Datasets:

ArXiv:
File size: 36,746 Bytes
0b1273f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Based on stable_diffusion_reference.py

from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import numpy as np
import PIL.Image
import torch

from diffusers import StableDiffusionXLPipeline
from diffusers.models.attention import BasicTransformerBlock
from diffusers.models.unet_2d_blocks import (
    CrossAttnDownBlock2D,
    CrossAttnUpBlock2D,
    DownBlock2D,
    UpBlock2D,
)
from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
from diffusers.utils import PIL_INTERPOLATION, logging
from diffusers.utils.torch_utils import randn_tensor


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import UniPCMultistepScheduler
        >>> from diffusers.utils import load_image

        >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")

        >>> pipe = StableDiffusionXLReferencePipeline.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0",
            torch_dtype=torch.float16,
            use_safetensors=True,
            variant="fp16").to('cuda:0')

        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
        >>> result_img = pipe(ref_image=input_image,
                        prompt="1girl",
                        num_inference_steps=20,
                        reference_attn=True,
                        reference_adain=True).images[0]

        >>> result_img.show()
        ```
"""


def torch_dfs(model: torch.nn.Module):
    result = [model]
    for child in model.children():
        result += torch_dfs(child)
    return result


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg


def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """
    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
    """
    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    # rescale the results from guidance (fixes overexposure)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
    return noise_cfg


class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
    def _default_height_width(self, height, width, image):
        # NOTE: It is possible that a list of images have different
        # dimensions for each image, so just checking the first image
        # is not _exactly_ correct, but it is simple.
        while isinstance(image, list):
            image = image[0]

        if height is None:
            if isinstance(image, PIL.Image.Image):
                height = image.height
            elif isinstance(image, torch.Tensor):
                height = image.shape[2]

            height = (height // 8) * 8  # round down to nearest multiple of 8

        if width is None:
            if isinstance(image, PIL.Image.Image):
                width = image.width
            elif isinstance(image, torch.Tensor):
                width = image.shape[3]

            width = (width // 8) * 8

        return height, width

    def prepare_image(
        self,
        image,
        width,
        height,
        batch_size,
        num_images_per_prompt,
        device,
        dtype,
        do_classifier_free_guidance=False,
        guess_mode=False,
    ):
        if not isinstance(image, torch.Tensor):
            if isinstance(image, PIL.Image.Image):
                image = [image]

            if isinstance(image[0], PIL.Image.Image):
                images = []

                for image_ in image:
                    image_ = image_.convert("RGB")
                    image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
                    image_ = np.array(image_)
                    image_ = image_[None, :]
                    images.append(image_)

                image = images

                image = np.concatenate(image, axis=0)
                image = np.array(image).astype(np.float32) / 255.0
                image = (image - 0.5) / 0.5
                image = image.transpose(0, 3, 1, 2)
                image = torch.from_numpy(image)

            elif isinstance(image[0], torch.Tensor):
                image = torch.stack(image, dim=0)

        image_batch_size = image.shape[0]

        if image_batch_size == 1:
            repeat_by = batch_size
        else:
            repeat_by = num_images_per_prompt

        image = image.repeat_interleave(repeat_by, dim=0)

        image = image.to(device=device, dtype=dtype)

        if do_classifier_free_guidance and not guess_mode:
            image = torch.cat([image] * 2)

        return image

    def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
        refimage = refimage.to(device=device)
        if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
            self.upcast_vae()
            refimage = refimage.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
        if refimage.dtype != self.vae.dtype:
            refimage = refimage.to(dtype=self.vae.dtype)
        # encode the mask image into latents space so we can concatenate it to the latents
        if isinstance(generator, list):
            ref_image_latents = [
                self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
                for i in range(batch_size)
            ]
            ref_image_latents = torch.cat(ref_image_latents, dim=0)
        else:
            ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
        ref_image_latents = self.vae.config.scaling_factor * ref_image_latents

        # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
        if ref_image_latents.shape[0] < batch_size:
            if not batch_size % ref_image_latents.shape[0] == 0:
                raise ValueError(
                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
                    f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
                    " Make sure the number of images that you pass is divisible by the total requested batch size."
                )
            ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)

        ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents

        # aligning device to prevent device errors when concating it with the latent model input
        ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
        return ref_image_latents

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        prompt_2: Optional[Union[str, List[str]]] = None,
        ref_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        denoising_end: Optional[float] = None,
        guidance_scale: float = 5.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt_2: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
        original_size: Optional[Tuple[int, int]] = None,
        crops_coords_top_left: Tuple[int, int] = (0, 0),
        target_size: Optional[Tuple[int, int]] = None,
        attention_auto_machine_weight: float = 1.0,
        gn_auto_machine_weight: float = 1.0,
        style_fidelity: float = 0.5,
        reference_attn: bool = True,
        reference_adain: bool = True,
    ):
        assert reference_attn or reference_adain, "`reference_attn` or `reference_adain` must be True."

