{{ message }}
Repository navigation
Expand file tree
/
Copy pathpipeline_utils.py
More file actions
627 lines (506 loc) · 28.5 KB
/
Copy pathpipeline_utils.py
File metadata and controls
627 lines (506 loc) · 28.5 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# 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.
import importlib
import inspect
import os
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
import diffusers
import PIL
from huggingface_hub import snapshot_download
from packaging import version
from PIL import Image
from tqdm.auto import tqdm
from . import __version__
from .configuration_utils import ConfigMixin
from .dynamic_modules_utils import get_class_from_dynamic_module
from .schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from .utils import (
CONFIG_NAME,
DIFFUSERS_CACHE,
ONNX_WEIGHTS_NAME,
WEIGHTS_NAME,
BaseOutput,
deprecate,
is_transformers_available,
logging,
)
if is_transformers_available():
import transformers
from transformers import PreTrainedModel
INDEX_FILE = "diffusion_pytorch_model.bin"
CUSTOM_PIPELINE_FILE_NAME = "pipeline.py"
DUMMY_MODULES_FOLDER = "diffusers.utils"
logger = logging.get_logger(__name__)
LOADABLE_CLASSES = {
"diffusers": {
"ModelMixin": ["save_pretrained", "from_pretrained"],
"SchedulerMixin": ["save_config", "from_config"],
"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"],
},
}
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`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
class DiffusionPipeline(ConfigMixin):
r"""
Base class for all models.
[`DiffusionPipeline`] takes care of storing all components (models, schedulers, processors) for diffusion pipelines
and handles methods for loading, downloading and saving models as well as a few methods common to all pipelines to:
- move all PyTorch modules to the device of your choice
- enabling/disabling the progress bar for the denoising iteration
Class attributes:
- **config_name** ([`str`]) -- name of the config file that will store the class and module names of all
components of the diffusion pipeline.
"""
config_name = "model_index.json"
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:
library = module.__module__.split(".")[0]
# check if the module is a pipeline module
pipeline_dir = module.__module__.split(".")[-2] if len(module.__module__.split(".")) > 2 else None
path = module.__module__.split(".")
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.
if library not in LOADABLE_CLASSES or is_pipeline_module:
library = pipeline_dir
# retrieve class_name
class_name = module.__class__.__name__
register_dict = {name: (library, class_name)}
# save model index config
self.register_to_config(**register_dict)
# set models
setattr(self, name, module)
def save_pretrained(self, save_directory: Union[str, os.PathLike]):
"""
Save all variables of the pipeline that can be saved and loaded as well as the pipelines configuration file to
a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading
method. The pipeline can easily be re-loaded using the `[`~DiffusionPipeline.from_pretrained`]` class method.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
"""
self.save_config(save_directory)
model_index_dict = dict(self.config)
model_index_dict.pop("_class_name")
model_index_dict.pop("_diffusers_version")
model_index_dict.pop("_module", None)
for pipeline_component_name in model_index_dict.keys():
sub_model = getattr(self, pipeline_component_name)
model_cls = sub_model.__class__
save_method_name = None
# search for the model's base class in LOADABLE_CLASSES
for library_name, library_classes in LOADABLE_CLASSES.items():
library = importlib.import_module(library_name)
for base_class, save_load_methods in library_classes.items():
class_candidate = getattr(library, base_class)
if 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
save_method = getattr(sub_model, save_method_name)
save_method(os.path.join(save_directory, pipeline_component_name))
def to(self, torch_device: Optional[Union[str, torch.device]] = None):
if torch_device is None:
return self
module_names, _ = self.extract_init_dict(dict(self.config))
for name in module_names.keys():
module = getattr(self, name)
if isinstance(module, torch.nn.Module):
if module.dtype == torch.float16 and str(torch_device) in ["cpu", "mps"]:
logger.warning(
"Pipelines loaded with `torch_dtype=torch.float16` cannot run with `cpu` or `mps` device. It"
" is not recommended to move them to `cpu` or `mps` as running them will fail. Please make"
" sure to use a `cuda` device to run the pipeline in inference. due to the lack of support for"
" `float16` operations on those devices in PyTorch. Please remove the"
" `torch_dtype=torch.float16` argument, or use a `cuda` device to run inference."
)
module.to(torch_device)
return self
@property
def device(self) -> torch.device:
r"""
Returns:
`torch.device`: The torch device on which the pipeline is located.
