File size: 41,387 Bytes
d77a781
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
import torch
from transformers import CLIPModel, CLIPTextModel, CLIPTokenizer
from omegaconf import OmegaConf
import matplotlib.pyplot as plt
import math
import imageio
from PIL import Image
import torchvision
import torch.nn.functional as F
import torch
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import time
import datetime
import torch
import sys
import os
from torchvision import datasets
import pickle

# StableDiffusion P2P implementation originally from https://github.com/bloc97/CrossAttentionControl
use_half_prec = True
if use_half_prec:
    from my_half_diffusers import AutoencoderKL, UNet2DConditionModel
    from my_half_diffusers.schedulers.scheduling_utils import SchedulerOutput
    from my_half_diffusers import LMSDiscreteScheduler, PNDMScheduler, DDPMScheduler, DDIMScheduler
else:
    from my_diffusers import AutoencoderKL, UNet2DConditionModel
    from my_diffusers.schedulers.scheduling_utils import SchedulerOutput
    from my_diffusers import LMSDiscreteScheduler, PNDMScheduler, DDPMScheduler, DDIMScheduler
torch_dtype = torch.float16 if use_half_prec else torch.float64
np_dtype = np.float16 if use_half_prec else np.float64



import random
from tqdm.auto import tqdm
from torch import autocast
from difflib import SequenceMatcher

# Build our CLIP model
model_path_clip = "openai/clip-vit-large-patch14"
clip_tokenizer = CLIPTokenizer.from_pretrained(model_path_clip)
clip_model = CLIPModel.from_pretrained(model_path_clip, torch_dtype=torch_dtype)
clip = clip_model.text_model


# Getting our HF Auth token
auth_token = os.environ.get('hf_auth')
if auth_token is None:
    with open('hf_auth', 'r') as f:
        auth_token = f.readlines()[0].strip()
model_path_diffusion = "CompVis/stable-diffusion-v1-4"
# Build our SD model
unet = UNet2DConditionModel.from_pretrained(model_path_diffusion, subfolder="unet", use_auth_token=auth_token, revision="fp16", torch_dtype=torch_dtype)
vae = AutoencoderKL.from_pretrained(model_path_diffusion, subfolder="vae", use_auth_token=auth_token, revision="fp16", torch_dtype=torch_dtype)

# Push to devices w/ double precision
device = 'cuda'
if use_half_prec:
    unet.to(device)
    vae.to(device)
    clip.to(device)
else:
    unet.double().to(device)
    vae.double().to(device)
    clip.double().to(device)
print("Loaded all models")


    
    
def EDICT_editing(im_path,
                  base_prompt,
                  edit_prompt,
                  use_p2p=False,
                  steps=50,
                  mix_weight=0.93,
                  init_image_strength=0.8,
                  guidance_scale=3,
                 run_baseline=False,
             width=512, height=512):
    """
    Main call of our research, performs editing with either EDICT or DDIM
    
    Args:
        im_path: path to image to run on
        base_prompt: conditional prompt to deterministically noise with
        edit_prompt: desired text conditoining
        steps: ddim steps
        mix_weight: Weight of mixing layers.
            Higher means more consistent generations but divergence in inversion
            Lower means opposite
            This is fairly tuned and can get good results
        init_image_strength: Editing strength. Higher = more dramatic edit. 
            Typically [0.6, 0.9] is good range.
            Definitely tunable per-image/maybe best results are at a different value
        guidance_scale: classifier-free guidance scale
            3 I've found is the best for both our method and basic DDIM inversion
            Higher can result in more distorted results
        run_baseline:
            VERY IMPORTANT
            True is EDICT, False is DDIM
    Output:
        PAIR of Images (tuple)
        If run_baseline=True then [0] will be edit and [1] will be original
        If run_baseline=False then they will be two nearly identical edited versions
    """
    # Resize/center crop to 512x512 (Can do higher res. if desired)
    if isinstance(im_path, str):
        orig_im = load_im_into_format_from_path(im_path)
    elif Image.isImageType(im_path):
        width, height = im_path.size
        
        
        # add max dim for sake of memory
        max_dim = max(width, height)
        if max_dim > 1024:
            factor = 1024 / max_dim
            width *= factor
            height *= factor
            width = int(width)
            height = int(height)
            im_path = im_path.resize((width, height))
            
        min_dim = min(width, height)
        if min_dim < 512:
            factor = 512 / min_dim
            width *= factor
            height *= factor
            width = int(width)
            height = int(height)
            im_path = im_path.resize((width, height))
            
        width = width - (width%64)
        height = height - (height%64)
        
        orig_im = im_path # general_crop(im_path, width, height)
    else:
        orig_im = im_path  
    
