declare-lab/tango2

1# This file is autogenerated by the command `make fix-copies`, do not edit.2from ..utils import DummyObject, requires_backends3 4 5class AutoencoderKL(metaclass=DummyObject):6    _backends = ["torch"]7 8    def __init__(self, *args, **kwargs):9        requires_backends(self, ["torch"])10 11    @classmethod12    def from_config(cls, *args, **kwargs):13        requires_backends(cls, ["torch"])14 15    @classmethod16    def from_pretrained(cls, *args, **kwargs):17        requires_backends(cls, ["torch"])18 19 20class ControlNetModel(metaclass=DummyObject):21    _backends = ["torch"]22 23    def __init__(self, *args, **kwargs):24        requires_backends(self, ["torch"])25 26    @classmethod27    def from_config(cls, *args, **kwargs):28        requires_backends(cls, ["torch"])29 30    @classmethod31    def from_pretrained(cls, *args, **kwargs):32        requires_backends(cls, ["torch"])33 34 35class ModelMixin(metaclass=DummyObject):36    _backends = ["torch"]37 38    def __init__(self, *args, **kwargs):39        requires_backends(self, ["torch"])40 41    @classmethod42    def from_config(cls, *args, **kwargs):43        requires_backends(cls, ["torch"])44 45    @classmethod46    def from_pretrained(cls, *args, **kwargs):47        requires_backends(cls, ["torch"])48 49 50class PriorTransformer(metaclass=DummyObject):51    _backends = ["torch"]52 53    def __init__(self, *args, **kwargs):54        requires_backends(self, ["torch"])55 56    @classmethod57    def from_config(cls, *args, **kwargs):58        requires_backends(cls, ["torch"])59 60    @classmethod61    def from_pretrained(cls, *args, **kwargs):62        requires_backends(cls, ["torch"])63 64 65class T5FilmDecoder(metaclass=DummyObject):66    _backends = ["torch"]67 68    def __init__(self, *args, **kwargs):69        requires_backends(self, ["torch"])70 71    @classmethod72    def from_config(cls, *args, **kwargs):73        requires_backends(cls, ["torch"])74 75    @classmethod76    def from_pretrained(cls, *args, **kwargs):77        requires_backends(cls, ["torch"])78 79 80class Transformer2DModel(metaclass=DummyObject):81    _backends = ["torch"]82 83    def __init__(self, *args, **kwargs):84        requires_backends(self, ["torch"])85 86    @classmethod87    def from_config(cls, *args, **kwargs):88        requires_backends(cls, ["torch"])89 90    @classmethod91    def from_pretrained(cls, *args, **kwargs):92        requires_backends(cls, ["torch"])93 94 95class UNet1DModel(metaclass=DummyObject):96    _backends = ["torch"]97 98    def __init__(self, *args, **kwargs):99        requires_backends(self, ["torch"])100 101    @classmethod102    def from_config(cls, *args, **kwargs):103        requires_backends(cls, ["torch"])104 105    @classmethod106    def from_pretrained(cls, *args, **kwargs):107        requires_backends(cls, ["torch"])108 109 110class UNet2DConditionModel(metaclass=DummyObject):111    _backends = ["torch"]112 113    def __init__(self, *args, **kwargs):114        requires_backends(self, ["torch"])115 116    @classmethod117    def from_config(cls, *args, **kwargs):118        requires_backends(cls, ["torch"])119 120    @classmethod121    def from_pretrained(cls, *args, **kwargs):122        requires_backends(cls, ["torch"])123 124 125class UNet2DModel(metaclass=DummyObject):126    _backends = ["torch"]127 128    def __init__(self, *args, **kwargs):129        requires_backends(self, ["torch"])130 131    @classmethod132    def from_config(cls, *args, **kwargs):133        requires_backends(cls, ["torch"])134 135    @classmethod136    def from_pretrained(cls, *args, **kwargs):137        requires_backends(cls, ["torch"])138 139 140class UNet3DConditionModel(metaclass=DummyObject):141    _backends = ["torch"]142 143    def __init__(self, *args, **kwargs):144        requires_backends(self, ["torch"])145 146    @classmethod147    def from_config(cls, *args, **kwargs):148        requires_backends(cls, ["torch"])149 150    @classmethod151    def from_pretrained(cls, *args, **kwargs):152        requires_backends(cls, ["torch"])153 154 155class VQModel(metaclass=DummyObject):156    _backends = ["torch"]157 158    def __init__(self, *args, **kwargs):159        requires_backends(self, ["torch"])160 161    @classmethod162    def from_config(cls, *args, **kwargs):163        requires_backends(cls, ["torch"])164 165    @classmethod166    def from_pretrained(cls, *args, **kwargs):167        requires_backends(cls, ["torch"])168 169 170def get_constant_schedule(*args, **kwargs):171    requires_backends(get_constant_schedule, ["torch"])172 173 174def get_constant_schedule_with_warmup(*args, **kwargs):175    requires_backends(get_constant_schedule_with_warmup, ["torch"])176 