"""
module_names, _ = self.extract_init_dict(dict(self.config))
for name in module_names.keys():
module = getattr(self, name)
if isinstance(module, torch.nn.Module):
return module.device
return torch.device("cpu")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Instantiate a PyTorch diffusion pipeline from pre-trained pipeline weights.
The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated).
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
weights are discarded.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *repo id* of a pretrained pipeline hosted inside a model repo on
https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like
`CompVis/ldm-text2im-large-256`.
- A path to a *directory* containing pipeline weights saved using
[`~DiffusionPipeline.save_pretrained`], e.g., `./my_pipeline_directory/`.
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
will be automatically derived from the model's weights.
custom_pipeline (`str`, *optional*):
<Tip warning={true}>
This is an experimental feature and is likely to change in the future.
</Tip>
Can be either:
- A string, the *repo id* of a custom pipeline hosted inside a model repo on
https://huggingface.co/. Valid repo ids have to be located under a user or organization name,
like `hf-internal-testing/diffusers-dummy-pipeline`.
<Tip>
It is required that the model repo has a file, called `pipeline.py` that defines the custom
pipeline.
</Tip>
- A string, the *file name* of a community pipeline hosted on GitHub under
https://github.com/huggingface/diffusers/tree/main/examples/community. Valid file names have to
match exactly the file name without `.py` located under the above link, *e.g.*
`clip_guided_stable_diffusion`.
<Tip>
Community pipelines are always loaded from the current `main` branch of GitHub.
</Tip>
- A path to a *directory* containing a custom pipeline, e.g., `./my_pipeline_directory/`.
<Tip>
It is required that the directory has a file, called `pipeline.py` that defines the custom
pipeline.
</Tip>
For more information on how to load and create custom pipelines, please have a look at [Loading and
Creating Custom
Pipelines](https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipelines)
torch_dtype (`str` or `torch.dtype`, *optional*):
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`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'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.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information. specify the folder name here.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
specific pipeline class. The overwritten components are then directly passed to the pipelines
`__init__` method. See example below for more information.
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"CompVis/stable-diffusion-v1-4"`
</Tip>
<Tip>
Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
this method in a firewalled environment.
</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("CompVis/stable-diffusion-v1-4")
>>> # Download pipeline, but overwrite scheduler
>>> from diffusers import LMSDiscreteScheduler
>>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
>>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", 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", False)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
torch_dtype = kwargs.pop("torch_dtype", None)
custom_pipeline = kwargs.pop("custom_pipeline", None)
provider = kwargs.pop("provider", None)
sess_options = kwargs.pop("sess_options", None)
device_map = kwargs.pop("device_map", None)
# 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):
config_dict = cls.get_config_dict(
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,
)
# make sure we only download sub-folders and `diffusers` filenames
folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
allow_patterns = [os.path.join(k, "*") for k in folder_names]
allow_patterns += [WEIGHTS_NAME, SCHEDULER_CONFIG_NAME, CONFIG_NAME, ONNX_WEIGHTS_NAME, cls.config_name]
if custom_pipeline is not None:
allow_patterns += [CUSTOM_PIPELINE_FILE_NAME]
requested_pipeline_class = config_dict.get("_class_name", cls.__name__)
user_agent = {"diffusers": __version__, "pipeline_class": requested_pipeline_class}
if custom_pipeline is not None:
user_agent["custom_pipeline"] = custom_pipeline
# download all allow_patterns
cached_folder = snapshot_download(
pretrained_model_name_or_path,
cache_dir=cache_dir,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
allow_patterns=allow_patterns,
user_agent=user_agent,
)
else:
cached_folder = pretrained_model_name_or_path
config_dict = cls.get_config_dict(cached_folder)
# 2. Load the pipeline class, if using custom module then load it from the hub
# if we load from explicit class, let's use it
if custom_pipeline is not None:
pipeline_class = get_class_from_dynamic_module(
custom_pipeline, module_file=CUSTOM_PIPELINE_FILE_NAME, cache_dir=custom_pipeline
)
elif cls != DiffusionPipeline:
pipeline_class = cls
else:
diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
pipeline_class = getattr(diffusers_module, config_dict["_class_name"])
# To be removed in 1.0.0
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)
# some modules can be passed directly to the init
# in this case they are already instantiated in `kwargs`
# extract them here
expected_modules = set(inspect.signature(pipeline_class.__init__).parameters.keys()) - set(["self"])
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
init_dict, unused_kwargs = pipeline_class.extract_init_dict(config_dict, **kwargs)
if len(unused_kwargs) > 0:
logger.warning(f"Keyword arguments {unused_kwargs} not recognized.")
