pipeline_utils.py 87 KB
Newer Older
1
# coding=utf-8
Patrick von Platen's avatar
Patrick von Platen committed
2
# Copyright 2023 The HuggingFace Inc. team.
3
4
5
6
7
8
9
10
11
12
13
14
15
16
# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
#
# 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.

17
import fnmatch
18
19
20
import importlib
import inspect
import os
21
import re
22
import sys
23
import warnings
24
25
from dataclasses import dataclass
from pathlib import Path
26
from typing import Any, Callable, Dict, List, Optional, Union
27
28
29

import numpy as np
import PIL
30
import torch
31
from huggingface_hub import ModelCard, create_repo, hf_hub_download, model_info, snapshot_download
32
from packaging import version
33
from requests.exceptions import HTTPError
34
35
from tqdm.auto import tqdm

36
37
import diffusers

38
from .. import __version__
39
40
41
42
43
from ..configuration_utils import ConfigMixin
from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT
from ..schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from ..utils import (
    CONFIG_NAME,
44
    DEPRECATED_REVISION_ARGS,
45
46
    DIFFUSERS_CACHE,
    HF_HUB_OFFLINE,
47
    SAFETENSORS_WEIGHTS_NAME,
48
49
50
51
52
    WEIGHTS_NAME,
    BaseOutput,
    deprecate,
    get_class_from_dynamic_module,
    is_accelerate_available,
53
    is_accelerate_version,
54
55
56
    is_torch_version,
    is_transformers_available,
    logging,
Patrick von Platen's avatar
Patrick von Platen committed
57
    numpy_to_pil,
58
)
Dhruv Nair's avatar
Dhruv Nair committed
59
from ..utils.torch_utils import is_compiled_module
60
61
62
63
64


if is_transformers_available():
    import transformers
    from transformers import PreTrainedModel
65
66
67
68
    from transformers.utils import FLAX_WEIGHTS_NAME as TRANSFORMERS_FLAX_WEIGHTS_NAME
    from transformers.utils import SAFE_WEIGHTS_NAME as TRANSFORMERS_SAFE_WEIGHTS_NAME
    from transformers.utils import WEIGHTS_NAME as TRANSFORMERS_WEIGHTS_NAME

69
from ..utils import FLAX_WEIGHTS_NAME, ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, PushToHubMixin
70
71


72
73
74
75
if is_accelerate_available():
    import accelerate


76
77
78
79
INDEX_FILE = "diffusion_pytorch_model.bin"
CUSTOM_PIPELINE_FILE_NAME = "pipeline.py"
DUMMY_MODULES_FOLDER = "diffusers.utils"
TRANSFORMERS_DUMMY_MODULES_FOLDER = "transformers.utils"
80
CONNECTED_PIPES_KEYS = ["prior"]
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


logger = logging.get_logger(__name__)


LOADABLE_CLASSES = {
    "diffusers": {
        "ModelMixin": ["save_pretrained", "from_pretrained"],
        "SchedulerMixin": ["save_pretrained", "from_pretrained"],
        "DiffusionPipeline": ["save_pretrained", "from_pretrained"],
        "OnnxRuntimeModel": ["save_pretrained", "from_pretrained"],
    },
    "transformers": {
        "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
        "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
        "PreTrainedModel": ["save_pretrained", "from_pretrained"],
        "FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
        "ProcessorMixin": ["save_pretrained", "from_pretrained"],
        "ImageProcessingMixin": ["save_pretrained", "from_pretrained"],
    },
    "onnxruntime.training": {
        "ORTModule": ["save_pretrained", "from_pretrained"],
    },
}

ALL_IMPORTABLE_CLASSES = {}
for library in LOADABLE_CLASSES:
    ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])


@dataclass
class ImagePipelineOutput(BaseOutput):
    """
    Output class for image pipelines.

    Args:
        images (`List[PIL.Image.Image]` or `np.ndarray`)
Steven Liu's avatar
Steven Liu committed
118
119
            List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
            num_channels)`.
120
121
122
123
124
125
126
127
128
129
130
131
    """

    images: Union[List[PIL.Image.Image], np.ndarray]


@dataclass
class AudioPipelineOutput(BaseOutput):
    """
    Output class for audio pipelines.

    Args:
        audios (`np.ndarray`)
Steven Liu's avatar
Steven Liu committed
132
            List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`.
133
134
135
136
137
    """

    audios: np.ndarray


138
def is_safetensors_compatible(filenames, variant=None, passed_components=None) -> bool:
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
    """
    Checking for safetensors compatibility:
    - By default, all models are saved with the default pytorch serialization, so we use the list of default pytorch
      files to know which safetensors files are needed.
    - The model is safetensors compatible only if there is a matching safetensors file for every default pytorch file.

    Converting default pytorch serialized filenames to safetensors serialized filenames:
    - For models from the diffusers library, just replace the ".bin" extension with ".safetensors"
    - For models from the transformers library, the filename changes from "pytorch_model" to "model", and the ".bin"
      extension is replaced with ".safetensors"
    """
    pt_filenames = []

    sf_filenames = set()

154
155
    passed_components = passed_components or []

156
157
158
    for filename in filenames:
        _, extension = os.path.splitext(filename)

159
160
161
        if len(filename.split("/")) == 2 and filename.split("/")[0] in passed_components:
            continue

162
163
164
165
166
167
168
169
170
171
        if extension == ".bin":
            pt_filenames.append(filename)
        elif extension == ".safetensors":
            sf_filenames.add(filename)

    for filename in pt_filenames:
        #  filename = 'foo/bar/baz.bam' -> path = 'foo/bar', filename = 'baz', extention = '.bam'
        path, filename = os.path.split(filename)
        filename, extension = os.path.splitext(filename)

172
173
        if filename.startswith("pytorch_model"):
            filename = filename.replace("pytorch_model", "model")
174
        else:
175
176
177
178
179
180
181
182
183
184
            filename = filename

        expected_sf_filename = os.path.join(path, filename)
        expected_sf_filename = f"{expected_sf_filename}.safetensors"

        if expected_sf_filename not in sf_filenames:
            logger.warning(f"{expected_sf_filename} not found")
            return False

    return True
185
186


187
def variant_compatible_siblings(filenames, variant=None) -> Union[List[os.PathLike], str]:
188
189
190
191
192
193
194
    weight_names = [
        WEIGHTS_NAME,
        SAFETENSORS_WEIGHTS_NAME,
        FLAX_WEIGHTS_NAME,
        ONNX_WEIGHTS_NAME,
        ONNX_EXTERNAL_WEIGHTS_NAME,
    ]
195
196
197
198
199
200
201
202

    if is_transformers_available():
        weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME, TRANSFORMERS_FLAX_WEIGHTS_NAME]

    # model_pytorch, diffusion_model_pytorch, ...
    weight_prefixes = [w.split(".")[0] for w in weight_names]
    # .bin, .safetensors, ...
    weight_suffixs = [w.split(".")[-1] for w in weight_names]
203
    # -00001-of-00002
204
    transformers_index_format = r"\d{5}-of-\d{5}"
205
206

    if variant is not None:
207
        # `diffusion_pytorch_model.fp16.bin` as well as `model.fp16-00001-of-00002.safetensors`
208
        variant_file_re = re.compile(
209
            rf"({'|'.join(weight_prefixes)})\.({variant}|{variant}-{transformers_index_format})\.({'|'.join(weight_suffixs)})$"
210
211
212
        )
        # `text_encoder/pytorch_model.bin.index.fp16.json`
        variant_index_re = re.compile(
213
            rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.{variant}\.json$"
214
        )
215

216
    # `diffusion_pytorch_model.bin` as well as `model-00001-of-00002.safetensors`
217
    non_variant_file_re = re.compile(
218
        rf"({'|'.join(weight_prefixes)})(-{transformers_index_format})?\.({'|'.join(weight_suffixs)})$"
219
    )
220
    # `text_encoder/pytorch_model.bin.index.json`
221
    non_variant_index_re = re.compile(rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.json")
222
223

    if variant is not None:
224
225
226
        variant_weights = {f for f in filenames if variant_file_re.match(f.split("/")[-1]) is not None}
        variant_indexes = {f for f in filenames if variant_index_re.match(f.split("/")[-1]) is not None}
        variant_filenames = variant_weights | variant_indexes
227
228
229
    else:
        variant_filenames = set()

230
231
232
    non_variant_weights = {f for f in filenames if non_variant_file_re.match(f.split("/")[-1]) is not None}
    non_variant_indexes = {f for f in filenames if non_variant_index_re.match(f.split("/")[-1]) is not None}
    non_variant_filenames = non_variant_weights | non_variant_indexes
233

