Datasets:

ArXiv:
File size: 37,699 Bytes
a798acd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
#
# Copyright 2023 The HuggingFace Inc. team.
# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import gc
import os
from collections import OrderedDict
from copy import copy
from typing import List, Optional, Union

import numpy as np
import onnx
import onnx_graphsurgeon as gs
import tensorrt as trt
import torch
from huggingface_hub import snapshot_download
from onnx import shape_inference
from polygraphy import cuda
from polygraphy.backend.common import bytes_from_path
from polygraphy.backend.onnx.loader import fold_constants
from polygraphy.backend.trt import (
    CreateConfig,
    Profile,
    engine_from_bytes,
    engine_from_network,
    network_from_onnx_path,
    save_engine,
)
from polygraphy.backend.trt import util as trt_util
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion import (
    StableDiffusionPipeline,
    StableDiffusionPipelineOutput,
    StableDiffusionSafetyChecker,
)
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import DIFFUSERS_CACHE, logging


"""
Installation instructions
python3 -m pip install --upgrade transformers diffusers>=0.16.0
python3 -m pip install --upgrade tensorrt>=8.6.1
python3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com
python3 -m pip install onnxruntime
"""

TRT_LOGGER = trt.Logger(trt.Logger.ERROR)
logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

# Map of numpy dtype -> torch dtype
numpy_to_torch_dtype_dict = {
    np.uint8: torch.uint8,
    np.int8: torch.int8,
    np.int16: torch.int16,
    np.int32: torch.int32,
    np.int64: torch.int64,
    np.float16: torch.float16,
    np.float32: torch.float32,
    np.float64: torch.float64,
    np.complex64: torch.complex64,
    np.complex128: torch.complex128,
}
if np.version.full_version >= "1.24.0":
    numpy_to_torch_dtype_dict[np.bool_] = torch.bool
else:
    numpy_to_torch_dtype_dict[np.bool] = torch.bool

# Map of torch dtype -> numpy dtype
torch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()}


def device_view(t):
    return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype])


class Engine:
    def __init__(self, engine_path):
        self.engine_path = engine_path
        self.engine = None
        self.context = None
        self.buffers = OrderedDict()
        self.tensors = OrderedDict()

    def __del__(self):
        [buf.free() for buf in self.buffers.values() if isinstance(buf, cuda.DeviceArray)]
        del self.engine
        del self.context
        del self.buffers
        del self.tensors

    def build(
        self,
        onnx_path,
        fp16,
        input_profile=None,
        enable_preview=False,
        enable_all_tactics=False,
        timing_cache=None,
        workspace_size=0,
    ):
        logger.warning(f"Building TensorRT engine for {onnx_path}: {self.engine_path}")
        p = Profile()
        if input_profile:
            for name, dims in input_profile.items():
                assert len(dims) == 3
                p.add(name, min=dims[0], opt=dims[1], max=dims[2])

        config_kwargs = {}

        config_kwargs["preview_features"] = [trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
        if enable_preview:
            # Faster dynamic shapes made optional since it increases engine build time.
            config_kwargs["preview_features"].append(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0805)
        if workspace_size > 0:
            config_kwargs["memory_pool_limits"] = {trt.MemoryPoolType.WORKSPACE: workspace_size}
        if not enable_all_tactics:
            config_kwargs["tactic_sources"] = []

        engine = engine_from_network(
            network_from_onnx_path(onnx_path, flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM]),
            config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **config_kwargs),
            save_timing_cache=timing_cache,
        )
        save_engine(engine, path=self.engine_path)

    def load(self):
        logger.warning(f"Loading TensorRT engine: {self.engine_path}")
        self.engine = engine_from_bytes(bytes_from_path(self.engine_path))

    def activate(self):
        self.context = self.engine.create_execution_context()

    def allocate_buffers(self, shape_dict=None, device="cuda"):
        for idx in range(trt_util.get_bindings_per_profile(self.engine)):
            binding = self.engine[idx]
            if shape_dict and binding in shape_dict:
                shape = shape_dict[binding]
            else:
                shape = self.engine.get_binding_shape(binding)
            dtype = trt.nptype(self.engine.get_binding_dtype(binding))
            if self.engine.binding_is_input(binding):
                self.context.set_binding_shape(idx, shape)
            tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device)
            self.tensors[binding] = tensor
            self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype)

    def infer(self, feed_dict, stream):
        start_binding, end_binding = trt_util.get_active_profile_bindings(self.context)
        # shallow copy of ordered dict
        device_buffers = copy(self.buffers)
        for name, buf in feed_dict.items():
            assert isinstance(buf, cuda.DeviceView)
            device_buffers[name] = buf
        bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()]
        noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr)
        if not noerror:
            raise ValueError("ERROR: inference failed.")

