Repository navigation
Expand file tree
/
Copy pathsingle_file_model.py
More file actions
550 lines (487 loc) · 24 KB
/
Copy pathsingle_file_model.py
File metadata and controls
550 lines (487 loc) · 24 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
# Copyright 2025 The HuggingFace Team. 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 re
from contextlib import nullcontext
import torch
from huggingface_hub.utils import validate_hf_hub_args
from typing_extensions import Self
from .. import __version__
from ..models.model_loading_utils import (
_caching_allocator_warmup,
_determine_device_map,
_expand_device_map,
)
from ..quantizers import DiffusersAutoQuantizer
from ..utils import deprecate, is_accelerate_available, is_torch_version, logging
from ..utils.torch_utils import empty_device_cache
from .single_file_utils import (
SingleFileComponentError,
convert_animatediff_checkpoint_to_diffusers,
convert_auraflow_transformer_checkpoint_to_diffusers,
convert_autoencoder_dc_checkpoint_to_diffusers,
convert_chroma_transformer_checkpoint_to_diffusers,
convert_controlnet_checkpoint,
convert_cosmos_transformer_checkpoint_to_diffusers,
convert_ernie_image_transformer_checkpoint_to_diffusers,
convert_flux2_transformer_checkpoint_to_diffusers,
convert_flux_transformer_checkpoint_to_diffusers,
convert_hidream_transformer_to_diffusers,
convert_hunyuan_video_transformer_to_diffusers,
convert_ldm_unet_checkpoint,
convert_ldm_vae_checkpoint,
convert_ltx2_audio_vae_to_diffusers,
convert_ltx2_transformer_to_diffusers,
convert_ltx2_vae_to_diffusers,
convert_ltx_transformer_checkpoint_to_diffusers,
convert_ltx_vae_checkpoint_to_diffusers,
convert_lumina2_to_diffusers,
convert_mochi_transformer_checkpoint_to_diffusers,
convert_sana_transformer_to_diffusers,
convert_sd3_transformer_checkpoint_to_diffusers,
convert_stable_cascade_unet_single_file_to_diffusers,
convert_wan_transformer_to_diffusers,
convert_wan_vae_to_diffusers,
convert_z_image_controlnet_checkpoint_to_diffusers,
convert_z_image_transformer_checkpoint_to_diffusers,
create_controlnet_diffusers_config_from_ldm,
create_unet_diffusers_config_from_ldm,
create_vae_diffusers_config_from_ldm,
fetch_diffusers_config,
fetch_original_config,
load_single_file_checkpoint,
)
logger = logging.get_logger(__name__)
if is_accelerate_available():
from accelerate import dispatch_model, init_empty_weights
from ..models.model_loading_utils import load_model_dict_into_meta
if is_torch_version(">=", "1.9.0") and is_accelerate_available():
_LOW_CPU_MEM_USAGE_DEFAULT = True
else:
_LOW_CPU_MEM_USAGE_DEFAULT = False
SINGLE_FILE_LOADABLE_CLASSES = {
"StableCascadeUNet": {
"checkpoint_mapping_fn": convert_stable_cascade_unet_single_file_to_diffusers,
},
"UNet2DConditionModel": {
"checkpoint_mapping_fn": convert_ldm_unet_checkpoint,
"config_mapping_fn": create_unet_diffusers_config_from_ldm,
"default_subfolder": "unet",
"legacy_kwargs": {
"num_in_channels": "in_channels", # Legacy kwargs supported by `from_single_file` mapped to new args
},
},
"AutoencoderKL": {
"checkpoint_mapping_fn": convert_ldm_vae_checkpoint,
"config_mapping_fn": create_vae_diffusers_config_from_ldm,
"default_subfolder": "vae",
},
"ControlNetModel": {
"checkpoint_mapping_fn": convert_controlnet_checkpoint,
"config_mapping_fn": create_controlnet_diffusers_config_from_ldm,
},
"SD3Transformer2DModel": {
"checkpoint_mapping_fn": convert_sd3_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
"MotionAdapter": {
"checkpoint_mapping_fn": convert_animatediff_checkpoint_to_diffusers,
},
"SparseControlNetModel": {
"checkpoint_mapping_fn": convert_animatediff_checkpoint_to_diffusers,
},
"FluxTransformer2DModel": {
"checkpoint_mapping_fn": convert_flux_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
"ChromaTransformer2DModel": {
"checkpoint_mapping_fn": convert_chroma_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
"ErnieImageTransformer2DModel": {
"checkpoint_mapping_fn": convert_ernie_image_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
"LTXVideoTransformer3DModel": {