234
    # all variant filenames will be used by default
235
    usable_filenames = set(variant_filenames)
236
237
238
239
240
241
242
243
244
245

    def convert_to_variant(filename):
        if "index" in filename:
            variant_filename = filename.replace("index", f"index.{variant}")
        elif re.compile(f"^(.*?){transformers_index_format}").match(filename) is not None:
            variant_filename = f"{filename.split('-')[0]}.{variant}-{'-'.join(filename.split('-')[1:])}"
        else:
            variant_filename = f"{filename.split('.')[0]}.{variant}.{filename.split('.')[1]}"
        return variant_filename

246
    for f in non_variant_filenames:
247
        variant_filename = convert_to_variant(f)
248
249
250
251
252
253
        if variant_filename not in usable_filenames:
            usable_filenames.add(f)

    return usable_filenames, variant_filenames


254
255
256
257
258
259
def warn_deprecated_model_variant(pretrained_model_name_or_path, use_auth_token, variant, revision, model_filenames):
    info = model_info(
        pretrained_model_name_or_path,
        use_auth_token=use_auth_token,
        revision=None,
    )
260
    filenames = {sibling.rfilename for sibling in info.siblings}
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
    comp_model_filenames, _ = variant_compatible_siblings(filenames, variant=revision)
    comp_model_filenames = [".".join(f.split(".")[:1] + f.split(".")[2:]) for f in comp_model_filenames]

    if set(comp_model_filenames) == set(model_filenames):
        warnings.warn(
            f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` even though you can load it via `variant=`{revision}`. Loading model variants via `revision='{revision}'` is deprecated and will be removed in diffusers v1. Please use `variant='{revision}'` instead.",
            FutureWarning,
        )
    else:
        warnings.warn(
            f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have the required variant filenames in the 'main' branch. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {revision} files' so that the correct variant file can be added.",
            FutureWarning,
        )


def maybe_raise_or_warn(
    library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module
):
    """Simple helper method to raise or warn in case incorrect module has been passed"""
    if not is_pipeline_module:
        library = importlib.import_module(library_name)
        class_obj = getattr(library, class_name)
        class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}

        expected_class_obj = None
        for class_name, class_candidate in class_candidates.items():
            if class_candidate is not None and issubclass(class_obj, class_candidate):
                expected_class_obj = class_candidate

290
291
292
293
294
295
296
297
        # Dynamo wraps the original model in a private class.
        # I didn't find a public API to get the original class.
        sub_model = passed_class_obj[name]
        model_cls = sub_model.__class__
        if is_compiled_module(sub_model):
            model_cls = sub_model._orig_mod.__class__

        if not issubclass(model_cls, expected_class_obj):
298
            raise ValueError(
299
                f"{passed_class_obj[name]} is of type: {model_cls}, but should be" f" {expected_class_obj}"
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
            )
    else:
        logger.warning(
            f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
            " has the correct type"
        )


def get_class_obj_and_candidates(library_name, class_name, importable_classes, pipelines, is_pipeline_module):
    """Simple helper method to retrieve class object of module as well as potential parent class objects"""
    if is_pipeline_module:
        pipeline_module = getattr(pipelines, library_name)

        class_obj = getattr(pipeline_module, class_name)
        class_candidates = {c: class_obj for c in importable_classes.keys()}
    else:
        # else we just import it from the library.
        library = importlib.import_module(library_name)

        class_obj = getattr(library, class_name)
        class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}

    return class_obj, class_candidates


325
326
327
def _get_pipeline_class(
    class_obj, config, load_connected_pipeline=False, custom_pipeline=None, cache_dir=None, revision=None
):
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
    if custom_pipeline is not None:
        if custom_pipeline.endswith(".py"):
            path = Path(custom_pipeline)
            # decompose into folder & file
            file_name = path.name
            custom_pipeline = path.parent.absolute()
        else:
            file_name = CUSTOM_PIPELINE_FILE_NAME

        return get_class_from_dynamic_module(
            custom_pipeline, module_file=file_name, cache_dir=cache_dir, revision=revision
        )

    if class_obj != DiffusionPipeline:
        return class_obj

    diffusers_module = importlib.import_module(class_obj.__module__.split(".")[0])
345
346
347
348
349
350
    class_name = config["_class_name"]

    if class_name.startswith("Flax"):
        class_name = class_name[4:]

    pipeline_cls = getattr(diffusers_module, class_name)
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365

    if load_connected_pipeline:
        from .auto_pipeline import _get_connected_pipeline

        connected_pipeline_cls = _get_connected_pipeline(pipeline_cls)
        if connected_pipeline_cls is not None:
            logger.info(
                f"Loading connected pipeline {connected_pipeline_cls.__name__} instead of {pipeline_cls.__name__} as specified via `load_connected_pipeline=True`"
            )
        else:
            logger.info(f"{pipeline_cls.__name__} has no connected pipeline class. Loading {pipeline_cls.__name__}.")

        pipeline_cls = connected_pipeline_cls or pipeline_cls

    return pipeline_cls
366
367


368
369
370
371
372
373
374
375
376
377
378
def load_sub_model(
    library_name: str,
    class_name: str,
    importable_classes: List[Any],
    pipelines: Any,
    is_pipeline_module: bool,
    pipeline_class: Any,
    torch_dtype: torch.dtype,
    provider: Any,
    sess_options: Any,
    device_map: Optional[Union[Dict[str, torch.device], str]],
379
380
381
    max_memory: Optional[Dict[Union[int, str], Union[int, str]]],
    offload_folder: Optional[Union[str, os.PathLike]],
    offload_state_dict: bool,
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
    model_variants: Dict[str, str],
    name: str,
    from_flax: bool,
    variant: str,
    low_cpu_mem_usage: bool,
    cached_folder: Union[str, os.PathLike],
):
    """Helper method to load the module `name` from `library_name` and `class_name`"""
    # retrieve class candidates
    class_obj, class_candidates = get_class_obj_and_candidates(
        library_name, class_name, importable_classes, pipelines, is_pipeline_module
    )

    load_method_name = None
    # retrive load method name
    for class_name, class_candidate in class_candidates.items():
        if class_candidate is not None and issubclass(class_obj, class_candidate):
            load_method_name = importable_classes[class_name][1]

    # if load method name is None, then we have a dummy module -> raise Error
    if load_method_name is None:
        none_module = class_obj.__module__
        is_dummy_path = none_module.startswith(DUMMY_MODULES_FOLDER) or none_module.startswith(
            TRANSFORMERS_DUMMY_MODULES_FOLDER
        )
        if is_dummy_path and "dummy" in none_module:
            # call class_obj for nice error message of missing requirements
            class_obj()

        raise ValueError(
            f"The component {class_obj} of {pipeline_class} cannot be loaded as it does not seem to have"
            f" any of the loading methods defined in {ALL_IMPORTABLE_CLASSES}."
        )

    load_method = getattr(class_obj, load_method_name)

    # add kwargs to loading method
    loading_kwargs = {}
    if issubclass(class_obj, torch.nn.Module):
        loading_kwargs["torch_dtype"] = torch_dtype
    if issubclass(class_obj, diffusers.OnnxRuntimeModel):
        loading_kwargs["provider"] = provider
        loading_kwargs["sess_options"] = sess_options

    is_diffusers_model = issubclass(class_obj, diffusers.ModelMixin)

    if is_transformers_available():
        transformers_version = version.parse(version.parse(transformers.__version__).base_version)
    else:
        transformers_version = "N/A"

    is_transformers_model = (
        is_transformers_available()
        and issubclass(class_obj, PreTrainedModel)
        and transformers_version >= version.parse("4.20.0")
    )

    # When loading a transformers model, if the device_map is None, the weights will be initialized as opposed to diffusers.
    # To make default loading faster we set the `low_cpu_mem_usage=low_cpu_mem_usage` flag which is `True` by default.
    # This makes sure that the weights won't be initialized which significantly speeds up loading.
    if is_diffusers_model or is_transformers_model:
        loading_kwargs["device_map"] = device_map
444
445
446
        loading_kwargs["max_memory"] = max_memory
        loading_kwargs["offload_folder"] = offload_folder
        loading_kwargs["offload_state_dict"] = offload_state_dict
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
        loading_kwargs["variant"] = model_variants.pop(name, None)
        if from_flax:
            loading_kwargs["from_flax"] = True

        # the following can be deleted once the minimum required `transformers` version
        # is higher than 4.27
        if (
            is_transformers_model
            and loading_kwargs["variant"] is not None
            and transformers_version < version.parse("4.27.0")
        ):
            raise ImportError(
                f"When passing `variant='{variant}'`, please make sure to upgrade your `transformers` version to at least 4.27.0.dev0"
            )
        elif is_transformers_model and loading_kwargs["variant"] is None:
            loading_kwargs.pop("variant")

        # if `from_flax` and model is transformer model, can currently not load with `low_cpu_mem_usage`
        if not (from_flax and is_transformers_model):
            loading_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
        else:
            loading_kwargs["low_cpu_mem_usage"] = False