        return self.tensors


class Optimizer:
    def __init__(self, onnx_graph):
        self.graph = gs.import_onnx(onnx_graph)

    def cleanup(self, return_onnx=False):
        self.graph.cleanup().toposort()
        if return_onnx:
            return gs.export_onnx(self.graph)

    def select_outputs(self, keep, names=None):
        self.graph.outputs = [self.graph.outputs[o] for o in keep]
        if names:
            for i, name in enumerate(names):
                self.graph.outputs[i].name = name

    def fold_constants(self, return_onnx=False):
        onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)
        self.graph = gs.import_onnx(onnx_graph)
        if return_onnx:
            return onnx_graph

    def infer_shapes(self, return_onnx=False):
        onnx_graph = gs.export_onnx(self.graph)
        if onnx_graph.ByteSize() > 2147483648:
            raise TypeError("ERROR: model size exceeds supported 2GB limit")
        else:
            onnx_graph = shape_inference.infer_shapes(onnx_graph)

        self.graph = gs.import_onnx(onnx_graph)
        if return_onnx:
            return onnx_graph


class BaseModel:
    def __init__(self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77):
        self.model = model
        self.name = "SD Model"
        self.fp16 = fp16
        self.device = device

        self.min_batch = 1
        self.max_batch = max_batch_size
        self.min_image_shape = 256  # min image resolution: 256x256
        self.max_image_shape = 1024  # max image resolution: 1024x1024
        self.min_latent_shape = self.min_image_shape // 8
        self.max_latent_shape = self.max_image_shape // 8

        self.embedding_dim = embedding_dim
        self.text_maxlen = text_maxlen

    def get_model(self):
        return self.model

    def get_input_names(self):
        pass

    def get_output_names(self):
        pass

    def get_dynamic_axes(self):
        return None

    def get_sample_input(self, batch_size, image_height, image_width):
        pass

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        return None

    def get_shape_dict(self, batch_size, image_height, image_width):
        return None

    def optimize(self, onnx_graph):
        opt = Optimizer(onnx_graph)
        opt.cleanup()
        opt.fold_constants()
        opt.infer_shapes()
        onnx_opt_graph = opt.cleanup(return_onnx=True)
        return onnx_opt_graph

    def check_dims(self, batch_size, image_height, image_width):
        assert batch_size >= self.min_batch and batch_size <= self.max_batch
        assert image_height % 8 == 0 or image_width % 8 == 0
        latent_height = image_height // 8
        latent_width = image_width // 8
        assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape
        assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape
        return (latent_height, latent_width)

    def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):
        min_batch = batch_size if static_batch else self.min_batch
        max_batch = batch_size if static_batch else self.max_batch
        latent_height = image_height // 8
        latent_width = image_width // 8
        min_image_height = image_height if static_shape else self.min_image_shape
        max_image_height = image_height if static_shape else self.max_image_shape
        min_image_width = image_width if static_shape else self.min_image_shape
        max_image_width = image_width if static_shape else self.max_image_shape
        min_latent_height = latent_height if static_shape else self.min_latent_shape
        max_latent_height = latent_height if static_shape else self.max_latent_shape
        min_latent_width = latent_width if static_shape else self.min_latent_shape
        max_latent_width = latent_width if static_shape else self.max_latent_shape
        return (
            min_batch,
            max_batch,
            min_image_height,
            max_image_height,
            min_image_width,
            max_image_width,
            min_latent_height,
            max_latent_height,
            min_latent_width,
            max_latent_width,
        )


def getOnnxPath(model_name, onnx_dir, opt=True):
    return os.path.join(onnx_dir, model_name + (".opt" if opt else "") + ".onnx")