"checkpoint_mapping_fn": convert_ltx_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
"AutoencoderKLLTXVideo": {
"checkpoint_mapping_fn": convert_ltx_vae_checkpoint_to_diffusers,
"default_subfolder": "vae",
},
"AutoencoderDC": {"checkpoint_mapping_fn": convert_autoencoder_dc_checkpoint_to_diffusers},
"MochiTransformer3DModel": {
"checkpoint_mapping_fn": convert_mochi_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
"HunyuanVideoTransformer3DModel": {
"checkpoint_mapping_fn": convert_hunyuan_video_transformer_to_diffusers,
"default_subfolder": "transformer",
},
"AuraFlowTransformer2DModel": {
"checkpoint_mapping_fn": convert_auraflow_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
"Lumina2Transformer2DModel": {
"checkpoint_mapping_fn": convert_lumina2_to_diffusers,
"default_subfolder": "transformer",
},
"SanaTransformer2DModel": {
"checkpoint_mapping_fn": convert_sana_transformer_to_diffusers,
"default_subfolder": "transformer",
},
"WanTransformer3DModel": {
"checkpoint_mapping_fn": convert_wan_transformer_to_diffusers,
"default_subfolder": "transformer",
},
"WanVACETransformer3DModel": {
"checkpoint_mapping_fn": convert_wan_transformer_to_diffusers,
"default_subfolder": "transformer",
},
"WanAnimateTransformer3DModel": {
"checkpoint_mapping_fn": convert_wan_transformer_to_diffusers,
"default_subfolder": "transformer",
},
"AutoencoderKLWan": {
"checkpoint_mapping_fn": convert_wan_vae_to_diffusers,
"default_subfolder": "vae",
},
"HiDreamImageTransformer2DModel": {
"checkpoint_mapping_fn": convert_hidream_transformer_to_diffusers,
"default_subfolder": "transformer",
},
"CosmosTransformer3DModel": {
"checkpoint_mapping_fn": convert_cosmos_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
"QwenImageTransformer2DModel": {
"checkpoint_mapping_fn": lambda checkpoint, **kwargs: checkpoint,
"default_subfolder": "transformer",
},
"Flux2Transformer2DModel": {
"checkpoint_mapping_fn": convert_flux2_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
"ZImageTransformer2DModel": {
"checkpoint_mapping_fn": convert_z_image_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
"ZImageControlNetModel": {
"checkpoint_mapping_fn": convert_z_image_controlnet_checkpoint_to_diffusers,
},
"LTX2VideoTransformer3DModel": {
"checkpoint_mapping_fn": convert_ltx2_transformer_to_diffusers,
"default_subfolder": "transformer",
},
"AutoencoderKLLTX2Video": {
"checkpoint_mapping_fn": convert_ltx2_vae_to_diffusers,
"default_subfolder": "vae",
},
"AutoencoderKLLTX2Audio": {
"checkpoint_mapping_fn": convert_ltx2_audio_vae_to_diffusers,
"default_subfolder": "audio_vae",
},
"MotifVideoTransformer3DModel": {
"checkpoint_mapping_fn": lambda checkpoint, **kwargs: checkpoint,
"default_subfolder": "transformer",
},
}
def _should_convert_state_dict_to_diffusers(model_state_dict, checkpoint_state_dict):
model_state_dict_keys = set(model_state_dict.keys())
checkpoint_state_dict_keys = set(checkpoint_state_dict.keys())
is_subset = model_state_dict_keys.issubset(checkpoint_state_dict_keys)
is_match = model_state_dict_keys == checkpoint_state_dict_keys
return not (is_subset and is_match)
def _get_single_file_loadable_mapping_class(cls):
diffusers_module = importlib.import_module(__name__.split(".")[0])
for loadable_class_str in SINGLE_FILE_LOADABLE_CLASSES:
loadable_class = getattr(diffusers_module, loadable_class_str)
if issubclass(cls, loadable_class):
return loadable_class_str
return None
def _get_mapping_function_kwargs(mapping_fn, **kwargs):
parameters = inspect.signature(mapping_fn).parameters
mapping_kwargs = {}
for parameter in parameters:
if parameter in kwargs:
mapping_kwargs[parameter] = kwargs[parameter]
return mapping_kwargs
class FromOriginalModelMixin:
"""
Load pretrained weights saved in the `.ckpt` or `.safetensors` format into a model.
"""
@classmethod
@validate_hf_hub_args
def from_single_file(cls, pretrained_model_link_or_path_or_dict: str | None = None, **kwargs) -> Self:
r"""
Instantiate a model from pretrained weights saved in the original `.ckpt` or `.safetensors` format. The model
is set in evaluation mode (`model.eval()`) by default.