    # compute latent pair (second one will be original latent if run_baseline=True)
    latents = coupled_stablediffusion(base_prompt,
                                     reverse=True,
                                      init_image=orig_im,
                                     init_image_strength=init_image_strength,
                                      steps=steps,
                                      mix_weight=mix_weight,
                                     guidance_scale=guidance_scale,
                                     run_baseline=run_baseline,
                                         width=width, height=height)
    # Denoise intermediate state with new conditioning
    gen = coupled_stablediffusion(edit_prompt if (not use_p2p) else base_prompt,
                                  None if (not use_p2p) else edit_prompt,
                                fixed_starting_latent=latents,
                                 init_image_strength=init_image_strength,
                                steps=steps,
                                mix_weight=mix_weight,
                                 guidance_scale=guidance_scale,
                                 run_baseline=run_baseline,
                                         width=width, height=height)
    
    return gen
    

def img2img_editing(im_path,
                  edit_prompt,
                  steps=50,
                  init_image_strength=0.7,
                  guidance_scale=3):
    """
    Basic SDEdit/img2img, given an image add some noise and denoise with prompt
    """
    orig_im = load_im_into_format_from_path(im_path)
    
    return baseline_stablediffusion(edit_prompt,
                                     init_image_strength=init_image_strength,
                                    steps=steps,
                                  init_image=orig_im,
                                 guidance_scale=guidance_scale)


def center_crop(im):
    width, height = im.size   # Get dimensions
    min_dim = min(width, height)
    left = (width - min_dim)/2
    top = (height - min_dim)/2
    right = (width + min_dim)/2
    bottom = (height + min_dim)/2

    # Crop the center of the image
    im = im.crop((left, top, right, bottom))
    return im



def general_crop(im, target_w, target_h):
    width, height = im.size   # Get dimensions
    min_dim = min(width, height)
    left = target_w / 2 # (width - min_dim)/2
    top = target_h / 2 # (height - min_dim)/2
    right = width - (target_w / 2) # (width + min_dim)/2
    bottom = height - (target_h / 2) # (height + min_dim)/2

    # Crop the center of the image
    im = im.crop((left, top, right, bottom))
    return im



def load_im_into_format_from_path(im_path):
    return center_crop(Image.open(im_path)).resize((512,512))


#### P2P STUFF #### 
def init_attention_weights(weight_tuples):
    tokens_length = clip_tokenizer.model_max_length
    weights = torch.ones(tokens_length)
    
    for i, w in weight_tuples:
        if i < tokens_length and i >= 0:
            weights[i] = w
    
    
    for name, module in unet.named_modules():
        module_name = type(module).__name__
        if module_name == "CrossAttention" and "attn2" in name:
            module.last_attn_slice_weights = weights.to(device)
        if module_name == "CrossAttention" and "attn1" in name:
            module.last_attn_slice_weights = None
    

def init_attention_edit(tokens, tokens_edit):
    tokens_length = clip_tokenizer.model_max_length
    mask = torch.zeros(tokens_length)
    indices_target = torch.arange(tokens_length, dtype=torch.long)
    indices = torch.zeros(tokens_length, dtype=torch.long)

    tokens = tokens.input_ids.numpy()[0]
    tokens_edit = tokens_edit.input_ids.numpy()[0]
    
    for name, a0, a1, b0, b1 in SequenceMatcher(None, tokens, tokens_edit).get_opcodes():
        if b0 < tokens_length:
            if name == "equal" or (name == "replace" and a1-a0 == b1-b0):
                mask[b0:b1] = 1
                indices[b0:b1] = indices_target[a0:a1]

    for name, module in unet.named_modules():
        module_name = type(module).__name__
        if module_name == "CrossAttention" and "attn2" in name:
            module.last_attn_slice_mask = mask.to(device)
            module.last_attn_slice_indices = indices.to(device)
        if module_name == "CrossAttention" and "attn1" in name:
            module.last_attn_slice_mask = None
            module.last_attn_slice_indices = None


def init_attention_func():
    def new_attention(self, query, key, value, sequence_length, dim):
        batch_size_attention = query.shape[0]
        hidden_states = torch.zeros(
            (batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
        )
        slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
        for i in range(hidden_states.shape[0] // slice_size):
            start_idx = i * slice_size
            end_idx = (i + 1) * slice_size
            attn_slice = (
                torch.einsum("b i d, b j d -> b i j", query[start_idx:end_idx], key[start_idx:end_idx]) * self.scale
            )
            attn_slice = attn_slice.softmax(dim=-1)
            