def getEnginePath(model_name, engine_dir):
    return os.path.join(engine_dir, model_name + ".plan")


def build_engines(
    models: dict,
    engine_dir,
    onnx_dir,
    onnx_opset,
    opt_image_height,
    opt_image_width,
    opt_batch_size=1,
    force_engine_rebuild=False,
    static_batch=False,
    static_shape=True,
    enable_preview=False,
    enable_all_tactics=False,
    timing_cache=None,
    max_workspace_size=0,
):
    built_engines = {}
    if not os.path.isdir(onnx_dir):
        os.makedirs(onnx_dir)
    if not os.path.isdir(engine_dir):
        os.makedirs(engine_dir)

    # Export models to ONNX
    for model_name, model_obj in models.items():
        engine_path = getEnginePath(model_name, engine_dir)
        if force_engine_rebuild or not os.path.exists(engine_path):
            logger.warning("Building Engines...")
            logger.warning("Engine build can take a while to complete")
            onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)
            onnx_opt_path = getOnnxPath(model_name, onnx_dir)
            if force_engine_rebuild or not os.path.exists(onnx_opt_path):
                if force_engine_rebuild or not os.path.exists(onnx_path):
                    logger.warning(f"Exporting model: {onnx_path}")
                    model = model_obj.get_model()
                    with torch.inference_mode(), torch.autocast("cuda"):
                        inputs = model_obj.get_sample_input(opt_batch_size, opt_image_height, opt_image_width)
                        torch.onnx.export(
                            model,
                            inputs,
                            onnx_path,
                            export_params=True,
                            opset_version=onnx_opset,
                            do_constant_folding=True,
                            input_names=model_obj.get_input_names(),
                            output_names=model_obj.get_output_names(),
                            dynamic_axes=model_obj.get_dynamic_axes(),
                        )
                    del model
                    torch.cuda.empty_cache()
                    gc.collect()
                else:
                    logger.warning(f"Found cached model: {onnx_path}")

                # Optimize onnx
                if force_engine_rebuild or not os.path.exists(onnx_opt_path):
                    logger.warning(f"Generating optimizing model: {onnx_opt_path}")
                    onnx_opt_graph = model_obj.optimize(onnx.load(onnx_path))
                    onnx.save(onnx_opt_graph, onnx_opt_path)
                else:
                    logger.warning(f"Found cached optimized model: {onnx_opt_path} ")

    # Build TensorRT engines
    for model_name, model_obj in models.items():
        engine_path = getEnginePath(model_name, engine_dir)
        engine = Engine(engine_path)
        onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)
        onnx_opt_path = getOnnxPath(model_name, onnx_dir)

        if force_engine_rebuild or not os.path.exists(engine.engine_path):
            engine.build(
                onnx_opt_path,
                fp16=True,
                input_profile=model_obj.get_input_profile(
                    opt_batch_size,
                    opt_image_height,
                    opt_image_width,
                    static_batch=static_batch,
                    static_shape=static_shape,
                ),
                enable_preview=enable_preview,
                timing_cache=timing_cache,
                workspace_size=max_workspace_size,
            )
        built_engines[model_name] = engine

    # Load and activate TensorRT engines
    for model_name, model_obj in models.items():
        engine = built_engines[model_name]
        engine.load()
        engine.activate()

    return built_engines


def runEngine(engine, feed_dict, stream):
    return engine.infer(feed_dict, stream)


class CLIP(BaseModel):
    def __init__(self, model, device, max_batch_size, embedding_dim):
        super(CLIP, self).__init__(
            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
        )
        self.name = "CLIP"

    def get_input_names(self):
        return ["input_ids"]

    def get_output_names(self):
        return ["text_embeddings", "pooler_output"]

    def get_dynamic_axes(self):
        return {"input_ids": {0: "B"}, "text_embeddings": {0: "B"}}