Parameters:
pretrained_model_link_or_path_or_dict (`str`, *optional*):
Can be either:
- A link to the `.safetensors` or `.ckpt` file (for example
`"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.safetensors"`) on the Hub.
- A path to a local *file* containing the weights of the component model.
- A state dict containing the component model weights.
config (`str`, *optional*):
- 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 the pipeline component
configs in Diffusers format.
subfolder (`str`, *optional*, defaults to `""`):
The subfolder location of a model file within a larger model repository on the Hub or locally.
original_config (`str`, *optional*):
Dict or path to a yaml file containing the configuration for the model in its original format.
If a dict is provided, it will be used to initialize the model configuration.
torch_dtype (`torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model with another dtype.
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.
cache_dir (`str | os.PathLike`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
proxies (`dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
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.
token (`str` or *bool*, *optional*):
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.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 and
is_accelerate_available() else `False`): 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.
disable_mmap ('bool', *optional*, defaults to 'False'):
Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to overwrite load and saveable variables (for example the pipeline components of the
specific pipeline class). The overwritten components are directly passed to the pipelines `__init__`
method. See example below for more information.
```py
>>> from diffusers import StableCascadeUNet
>>> ckpt_path = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite.safetensors"
>>> model = StableCascadeUNet.from_single_file(ckpt_path)
```
"""
mapping_class_name = _get_single_file_loadable_mapping_class(cls)
# if class_name not in SINGLE_FILE_LOADABLE_CLASSES:
if mapping_class_name is None:
raise ValueError(
f"FromOriginalModelMixin is currently only compatible with {', '.join(SINGLE_FILE_LOADABLE_CLASSES.keys())}"
)
pretrained_model_link_or_path = kwargs.get("pretrained_model_link_or_path", None)
if pretrained_model_link_or_path is not None:
deprecation_message = (
"Please use `pretrained_model_link_or_path_or_dict` argument instead for model classes"
)
deprecate("pretrained_model_link_or_path", "1.0.0", deprecation_message)
pretrained_model_link_or_path_or_dict = pretrained_model_link_or_path
config = kwargs.pop("config", None)
original_config = kwargs.pop("original_config", None)
if config is not None and original_config is not None:
raise ValueError(
"`from_single_file` cannot accept both `config` and `original_config` arguments. Please provide only one of these arguments"
)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
token = kwargs.pop("token", None)
cache_dir = kwargs.pop("cache_dir", None)
local_files_only = kwargs.pop("local_files_only", None)
subfolder = kwargs.pop("subfolder", None)
revision = kwargs.pop("revision", None)
config_revision = kwargs.pop("config_revision", None)
torch_dtype = kwargs.pop("torch_dtype", None)
quantization_config = kwargs.pop("quantization_config", None)
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
device = kwargs.pop("device", None)
disable_mmap = kwargs.pop("disable_mmap", False)
device_map = kwargs.pop("device_map", None)
user_agent = {
"diffusers": __version__,
"file_type": "single_file",
"framework": "pytorch",
}
# In order to ensure popular quantization methods are supported. Can be disable with `disable_telemetry`
if quantization_config is not None:
user_agent["quant"] = quantization_config.quant_method.value
if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
torch_dtype = torch.float32
logger.warning(
f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`."
)
if isinstance(pretrained_model_link_or_path_or_dict, dict):
checkpoint = pretrained_model_link_or_path_or_dict
else:
checkpoint = load_single_file_checkpoint(
pretrained_model_link_or_path_or_dict,
force_download=force_download,
proxies=proxies,
token=token,
cache_dir=cache_dir,
local_files_only=local_files_only,
revision=revision,
disable_mmap=disable_mmap,
user_agent=user_agent,
)
if quantization_config is not None:
hf_quantizer = DiffusersAutoQuantizer.from_config(quantization_config)
hf_quantizer.validate_environment()
torch_dtype = hf_quantizer.update_torch_dtype(torch_dtype)
else:
hf_quantizer = None
mapping_functions = SINGLE_FILE_LOADABLE_CLASSES[mapping_class_name]
checkpoint_mapping_fn = mapping_functions["checkpoint_mapping_fn"]
if original_config is not None:
if "config_mapping_fn" in mapping_functions:
config_mapping_fn = mapping_functions["config_mapping_fn"]
else:
config_mapping_fn = None
if config_mapping_fn is None:
raise ValueError(
(
f"`original_config` has been provided for {mapping_class_name} but no mapping function"
"was found to convert the original config to a Diffusers config in"
"`diffusers.loaders.single_file_utils`"
)
)
if isinstance(original_config, str):
# If original_config is a URL or filepath fetch the original_config dict
original_config = fetch_original_config(original_config, local_files_only=local_files_only)
config_mapping_kwargs = _get_mapping_function_kwargs(config_mapping_fn, **kwargs)
diffusers_model_config = config_mapping_fn(
original_config=original_config,
checkpoint=checkpoint,
**config_mapping_kwargs,
)
else:
if config is not None:
if isinstance(config, str):
default_pretrained_model_config_name = config
else:
raise ValueError(
(
"Invalid `config` argument. Please provide a string representing a repo id"
"or path to a local Diffusers model repo."