            if self.use_last_attn_slice:
                if self.last_attn_slice_mask is not None:
                    new_attn_slice = torch.index_select(self.last_attn_slice, -1, self.last_attn_slice_indices)
                    attn_slice = attn_slice * (1 - self.last_attn_slice_mask) + new_attn_slice * self.last_attn_slice_mask
                else:
                    attn_slice = self.last_attn_slice
                
                self.use_last_attn_slice = False
                    
            if self.save_last_attn_slice:
                self.last_attn_slice = attn_slice
                self.save_last_attn_slice = False
                
            if self.use_last_attn_weights and self.last_attn_slice_weights is not None:
                attn_slice = attn_slice * self.last_attn_slice_weights
                self.use_last_attn_weights = False

            attn_slice = torch.einsum("b i j, b j d -> b i d", attn_slice, value[start_idx:end_idx])

            hidden_states[start_idx:end_idx] = attn_slice

        # reshape hidden_states
        hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
        return hidden_states

    for name, module in unet.named_modules():
        module_name = type(module).__name__
        if module_name == "CrossAttention":
            module.last_attn_slice = None
            module.use_last_attn_slice = False
            module.use_last_attn_weights = False
            module.save_last_attn_slice = False
            module._attention = new_attention.__get__(module, type(module))
            
def use_last_tokens_attention(use=True):
    for name, module in unet.named_modules():
        module_name = type(module).__name__
        if module_name == "CrossAttention" and "attn2" in name:
            module.use_last_attn_slice = use
            
def use_last_tokens_attention_weights(use=True):
    for name, module in unet.named_modules():
        module_name = type(module).__name__
        if module_name == "CrossAttention" and "attn2" in name:
            module.use_last_attn_weights = use
            
def use_last_self_attention(use=True):
    for name, module in unet.named_modules():
        module_name = type(module).__name__
        if module_name == "CrossAttention" and "attn1" in name:
            module.use_last_attn_slice = use
            
def save_last_tokens_attention(save=True):
    for name, module in unet.named_modules():
        module_name = type(module).__name__
        if module_name == "CrossAttention" and "attn2" in name:
            module.save_last_attn_slice = save
            
def save_last_self_attention(save=True):
    for name, module in unet.named_modules():
        module_name = type(module).__name__
        if module_name == "CrossAttention" and "attn1" in name:
            module.save_last_attn_slice = save
####################################


##### BASELINE ALGORITHM, ONLY USED NOW FOR SDEDIT ####3

@torch.no_grad()
def baseline_stablediffusion(prompt="",
                    prompt_edit=None,
                             null_prompt='',
                    prompt_edit_token_weights=[],
                    prompt_edit_tokens_start=0.0,
                    prompt_edit_tokens_end=1.0,
                    prompt_edit_spatial_start=0.0,
                    prompt_edit_spatial_end=1.0,
                    clip_start=0.0,
                    clip_end=1.0,
                    guidance_scale=7,
                    steps=50,
                    seed=1,
                    width=512, height=512,
                    init_image=None, init_image_strength=0.5,
                    fixed_starting_latent = None,
                   prev_image= None,
                   grid=None,
                   clip_guidance=None,
                   clip_guidance_scale=1,
                   num_cutouts=4,
                   cut_power=1,
                   scheduler_str='lms',
                    return_latent=False,
                            one_pass=False,
                            normalize_noise_pred=False):
    width = width - width % 64
    height = height - height % 64
    
    #If seed is None, randomly select seed from 0 to 2^32-1
    if seed is None: seed = random.randrange(2**32 - 1)
    generator = torch.cuda.manual_seed(seed)
    
    #Set inference timesteps to scheduler
    scheduler_dict = {'ddim':DDIMScheduler,
                     'lms':LMSDiscreteScheduler,
                     'pndm':PNDMScheduler,
                     'ddpm':DDPMScheduler}
    scheduler_call = scheduler_dict[scheduler_str]
    if scheduler_str == 'ddim':
        scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012,
                                     beta_schedule="scaled_linear",
                                     clip_sample=False, set_alpha_to_one=False)
    else:
        scheduler = scheduler_call(beta_schedule="scaled_linear",
                              num_train_timesteps=1000)

    scheduler.set_timesteps(steps)
    if prev_image is not None:
        prev_scheduler = LMSDiscreteScheduler(beta_start=0.00085,
                                         beta_end=0.012,
                                              beta_schedule="scaled_linear",
                                         num_train_timesteps=1000)
        prev_scheduler.set_timesteps(steps)
    
