File size: 36,210 Bytes
c9ea4f0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
from collections import namedtuple
from copy import copy
from itertools import permutations, chain
import random
import csv
import os.path
from io import StringIO
from PIL import Image
import numpy as np

import modules.scripts as scripts
import gradio as gr

from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion, errors
from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
from modules.shared import opts, state
import modules.shared as shared
import modules.sd_samplers
import modules.sd_models
import modules.sd_vae
import re

from modules.ui_components import ToolButton

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 confirm_samplers(p, xs):
    for x in xs:
        if x.lower() not in sd_samplers.samplers_map:
            raise RuntimeError(f"Unknown sampler: {x}")


def apply_checkpoint(p, x, xs):
    info = modules.sd_models.get_closet_checkpoint_match(x)
    if info is None:
        raise RuntimeError(f"Unknown checkpoint: {x}")
    p.override_settings['sd_model_checkpoint'] = info.name


def confirm_checkpoints(p, xs):
    for x in xs:
        if modules.sd_models.get_closet_checkpoint_match(x) is None:
            raise RuntimeError(f"Unknown checkpoint: {x}")


def confirm_checkpoints_or_none(p, xs):
    for x in xs:
        if x in (None, "", "None", "none"):
            continue

        if modules.sd_models.get_closet_checkpoint_match(x) is None:
            raise RuntimeError(f"Unknown checkpoint: {x}")


def 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, boolean: bool = False):
    def fun(p, x, xs):
        if boolean:
            x = True if x.lower() == "true" else False
        p.override_settings[field] = x
    return fun


def boolean_choice(reverse: bool = False):
    def choice():
        return ["False", "True"] if reverse else ["True", "False"]
    return choice


def format_value_add_label(p, opt, x):
    if type(x) == float:
        x = round(x, 8)

    return f"{opt.label}: {x}"


def format_value(p, opt, x):
    if type(x) == float:
        x = round(x, 8)
    return x


def format_value_join_list(p, opt, x):
    return ", ".join(x)


def do_nothing(p, x, xs):
    pass


def format_nothing(p, opt, x):
    return ""


def format_remove_path(p, opt, x):
    return os.path.basename(x)


def str_permutations(x):
    """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
    return x


def list_to_csv_string(data_list):
    with StringIO() as o:
        csv.writer(o).writerow(data_list)
        return o.getvalue().strip()


def csv_string_to_list_strip(data_str):
    return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str)))))


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_field("sampler_name"), format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers if x.name not in opts.hide_samplers]),
    AxisOptionTxt2Img("Hires sampler", str, apply_field("hr_sampler_name"), confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img if x.name not in opts.hide_samplers]),
    AxisOptionImg2Img("Sampler", str, apply_field("sampler_name"), format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img if x.name not in opts.hide_samplers]),
    AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_remove_path, confirm=confirm_checkpoints, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list, key=str.casefold)),
    AxisOption("Negative Guidance minimum sigma", float, apply_field("s_min_uncond")),
    AxisOption("Sigma Churn", float, apply_field("s_churn")),
    AxisOption("Sigma min", float, apply_field("s_tmin")),
    AxisOption("Sigma max", float, apply_field("s_tmax")),
    AxisOption("Sigma noise", float, apply_field("s_noise")),
    AxisOption("Schedule type", str, apply_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")),
    AxisOption("Initial noise multiplier", float, apply_field("initial_noise_multiplier")),
    AxisOption("Extra noise", float, apply_override("img2img_extra_noise")),
    AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
    AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
    AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
    AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
    AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
    AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
    AxisOption("Token merging ratio", float, apply_override('token_merging_ratio')),
    AxisOption("Token merging ratio high-res", float, apply_override('token_merging_ratio_hr')),
    AxisOption("Always discard next-to-last sigma", str, apply_override('always_discard_next_to_last_sigma', boolean=True), choices=boolean_choice(reverse=True)),
    AxisOption("SGM noise multiplier", str, apply_override('sgm_noise_multiplier', boolean=True), choices=boolean_choice(reverse=True)),
    AxisOption("Refiner checkpoint", str, apply_field('refiner_checkpoint'), format_value=format_remove_path, confirm=confirm_checkpoints_or_none, cost=1.0, choices=lambda: ['None'] + sorted(sd_models.checkpoints_list, key=str.casefold)),
    AxisOption("Refiner switch at", float, apply_field('refiner_switch_at')),
    AxisOption("RNG source", str, apply_override("randn_source"), choices=lambda: ["GPU", "CPU", "NV"]),
]


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:
            # 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)