    # check if the module is in a subdirectory
    if os.path.isdir(os.path.join(cached_folder, name)):
        loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
    else:
        # else load from the root directory
        loaded_sub_model = load_method(cached_folder, **loading_kwargs)

    return loaded_sub_model


480
class DiffusionPipeline(ConfigMixin, PushToHubMixin):
481
    r"""
Steven Liu's avatar
Steven Liu committed
482
    Base class for all pipelines.
483

Steven Liu's avatar
Steven Liu committed
484
485
    [`DiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and
    provides methods for loading, downloading and saving models. It also includes methods to:
486
487

        - move all PyTorch modules to the device of your choice
488
        - enable/disable the progress bar for the denoising iteration
489
490
491

    Class attributes:

Steven Liu's avatar
Steven Liu committed
492
493
        - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
          diffusion pipeline's components.
494
        - **_optional_components** (`List[str]`) -- List of all optional components that don't have to be passed to the
Steven Liu's avatar
Steven Liu committed
495
          pipeline to function (should be overridden by subclasses).
496
497
    """
    config_name = "model_index.json"
498
    model_cpu_offload_seq = None
499
    _optional_components = []
500
    _exclude_from_cpu_offload = []
501
    _load_connected_pipes = False
502
    _is_onnx = False
503
504
505
506
507
508
509
510
511
512

    def register_modules(self, **kwargs):
        # import it here to avoid circular import
        from diffusers import pipelines

        for name, module in kwargs.items():
            # retrieve library
            if module is None:
                register_dict = {name: (None, None)}
            else:
513
                # register the config from the original module, not the dynamo compiled one
514
                if is_compiled_module(module):
515
516
517
                    not_compiled_module = module._orig_mod
                else:
                    not_compiled_module = module
518

519
                library = not_compiled_module.__module__.split(".")[0]
520
521

                # check if the module is a pipeline module
522
                module_path_items = not_compiled_module.__module__.split(".")
523
524
                pipeline_dir = module_path_items[-2] if len(module_path_items) > 2 else None

525
                path = not_compiled_module.__module__.split(".")
526
527
528
529
530
                is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)

                # if library is not in LOADABLE_CLASSES, then it is a custom module.
                # Or if it's a pipeline module, then the module is inside the pipeline
                # folder so we set the library to module name.
531
                if is_pipeline_module:
532
                    library = pipeline_dir
533
                elif library not in LOADABLE_CLASSES:
534
                    library = not_compiled_module.__module__
535
536

                # retrieve class_name
537
                class_name = not_compiled_module.__class__.__name__
538
539
540
541
542
543
544
545
546

                register_dict = {name: (library, class_name)}

            # save model index config
            self.register_to_config(**register_dict)

            # set models
            setattr(self, name, module)

547
    def __setattr__(self, name: str, value: Any):
548
        if name in self.__dict__ and hasattr(self.config, name):
549
550
            # We need to overwrite the config if name exists in config
            if isinstance(getattr(self.config, name), (tuple, list)):
551
                if value is not None and self.config[name][0] is not None:
552
553
554
555
556
557
558
559
560
561
                    class_library_tuple = (value.__module__.split(".")[0], value.__class__.__name__)
                else:
                    class_library_tuple = (None, None)

                self.register_to_config(**{name: class_library_tuple})
            else:
                self.register_to_config(**{name: value})

        super().__setattr__(name, value)

562
563
564
    def save_pretrained(
        self,
        save_directory: Union[str, os.PathLike],
565
        safe_serialization: bool = True,
566
        variant: Optional[str] = None,
567
568
        push_to_hub: bool = False,
        **kwargs,
569
570
    ):
        """
Steven Liu's avatar
Steven Liu committed
571
572
573
        Save all saveable variables of the pipeline to a directory. A pipeline variable can be saved and loaded if its
        class implements both a save and loading method. The pipeline is easily reloaded using the
        [`~DiffusionPipeline.from_pretrained`] class method.
574
575
576

        Arguments:
            save_directory (`str` or `os.PathLike`):
Steven Liu's avatar
Steven Liu committed
577
                Directory to save a pipeline to. Will be created if it doesn't exist.
578
            safe_serialization (`bool`, *optional*, defaults to `True`):
Steven Liu's avatar
Steven Liu committed
579
                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
580
            variant (`str`, *optional*):
Steven Liu's avatar
Steven Liu committed
581
                If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
582
583
584
585
586
587
            push_to_hub (`bool`, *optional*, defaults to `False`):
                Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                namespace).
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
588
589
        """
        model_index_dict = dict(self.config)
590
591
        model_index_dict.pop("_class_name", None)
        model_index_dict.pop("_diffusers_version", None)
592
        model_index_dict.pop("_module", None)
593
        model_index_dict.pop("_name_or_path", None)
594

595
596
597
598
599
600
601
602
        if push_to_hub:
            commit_message = kwargs.pop("commit_message", None)
            private = kwargs.pop("private", False)
            create_pr = kwargs.pop("create_pr", False)
            token = kwargs.pop("token", None)
            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
            repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id

603
604
605
606
607
608
609
610
611
612
613
614
615
616
        expected_modules, optional_kwargs = self._get_signature_keys(self)

        def is_saveable_module(name, value):
            if name not in expected_modules:
                return False
            if name in self._optional_components and value[0] is None:
                return False
            return True

        model_index_dict = {k: v for k, v in model_index_dict.items() if is_saveable_module(k, v)}
        for pipeline_component_name in model_index_dict.keys():
            sub_model = getattr(self, pipeline_component_name)
            model_cls = sub_model.__class__

617
618
619
620
621
622
            # Dynamo wraps the original model in a private class.
            # I didn't find a public API to get the original class.
            if is_compiled_module(sub_model):
                sub_model = sub_model._orig_mod
                model_cls = sub_model.__class__

623
624
625
            save_method_name = None
            # search for the model's base class in LOADABLE_CLASSES
            for library_name, library_classes in LOADABLE_CLASSES.items():
626
627
628
629
630
631
632
                if library_name in sys.modules:
                    library = importlib.import_module(library_name)
                else:
                    logger.info(
                        f"{library_name} is not installed. Cannot save {pipeline_component_name} as {library_classes} from {library_name}"
                    )

633
634
635
636
637
638
639
640
641
                for base_class, save_load_methods in library_classes.items():
                    class_candidate = getattr(library, base_class, None)
                    if class_candidate is not None and issubclass(model_cls, class_candidate):
                        # if we found a suitable base class in LOADABLE_CLASSES then grab its save method
                        save_method_name = save_load_methods[0]
                        break
                if save_method_name is not None:
                    break

642
643
644
645
646
647
            if save_method_name is None:
                logger.warn(f"self.{pipeline_component_name}={sub_model} of type {type(sub_model)} cannot be saved.")
                # make sure that unsaveable components are not tried to be loaded afterward
                self.register_to_config(**{pipeline_component_name: (None, None)})
                continue

648
649
650
651
652
            save_method = getattr(sub_model, save_method_name)

            # Call the save method with the argument safe_serialization only if it's supported
            save_method_signature = inspect.signature(save_method)
            save_method_accept_safe = "safe_serialization" in save_method_signature.parameters
653
654
655
            save_method_accept_variant = "variant" in save_method_signature.parameters

            save_kwargs = {}
656
            if save_method_accept_safe:
657
658
659
660
661
                save_kwargs["safe_serialization"] = safe_serialization
            if save_method_accept_variant:
                save_kwargs["variant"] = variant

            save_method(os.path.join(save_directory, pipeline_component_name), **save_kwargs)
662

663
664
665
        # finally save the config
        self.save_config(save_directory)

666
667
668
669
670
671
672
673
674
        if push_to_hub:
            self._upload_folder(
                save_directory,
                repo_id,
                token=token,
                commit_message=commit_message,
                create_pr=create_pr,
            )

675
676
677
678
679
680
681
    def to(
        self,
        torch_device: Optional[Union[str, torch.device]] = None,
        torch_dtype: Optional[torch.dtype] = None,
        silence_dtype_warnings: bool = False,
    ):
        if torch_device is None and torch_dtype is None:
682
683
            return self

684
685
686
687
688
        # throw warning if pipeline is in "offloaded"-mode but user tries to manually set to GPU.
        def module_is_sequentially_offloaded(module):
            if not is_accelerate_available() or is_accelerate_version("<", "0.14.0"):
                return False

Patrick von Platen's avatar
Patrick von Platen committed
689
690
691
            return hasattr(module, "_hf_hook") and not isinstance(
                module._hf_hook, (accelerate.hooks.CpuOffload, accelerate.hooks.AlignDevicesHook)
            )
692
693
694
695
696
697
698
699
700
701
702

        def module_is_offloaded(module):
            if not is_accelerate_available() or is_accelerate_version("<", "0.17.0.dev0"):
                return False

            return hasattr(module, "_hf_hook") and isinstance(module._hf_hook, accelerate.hooks.CpuOffload)