    #Preprocess image if it exists (img2img)
    if init_image is not None:
        init_image = init_image.resize((width, height), resample=Image.Resampling.LANCZOS)
        init_image = np.array(init_image).astype(np_dtype) / 255.0 * 2.0 - 1.0
        init_image = torch.from_numpy(init_image[np.newaxis, ...].transpose(0, 3, 1, 2))

        #If there is alpha channel, composite alpha for white, as the diffusion model does not support alpha channel
        if init_image.shape[1] > 3:
            init_image = init_image[:, :3] * init_image[:, 3:] + (1 - init_image[:, 3:])

        #Move image to GPU
        init_image = init_image.to(device)

        #Encode image
        with autocast(device):
            init_latent = vae.encode(init_image).latent_dist.sample(generator=generator) * 0.18215

        t_start = steps - int(steps * init_image_strength)
            
    else:
        init_latent = torch.zeros((1, unet.in_channels, height // 8, width // 8), device=device)
        t_start = 0
    
    #Generate random normal noise
    if fixed_starting_latent is None:
        noise = torch.randn(init_latent.shape, generator=generator, device=device, dtype=unet.dtype)
        if scheduler_str == 'ddim':
            if init_image is not None:
                raise notImplementedError
                latent = scheduler.add_noise(init_latent, noise,
                                         1000 - int(1000 * init_image_strength)).to(device)
            else:
                latent = noise
        else:
            latent = scheduler.add_noise(init_latent, noise,
                                         t_start).to(device)
    else:
        latent = fixed_starting_latent
        t_start = steps - int(steps * init_image_strength)
    
    if prev_image is not None:
        #Resize and prev_image for numpy b h w c -> torch b c h w
        prev_image = prev_image.resize((width, height), resample=Image.Resampling.LANCZOS)
        prev_image = np.array(prev_image).astype(np_dtype) / 255.0 * 2.0 - 1.0
        prev_image = torch.from_numpy(prev_image[np.newaxis, ...].transpose(0, 3, 1, 2))
        
        #If there is alpha channel, composite alpha for white, as the diffusion model does not support alpha channel
        if prev_image.shape[1] > 3:
            prev_image = prev_image[:, :3] * prev_image[:, 3:] + (1 - prev_image[:, 3:])
            
        #Move image to GPU
        prev_image = prev_image.to(device)
        
        #Encode image
        with autocast(device):
            prev_init_latent = vae.encode(prev_image).latent_dist.sample(generator=generator) * 0.18215
            
        t_start = steps - int(steps * init_image_strength)
        
        prev_latent = prev_scheduler.add_noise(prev_init_latent, noise, t_start).to(device)
    else:
        prev_latent = None
        
    
    #Process clip
    with autocast(device):
        tokens_unconditional = clip_tokenizer(null_prompt, padding="max_length", max_length=clip_tokenizer.model_max_length, truncation=True, return_tensors="pt", return_overflowing_tokens=True)
        embedding_unconditional = clip(tokens_unconditional.input_ids.to(device)).last_hidden_state

        tokens_conditional = clip_tokenizer(prompt, padding="max_length", max_length=clip_tokenizer.model_max_length, truncation=True, return_tensors="pt", return_overflowing_tokens=True)
        embedding_conditional = clip(tokens_conditional.input_ids.to(device)).last_hidden_state

        #Process prompt editing
        assert not ((prompt_edit is not None) and (prev_image is not None))
        if prompt_edit is not None:
            tokens_conditional_edit = clip_tokenizer(prompt_edit, padding="max_length", max_length=clip_tokenizer.model_max_length, truncation=True, return_tensors="pt", return_overflowing_tokens=True)
            embedding_conditional_edit = clip(tokens_conditional_edit.input_ids.to(device)).last_hidden_state
            init_attention_edit(tokens_conditional, tokens_conditional_edit)
        elif prev_image is not None:
            init_attention_edit(tokens_conditional, tokens_conditional)
            
            
        init_attention_func()
        init_attention_weights(prompt_edit_token_weights)
            
        timesteps = scheduler.timesteps[t_start:]
        # print(timesteps)
        
        assert isinstance(guidance_scale, int)
        num_cycles = 1 # guidance_scale + 1
        
        last_noise_preds = None
        for i, t in tqdm(enumerate(timesteps), total=len(timesteps)):
            t_index = t_start + i
            
            latent_model_input = latent
            if scheduler_str=='lms':
                sigma = scheduler.sigmas[t_index] # last is first and first is last
                latent_model_input = (latent_model_input / ((sigma**2 + 1) ** 0.5)).to(unet.dtype)
            else:
                assert scheduler_str in ['ddim', 'pndm', 'ddpm']