    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"))
                    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.Column():
                csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))

        with gr.Row(variant="compact", elem_id="swap_axes"):
            swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
            swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
            swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")

        def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown):
            return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown

        xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
        swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
        yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
        swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
        xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
        swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)

        def fill(axis_type, csv_mode):
            axis = self.current_axis_options[axis_type]
            if axis.choices:
                if csv_mode:
                    return list_to_csv_string(axis.choices()), gr.update()
                else:
                    return gr.update(), axis.choices()
            else:
                return gr.update(), gr.update()

        fill_x_button.click(fn=fill, inputs=[x_type, csv_mode], outputs=[x_values, x_values_dropdown])
        fill_y_button.click(fn=fill, inputs=[y_type, csv_mode], outputs=[y_values, y_values_dropdown])
        fill_z_button.click(fn=fill, inputs=[z_type, csv_mode], outputs=[z_values, z_values_dropdown])

        def select_axis(axis_type, axis_values, axis_values_dropdown, csv_mode):
            choices = self.current_axis_options[axis_type].choices
            has_choices = choices is not None

            if has_choices:
                choices = choices()
                if csv_mode:
                    if axis_values_dropdown:
                        axis_values = list_to_csv_string(list(filter(lambda x: x in choices, axis_values_dropdown)))
                        axis_values_dropdown = []
                else:
                    if axis_values:
                        axis_values_dropdown = list(filter(lambda x: x in choices, csv_string_to_list_strip(axis_values)))
                        axis_values = ""

            return (gr.Button.update(visible=has_choices), gr.Textbox.update(visible=not has_choices or csv_mode, value=axis_values),
                    gr.update(choices=choices if has_choices else None, visible=has_choices and not csv_mode, value=axis_values_dropdown))

        x_type.change(fn=select_axis, inputs=[x_type, x_values, x_values_dropdown, csv_mode], outputs=[fill_x_button, x_values, x_values_dropdown])
        y_type.change(fn=select_axis, inputs=[y_type, y_values, y_values_dropdown, csv_mode], outputs=[fill_y_button, y_values, y_values_dropdown])
        z_type.change(fn=select_axis, inputs=[z_type, z_values, z_values_dropdown, csv_mode], outputs=[fill_z_button, z_values, z_values_dropdown])

        def change_choice_mode(csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown):
            _fill_x_button, _x_values, _x_values_dropdown = select_axis(x_type, x_values, x_values_dropdown, csv_mode)
            _fill_y_button, _y_values, _y_values_dropdown = select_axis(y_type, y_values, y_values_dropdown, csv_mode)
            _fill_z_button, _z_values, _z_values_dropdown = select_axis(z_type, z_values, z_values_dropdown, csv_mode)
            return _fill_x_button, _x_values, _x_values_dropdown, _fill_y_button, _y_values, _y_values_dropdown, _fill_z_button, _z_values, _z_values_dropdown

        csv_mode.change(fn=change_choice_mode, inputs=[csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown], outputs=[fill_x_button, x_values, x_values_dropdown, fill_y_button, y_values, y_values_dropdown, fill_z_button, z_values, z_values_dropdown])

        def get_dropdown_update_from_params(axis, params):
            val_key = f"{axis} Values"
            vals = params.get(val_key, "")
            valslist = csv_string_to_list_strip(vals)
            return gr.update(value=valslist)

        self.infotext_fields = (
            (x_type, "X Type"),
            (x_values, "X Values"),
            (x_values_dropdown, lambda params: get_dropdown_update_from_params("X", params)),
            (y_type, "Y Type"),
            (y_values, "Y Values"),
            (y_values_dropdown, lambda params: get_dropdown_update_from_params("Y", params)),
            (z_type, "Z Type"),
            (z_values, "Z Values"),
            (z_values_dropdown, lambda params: get_dropdown_update_from_params("Z", params)),
        )

        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size, csv_mode]

    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, csv_mode):
        if not no_fixed_seeds:
            modules.processing.fix_seed(p)

        if not opts.return_grid:
            p.batch_size = 1

        def process_axis(opt, vals, vals_dropdown):
            if opt.label == 'Nothing':
                return [0]

            if opt.choices is not None and not csv_mode:
                valslist = vals_dropdown
            else:
                valslist = csv_string_to_list_strip(vals)

            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]
        if x_opt.choices is not None and not csv_mode:
            x_values = list_to_csv_string(x_values_dropdown)
        xs = process_axis(x_opt, x_values, x_values_dropdown)

        y_opt = self.current_axis_options[y_type]
        if y_opt.choices is not None and not csv_mode:
            y_values = list_to_csv_string(y_values_dropdown)
        ys = process_axis(y_opt, y_values, y_values_dropdown)

        z_opt = self.current_axis_options[z_type]
        if z_opt.choices is not None and not csv_mode:
            z_values = list_to_csv_string(z_values_dropdown)
        zs = process_axis(z_opt, z_values, z_values_dropdown)

        # 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)'

        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)

        # 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):
            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)

            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])

                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)

                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)

        # 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:
            # 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 _ in range(z_count):
                del processed.images[1]
                del processed.all_prompts[1]
                del processed.all_seeds[1]
                del processed.infotexts[1]

        return processed