        # 0. Default height and width to unet
        # height, width = self._default_height_width(height, width, ref_image)

        height = height or self.default_sample_size * self.vae_scale_factor
        width = width or self.default_sample_size * self.vae_scale_factor
        original_size = original_size or (height, width)
        target_size = target_size or (height, width)

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            prompt_2,
            height,
            width,
            callback_steps,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        )

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
        )
        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self.encode_prompt(
            prompt=prompt,
            prompt_2=prompt_2,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
        )
        # 4. Preprocess reference image
        ref_image = self.prepare_image(
            image=ref_image,
            width=width,
            height=height,
            batch_size=batch_size * num_images_per_prompt,
            num_images_per_prompt=num_images_per_prompt,
            device=device,
            dtype=prompt_embeds.dtype,
        )

        # 5. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)

        timesteps = self.scheduler.timesteps

        # 6. Prepare latent variables
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )
        # 7. Prepare reference latent variables
        ref_image_latents = self.prepare_ref_latents(
            ref_image,
            batch_size * num_images_per_prompt,
            prompt_embeds.dtype,
            device,
            generator,
            do_classifier_free_guidance,
        )

        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 9. Modify self attebtion and group norm
        MODE = "write"
        uc_mask = (
            torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)
            .type_as(ref_image_latents)
            .bool()
        )

        def hacked_basic_transformer_inner_forward(
            self,
            hidden_states: torch.FloatTensor,
            attention_mask: Optional[torch.FloatTensor] = None,
            encoder_hidden_states: Optional[torch.FloatTensor] = None,
            encoder_attention_mask: Optional[torch.FloatTensor] = None,
            timestep: Optional[torch.LongTensor] = None,
            cross_attention_kwargs: Dict[str, Any] = None,
            class_labels: Optional[torch.LongTensor] = None,
        ):
            if self.use_ada_layer_norm:
                norm_hidden_states = self.norm1(hidden_states, timestep)
            elif self.use_ada_layer_norm_zero:
                norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
                    hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
                )
            else:
                norm_hidden_states = self.norm1(hidden_states)

            # 1. Self-Attention
            cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
            if self.only_cross_attention:
                attn_output = self.attn1(
                    norm_hidden_states,
                    encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
                    attention_mask=attention_mask,
                    **cross_attention_kwargs,
                )
            else:
                if MODE == "write":
                    self.bank.append(norm_hidden_states.detach().clone())
                    attn_output = self.attn1(
                        norm_hidden_states,
                        encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
                        attention_mask=attention_mask,
                        **cross_attention_kwargs,
                    )
                if MODE == "read":
                    if attention_auto_machine_weight > self.attn_weight:
                        attn_output_uc = self.attn1(
                            norm_hidden_states,
                            encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),
                            # attention_mask=attention_mask,
                            **cross_attention_kwargs,
                        )
                        attn_output_c = attn_output_uc.clone()
                        if do_classifier_free_guidance and style_fidelity > 0:
                            attn_output_c[uc_mask] = self.attn1(
                                norm_hidden_states[uc_mask],
                                encoder_hidden_states=norm_hidden_states[uc_mask],
                                **cross_attention_kwargs,
                            )
                        attn_output = style_fidelity * attn_output_c + (1.0 - style_fidelity) * attn_output_uc
                        self.bank.clear()
                    else:
                        attn_output = self.attn1(
                            norm_hidden_states,
                            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
                            attention_mask=attention_mask,
                            **cross_attention_kwargs,
                        )
            if self.use_ada_layer_norm_zero:
                attn_output = gate_msa.unsqueeze(1) * attn_output
            hidden_states = attn_output + hidden_states

            if self.attn2 is not None:
                norm_hidden_states = (
                    self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
                )