        # .to("cuda") would raise an error if the pipeline is sequentially offloaded, so we raise our own to make it clearer
        pipeline_is_sequentially_offloaded = any(
            module_is_sequentially_offloaded(module) for _, module in self.components.items()
        )
Patrick von Platen's avatar
Patrick von Platen committed
703
        if pipeline_is_sequentially_offloaded and torch_device and torch.device(torch_device).type == "cuda":
704
705
706
707
708
709
            raise ValueError(
                "It seems like you have activated sequential model offloading by calling `enable_sequential_cpu_offload`, but are now attempting to move the pipeline to GPU. This is not compatible with offloading. Please, move your pipeline `.to('cpu')` or consider removing the move altogether if you use sequential offloading."
            )

        # Display a warning in this case (the operation succeeds but the benefits are lost)
        pipeline_is_offloaded = any(module_is_offloaded(module) for _, module in self.components.items())
Patrick von Platen's avatar
Patrick von Platen committed
710
        if pipeline_is_offloaded and torch_device and torch.device(torch_device).type == "cuda":
711
712
713
714
            logger.warning(
                f"It seems like you have activated model offloading by calling `enable_model_cpu_offload`, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components {', '.join(self.components.keys())} to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: `pipeline.to('cpu')` or removing the move altogether if you use offloading."
            )

715
        module_names, _ = self._get_signature_keys(self)
716
717
        modules = [getattr(self, n, None) for n in module_names]
        modules = [m for m in modules if isinstance(m, torch.nn.Module)]
718

719
        is_offloaded = pipeline_is_offloaded or pipeline_is_sequentially_offloaded
720
        for module in modules:
Patrick von Platen's avatar
Patrick von Platen committed
721
722
723
724
725
726
727
728
729
730
731
732
733
734
            is_loaded_in_8bit = hasattr(module, "is_loaded_in_8bit") and module.is_loaded_in_8bit

            if is_loaded_in_8bit and torch_dtype is not None:
                logger.warning(
                    f"The module '{module.__class__.__name__}' has been loaded in 8bit and conversion to {torch_dtype} is not yet supported. Module is still in 8bit precision."
                )

            if is_loaded_in_8bit and torch_device is not None:
                logger.warning(
                    f"The module '{module.__class__.__name__}' has been loaded in 8bit and moving it to {torch_dtype} via `.to()` is not yet supported. Module is still on {module.device}."
                )
            else:
                module.to(torch_device, torch_dtype)

735
736
737
738
739
740
741
742
743
744
745
746
747
            if (
                module.dtype == torch.float16
                and str(torch_device) in ["cpu"]
                and not silence_dtype_warnings
                and not is_offloaded
            ):
                logger.warning(
                    "Pipelines loaded with `torch_dtype=torch.float16` cannot run with `cpu` device. It"
                    " is not recommended to move them to `cpu` as running them will fail. Please make"
                    " sure to use an accelerator to run the pipeline in inference, due to the lack of"
                    " support for`float16` operations on this device in PyTorch. Please, remove the"
                    " `torch_dtype=torch.float16` argument, or use another device for inference."
                )
748
749
750
751
752
753
754
755
        return self

    @property
    def device(self) -> torch.device:
        r"""
        Returns:
            `torch.device`: The torch device on which the pipeline is located.
        """
756
        module_names, _ = self._get_signature_keys(self)
757
758
759
760
761
        modules = [getattr(self, n, None) for n in module_names]
        modules = [m for m in modules if isinstance(m, torch.nn.Module)]

        for module in modules:
            return module.device
762

763
764
765
766
767
        return torch.device("cpu")

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
        r"""
Steven Liu's avatar
Steven Liu committed
768
        Instantiate a PyTorch diffusion pipeline from pretrained pipeline weights.
769

Steven Liu's avatar
Steven Liu committed
770
        The pipeline is set in evaluation mode (`model.eval()`) by default.
771

Steven Liu's avatar
Steven Liu committed
772
        If you get the error message below, you need to finetune the weights for your downstream task:
773

Steven Liu's avatar
Steven Liu committed
774
775
776
777
778
        ```
        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
        - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
        ```
779
780
781
782
783

        Parameters:
            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
                Can be either:

Steven Liu's avatar
Steven Liu committed
784
785
786
787
788
                    - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
                      hosted on the Hub.
                    - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
                      saved using
                    [`~DiffusionPipeline.save_pretrained`].
789
            torch_dtype (`str` or `torch.dtype`, *optional*):
Steven Liu's avatar
Steven Liu committed
790
791
                Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
                dtype is automatically derived from the model's weights.
792
793
794
795
            custom_pipeline (`str`, *optional*):

                <Tip warning={true}>

Steven Liu's avatar
Steven Liu committed
796
                🧪 This is an experimental feature and may change in the future.
797
798
799
800
801

                </Tip>

                Can be either:

Steven Liu's avatar
Steven Liu committed
802
803
804
                    - A string, the *repo id* (for example `hf-internal-testing/diffusers-dummy-pipeline`) of a custom
                      pipeline hosted on the Hub. The repository must contain a file called pipeline.py that defines
                      the custom pipeline.
805
                    - A string, the *file name* of a community pipeline hosted on GitHub under
Steven Liu's avatar
Steven Liu committed
806
807
808
809
810
811
                      [Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file
                      names must match the file name and not the pipeline script (`clip_guided_stable_diffusion`
                      instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the
                      current main branch of GitHub.
                    - A path to a directory (`./my_pipeline_directory/`) containing a custom pipeline. The directory
                      must contain a file called `pipeline.py` that defines the custom pipeline.
812
813
814
815
816
817
818

                For more information on how to load and create custom pipelines, please have a look at [Loading and
                Adding Custom
                Pipelines](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview)
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
819
            cache_dir (`Union[str, os.PathLike]`, *optional*):
Steven Liu's avatar
Steven Liu committed
820
821
                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                is not used.
822
            resume_download (`bool`, *optional*, defaults to `False`):
Steven Liu's avatar
Steven Liu committed
823
824
                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
                incompletely downloaded files are deleted.
825
            proxies (`Dict[str, str]`, *optional*):
Steven Liu's avatar
Steven Liu committed
826
                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
827
828
829
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
Steven Liu's avatar
Steven Liu committed
830
831
832
            local_files_only (`bool`, *optional*, defaults to `False`):
                Whether to only load local model weights and configuration files or not. If set to `True`, the model
                won't be downloaded from the Hub.
833
            use_auth_token (`str` or *bool*, *optional*):
Steven Liu's avatar
Steven Liu committed
834
835
                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                `diffusers-cli login` (stored in `~/.huggingface`) is used.
836
            revision (`str`, *optional*, defaults to `"main"`):
Steven Liu's avatar
Steven Liu committed
837
838
839
                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                allowed by Git.
            custom_revision (`str`, *optional*, defaults to `"main"`):
840
                The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
Steven Liu's avatar
Steven Liu committed
841
842
                `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
                custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
843
            mirror (`str`, *optional*):
Steven Liu's avatar
Steven Liu committed
844
845
846
                Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
                information.
847
            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
Steven Liu's avatar
Steven Liu committed
848
849
                A map that specifies where each submodule should go. It doesn’t need to be defined for each
                parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
850
851
                same device.

Steven Liu's avatar
Steven Liu committed
852
                Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
853
854
                more information about each option see [designing a device
                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
855
            max_memory (`Dict`, *optional*):
Steven Liu's avatar
Steven Liu committed
856
857
                A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
                each GPU and the available CPU RAM if unset.
858
            offload_folder (`str` or `os.PathLike`, *optional*):
Steven Liu's avatar
Steven Liu committed
859
                The path to offload weights if device_map contains the value `"disk"`.
860
            offload_state_dict (`bool`, *optional*):
Steven Liu's avatar
Steven Liu committed
861
862
863
                If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
                the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
                when there is some disk offload.
864
            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Steven Liu's avatar
Steven Liu committed
865
866
867
868
                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
                argument to `True` will raise an error.
869
            use_safetensors (`bool`, *optional*, defaults to `None`):
Steven Liu's avatar
Steven Liu committed
870
871
872
                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
                weights. If set to `False`, safetensors weights are not loaded.
873
874
875
876
877
            use_onnx (`bool`, *optional*, defaults to `None`):
                If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights
                will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is
                `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending
                with `.onnx` and `.pb`.
878
            kwargs (remaining dictionary of keyword arguments, *optional*):
Steven Liu's avatar
Steven Liu committed
879
880
881
                Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
                class). The overwritten components are passed directly to the pipelines `__init__` method. See example
                below for more information.
882
            variant (`str`, *optional*):
Steven Liu's avatar
Steven Liu committed
883
884
                Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
                loading `from_flax`.
885
886
887

        <Tip>

Steven Liu's avatar
Steven Liu committed
888
889
        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
        `huggingface-cli login`.
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

        </Tip>

        Examples:

        ```py
        >>> from diffusers import DiffusionPipeline

        >>> # Download pipeline from huggingface.co and cache.
        >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")

        >>> # Download pipeline that requires an authorization token
        >>> # For more information on access tokens, please refer to this section
        >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
        >>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")