init_kwargs = {}
# import it here to avoid circular import
from diffusers import pipelines
# 3. Load each module in the pipeline
for name, (library_name, class_name) in init_dict.items():
# 3.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names
if class_name.startswith("Flax"):
class_name = class_name[4:]
is_pipeline_module = hasattr(pipelines, library_name)
loaded_sub_model = None
sub_model_should_be_defined = True
# if the model is in a pipeline module, then we load it from the pipeline
if name in passed_class_obj:
# 1. check that passed_class_obj has correct parent class
if not is_pipeline_module:
library = importlib.import_module(library_name)
class_obj = getattr(library, class_name)
importable_classes = LOADABLE_CLASSES[library_name]
class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
expected_class_obj = None
for class_name, class_candidate in class_candidates.items():
if issubclass(class_obj, class_candidate):
expected_class_obj = class_candidate
if not issubclass(passed_class_obj[name].__class__, expected_class_obj):
raise ValueError(
f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be"
f" {expected_class_obj}"
)
elif passed_class_obj[name] is None:
logger.warn(
f"You have passed `None` for {name} to disable its functionality in {pipeline_class}. Note"
f" that this might lead to problems when using {pipeline_class} and is not recommended."
)
sub_model_should_be_defined = False
else:
logger.warn(
f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
" has the correct type"
)
# set passed class object
loaded_sub_model = passed_class_obj[name]
elif is_pipeline_module:
pipeline_module = getattr(pipelines, library_name)
class_obj = getattr(pipeline_module, class_name)
importable_classes = ALL_IMPORTABLE_CLASSES
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)
importable_classes = LOADABLE_CLASSES[library_name]
class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
if loaded_sub_model is None and sub_model_should_be_defined:
load_method_name = None
for class_name, class_candidate in class_candidates.items():
if issubclass(class_obj, class_candidate):
load_method_name = importable_classes[class_name][1]
if load_method_name is None:
none_module = class_obj.__module__
if none_module.startswith(DUMMY_MODULES_FOLDER) 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)
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)
is_transformers_model = (
is_transformers_available()
and issubclass(class_obj, PreTrainedModel)
and version.parse(version.parse(transformers.__version__).base_version) >= version.parse("4.20.0")
)
if is_diffusers_model or is_transformers_model:
loading_kwargs["device_map"] = device_map
# 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)
init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
# 4. Potentially add passed objects if expected
missing_modules = set(expected_modules) - set(init_kwargs.keys())
if len(missing_modules) > 0 and missing_modules <= set(passed_class_obj.keys()):
for module in missing_modules:
init_kwargs[module] = passed_class_obj[module]
elif len(missing_modules) > 0:
passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys()))
raise ValueError(
f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
)
# 5. Instantiate the pipeline
model = pipeline_class(**init_kwargs)
return model
@property
def components(self) -> Dict[str, Any]:
r"""
The `self.compenents` property can be useful to run different pipelines with the same weights and
configurations to not have to re-allocate memory.
Examples:
```py
>>> from diffusers import (
... StableDiffusionPipeline,
... StableDiffusionImg2ImgPipeline,
... StableDiffusionInpaintPipeline,
... )
>>> img2text = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
>>> img2img = StableDiffusionImg2ImgPipeline(**img2text.components)
>>> inpaint = StableDiffusionInpaintPipeline(**img2text.components)
```
Returns:
A dictionaly containing all the modules needed to initialize the pipleline.
"""
components = {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
expected_modules = set(inspect.signature(self.__init__).parameters.keys()) - set(["self"])
if set(components.keys()) != expected_modules:
raise ValueError(
f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected"
f" {expected_modules} to be defined, but {components} are defined."
)
return components
@staticmethod
def numpy_to_pil(images):
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def progress_bar(self, iterable):
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)}."
)
return tqdm(iterable, **self._progress_bar_config)
def set_progress_bar_config(self, **kwargs):
self._progress_bar_config = kwargs
You can’t perform that action at this time.