            #Predict the unconditional noise residual

            if len(t.shape) == 0:
                t = t[None].to(unet.device)
            noise_pred_uncond = unet(latent_model_input, t, encoder_hidden_states=embedding_unconditional,
                                   ).sample

            if prev_latent is not None:
                prev_latent_model_input = prev_latent
                prev_latent_model_input = (prev_latent_model_input / ((sigma**2 + 1) ** 0.5)).to(unet.dtype)
                prev_noise_pred_uncond = unet(prev_latent_model_input, t,
                                              encoder_hidden_states=embedding_unconditional,
                                       ).sample
            # noise_pred_uncond = unet(latent_model_input, t,
            #                          encoder_hidden_states=embedding_unconditional)['sample']

            #Prepare the Cross-Attention layers
            if prompt_edit is not None or prev_latent is not None:
                save_last_tokens_attention()
                save_last_self_attention()
            else:
                #Use weights on non-edited prompt when edit is None
                use_last_tokens_attention_weights()

            #Predict the conditional noise residual and save the cross-attention layer activations
            if prev_latent is not None:
                raise NotImplementedError # I totally lost track of what this is
                prev_noise_pred_cond = unet(prev_latent_model_input, t, encoder_hidden_states=embedding_conditional,
                                      ).sample
            else:
                noise_pred_cond = unet(latent_model_input, t, encoder_hidden_states=embedding_conditional,
                                      ).sample

            #Edit the Cross-Attention layer activations
            t_scale = t / scheduler.num_train_timesteps
            if prompt_edit is not None or prev_latent is not None:
                if t_scale >= prompt_edit_tokens_start and t_scale <= prompt_edit_tokens_end:
                    use_last_tokens_attention()
                if t_scale >= prompt_edit_spatial_start and t_scale <= prompt_edit_spatial_end:
                    use_last_self_attention()

                #Use weights on edited prompt
                use_last_tokens_attention_weights()

                #Predict the edited conditional noise residual using the cross-attention masks
                if prompt_edit is not None:
                    noise_pred_cond = unet(latent_model_input, t,
                                           encoder_hidden_states=embedding_conditional_edit).sample

            #Perform guidance
            # if i%(num_cycles)==0: # cycle_i+1==num_cycles:
            """
            if cycle_i+1==num_cycles:
                noise_pred = noise_pred_uncond
            else:
                noise_pred = noise_pred_cond - noise_pred_uncond

            """
            if last_noise_preds is not None:
                # print( (last_noise_preds[0]*noise_pred_uncond).sum(), (last_noise_preds[1]*noise_pred_cond).sum())
                # print(F.cosine_similarity(last_noise_preds[0].flatten(), noise_pred_uncond.flatten(), dim=0),
                #      F.cosine_similarity(last_noise_preds[1].flatten(), noise_pred_cond.flatten(), dim=0))
                last_grad= last_noise_preds[1] - last_noise_preds[0]
                new_grad = noise_pred_cond - noise_pred_uncond
                # print( F.cosine_similarity(last_grad.flatten(), new_grad.flatten(), dim=0))
            last_noise_preds = (noise_pred_uncond, noise_pred_cond)

            use_cond_guidance = True 
            if use_cond_guidance:
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
            else:
                noise_pred = noise_pred_uncond
            if clip_guidance is not None and t_scale >= clip_start and t_scale <= clip_end:
                noise_pred, latent = new_cond_fn(latent, t, t_index,
                                                 embedding_conditional, noise_pred,clip_guidance,
                                                clip_guidance_scale, 
                                                num_cutouts, 
                                                scheduler, unet,use_cutouts=True,
                                                cut_power=cut_power)
            if normalize_noise_pred:
                noise_pred = noise_pred * noise_pred_uncond.norm() /  noise_pred.norm()
            if scheduler_str == 'ddim':
                latent = forward_step(scheduler, noise_pred,
                                        t,
                                        latent).prev_sample
            else:
                latent = scheduler.step(noise_pred,
                                        t_index,
                                        latent).prev_sample

            if prev_latent is not None:
                prev_noise_pred = prev_noise_pred_uncond + guidance_scale * (prev_noise_pred_cond - prev_noise_pred_uncond)
                prev_latent = prev_scheduler.step(prev_noise_pred, t_index, prev_latent).prev_sample
            if one_pass: break