        >>> # Use a different scheduler
        >>> from diffusers import LMSDiscreteScheduler

        >>> scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
        >>> pipeline.scheduler = scheduler
        ```
        """
        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
        resume_download = kwargs.pop("resume_download", False)
        force_download = kwargs.pop("force_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
        use_auth_token = kwargs.pop("use_auth_token", None)
        revision = kwargs.pop("revision", None)
920
        from_flax = kwargs.pop("from_flax", False)
921
922
923
924
925
926
        torch_dtype = kwargs.pop("torch_dtype", None)
        custom_pipeline = kwargs.pop("custom_pipeline", None)
        custom_revision = kwargs.pop("custom_revision", None)
        provider = kwargs.pop("provider", None)
        sess_options = kwargs.pop("sess_options", None)
        device_map = kwargs.pop("device_map", None)
927
928
929
        max_memory = kwargs.pop("max_memory", None)
        offload_folder = kwargs.pop("offload_folder", None)
        offload_state_dict = kwargs.pop("offload_state_dict", False)
930
        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
931
        variant = kwargs.pop("variant", None)
932
        use_safetensors = kwargs.pop("use_safetensors", None)
933
        use_onnx = kwargs.pop("use_onnx", None)
934
        load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
935
936
937
938

        # 1. Download the checkpoints and configs
        # use snapshot download here to get it working from from_pretrained
        if not os.path.isdir(pretrained_model_name_or_path):
939
            cached_folder = cls.download(
940
941
942
943
944
945
946
947
                pretrained_model_name_or_path,
                cache_dir=cache_dir,
                resume_download=resume_download,
                force_download=force_download,
                proxies=proxies,
                local_files_only=local_files_only,
                use_auth_token=use_auth_token,
                revision=revision,
948
                from_flax=from_flax,
949
                use_safetensors=use_safetensors,
950
                use_onnx=use_onnx,
951
                custom_pipeline=custom_pipeline,
952
                custom_revision=custom_revision,
953
                variant=variant,
954
                load_connected_pipeline=load_connected_pipeline,
955
                **kwargs,
956
957
958
959
            )
        else:
            cached_folder = pretrained_model_name_or_path

960
961
        config_dict = cls.load_config(cached_folder)

Patrick von Platen's avatar
Patrick von Platen committed
962
963
964
        # pop out "_ignore_files" as it is only needed for download
        config_dict.pop("_ignore_files", None)

965
966
967
        # 2. Define which model components should load variants
        # We retrieve the information by matching whether variant
        # model checkpoints exist in the subfolders
968
969
970
971
972
        model_variants = {}
        if variant is not None:
            for folder in os.listdir(cached_folder):
                folder_path = os.path.join(cached_folder, folder)
                is_folder = os.path.isdir(folder_path) and folder in config_dict
973
974
975
                variant_exists = is_folder and any(
                    p.split(".")[1].startswith(variant) for p in os.listdir(folder_path)
                )
976
977
978
                if variant_exists:
                    model_variants[folder] = variant

979
        # 3. Load the pipeline class, if using custom module then load it from the hub
980
        # if we load from explicit class, let's use it
981
        pipeline_class = _get_pipeline_class(
982
983
984
985
986
987
            cls,
            config_dict,
            load_connected_pipeline=load_connected_pipeline,
            custom_pipeline=custom_pipeline,
            cache_dir=cache_dir,
            revision=custom_revision,
988
        )
989

990
        # DEPRECATED: To be removed in 1.0.0
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
        if pipeline_class.__name__ == "StableDiffusionInpaintPipeline" and version.parse(
            version.parse(config_dict["_diffusers_version"]).base_version
        ) <= version.parse("0.5.1"):
            from diffusers import StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy

            pipeline_class = StableDiffusionInpaintPipelineLegacy

            deprecation_message = (
                "You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the"
                f" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For"
                " better inpainting results, we strongly suggest using Stable Diffusion's official inpainting"
                " checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting instead or adapting your"
                f" checkpoint {pretrained_model_name_or_path} to the format of"
                " https://huggingface.co/runwayml/stable-diffusion-inpainting. Note that we do not actively maintain"
                " the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0."
            )
            deprecate("StableDiffusionInpaintPipelineLegacy", "1.0.0", deprecation_message, standard_warn=False)

1009
1010
1011
        # 4. Define expected modules given pipeline signature
        # and define non-None initialized modules (=`init_kwargs`)

1012
1013
1014
1015
1016
1017
1018
1019
1020
        # some modules can be passed directly to the init
        # in this case they are already instantiated in `kwargs`
        # extract them here
        expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class)
        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
        passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}

        init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)

1021
1022
1023
1024
1025
1026
        # define init kwargs and make sure that optional component modules are filtered out
        init_kwargs = {
            k: init_dict.pop(k)
            for k in optional_kwargs
            if k in init_dict and k not in pipeline_class._optional_components
        }
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
        init_kwargs = {**init_kwargs, **passed_pipe_kwargs}

        # remove `null` components
        def load_module(name, value):
            if value[0] is None:
                return False
            if name in passed_class_obj and passed_class_obj[name] is None:
                return False
            return True

        init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}

1039
1040
1041
1042
1043
1044
1045
1046
        # Special case: safety_checker must be loaded separately when using `from_flax`
        if from_flax and "safety_checker" in init_dict and "safety_checker" not in passed_class_obj:
            raise NotImplementedError(
                "The safety checker cannot be automatically loaded when loading weights `from_flax`."
                " Please, pass `safety_checker=None` to `from_pretrained`, and load the safety checker"
                " separately if you need it."
            )

1047
        # 5. Throw nice warnings / errors for fast accelerate loading
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
        if len(unused_kwargs) > 0:
            logger.warning(
                f"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored."
            )

        if low_cpu_mem_usage and not is_accelerate_available():
            low_cpu_mem_usage = False
            logger.warning(
                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
                " install accelerate\n```\n."
            )

        if device_map is not None and not is_torch_version(">=", "1.9.0"):
            raise NotImplementedError(
                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
                " `device_map=None`."
            )

        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
            raise NotImplementedError(
                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
                " `low_cpu_mem_usage=False`."
            )

        if low_cpu_mem_usage is False and device_map is not None:
            raise ValueError(
                f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
                " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
            )

        # import it here to avoid circular import
        from diffusers import pipelines

1083
        # 6. Load each module in the pipeline
1084
        for name, (library_name, class_name) in tqdm(init_dict.items(), desc="Loading pipeline components..."):
1085
            # 6.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names
1086
1087
1088
            if class_name.startswith("Flax"):
                class_name = class_name[4:]

1089
            # 6.2 Define all importable classes
1090
            is_pipeline_module = hasattr(pipelines, library_name)
1091
            importable_classes = ALL_IMPORTABLE_CLASSES
1092
1093
            loaded_sub_model = None

1094
            # 6.3 Use passed sub model or load class_name from library_name
1095
            if name in passed_class_obj:
1096
1097
1098
1099
1100
                # if the model is in a pipeline module, then we load it from the pipeline
                # check that passed_class_obj has correct parent class
                maybe_raise_or_warn(
                    library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module
                )
1101
1102
1103

                loaded_sub_model = passed_class_obj[name]
            else:
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
                # load sub model
                loaded_sub_model = load_sub_model(
                    library_name=library_name,
                    class_name=class_name,
                    importable_classes=importable_classes,
                    pipelines=pipelines,
                    is_pipeline_module=is_pipeline_module,
                    pipeline_class=pipeline_class,
                    torch_dtype=torch_dtype,
                    provider=provider,
                    sess_options=sess_options,
                    device_map=device_map,
1116
1117
1118
                    max_memory=max_memory,
                    offload_folder=offload_folder,
                    offload_state_dict=offload_state_dict,
1119
1120
1121
1122
1123
1124
                    model_variants=model_variants,
                    name=name,
                    from_flax=from_flax,
                    variant=variant,
                    low_cpu_mem_usage=low_cpu_mem_usage,
                    cached_folder=cached_folder,
1125
                )
1126
1127
1128
                logger.info(
                    f"Loaded {name} as {class_name} from `{name}` subfolder of {pretrained_model_name_or_path}."
                )
1129
1130
1131

            init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)

