
|
|
elif opt.type == str_permutations:
|
|
valslist = list(permutations(valslist))
|
|
|
|
valslist = [opt.type(x) for x in valslist]
|
|
|
|
# Confirm options are valid before starting
|
|
if opt.confirm:
|
|
opt.confirm(p, valslist)
|
|
|
|
return valslist
|
|
|
|
x_opt = self.current_axis_options[x_type]
|
|
xs = process_axis(x_opt, x_values)
|
|
|
|
y_opt = self.current_axis_options[y_type]
|
|
ys = process_axis(y_opt, y_values)
|
|
|
|
z_opt = self.current_axis_options[z_type]
|
|
zs = process_axis(z_opt, z_values)
|
|
|
|
# this could be moved to common code, but unlikely to be ever triggered anywhere else
|
|
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)
|
|
|
|
grid_infotext = [None]
|
|
|
|
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'
|
|
|
|
def cell(x, y, z):
|
|
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)
|
|
|
|
if grid_infotext[0] is None:
|
|
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])
|
|
|
|
if z_opt.label != 'Nothing':
|
|
pc.extra_generation_params["Z Type"] = z_opt.label
|
|
pc.extra_generation_params["Z Values"] = z_values
|
|
if z_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
|
|
pc.extra_generation_params["Fixed Z Values"] = ", ".join([str(z) for z in zs])
|
|
|
|
grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
|
|
|
|
return res
|
|
|
|
with SharedSettingsStackHelper():
|
|
processed = draw_xyz_grid(
|
|
p,
|
|
xs=xs,
|
|
ys=ys,
|
|
zs=zs,
|
|
x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
|
|
y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
|
|
z_labels=[z_opt.format_value(p, z_opt, z) for z in zs],
|
|
cell=cell,
|
|
draw_legend=draw_legend,
|
|
include_lone_images=include_lone_images,
|
|
include_sub_grids=include_sub_grids,
|
|
first_axes_processed=first_axes_processed,
|
|
second_axes_processed=second_axes_processed,
|
|
margin_size=margin_size
|
|
)
|
|
|
|
if not processed.images:
|
|
# It broke, no further handling needed.
|
|
return processed
|
|
|
|
z_count = len(zs)
|
|
|
|
if not include_lone_images:
|
|
# Don't need sub-images anymore, drop from list:
|
|
processed.images = processed.images[:z_count+1]
|
|
|
|
if opts.grid_save:
|
|
# Auto-save main and sub-grids:
|
|
grid_count = z_count + 1 if z_count > 1 else 1
|
|
for g in range(grid_count):
|
|
#TODO: See previous comment about intentional data misalignment.
|
|
adj_g = g-1 if g > 0 else g
|
|
images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
|
|
|
|
if not include_sub_grids:
|
|
# Done with sub-grids, drop all related information:
|
|
for sg in range(z_count):
|
|
del processed.images[1]
|
|
del processed.all_prompts[1]
|
|
del processed.all_seeds[1]
|
|
del processed.infotexts[1]
|
|
|
|
return processed
|