        #scale and decode the image latents with vae
        if return_latent: return latent
        latent = latent / 0.18215
        image = vae.decode(latent.to(vae.dtype)).sample

    image = (image / 2 + 0.5).clamp(0, 1)
    image = image.cpu().permute(0, 2, 3, 1).numpy()
    image = (image[0] * 255).round().astype("uint8")
    return Image.fromarray(image)
####################################

#### HELPER FUNCTIONS FOR OUR METHOD #####

def get_alpha_and_beta(t, scheduler):
    # want to run this for both current and previous timnestep
    if t.dtype==torch.long:
        alpha = scheduler.alphas_cumprod[t]
        return alpha, 1-alpha
    
    if t<0:
        return scheduler.final_alpha_cumprod, 1 - scheduler.final_alpha_cumprod

    
    low = t.floor().long()
    high = t.ceil().long()
    rem = t - low
    
    low_alpha = scheduler.alphas_cumprod[low]
    high_alpha = scheduler.alphas_cumprod[high]
    interpolated_alpha = low_alpha * rem + high_alpha * (1-rem)
    interpolated_beta = 1 - interpolated_alpha
    return interpolated_alpha, interpolated_beta
    

# A DDIM forward step function
def forward_step(
    self,
    model_output,
    timestep: int,
    sample,
    eta: float = 0.0,
    use_clipped_model_output: bool = False,
    generator=None,
    return_dict: bool = True,
    use_double=False,
) :
    if self.num_inference_steps is None:
        raise ValueError(
            "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
        )

    prev_timestep = timestep - self.config.num_train_timesteps / self.num_inference_steps
        
    if timestep > self.timesteps.max():
        raise NotImplementedError("Need to double check what the overflow is")
  
    alpha_prod_t, beta_prod_t = get_alpha_and_beta(timestep, self)
    alpha_prod_t_prev, _ = get_alpha_and_beta(prev_timestep, self)
    
    
    alpha_quotient = ((alpha_prod_t / alpha_prod_t_prev)**0.5)
    first_term =  (1./alpha_quotient) * sample
    second_term = (1./alpha_quotient) * (beta_prod_t ** 0.5) * model_output
    third_term = ((1 - alpha_prod_t_prev)**0.5) * model_output
    return first_term - second_term + third_term
                
# A DDIM reverse step function, the inverse of above
def reverse_step(
    self,
    model_output,
    timestep: int,
    sample,
    eta: float = 0.0,
    use_clipped_model_output: bool = False,
    generator=None,
    return_dict: bool = True,
    use_double=False,
) :
    if self.num_inference_steps is None:
        raise ValueError(
            "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
        )

    prev_timestep = timestep - self.config.num_train_timesteps / self.num_inference_steps
   
    if timestep > self.timesteps.max():
        raise NotImplementedError
    else:
        alpha_prod_t = self.alphas_cumprod[timestep]
        
    alpha_prod_t, beta_prod_t = get_alpha_and_beta(timestep, self)
    alpha_prod_t_prev, _ = get_alpha_and_beta(prev_timestep, self)
    
    alpha_quotient = ((alpha_prod_t / alpha_prod_t_prev)**0.5)
    
    first_term =  alpha_quotient * sample
    second_term = ((beta_prod_t)**0.5) * model_output
    third_term = alpha_quotient * ((1 - alpha_prod_t_prev)**0.5) * model_output
    return first_term + second_term - third_term  
 



@torch.no_grad()
def latent_to_image(latent):
    image = vae.decode(latent.to(vae.dtype)/0.18215).sample
    image = prep_image_for_return(image)
    return image

def prep_image_for_return(image):
    image = (image / 2 + 0.5).clamp(0, 1)
    image = image.cpu().permute(0, 2, 3, 1).numpy()
    image = (image[0] * 255).round().astype("uint8")
    image = Image.fromarray(image)
    return image

#############################

##### MAIN EDICT FUNCTION #######
# Use EDICT_editing to perform calls

@torch.no_grad()
def coupled_stablediffusion(prompt="",
                           prompt_edit=None,
                            null_prompt='',
                            prompt_edit_token_weights=[],
                            prompt_edit_tokens_start=0.0,
                            prompt_edit_tokens_end=1.0,
                            prompt_edit_spatial_start=0.0,
                            prompt_edit_spatial_end=1.0,
                            guidance_scale=7.0, steps=50,
                            seed=1, width=512, height=512,
                            init_image=None, init_image_strength=1.0,
                           run_baseline=False,
                           use_lms=False,
                           leapfrog_steps=True,
                          reverse=False,
                          return_latents=False,
                          fixed_starting_latent=None,
                           beta_schedule='scaled_linear',
                            mix_weight=0.93):
    #If seed is None, randomly select seed from 0 to 2^32-1
    if seed is None: seed = random.randrange(2**32 - 1)
    generator = torch.cuda.manual_seed(seed)

