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xyz grid optimizations

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This commit is contained in:
Vladimir Mandic
2023-05-09 10:41:23 -04:00
parent 1288aec459
commit d8a2c32918
5 changed files with 84 additions and 139 deletions
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scripts/xyz_grid.py
+78 -133
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@@ -1,3 +1,5 @@
# pylint: disable=unused-argument, attribute-defined-outside-init
import re
import csv
import random
@@ -12,47 +14,36 @@ import modules.scripts as scripts
import modules.shared as shared
from modules import images, sd_samplers, processing, sd_models, sd_vae
from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
from modules.shared import opts, state
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)
shared.log.warning(f"XYZ grid: prompt S/R did not find {xs[0]} in prompt or negative prompt.")
else:
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
@@ -63,39 +54,42 @@ def apply_order(p, x, xs):
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
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
else:
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}")
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
def apply_checkpoint(p, x, xs):
if x == shared.opts.sd_model_checkpoint:
return
info = sd_models.get_closet_checkpoint_match(x)
if info is None:
raise RuntimeError(f"Unknown checkpoint: {x}")
sd_models.reload_model_weights(shared.sd_model, info)
shared.log.warning(f"XYZ grid: unknown checkpoint: {x}")
else:
sd_models.reload_model_weights(shared.sd_model, info)
def confirm_checkpoints(p, xs):
for x in xs:
if sd_models.get_closet_checkpoint_match(x) is None:
raise RuntimeError(f"Unknown checkpoint: {x}")
shared.log.warning(f"XYZ grid: Unknown checkpoint: {x}")
def apply_clip_skip(p, x, xs):
opts.data["CLIP_stop_at_last_layers"] = x
shared.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
shared.opts.data["use_scale_latent_for_hires_fix"] = True
else:
opts.data["use_scale_latent_for_hires_fix"] = False
shared.opts.data["use_scale_latent_for_hires_fix"] = False
def find_vae(name: str):
@@ -106,7 +100,7 @@ def find_vae(name: str):
else:
choices = [x for x in sorted(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")
shared.log.warning(f"No VAE found for {name}; using automatic")
return sd_vae.unspecified
else:
return sd_vae.vae_dict[choices[0]]
@@ -123,13 +117,13 @@ def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
def apply_fallback(p, x, xs):
sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
if sampler_name is None:
raise RuntimeError(f"Unknown sampler: {x}")
opts.data["xyz_fallback_sampler"] = sampler_name
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
else:
shared.opts.data["xyz_fallback_sampler"] = sampler_name
def apply_uni_pc_order(p, x, xs):
opts.data["uni_pc_order"] = min(x, p.steps - 1)
shared.opts.data["uni_pc_order"] = min(x, p.steps - 1)
def apply_face_restore(p, opt, x):
@@ -142,23 +136,25 @@ def apply_face_restore(p, opt, x):
p.face_restoration_model = 'GFPGAN'
else:
is_active = opt in ('true', 'yes', 'y', '1')
p.restore_faces = is_active
def apply_token_merging_ratio_hr(p, x, xs):
opts.data["token_merging_ratio_hr"] = x
shared.opts.data["token_merging_ratio_hr"] = x
def apply_token_merging_ratio(p, x, xs):
opts.data["token_merging_ratio"] = x
shared.opts.data["token_merging_ratio"] = x
def apply_token_merging_random(p, x, xs):
is_active = x.lower() in ('true', 'yes', 'y', '1')
opts.data["token_merging_random"] = is_active
shared.opts.data["token_merging_random"] = is_active
def format_value_add_label(p, opt, x):
if type(x) == float:
x = round(x, 8)
return f"{opt.label}: {x}"
@@ -186,11 +182,11 @@ def str_permutations(x):
class AxisOption:
def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None):
def __init__(self, label, tipe, apply, fmt=format_value_add_label, confirm=None, cost=0.0, choices=None):
self.label = label
self.type = type
self.type = tipe
self.apply = apply
self.format_value = format_value
self.format_value = fmt
self.confirm = confirm
self.cost = cost
self.choices = choices
@@ -208,7 +204,7 @@ class AxisOptionTxt2Img(AxisOption):
axis_options = [
AxisOption("Nothing", str, do_nothing, format_value=format_nothing),
AxisOption("Nothing", str, do_nothing, fmt=format_nothing),
AxisOption("Seed", int, apply_field("seed")),
AxisOption("Var. seed", int, apply_field("subseed")),
AxisOption("Var. strength", float, apply_field("subseed_strength")),
@@ -216,11 +212,11 @@ axis_options = [
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("Prompt S/R", str, apply_prompt, fmt=format_value),
AxisOption("Prompt order", str_permutations, apply_order, fmt=format_value_join_list),
AxisOptionTxt2Img("Sampler", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
AxisOptionImg2Img("Sampler", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
AxisOption("Checkpoint name", str, apply_checkpoint, fmt=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")),
@@ -229,12 +225,12 @@ axis_options = [
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]]),
AxisOptionTxt2Img("Fallback latent upscaler sampler", str, apply_fallback, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
AxisOptionTxt2Img("Fallback latent upscaler sampler", str, apply_fallback, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
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),
AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
AxisOption("Face restore", str, apply_face_restore, fmt=format_value),
AxisOption("ToMe ratio",float,apply_token_merging_ratio),
AxisOption("ToMe ratio for Hires fix",float,apply_token_merging_ratio_hr),
AxisOption("ToMe random pertubations",str,apply_token_merging_random, choices = lambda: ["Yes","No"])
@@ -245,12 +241,9 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
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
shared.state.job_count = list_size * p.n_iter
def process_cell(x, y, z, ix, iy, iz):
nonlocal processed_result
@@ -258,10 +251,8 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
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}"
shared.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:
# Use our first processed result object as a template container to hold our full results
processed_result = copy(processed)
@@ -270,7 +261,6 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
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.