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        self.check_dims(batch_size, image_height, image_width)
        min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(
            batch_size, image_height, image_width, static_batch, static_shape
        )
        return {
            "input_ids": [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]
        }

    def get_shape_dict(self, batch_size, image_height, image_width):
        self.check_dims(batch_size, image_height, image_width)
        return {
            "input_ids": (batch_size, self.text_maxlen),
            "text_embeddings": (batch_size, self.text_maxlen, self.embedding_dim),
        }

    def get_sample_input(self, batch_size, image_height, image_width):
        self.check_dims(batch_size, image_height, image_width)
        return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)

    def optimize(self, onnx_graph):
        opt = Optimizer(onnx_graph)
        opt.select_outputs([0])  # delete graph output#1
        opt.cleanup()
        opt.fold_constants()
        opt.infer_shapes()
        opt.select_outputs([0], names=["text_embeddings"])  # rename network output
        opt_onnx_graph = opt.cleanup(return_onnx=True)
        return opt_onnx_graph


def make_CLIP(model, device, max_batch_size, embedding_dim, inpaint=False):
    return CLIP(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)


class UNet(BaseModel):
    def __init__(
        self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77, unet_dim=4
    ):
        super(UNet, self).__init__(
            model=model,
            fp16=fp16,
            device=device,
            max_batch_size=max_batch_size,
            embedding_dim=embedding_dim,
            text_maxlen=text_maxlen,
        )
        self.unet_dim = unet_dim
        self.name = "UNet"

    def get_input_names(self):
        return ["sample", "timestep", "encoder_hidden_states"]

    def get_output_names(self):
        return ["latent"]

    def get_dynamic_axes(self):
        return {
            "sample": {0: "2B", 2: "H", 3: "W"},
            "encoder_hidden_states": {0: "2B"},
            "latent": {0: "2B", 2: "H", 3: "W"},
        }

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        (
            min_batch,
            max_batch,
            _,
            _,
            _,
            _,
            min_latent_height,
            max_latent_height,
            min_latent_width,
            max_latent_width,
        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
        return {
            "sample": [
                (2 * min_batch, self.unet_dim, min_latent_height, min_latent_width),
                (2 * batch_size, self.unet_dim, latent_height, latent_width),
                (2 * max_batch, self.unet_dim, max_latent_height, max_latent_width),
            ],
            "encoder_hidden_states": [
                (2 * min_batch, self.text_maxlen, self.embedding_dim),
                (2 * batch_size, self.text_maxlen, self.embedding_dim),
                (2 * max_batch, self.text_maxlen, self.embedding_dim),
            ],
        }

    def get_shape_dict(self, batch_size, image_height, image_width):
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        return {
            "sample": (2 * batch_size, self.unet_dim, latent_height, latent_width),
            "encoder_hidden_states": (2 * batch_size, self.text_maxlen, self.embedding_dim),
            "latent": (2 * batch_size, 4, latent_height, latent_width),
        }

    def get_sample_input(self, batch_size, image_height, image_width):
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        dtype = torch.float16 if self.fp16 else torch.float32
        return (
            torch.randn(
                2 * batch_size, self.unet_dim, latent_height, latent_width, dtype=torch.float32, device=self.device
            ),
            torch.tensor([1.0], dtype=torch.float32, device=self.device),
            torch.randn(2 * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
        )


def make_UNet(model, device, max_batch_size, embedding_dim, inpaint=False):
    return UNet(
        model,
        fp16=True,
        device=device,
        max_batch_size=max_batch_size,
        embedding_dim=embedding_dim,
        unet_dim=(9 if inpaint else 4),
    )


class VAE(BaseModel):
    def __init__(self, model, device, max_batch_size, embedding_dim):
        super(VAE, self).__init__(
            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
        )
        self.name = "VAE decoder"

    def get_input_names(self):
        return ["latent"]

    def get_output_names(self):
        return ["images"]

    def get_dynamic_axes(self):
        return {"latent": {0: "B", 2: "H", 3: "W"}, "images": {0: "B", 2: "8H", 3: "8W"}}

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        (
            min_batch,
            max_batch,
            _,
            _,
            _,
            _,
            min_latent_height,
            max_latent_height,
            min_latent_width,
            max_latent_width,
        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
        return {
            "latent": [
                (min_batch, 4, min_latent_height, min_latent_width),
                (batch_size, 4, latent_height, latent_width),
                (max_batch, 4, max_latent_height, max_latent_width),
            ]
        }

    def get_shape_dict(self, batch_size, image_height, image_width):
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        return {
            "latent": (batch_size, 4, latent_height, latent_width),
            "images": (batch_size, 3, image_height, image_width),
        }

    def get_sample_input(self, batch_size, image_height, image_width):
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        return torch.randn(batch_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device)


def make_VAE(model, device, max_batch_size, embedding_dim, inpaint=False):
    return VAE(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)


class TensorRTStableDiffusionPipeline(StableDiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using TensorRT accelerated Stable Diffusion.