    def image_to_latent(im):
        if isinstance(im, torch.Tensor):
            # assume it's the latent
            # used to avoid clipping new generation before inversion
            init_latent = im.to(device)
        else:
            #Resize and transpose for numpy b h w c -> torch b c h w
            im = im.resize((width, height), resample=Image.Resampling.LANCZOS)
            im = np.array(im).astype(np_dtype) / 255.0 * 2.0 - 1.0
            # check if black and white
            if len(im.shape) < 3:
                im = np.stack([im for _ in range(3)], axis=2) # putting at end b/c channels
                
            im = torch.from_numpy(im[np.newaxis, ...].transpose(0, 3, 1, 2))

            #If there is alpha channel, composite alpha for white, as the diffusion model does not support alpha channel
            if im.shape[1] > 3:
                im = im[:, :3] * im[:, 3:] + (1 - im[:, 3:])

            #Move image to GPU
            im = im.to(device)
            #Encode image
            if use_half_prec:
                init_latent = vae.encode(im).latent_dist.sample(generator=generator) * 0.18215
            else:
                with autocast(device):
                    init_latent = vae.encode(im).latent_dist.sample(generator=generator) * 0.18215
            return init_latent
    assert not use_lms, "Can't invert LMS the same as DDIM"
    if run_baseline: leapfrog_steps=False
    #Change size to multiple of 64 to prevent size mismatches inside model
    width = width - width % 64
    height = height - height % 64
    
    
    #Preprocess image if it exists (img2img)
    if init_image is not None:
        assert reverse # want to be performing deterministic noising 
        # can take either pair (output of generative process) or single image
        if isinstance(init_image, list):
            if isinstance(init_image[0], torch.Tensor):
                init_latent = [t.clone() for t in init_image]
            else:
                init_latent = [image_to_latent(im) for im in init_image]
        else:
            init_latent = image_to_latent(init_image)
        # this is t_start for forward, t_end for reverse
        t_limit = steps - int(steps * init_image_strength)
    else:
        assert not reverse, 'Need image to reverse from'
        init_latent = torch.zeros((1, unet.in_channels, height // 8, width // 8), device=device)
        t_limit = 0
    
    if reverse:
        latent = init_latent
    else:
        #Generate random normal noise
        noise = torch.randn(init_latent.shape,
                            generator=generator,
                            device=device,
                           dtype=torch_dtype)
        if fixed_starting_latent is None:
            latent = noise
        else:
            if isinstance(fixed_starting_latent, list):
                latent = [l.clone() for l in fixed_starting_latent]
            else:
                latent = fixed_starting_latent.clone()
            t_limit = steps - int(steps * init_image_strength)
    if isinstance(latent, list): # initializing from pair of images
        latent_pair = latent
    else: # initializing from noise
        latent_pair = [latent.clone(), latent.clone()]
        
    
    if steps==0:
        if init_image is not None:
            return image_to_latent(init_image)
        else:
            image = vae.decode(latent.to(vae.dtype) / 0.18215).sample
            return prep_image_for_return(image)
    
    #Set inference timesteps to scheduler
    schedulers = []
    for i in range(2):
        # num_raw_timesteps = max(1000, steps)
        scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012,
                                     beta_schedule=beta_schedule,
                                  num_train_timesteps=1000,
                                     clip_sample=False,
                                  set_alpha_to_one=False)
        scheduler.set_timesteps(steps)
        schedulers.append(scheduler)
    
    with autocast(device):
        # CLIP Text Embeddings
        tokens_unconditional = clip_tokenizer(null_prompt, padding="max_length",
                                              max_length=clip_tokenizer.model_max_length,
                                              truncation=True, return_tensors="pt", 
                                              return_overflowing_tokens=True)
        embedding_unconditional = clip(tokens_unconditional.input_ids.to(device)).last_hidden_state

        tokens_conditional = clip_tokenizer(prompt, padding="max_length", 
                                            max_length=clip_tokenizer.model_max_length,
                                            truncation=True, return_tensors="pt", 
                                            return_overflowing_tokens=True)
        embedding_conditional = clip(tokens_conditional.input_ids.to(device)).last_hidden_state