@@ -287,7 +277,6 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
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':
@@ -321,14 +310,14 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
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.")
shared.log.error("XYZ grid: 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")
shared.log.error("XYZ grid: Failed to return even a single processed image")
return Processed(p, [])
z_count = len(zs)
sub_grids = [None] * z_count
# 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)
@@ -339,7 +328,6 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
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:
@@ -348,36 +336,39 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
#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):
#Save overridden settings so they can be restored later.
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
self.token_merging_ratio_hr = opts.token_merging_ratio_hr
self.token_merging_ratio = opts.token_merging_ratio
self.token_merging_random = opts.token_merging_random
self.CLIP_stop_at_last_layers = shared.opts.CLIP_stop_at_last_layers
self.vae = shared.opts.sd_vae
self.uni_pc_order = shared.opts.uni_pc_order
self.token_merging_ratio_hr = shared.opts.token_merging_ratio_hr
self.token_merging_ratio = shared.opts.token_merging_ratio
self.token_merging_random = shared.opts.token_merging_random
self.sd_model_checkpoint = shared.opts.sd_model_checkpoint
self.sd_vae_checkpoint = shared.opts.sd_vae
def __exit__(self, exc_type, exc_value, tb):
#Restore overriden settings after plot generation.
opts.data["sd_vae"] = self.vae
opts.data["uni_pc_order"] = self.uni_pc_order
sd_models.reload_model_weights()
sd_vae.reload_vae_weights()
shared.opts.data["sd_vae"] = self.vae
shared.opts.data["uni_pc_order"] = self.uni_pc_order
shared.opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
shared.opts.data["token_merging_ratio_hr"] = self.token_merging_ratio_hr
shared.opts.data["token_merging_ratio"] = self.token_merging_ratio
shared.opts.data["token_merging_random"] = self.token_merging_random
if self.sd_model_checkpoint != shared.opts.sd_model_checkpoint:
shared.opts.data["sd_model_checkpoint"] = self.sd_model_checkpoint
sd_models.reload_model_weights()
if self.sd_vae_checkpoint != shared.opts.sd_vae:
shared.opts.data["sd_vae"] = self.sd_vae_checkpoint
sd_vae.reload_vae_weights()
opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
opts.data["token_merging_ratio_hr"] = self.token_merging_ratio_hr
opts.data["token_merging_ratio"] = self.token_merging_ratio
opts.data["token_merging_random"] = self.token_merging_random
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*")
@@ -388,7 +379,6 @@ class Script(scripts.Script):
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():
@@ -408,7 +398,6 @@ class Script(scripts.Script):
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"):
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"))
@@ -416,7 +405,6 @@ class Script(scripts.Script):
include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
with gr.Row(variant="compact", elem_id="axis_options"):
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")
@@ -475,25 +463,21 @@ class Script(scripts.Script):
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):
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): # pylint: disable=arguments-differ
shared.log.debug(f'xyzgrid: {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:
processing.fix_seed(p)
if not opts.return_grid:
if not shared.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)
@@ -501,21 +485,17 @@ class Script(scripts.Script):
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)
@@ -523,48 +503,38 @@ class Script(scripts.Script):
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]
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)
# this could be moved to common code, but unlikely to be ever triggered anywhere else
Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
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)'
assert grid_mp < shared.opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {shared.opts.img_max_size_mp} MPixels)'
def fix_axis_seeds(axis_opt, axis_list):
if axis_opt.label in ['Seed', 'Var. seed']:
@@ -585,7 +555,6 @@ class Script(scripts.Script):
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)
@@ -597,21 +566,13 @@ class Script(scripts.Script):
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})")
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.
shared.log.info(f"XYZ grid: images={len(xs)*len(ys)*len(zs)*image_cell_count} grid={len(zs)} {len(xs)}x{len(ys)} cells={len(zs)} steps={total_steps}")
shared.state.xyz_plot_x = AxisInfo(x_opt, xs)
shared.state.xyz_plot_y = AxisInfo(y_opt, ys)
shared.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:
@@ -632,41 +593,33 @@ class Script(scripts.Script):
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)
# 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])
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)
@@ -676,9 +629,7 @@ class Script(scripts.Script):
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():
@@ -702,23 +653,18 @@ class Script(scripts.Script):
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]
if opts.grid_save:
if shared.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):
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)
images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=shared.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):
@@ -726,5 +672,4 @@ class Script(scripts.Script):
del processed.all_prompts[1]
del processed.all_seeds[1]
del processed.infotexts[1]
return processed