                # 2. Cross-Attention
                attn_output = self.attn2(
                    norm_hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=encoder_attention_mask,
                    **cross_attention_kwargs,
                )
                hidden_states = attn_output + hidden_states

            # 3. Feed-forward
            norm_hidden_states = self.norm3(hidden_states)

            if self.use_ada_layer_norm_zero:
                norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

            ff_output = self.ff(norm_hidden_states)

            if self.use_ada_layer_norm_zero:
                ff_output = gate_mlp.unsqueeze(1) * ff_output

            hidden_states = ff_output + hidden_states

            return hidden_states

        def hacked_mid_forward(self, *args, **kwargs):
            eps = 1e-6
            x = self.original_forward(*args, **kwargs)
            if MODE == "write":
                if gn_auto_machine_weight >= self.gn_weight:
                    var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
                    self.mean_bank.append(mean)
                    self.var_bank.append(var)
            if MODE == "read":
                if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
                    var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
                    std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
                    mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))
                    var_acc = sum(self.var_bank) / float(len(self.var_bank))
                    std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
                    x_uc = (((x - mean) / std) * std_acc) + mean_acc
                    x_c = x_uc.clone()
                    if do_classifier_free_guidance and style_fidelity > 0:
                        x_c[uc_mask] = x[uc_mask]
                    x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc
                self.mean_bank = []
                self.var_bank = []
            return x

        def hack_CrossAttnDownBlock2D_forward(
            self,
            hidden_states: torch.FloatTensor,
            temb: Optional[torch.FloatTensor] = None,
            encoder_hidden_states: Optional[torch.FloatTensor] = None,
            attention_mask: Optional[torch.FloatTensor] = None,
            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
            encoder_attention_mask: Optional[torch.FloatTensor] = None,
        ):
            eps = 1e-6

            # TODO(Patrick, William) - attention mask is not used
            output_states = ()

            for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
                hidden_states = resnet(hidden_states, temb)
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )[0]
                if MODE == "write":
                    if gn_auto_machine_weight >= self.gn_weight:
                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
                        self.mean_bank.append([mean])
                        self.var_bank.append([var])
                if MODE == "read":
                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
                        hidden_states_c = hidden_states_uc.clone()
                        if do_classifier_free_guidance and style_fidelity > 0:
                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc

                output_states = output_states + (hidden_states,)

            if MODE == "read":
                self.mean_bank = []
                self.var_bank = []

            if self.downsamplers is not None:
                for downsampler in self.downsamplers:
                    hidden_states = downsampler(hidden_states)

                output_states = output_states + (hidden_states,)

            return hidden_states, output_states

        def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
            eps = 1e-6

            output_states = ()

            for i, resnet in enumerate(self.resnets):
                hidden_states = resnet(hidden_states, temb)

                if MODE == "write":
                    if gn_auto_machine_weight >= self.gn_weight:
                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
                        self.mean_bank.append([mean])
                        self.var_bank.append([var])
                if MODE == "read":
                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
                        hidden_states_c = hidden_states_uc.clone()
                        if do_classifier_free_guidance and style_fidelity > 0:
                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc

                output_states = output_states + (hidden_states,)

            if MODE == "read":
                self.mean_bank = []
                self.var_bank = []

            if self.downsamplers is not None:
                for downsampler in self.downsamplers:
                    hidden_states = downsampler(hidden_states)

                output_states = output_states + (hidden_states,)

            return hidden_states, output_states

        def hacked_CrossAttnUpBlock2D_forward(
            self,
            hidden_states: torch.FloatTensor,
            res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
            temb: Optional[torch.FloatTensor] = None,
            encoder_hidden_states: Optional[torch.FloatTensor] = None,
            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
            upsample_size: Optional[int] = None,
            attention_mask: Optional[torch.FloatTensor] = None,
            encoder_attention_mask: Optional[torch.FloatTensor] = None,
        ):
            eps = 1e-6
            # TODO(Patrick, William) - attention mask is not used
            for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
                # pop res hidden states
                res_hidden_states = res_hidden_states_tuple[-1]
                res_hidden_states_tuple = res_hidden_states_tuple[:-1]
                hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
                hidden_states = resnet(hidden_states, temb)
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )[0]

                if MODE == "write":
                    if gn_auto_machine_weight >= self.gn_weight:
                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
                        self.mean_bank.append([mean])
                        self.var_bank.append([var])
                if MODE == "read":
                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
                        hidden_states_c = hidden_states_uc.clone()
                        if do_classifier_free_guidance and style_fidelity > 0:
                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc

            if MODE == "read":
                self.mean_bank = []
                self.var_bank = []

            if self.upsamplers is not None:
                for upsampler in self.upsamplers:
                    hidden_states = upsampler(hidden_states, upsample_size)

            return hidden_states

        def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
            eps = 1e-6
            for i, resnet in enumerate(self.resnets):
                # pop res hidden states
                res_hidden_states = res_hidden_states_tuple[-1]
                res_hidden_states_tuple = res_hidden_states_tuple[:-1]
                hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
                hidden_states = resnet(hidden_states, temb)

                if MODE == "write":
                    if gn_auto_machine_weight >= self.gn_weight:
                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
                        self.mean_bank.append([mean])
                        self.var_bank.append([var])
                if MODE == "read":
                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
                        hidden_states_c = hidden_states_uc.clone()
                        if do_classifier_free_guidance and style_fidelity > 0:
                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc

            if MODE == "read":
                self.mean_bank = []
                self.var_bank = []

            if self.upsamplers is not None:
                for upsampler in self.upsamplers:
                    hidden_states = upsampler(hidden_states, upsample_size)

            return hidden_states

        if reference_attn:
            attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock)]
            attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])

            for i, module in enumerate(attn_modules):
                module._original_inner_forward = module.forward
                module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
                module.bank = []
                module.attn_weight = float(i) / float(len(attn_modules))

        if reference_adain:
            gn_modules = [self.unet.mid_block]
            self.unet.mid_block.gn_weight = 0

            down_blocks = self.unet.down_blocks
            for w, module in enumerate(down_blocks):
                module.gn_weight = 1.0 - float(w) / float(len(down_blocks))
                gn_modules.append(module)

            up_blocks = self.unet.up_blocks
            for w, module in enumerate(up_blocks):
                module.gn_weight = float(w) / float(len(up_blocks))
                gn_modules.append(module)

            for i, module in enumerate(gn_modules):
                if getattr(module, "original_forward", None) is None:
                    module.original_forward = module.forward
                if i == 0:
                    # mid_block
                    module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)
                elif isinstance(module, CrossAttnDownBlock2D):
                    module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)
                elif isinstance(module, DownBlock2D):
                    module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)
                elif isinstance(module, CrossAttnUpBlock2D):
                    module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)
                elif isinstance(module, UpBlock2D):
                    module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)
                module.mean_bank = []
                module.var_bank = []
                module.gn_weight *= 2

        # 10. Prepare added time ids & embeddings
        add_text_embeds = pooled_prompt_embeds
        add_time_ids = self._get_add_time_ids(
            original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
        )

        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)

        prompt_embeds = prompt_embeds.to(device)
        add_text_embeds = add_text_embeds.to(device)
        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)

        # 11. Denoising loop
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

        # 10.1 Apply denoising_end
        if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps
                    - (denoising_end * self.scheduler.config.num_train_timesteps)
                )
            )
            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]

        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents

                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}

                # ref only part
                noise = randn_tensor(
                    ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
                )
                ref_xt = self.scheduler.add_noise(
                    ref_image_latents,
                    noise,
                    t.reshape(
                        1,
                    ),
                )
                ref_xt = self.scheduler.scale_model_input(ref_xt, t)

                MODE = "write"

                self.unet(
                    ref_xt,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                    added_cond_kwargs=added_cond_kwargs,
                    return_dict=False,
                )

                # predict the noise residual
                MODE = "read"
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                    added_cond_kwargs=added_cond_kwargs,
                    return_dict=False,
                )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                if do_classifier_free_guidance and guidance_rescale > 0.0:
                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        if not output_type == "latent":
            # make sure the VAE is in float32 mode, as it overflows in float16
            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

            if needs_upcasting:
                self.upcast_vae()
                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)

            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]

            # cast back to fp16 if needed
            if needs_upcasting:
                self.vae.to(dtype=torch.float16)
        else:
            image = latents
            return StableDiffusionXLPipelineOutput(images=image)

        # apply watermark if available
        if self.watermark is not None:
            image = self.watermark.apply_watermark(image)

        image = self.image_processor.postprocess(image, output_type=output_type)

        # Offload last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image,)

        return StableDiffusionXLPipelineOutput(images=image)