    This model inherits from [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPFeatureExtractor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: DDIMScheduler,
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPFeatureExtractor,
        requires_safety_checker: bool = True,
        stages=["clip", "unet", "vae"],
        image_height: int = 768,
        image_width: int = 768,
        max_batch_size: int = 16,
        # ONNX export parameters
        onnx_opset: int = 17,
        onnx_dir: str = "onnx",
        # TensorRT engine build parameters
        engine_dir: str = "engine",
        build_preview_features: bool = True,
        force_engine_rebuild: bool = False,
        timing_cache: str = "timing_cache",
    ):
        super().__init__(
            vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker
        )

        self.vae.forward = self.vae.decode

        self.stages = stages
        self.image_height, self.image_width = image_height, image_width
        self.inpaint = False
        self.onnx_opset = onnx_opset
        self.onnx_dir = onnx_dir
        self.engine_dir = engine_dir
        self.force_engine_rebuild = force_engine_rebuild
        self.timing_cache = timing_cache
        self.build_static_batch = False
        self.build_dynamic_shape = False
        self.build_preview_features = build_preview_features

        self.max_batch_size = max_batch_size
        # TODO: Restrict batch size to 4 for larger image dimensions as a WAR for TensorRT limitation.
        if self.build_dynamic_shape or self.image_height > 512 or self.image_width > 512:
            self.max_batch_size = 4

        self.stream = None  # loaded in loadResources()
        self.models = {}  # loaded in __loadModels()
        self.engine = {}  # loaded in build_engines()

    def __loadModels(self):
        # Load pipeline models
        self.embedding_dim = self.text_encoder.config.hidden_size
        models_args = {
            "device": self.torch_device,
            "max_batch_size": self.max_batch_size,
            "embedding_dim": self.embedding_dim,
            "inpaint": self.inpaint,
        }
        if "clip" in self.stages:
            self.models["clip"] = make_CLIP(self.text_encoder, **models_args)
        if "unet" in self.stages:
            self.models["unet"] = make_UNet(self.unet, **models_args)
        if "vae" in self.stages:
            self.models["vae"] = make_VAE(self.vae, **models_args)

    @classmethod
    def set_cached_folder(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", False)
        use_auth_token = kwargs.pop("use_auth_token", None)
        revision = kwargs.pop("revision", None)

        cls.cached_folder = (
            pretrained_model_name_or_path
            if os.path.isdir(pretrained_model_name_or_path)
            else snapshot_download(
                pretrained_model_name_or_path,
                cache_dir=cache_dir,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                use_auth_token=use_auth_token,
                revision=revision,
            )
        )

    def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings: bool = False):
        super().to(torch_device, silence_dtype_warnings=silence_dtype_warnings)

        self.onnx_dir = os.path.join(self.cached_folder, self.onnx_dir)
        self.engine_dir = os.path.join(self.cached_folder, self.engine_dir)
        self.timing_cache = os.path.join(self.cached_folder, self.timing_cache)

        # set device
        self.torch_device = self._execution_device
        logger.warning(f"Running inference on device: {self.torch_device}")

        # load models
        self.__loadModels()

        # build engines
        self.engine = build_engines(
            self.models,
            self.engine_dir,
            self.onnx_dir,
            self.onnx_opset,
            opt_image_height=self.image_height,
            opt_image_width=self.image_width,
            force_engine_rebuild=self.force_engine_rebuild,
            static_batch=self.build_static_batch,
            static_shape=not self.build_dynamic_shape,
            enable_preview=self.build_preview_features,
            timing_cache=self.timing_cache,
        )

        return self

    def __encode_prompt(self, prompt, negative_prompt):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
             prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
        """
        # Tokenize prompt
        text_input_ids = (
            self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            .input_ids.type(torch.int32)
            .to(self.torch_device)
        )

        text_input_ids_inp = device_view(text_input_ids)
        # NOTE: output tensor for CLIP must be cloned because it will be overwritten when called again for negative prompt
        text_embeddings = runEngine(self.engine["clip"], {"input_ids": text_input_ids_inp}, self.stream)[
            "text_embeddings"
        ].clone()