1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
        if pipeline_class._load_connected_pipes and os.path.isfile(os.path.join(cached_folder, "README.md")):
            modelcard = ModelCard.load(os.path.join(cached_folder, "README.md"))
            connected_pipes = {prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS}
            load_kwargs = {
                "cache_dir": cache_dir,
                "resume_download": resume_download,
                "force_download": force_download,
                "proxies": proxies,
                "local_files_only": local_files_only,
                "use_auth_token": use_auth_token,
                "revision": revision,
                "torch_dtype": torch_dtype,
                "custom_pipeline": custom_pipeline,
                "custom_revision": custom_revision,
                "provider": provider,
                "sess_options": sess_options,
                "device_map": device_map,
                "max_memory": max_memory,
                "offload_folder": offload_folder,
                "offload_state_dict": offload_state_dict,
                "low_cpu_mem_usage": low_cpu_mem_usage,
                "variant": variant,
                "use_safetensors": use_safetensors,
            }
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167

            def get_connected_passed_kwargs(prefix):
                connected_passed_class_obj = {
                    k.replace(f"{prefix}_", ""): w for k, w in passed_class_obj.items() if k.split("_")[0] == prefix
                }
                connected_passed_pipe_kwargs = {
                    k.replace(f"{prefix}_", ""): w for k, w in passed_pipe_kwargs.items() if k.split("_")[0] == prefix
                }

                connected_passed_kwargs = {**connected_passed_class_obj, **connected_passed_pipe_kwargs}
                return connected_passed_kwargs

1168
            connected_pipes = {
1169
1170
1171
                prefix: DiffusionPipeline.from_pretrained(
                    repo_id, **load_kwargs.copy(), **get_connected_passed_kwargs(prefix)
                )
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
                for prefix, repo_id in connected_pipes.items()
                if repo_id is not None
            }

            for prefix, connected_pipe in connected_pipes.items():
                # add connected pipes to `init_kwargs` with <prefix>_<component_name>, e.g. "prior_text_encoder"
                init_kwargs.update(
                    {"_".join([prefix, name]): component for name, component in connected_pipe.components.items()}
                )

1182
        # 7. Potentially add passed objects if expected
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
        missing_modules = set(expected_modules) - set(init_kwargs.keys())
        passed_modules = list(passed_class_obj.keys())
        optional_modules = pipeline_class._optional_components
        if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules):
            for module in missing_modules:
                init_kwargs[module] = passed_class_obj.get(module, None)
        elif len(missing_modules) > 0:
            passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs
            raise ValueError(
                f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
            )

1195
        # 8. Instantiate the pipeline
1196
        model = pipeline_class(**init_kwargs)
1197
1198
1199

        # 9. Save where the model was instantiated from
        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
1200
1201
        return model

1202
1203
1204
1205
    @property
    def name_or_path(self) -> str:
        return getattr(self.config, "_name_or_path", None)

1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
    @property
    def _execution_device(self):
        r"""
        Returns the device on which the pipeline's models will be executed. After calling
        [`~DiffusionPipeline.enable_sequential_cpu_offload`] the execution device can only be inferred from
        Accelerate's module hooks.
        """
        for name, model in self.components.items():
            if not isinstance(model, torch.nn.Module) or name in self._exclude_from_cpu_offload:
                continue

            if not hasattr(model, "_hf_hook"):
                return self.device
            for module in model.modules():
                if (
                    hasattr(module, "_hf_hook")
                    and hasattr(module._hf_hook, "execution_device")
                    and module._hf_hook.execution_device is not None
                ):
                    return torch.device(module._hf_hook.execution_device)
        return self.device

1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
    def enable_model_cpu_offload(self, gpu_id: int = 0, device: Union[torch.device, str] = "cuda"):
        r"""
        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
        """
        if self.model_cpu_offload_seq is None:
            raise ValueError(
                "Model CPU offload cannot be enabled because no `model_cpu_offload_seq` class attribute is set."
            )

        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
            from accelerate import cpu_offload_with_hook
        else:
            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")

        device = torch.device(f"cuda:{gpu_id}")

        if self.device.type != "cpu":
            self.to("cpu", silence_dtype_warnings=True)
            device_mod = getattr(torch, self.device.type, None)
            if hasattr(device_mod, "empty_cache") and device_mod.is_available():
                device_mod.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        all_model_components = {k: v for k, v in self.components.items() if isinstance(v, torch.nn.Module)}

        self._all_hooks = []
        hook = None
        for model_str in self.model_cpu_offload_seq.split("->"):
            model = all_model_components.pop(model_str)
            if not isinstance(model, torch.nn.Module):
                continue

            _, hook = cpu_offload_with_hook(model, device, prev_module_hook=hook)
            self._all_hooks.append(hook)

        # CPU offload models that are not in the seq chain unless they are explicitly excluded
        # these models will stay on CPU until maybe_free_model_hooks is called
        # some models cannot be in the seq chain because they are iteratively called, such as controlnet
        for name, model in all_model_components.items():
            if not isinstance(model, torch.nn.Module):
                continue

            if name in self._exclude_from_cpu_offload:
                model.to(device)
            else:
                _, hook = cpu_offload_with_hook(model, device)
                self._all_hooks.append(hook)

    def maybe_free_model_hooks(self):
        r"""
        TODO: Better doc string
        """
        if not hasattr(self, "_all_hooks") or len(self._all_hooks) == 0:
            # `enable_model_cpu_offload` has not be called, so silently do nothing
            return

        for hook in self._all_hooks:
            # offload model and remove hook from model
            hook.offload()
            hook.remove()

        # make sure the model is in the same state as before calling it
        self.enable_model_cpu_offload()

1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
    def enable_sequential_cpu_offload(self, gpu_id: int = 0, device: Union[torch.device, str] = "cuda"):
        r"""
        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
        Note that offloading happens on a submodule basis. Memory savings are higher than with
        `enable_model_cpu_offload`, but performance is lower.
        """
        if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
            from accelerate import cpu_offload
        else:
            raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")

        if device == "cuda":
            device = torch.device(f"{device}:{gpu_id}")

        if self.device.type != "cpu":
            self.to("cpu", silence_dtype_warnings=True)
1312
1313
1314
            device_mod = getattr(torch, self.device.type, None)
            if hasattr(device_mod, "empty_cache") and device_mod.is_available():
                device_mod.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327

        for name, model in self.components.items():
            if not isinstance(model, torch.nn.Module):
                continue

            if name in self._exclude_from_cpu_offload:
                model.to(device)
            else:
                # make sure to offload buffers if not all high level weights
                # are of type nn.Module
                offload_buffers = len(model._parameters) > 0
                cpu_offload(model, device, offload_buffers=offload_buffers)

1328
1329
1330
    @classmethod
    def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
        r"""
Steven Liu's avatar
Steven Liu committed
1331
        Download and cache a PyTorch diffusion pipeline from pretrained pipeline weights.
1332
1333

        Parameters:
Steven Liu's avatar
Steven Liu committed
1334
            pretrained_model_name (`str` or `os.PathLike`, *optional*):
Steven Liu's avatar
Steven Liu committed
1335
                A string, the *repository id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
Steven Liu's avatar
Steven Liu committed
1336
                hosted on the Hub.
1337
1338
1339
            custom_pipeline (`str`, *optional*):
                Can be either:

Steven Liu's avatar
Steven Liu committed
1340
                    - A string, the *repository id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained
Steven Liu's avatar
Steven Liu committed
1341
1342
                      pipeline hosted on the Hub. The repository must contain a file called `pipeline.py` that defines
                      the custom pipeline.
1343
1344

                    - A string, the *file name* of a community pipeline hosted on GitHub under
Steven Liu's avatar
Steven Liu committed
1345
1346
1347
1348
                      [Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file
                      names must match the file name and not the pipeline script (`clip_guided_stable_diffusion`
                      instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the
                      current `main` branch of GitHub.
1349

Steven Liu's avatar
Steven Liu committed
1350
1351
                    - A path to a *directory* (`./my_pipeline_directory/`) containing a custom pipeline. The directory
                      must contain a file called `pipeline.py` that defines the custom pipeline.
1352

Steven Liu's avatar
Steven Liu committed
1353
                <Tip warning={true}>
1354

Steven Liu's avatar
Steven Liu committed
1355
                🧪 This is an experimental feature and may change in the future.
1356

Steven Liu's avatar
Steven Liu committed
1357
                </Tip>
1358

Steven Liu's avatar
Steven Liu committed
1359
1360
                For more information on how to load and create custom pipelines, take a look at [How to contribute a
                community pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/contribute_pipeline).
1361
1362
1363
1364
1365

            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
Steven Liu's avatar
Steven Liu committed
1366
                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
Steven Liu's avatar
Steven Liu committed
1367
                incompletely downloaded files are deleted.
1368
            proxies (`Dict[str, str]`, *optional*):
Steven Liu's avatar
Steven Liu committed
1369
                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
1370
1371
1372
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
Steven Liu's avatar
Steven Liu committed
1373
1374
1375
            local_files_only (`bool`, *optional*, defaults to `False`):
                Whether to only load local model weights and configuration files or not. If set to `True`, the model
                won't be downloaded from the Hub.
1376
            use_auth_token (`str` or *bool*, *optional*):
Steven Liu's avatar
Steven Liu committed
1377
1378
                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                `diffusers-cli login` (stored in `~/.huggingface`) is used.
1379
            revision (`str`, *optional*, defaults to `"main"`):
Steven Liu's avatar
Steven Liu committed
1380
1381
                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                allowed by Git.
Steven Liu's avatar
Steven Liu committed
1382
            custom_revision (`str`, *optional*, defaults to `"main"`):
1383
                The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
Steven Liu's avatar
Steven Liu committed
1384
1385
                `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
                custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
1386
            mirror (`str`, *optional*):
Steven Liu's avatar
Steven Liu committed
1387
1388
1389
                Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
                information.
1390
            variant (`str`, *optional*):
Steven Liu's avatar
Steven Liu committed
1391
1392
                Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
                loading `from_flax`.
1393
1394
1395
1396
1397
1398
1399
1400
1401
            use_safetensors (`bool`, *optional*, defaults to `None`):
                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
                weights. If set to `False`, safetensors weights are not loaded.
            use_onnx (`bool`, *optional*, defaults to `False`):
                If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights
                will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is
                `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending
                with `.onnx` and `.pb`.
Steven Liu's avatar
Steven Liu committed
1402
1403
1404
1405