        #Process prompt editing (if running Prompt-to-Prompt)
        if prompt_edit is not None:
            tokens_conditional_edit = clip_tokenizer(prompt_edit, padding="max_length", 
                                                     max_length=clip_tokenizer.model_max_length,
                                                     truncation=True, return_tensors="pt", 
                                                     return_overflowing_tokens=True)
            embedding_conditional_edit = clip(tokens_conditional_edit.input_ids.to(device)).last_hidden_state

            init_attention_edit(tokens_conditional, tokens_conditional_edit)

        init_attention_func()
        init_attention_weights(prompt_edit_token_weights)

        timesteps = schedulers[0].timesteps[t_limit:]
        if reverse: timesteps = timesteps.flip(0)

        for i, t in tqdm(enumerate(timesteps), total=len(timesteps)):
            t_scale = t / schedulers[0].num_train_timesteps

            if (reverse) and (not run_baseline):
                # Reverse mixing layer
                new_latents = [l.clone() for l in latent_pair]
                new_latents[1] = (new_latents[1].clone() - (1-mix_weight)*new_latents[0].clone()) / mix_weight
                new_latents[0] = (new_latents[0].clone() - (1-mix_weight)*new_latents[1].clone()) / mix_weight
                latent_pair = new_latents

            # alternate EDICT steps
            for latent_i in range(2): 
                if run_baseline and latent_i==1: continue # just have one sequence for baseline
                # this modifies latent_pair[i] while using 
                # latent_pair[(i+1)%2]
                if reverse and (not run_baseline):
                    if leapfrog_steps:
                        # what i would be from going other way
                        orig_i = len(timesteps) - (i+1) 
                        offset = (orig_i+1) % 2
                        latent_i = (latent_i + offset) % 2
                    else:
                        # Do 1 then 0
                        latent_i = (latent_i+1)%2
                else:
                    if leapfrog_steps:
                        offset = i%2
                        latent_i = (latent_i + offset) % 2

                latent_j = ((latent_i+1) % 2) if not run_baseline else latent_i

                latent_model_input = latent_pair[latent_j]
                latent_base = latent_pair[latent_i]

                #Predict the unconditional noise residual
                noise_pred_uncond = unet(latent_model_input, t, 
                                         encoder_hidden_states=embedding_unconditional).sample

                #Prepare the Cross-Attention layers
                if prompt_edit is not None:
                    save_last_tokens_attention()
                    save_last_self_attention()
                else:
                    #Use weights on non-edited prompt when edit is None
                    use_last_tokens_attention_weights()

                #Predict the conditional noise residual and save the cross-attention layer activations
                noise_pred_cond = unet(latent_model_input, t, 
                                       encoder_hidden_states=embedding_conditional).sample

                #Edit the Cross-Attention layer activations
                if prompt_edit is not None:
                    t_scale = t / schedulers[0].num_train_timesteps
                    if t_scale >= prompt_edit_tokens_start and t_scale <= prompt_edit_tokens_end:
                        use_last_tokens_attention()
                    if t_scale >= prompt_edit_spatial_start and t_scale <= prompt_edit_spatial_end:
                        use_last_self_attention()

                    #Use weights on edited prompt
                    use_last_tokens_attention_weights()

                    #Predict the edited conditional noise residual using the cross-attention masks
                    noise_pred_cond = unet(latent_model_input,
                                           t, 
                                           encoder_hidden_states=embedding_conditional_edit).sample

                #Perform guidance
                grad = (noise_pred_cond - noise_pred_uncond)
                noise_pred = noise_pred_uncond + guidance_scale * grad


                step_call = reverse_step if reverse else forward_step
                new_latent = step_call(schedulers[latent_i],
                                          noise_pred,
                                            t,
                                            latent_base)# .prev_sample
                new_latent = new_latent.to(latent_base.dtype)

                latent_pair[latent_i] = new_latent

            if (not reverse) and (not run_baseline):
                # Mixing layer (contraction) during generative process
                new_latents = [l.clone() for l in latent_pair]
                new_latents[0] = (mix_weight*new_latents[0] + (1-mix_weight)*new_latents[1]).clone() 
                new_latents[1] = ((1-mix_weight)*new_latents[0] + (mix_weight)*new_latents[1]).clone() 
                latent_pair = new_latents

        #scale and decode the image latents with vae, can return latents instead of images
        if reverse or return_latents:
            results = [latent_pair]
            return results if len(results)>1 else results[0]

        # decode latents to iamges
        images = []
        for latent_i in range(2):
            latent = latent_pair[latent_i] / 0.18215
            image = vae.decode(latent.to(vae.dtype)).sample
            images.append(image)

    # Return images
    return_arr = []
    for image in images:
        image = prep_image_for_return(image)
        return_arr.append(image)
    results = [return_arr]
    return results if len(results)>1 else results[0]