)
)
else:
config = fetch_diffusers_config(checkpoint)
default_pretrained_model_config_name = config["pretrained_model_name_or_path"]
if "default_subfolder" in mapping_functions:
subfolder = mapping_functions["default_subfolder"]
subfolder = subfolder or config.pop(
"subfolder", None
) # some configs contain a subfolder key, e.g. StableCascadeUNet
diffusers_model_config = cls.load_config(
pretrained_model_name_or_path=default_pretrained_model_config_name,
subfolder=subfolder,
local_files_only=local_files_only,
token=token,
revision=config_revision,
)
expected_kwargs, optional_kwargs = cls._get_signature_keys(cls)
# Map legacy kwargs to new kwargs
if "legacy_kwargs" in mapping_functions:
legacy_kwargs = mapping_functions["legacy_kwargs"]
for legacy_key, new_key in legacy_kwargs.items():
if legacy_key in kwargs:
kwargs[new_key] = kwargs.pop(legacy_key)
model_kwargs = {k: kwargs.get(k) for k in kwargs if k in expected_kwargs or k in optional_kwargs}
diffusers_model_config.update(model_kwargs)
ctx = init_empty_weights if low_cpu_mem_usage else nullcontext
with ctx():
model = cls.from_config(diffusers_model_config)
model_state_dict = model.state_dict()
# Check if `_keep_in_fp32_modules` is not None
use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (
(torch_dtype == torch.float16) or hasattr(hf_quantizer, "use_keep_in_fp32_modules")
)
if use_keep_in_fp32_modules:
keep_in_fp32_modules = cls._keep_in_fp32_modules
if not isinstance(keep_in_fp32_modules, list):
keep_in_fp32_modules = [keep_in_fp32_modules]
else:
keep_in_fp32_modules = []
# Now that the model is loaded, we can determine the `device_map`
device_map = _determine_device_map(model, device_map, None, torch_dtype, keep_in_fp32_modules, hf_quantizer)
if device_map is not None:
expanded_device_map = _expand_device_map(device_map, model_state_dict.keys())
_caching_allocator_warmup(model, expanded_device_map, torch_dtype, hf_quantizer)
checkpoint_mapping_kwargs = _get_mapping_function_kwargs(checkpoint_mapping_fn, **kwargs)
if _should_convert_state_dict_to_diffusers(model_state_dict, checkpoint):
diffusers_format_checkpoint = checkpoint_mapping_fn(
config=diffusers_model_config,
checkpoint=checkpoint,
**checkpoint_mapping_kwargs,
)
else:
diffusers_format_checkpoint = checkpoint
if not diffusers_format_checkpoint:
raise SingleFileComponentError(
f"Failed to load {mapping_class_name}. Weights for this component appear to be missing in the checkpoint."
)
if hf_quantizer is not None:
hf_quantizer.preprocess_model(
model=model,
device_map=None,
state_dict=diffusers_format_checkpoint,
keep_in_fp32_modules=keep_in_fp32_modules,
)
device_map = None
if low_cpu_mem_usage:
param_device = torch.device(device) if device else torch.device("cpu")
empty_state_dict = model.state_dict()
unexpected_keys = [
param_name for param_name in diffusers_format_checkpoint if param_name not in empty_state_dict
]
device_map = {"": param_device}
load_model_dict_into_meta(
model,
diffusers_format_checkpoint,
dtype=torch_dtype,
device_map=device_map,
hf_quantizer=hf_quantizer,
keep_in_fp32_modules=keep_in_fp32_modules,
unexpected_keys=unexpected_keys,
)
empty_device_cache()
else:
_, unexpected_keys = model.load_state_dict(diffusers_format_checkpoint, strict=False)
if model._keys_to_ignore_on_load_unexpected is not None:
for pat in model._keys_to_ignore_on_load_unexpected:
unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
if len(unexpected_keys) > 0:
logger.warning(
f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
)
if hf_quantizer is not None:
hf_quantizer.postprocess_model(model)
model.hf_quantizer = hf_quantizer
if torch_dtype is not None and hf_quantizer is None:
model.to(torch_dtype)
model.eval()
if device_map is not None:
device_map_kwargs = {"device_map": device_map}
dispatch_model(model, **device_map_kwargs)
return model