        Returns:
            `os.PathLike`:
                A path to the downloaded pipeline.
1406
1407
1408

        <Tip>

Steven Liu's avatar
Steven Liu committed
1409
1410
        To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with
        `huggingface-cli login`.
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423

        </Tip>

        """
        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
        resume_download = kwargs.pop("resume_download", False)
        force_download = kwargs.pop("force_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
        use_auth_token = kwargs.pop("use_auth_token", None)
        revision = kwargs.pop("revision", None)
        from_flax = kwargs.pop("from_flax", False)
        custom_pipeline = kwargs.pop("custom_pipeline", None)
1424
        custom_revision = kwargs.pop("custom_revision", None)
1425
        variant = kwargs.pop("variant", None)
1426
        use_safetensors = kwargs.pop("use_safetensors", None)
1427
        use_onnx = kwargs.pop("use_onnx", None)
1428
        load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
1429
1430
1431

        allow_pickle = False
        if use_safetensors is None:
1432
            use_safetensors = True
1433
            allow_pickle = True
1434
1435
1436
1437

        allow_patterns = None
        ignore_patterns = None

1438
        model_info_call_error: Optional[Exception] = None
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
        if not local_files_only:
            try:
                info = model_info(
                    pretrained_model_name,
                    use_auth_token=use_auth_token,
                    revision=revision,
                )
            except HTTPError as e:
                logger.warn(f"Couldn't connect to the Hub: {e}.\nWill try to load from local cache.")
                local_files_only = True
1449
                model_info_call_error = e  # save error to reraise it if model is not cached locally
1450

1451
1452
1453
1454
1455
        if not local_files_only:
            config_file = hf_hub_download(
                pretrained_model_name,
                cls.config_name,
                cache_dir=cache_dir,
1456
                revision=revision,
1457
1458
1459
1460
1461
1462
1463
                proxies=proxies,
                force_download=force_download,
                resume_download=resume_download,
                use_auth_token=use_auth_token,
            )

            config_dict = cls._dict_from_json_file(config_file)
Patrick von Platen's avatar
Patrick von Platen committed
1464
1465
1466

            ignore_filenames = config_dict.pop("_ignore_files", [])

1467
1468
1469
            # retrieve all folder_names that contain relevant files
            folder_names = [k for k, v in config_dict.items() if isinstance(v, list)]

1470
            filenames = {sibling.rfilename for sibling in info.siblings}
1471
1472
            model_filenames, variant_filenames = variant_compatible_siblings(filenames, variant=variant)

1473
1474
1475
1476
1477
1478
1479
1480
1481
            if len(variant_filenames) == 0 and variant is not None:
                deprecation_message = (
                    f"You are trying to load the model files of the `variant={variant}`, but no such modeling files are available."
                    f"The default model files: {model_filenames} will be loaded instead. Make sure to not load from `variant={variant}`"
                    "if such variant modeling files are not available. Doing so will lead to an error in v0.22.0 as defaulting to non-variant"
                    "modeling files is deprecated."
                )
                deprecate("no variant default", "0.22.0", deprecation_message, standard_warn=False)

Patrick von Platen's avatar
Patrick von Platen committed
1482
1483
1484
1485
            # remove ignored filenames
            model_filenames = set(model_filenames) - set(ignore_filenames)
            variant_filenames = set(variant_filenames) - set(ignore_filenames)

1486
1487
1488
            # if the whole pipeline is cached we don't have to ping the Hub
            if revision in DEPRECATED_REVISION_ARGS and version.parse(
                version.parse(__version__).base_version
Patrick von Platen's avatar
Patrick von Platen committed
1489
            ) >= version.parse("0.22.0"):
1490
1491
1492
1493
                warn_deprecated_model_variant(
                    pretrained_model_name, use_auth_token, variant, revision, model_filenames
                )

1494
            model_folder_names = {os.path.split(f)[0] for f in model_filenames if os.path.split(f)[0] in folder_names}
1495
1496
1497
1498
1499
1500

            # all filenames compatible with variant will be added
            allow_patterns = list(model_filenames)

            # allow all patterns from non-model folders
            # this enables downloading schedulers, tokenizers, ...
1501
            allow_patterns += [f"{k}/*" for k in folder_names if k not in model_folder_names]
1502
            # also allow downloading config.json files with the model
1503
            allow_patterns += [os.path.join(k, "config.json") for k in model_folder_names]
1504
1505
1506
1507
1508
1509
1510
1511

            allow_patterns += [
                SCHEDULER_CONFIG_NAME,
                CONFIG_NAME,
                cls.config_name,
                CUSTOM_PIPELINE_FILE_NAME,
            ]

1512
1513
            # retrieve passed components that should not be downloaded
            pipeline_class = _get_pipeline_class(
1514
1515
1516
1517
1518
1519
                cls,
                config_dict,
                load_connected_pipeline=load_connected_pipeline,
                custom_pipeline=custom_pipeline,
                cache_dir=cache_dir,
                revision=custom_revision,
1520
1521
1522
1523
            )
            expected_components, _ = cls._get_signature_keys(pipeline_class)
            passed_components = [k for k in expected_components if k in kwargs]

1524
1525
1526
            if (
                use_safetensors
                and not allow_pickle
1527
1528
1529
                and not is_safetensors_compatible(
                    model_filenames, variant=variant, passed_components=passed_components
                )
1530
1531
1532
1533
            ):
                raise EnvironmentError(
                    f"Could not found the necessary `safetensors` weights in {model_filenames} (variant={variant})"
                )
1534
1535
            if from_flax:
                ignore_patterns = ["*.bin", "*.safetensors", "*.onnx", "*.pb"]
1536
1537
1538
            elif use_safetensors and is_safetensors_compatible(
                model_filenames, variant=variant, passed_components=passed_components
            ):
1539
1540
                ignore_patterns = ["*.bin", "*.msgpack"]

1541
1542
1543
1544
                use_onnx = use_onnx if use_onnx is not None else pipeline_class._is_onnx
                if not use_onnx:
                    ignore_patterns += ["*.onnx", "*.pb"]

1545
1546
                safetensors_variant_filenames = {f for f in variant_filenames if f.endswith(".safetensors")}
                safetensors_model_filenames = {f for f in model_filenames if f.endswith(".safetensors")}
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
                if (
                    len(safetensors_variant_filenames) > 0
                    and safetensors_model_filenames != safetensors_variant_filenames
                ):
                    logger.warn(
                        f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(safetensors_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(safetensors_model_filenames - safetensors_variant_filenames)}\nIf this behavior is not expected, please check your folder structure."
                    )
            else:
                ignore_patterns = ["*.safetensors", "*.msgpack"]

1557
1558
1559
1560
                use_onnx = use_onnx if use_onnx is not None else pipeline_class._is_onnx
                if not use_onnx:
                    ignore_patterns += ["*.onnx", "*.pb"]

1561
1562
                bin_variant_filenames = {f for f in variant_filenames if f.endswith(".bin")}
                bin_model_filenames = {f for f in model_filenames if f.endswith(".bin")}
1563
1564
1565
1566
1567
                if len(bin_variant_filenames) > 0 and bin_model_filenames != bin_variant_filenames:
                    logger.warn(
                        f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(bin_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(bin_model_filenames - bin_variant_filenames)}\nIf this behavior is not expected, please check your folder structure."
                    )

1568
1569
1570
1571
            # Don't download any objects that are passed
            allow_patterns = [
                p for p in allow_patterns if not (len(p.split("/")) == 2 and p.split("/")[0] in passed_components)
            ]
1572
1573
1574
1575

            if pipeline_class._load_connected_pipes:
                allow_patterns.append("README.md")

1576
1577
1578
            # Don't download index files of forbidden patterns either
            ignore_patterns = ignore_patterns + [f"{i}.index.*json" for i in ignore_patterns]

1579
1580
1581
1582
1583
            re_ignore_pattern = [re.compile(fnmatch.translate(p)) for p in ignore_patterns]
            re_allow_pattern = [re.compile(fnmatch.translate(p)) for p in allow_patterns]

            expected_files = [f for f in filenames if not any(p.match(f) for p in re_ignore_pattern)]
            expected_files = [f for f in expected_files if any(p.match(f) for p in re_allow_pattern)]
1584