        # Tokenize negative prompt
        uncond_input_ids = (
            self.tokenizer(
                negative_prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            .input_ids.type(torch.int32)
            .to(self.torch_device)
        )
        uncond_input_ids_inp = device_view(uncond_input_ids)
        uncond_embeddings = runEngine(self.engine["clip"], {"input_ids": uncond_input_ids_inp}, self.stream)[
            "text_embeddings"
        ]

        # Concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes for classifier free guidance
        text_embeddings = torch.cat([uncond_embeddings, text_embeddings]).to(dtype=torch.float16)

        return text_embeddings

    def __denoise_latent(
        self, latents, text_embeddings, timesteps=None, step_offset=0, mask=None, masked_image_latents=None
    ):
        if not isinstance(timesteps, torch.Tensor):
            timesteps = self.scheduler.timesteps
        for step_index, timestep in enumerate(timesteps):
            # Expand the latents if we are doing classifier free guidance
            latent_model_input = torch.cat([latents] * 2)
            latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep)
            if isinstance(mask, torch.Tensor):
                latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)

            # Predict the noise residual
            timestep_float = timestep.float() if timestep.dtype != torch.float32 else timestep

            sample_inp = device_view(latent_model_input)
            timestep_inp = device_view(timestep_float)
            embeddings_inp = device_view(text_embeddings)
            noise_pred = runEngine(
                self.engine["unet"],
                {"sample": sample_inp, "timestep": timestep_inp, "encoder_hidden_states": embeddings_inp},
                self.stream,
            )["latent"]

            # Perform guidance
            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
            noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)

            latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample

        latents = 1.0 / 0.18215 * latents
        return latents

    def __decode_latent(self, latents):
        images = runEngine(self.engine["vae"], {"latent": device_view(latents)}, self.stream)["images"]
        images = (images / 2 + 0.5).clamp(0, 1)
        return images.cpu().permute(0, 2, 3, 1).float().numpy()

    def __loadResources(self, image_height, image_width, batch_size):
        self.stream = cuda.Stream()

        # Allocate buffers for TensorRT engine bindings
        for model_name, obj in self.models.items():
            self.engine[model_name].allocate_buffers(
                shape_dict=obj.get_shape_dict(batch_size, image_height, image_width), device=self.torch_device
            )

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.

        """
        self.generator = generator
        self.denoising_steps = num_inference_steps
        self.guidance_scale = guidance_scale

        # Pre-compute latent input scales and linear multistep coefficients
        self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device)

        # Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
            prompt = [prompt]
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            raise ValueError(f"Expected prompt to be of type list or str but got {type(prompt)}")

        if negative_prompt is None:
            negative_prompt = [""] * batch_size

        if negative_prompt is not None and isinstance(negative_prompt, str):
            negative_prompt = [negative_prompt]

        assert len(prompt) == len(negative_prompt)

        if batch_size > self.max_batch_size:
            raise ValueError(
                f"Batch size {len(prompt)} is larger than allowed {self.max_batch_size}. If dynamic shape is used, then maximum batch size is 4"
            )

        # load resources
        self.__loadResources(self.image_height, self.image_width, batch_size)

        with torch.inference_mode(), torch.autocast("cuda"), trt.Runtime(TRT_LOGGER):
            # CLIP text encoder
            text_embeddings = self.__encode_prompt(prompt, negative_prompt)

            # Pre-initialize latents
            num_channels_latents = self.unet.in_channels
            latents = self.prepare_latents(
                batch_size,
                num_channels_latents,
                self.image_height,
                self.image_width,
                torch.float32,
                self.torch_device,
                generator,
            )

            # UNet denoiser
            latents = self.__denoise_latent(latents, text_embeddings)

            # VAE decode latent
            images = self.__decode_latent(latents)

        images, has_nsfw_concept = self.run_safety_checker(images, self.torch_device, text_embeddings.dtype)
        images = self.numpy_to_pil(images)
        return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)