1585
1586
            snapshot_folder = Path(config_file).parent
            pipeline_is_cached = all((snapshot_folder / f).is_file() for f in expected_files)
1587

1588
            if pipeline_is_cached and not force_download:
1589
1590
1591
                # if the pipeline is cached, we can directly return it
                # else call snapshot_download
                return snapshot_folder
1592

1593
1594
1595
        user_agent = {"pipeline_class": cls.__name__}
        if custom_pipeline is not None and not custom_pipeline.endswith(".py"):
            user_agent["custom_pipeline"] = custom_pipeline
1596
1597

        # download all allow_patterns - ignore_patterns
1598
        try:
1599
            cached_folder = snapshot_download(
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
                pretrained_model_name,
                cache_dir=cache_dir,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                use_auth_token=use_auth_token,
                revision=revision,
                allow_patterns=allow_patterns,
                ignore_patterns=ignore_patterns,
                user_agent=user_agent,
            )
1611

1612
1613
1614
1615
1616
            # retrieve pipeline class from local file
            cls_name = cls.load_config(os.path.join(cached_folder, "model_index.json")).get("_class_name", None)
            pipeline_class = getattr(diffusers, cls_name, None)

            if pipeline_class is not None and pipeline_class._load_connected_pipes:
1617
1618
1619
                modelcard = ModelCard.load(os.path.join(cached_folder, "README.md"))
                connected_pipes = sum([getattr(modelcard.data, k, []) for k in CONNECTED_PIPES_KEYS], [])
                for connected_pipe_repo_id in connected_pipes:
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
                    download_kwargs = {
                        "cache_dir": cache_dir,
                        "resume_download": resume_download,
                        "force_download": force_download,
                        "proxies": proxies,
                        "local_files_only": local_files_only,
                        "use_auth_token": use_auth_token,
                        "variant": variant,
                        "use_safetensors": use_safetensors,
                    }
                    DiffusionPipeline.download(connected_pipe_repo_id, **download_kwargs)
1631
1632
1633

            return cached_folder

1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
        except FileNotFoundError:
            # Means we tried to load pipeline with `local_files_only=True` but the files have not been found in local cache.
            # This can happen in two cases:
            # 1. If the user passed `local_files_only=True`                    => we raise the error directly
            # 2. If we forced `local_files_only=True` when `model_info` failed => we raise the initial error
            if model_info_call_error is None:
                # 1. user passed `local_files_only=True`
                raise
            else:
                # 2. we forced `local_files_only=True` when `model_info` failed
                raise EnvironmentError(
                    f"Cannot load model {pretrained_model_name}: model is not cached locally and an error occured"
                    " while trying to fetch metadata from the Hub. Please check out the root cause in the stacktrace"
                    " above."
                ) from model_info_call_error
1649

1650
1651
1652
1653
1654
    @staticmethod
    def _get_signature_keys(obj):
        parameters = inspect.signature(obj.__init__).parameters
        required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
        optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
1655
        expected_modules = set(required_parameters.keys()) - {"self"}
1656
1657
1658
1659
1660
1661
        return expected_modules, optional_parameters

    @property
    def components(self) -> Dict[str, Any]:
        r"""
        The `self.components` property can be useful to run different pipelines with the same weights and
Steven Liu's avatar
Steven Liu committed
1662
1663
1664
1665
        configurations without reallocating additional memory.

        Returns (`dict`):
            A dictionary containing all the modules needed to initialize the pipeline.
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688

        Examples:

        ```py
        >>> from diffusers import (
        ...     StableDiffusionPipeline,
        ...     StableDiffusionImg2ImgPipeline,
        ...     StableDiffusionInpaintPipeline,
        ... )

        >>> text2img = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
        >>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)
        ```
        """
        expected_modules, optional_parameters = self._get_signature_keys(self)
        components = {
            k: getattr(self, k) for k in self.config.keys() if not k.startswith("_") and k not in optional_parameters
        }

        if set(components.keys()) != expected_modules:
            raise ValueError(
                f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected"
1689
                f" {expected_modules} to be defined, but {components.keys()} are defined."
1690
1691
1692
1693
1694
1695
1696
            )

        return components

    @staticmethod
    def numpy_to_pil(images):
        """
Steven Liu's avatar
Steven Liu committed
1697
        Convert a NumPy image or a batch of images to a PIL image.
1698
        """
Patrick von Platen's avatar
Patrick von Platen committed
1699
        return numpy_to_pil(images)
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718

    def progress_bar(self, iterable=None, total=None):
        if not hasattr(self, "_progress_bar_config"):
            self._progress_bar_config = {}
        elif not isinstance(self._progress_bar_config, dict):
            raise ValueError(
                f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
            )

        if iterable is not None:
            return tqdm(iterable, **self._progress_bar_config)
        elif total is not None:
            return tqdm(total=total, **self._progress_bar_config)
        else:
            raise ValueError("Either `total` or `iterable` has to be defined.")

    def set_progress_bar_config(self, **kwargs):
        self._progress_bar_config = kwargs

1719
    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
1720
        r"""
1721
1722
1723
        Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this
        option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
        up during training is not guaranteed.
1724

Steven Liu's avatar
Steven Liu committed
1725
        <Tip warning={true}>
1726

Steven Liu's avatar
Steven Liu committed
1727
1728
1729
1730
        ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
        precedent.

        </Tip>
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750

        Parameters:
            attention_op (`Callable`, *optional*):
                Override the default `None` operator for use as `op` argument to the
                [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
                function of xFormers.

        Examples:

        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

        >>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
        >>> pipe = pipe.to("cuda")
        >>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
        >>> # Workaround for not accepting attention shape using VAE for Flash Attention
        >>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)
        ```
1751
        """
1752
        self.set_use_memory_efficient_attention_xformers(True, attention_op)
1753
1754
1755

    def disable_xformers_memory_efficient_attention(self):
        r"""
Steven Liu's avatar
Steven Liu committed
1756
        Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).
1757
1758
1759
        """
        self.set_use_memory_efficient_attention_xformers(False)

1760
1761
1762
    def set_use_memory_efficient_attention_xformers(
        self, valid: bool, attention_op: Optional[Callable] = None
    ) -> None:
1763
1764
1765
1766
1767
        # Recursively walk through all the children.
        # Any children which exposes the set_use_memory_efficient_attention_xformers method
        # gets the message
        def fn_recursive_set_mem_eff(module: torch.nn.Module):
            if hasattr(module, "set_use_memory_efficient_attention_xformers"):
1768
                module.set_use_memory_efficient_attention_xformers(valid, attention_op)
1769
1770
1771
1772

            for child in module.children():
                fn_recursive_set_mem_eff(child)

1773
1774
1775
        module_names, _ = self._get_signature_keys(self)
        modules = [getattr(self, n, None) for n in module_names]
        modules = [m for m in modules if isinstance(m, torch.nn.Module)]
1776

1777
1778
        for module in modules:
            fn_recursive_set_mem_eff(module)
1779
1780
1781

    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
        r"""
1782
        Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
        in slices to compute attention in several steps. For more than one attention head, the computation is performed
        sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.

        <Tip warning={true}>

        ⚠️ Don't enable attention slicing if you're already using `scaled_dot_product_attention` (SDPA) from PyTorch
        2.0 or xFormers. These attention computations are already very memory efficient so you won't need to enable
        this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!

        </Tip>
1793
1794
1795
1796

        Args:
            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
Alexander Pivovarov's avatar
Alexander Pivovarov committed
1797
                `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
1798
1799
                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                must be a multiple of `slice_size`.
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816

        Examples:

        ```py
        >>> import torch
        >>> from diffusers import StableDiffusionPipeline

        >>> pipe = StableDiffusionPipeline.from_pretrained(
        ...     "runwayml/stable-diffusion-v1-5",
        ...     torch_dtype=torch.float16,
        ...     use_safetensors=True,
        ... )

        >>> prompt = "a photo of an astronaut riding a horse on mars"
        >>> pipe.enable_attention_slicing()
        >>> image = pipe(prompt).images[0]
        ```
1817
1818
1819
1820
1821
        """
        self.set_attention_slice(slice_size)

    def disable_attention_slicing(self):
        r"""
Steven Liu's avatar
Steven Liu committed
1822
1823
        Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is
        computed in one step.
1824
1825
1826
1827
1828
        """
        # set slice_size = `None` to disable `attention slicing`
        self.enable_attention_slicing(None)

    def set_attention_slice(self, slice_size: Optional[int]):
1829
1830
1831
        module_names, _ = self._get_signature_keys(self)
        modules = [getattr(self, n, None) for n in module_names]
        modules = [m for m in modules if isinstance(m, torch.nn.Module) and hasattr(m, "set_attention_slice")]
1832

1833
1834
        for module in modules:
            module.set_attention_slice(slice_size)