177 178def get_cosine_schedule_with_warmup(*args, **kwargs):179    requires_backends(get_cosine_schedule_with_warmup, ["torch"])180 181 182def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):183    requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"])184 185 186def get_linear_schedule_with_warmup(*args, **kwargs):187    requires_backends(get_linear_schedule_with_warmup, ["torch"])188 189 190def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):191    requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"])192 193 194def get_scheduler(*args, **kwargs):195    requires_backends(get_scheduler, ["torch"])196 197 198class AudioPipelineOutput(metaclass=DummyObject):199    _backends = ["torch"]200 201    def __init__(self, *args, **kwargs):202        requires_backends(self, ["torch"])203 204    @classmethod205    def from_config(cls, *args, **kwargs):206        requires_backends(cls, ["torch"])207 208    @classmethod209    def from_pretrained(cls, *args, **kwargs):210        requires_backends(cls, ["torch"])211 212 213class DanceDiffusionPipeline(metaclass=DummyObject):214    _backends = ["torch"]215 216    def __init__(self, *args, **kwargs):217        requires_backends(self, ["torch"])218 219    @classmethod220    def from_config(cls, *args, **kwargs):221        requires_backends(cls, ["torch"])222 223    @classmethod224    def from_pretrained(cls, *args, **kwargs):225        requires_backends(cls, ["torch"])226 227 228class DDIMPipeline(metaclass=DummyObject):229    _backends = ["torch"]230 231    def __init__(self, *args, **kwargs):232        requires_backends(self, ["torch"])233 234    @classmethod235    def from_config(cls, *args, **kwargs):236        requires_backends(cls, ["torch"])237 238    @classmethod239    def from_pretrained(cls, *args, **kwargs):240        requires_backends(cls, ["torch"])241 242 243class DDPMPipeline(metaclass=DummyObject):244    _backends = ["torch"]245 246    def __init__(self, *args, **kwargs):247        requires_backends(self, ["torch"])248 249    @classmethod250    def from_config(cls, *args, **kwargs):251        requires_backends(cls, ["torch"])252 253    @classmethod254    def from_pretrained(cls, *args, **kwargs):255        requires_backends(cls, ["torch"])256 257 258class DiffusionPipeline(metaclass=DummyObject):259    _backends = ["torch"]260 261    def __init__(self, *args, **kwargs):262        requires_backends(self, ["torch"])263 264    @classmethod265    def from_config(cls, *args, **kwargs):266        requires_backends(cls, ["torch"])267 268    @classmethod269    def from_pretrained(cls, *args, **kwargs):270        requires_backends(cls, ["torch"])271 272 273class DiTPipeline(metaclass=DummyObject):274    _backends = ["torch"]275 276    def __init__(self, *args, **kwargs):277        requires_backends(self, ["torch"])278 279    @classmethod280    def from_config(cls, *args, **kwargs):281        requires_backends(cls, ["torch"])282 283    @classmethod284    def from_pretrained(cls, *args, **kwargs):285        requires_backends(cls, ["torch"])286 287 288class ImagePipelineOutput(metaclass=DummyObject):289    _backends = ["torch"]290 291    def __init__(self, *args, **kwargs):292        requires_backends(self, ["torch"])293 294    @classmethod295    def from_config(cls, *args, **kwargs):296        requires_backends(cls, ["torch"])297 298    @classmethod299    def from_pretrained(cls, *args, **kwargs):300        requires_backends(cls, ["torch"])301 302 303class KarrasVePipeline(metaclass=DummyObject):304    _backends = ["torch"]305 306    def __init__(self, *args, **kwargs):307        requires_backends(self, ["torch"])308 309    @classmethod310    def from_config(cls, *args, **kwargs):311        requires_backends(cls, ["torch"])312 313    @classmethod314    def from_pretrained(cls, *args, **kwargs):315        requires_backends(cls, ["torch"])316 317 318class LDMPipeline(metaclass=DummyObject):319    _backends = ["torch"]320 321    def __init__(self, *args, **kwargs):322        requires_backends(self, ["torch"])323 324    @classmethod325    def from_config(cls, *args, **kwargs):326        requires_backends(cls, ["torch"])327 328    @classmethod329    def from_pretrained(cls, *args, **kwargs):330        requires_backends(cls, ["torch"])331 332 333class LDMSuperResolutionPipeline(metaclass=DummyObject):334    _backends = ["torch"]335 336    def __init__(self, *args, **kwargs):337        requires_backends(self, ["torch"])338 339    @classmethod340    def from_config(cls, *args, **kwargs):341        requires_backends(cls, ["torch"])342 343    @classmethod344    def from_pretrained(cls, *args, **kwargs):345        requires_backends(cls, ["torch"])346 347 348class PNDMPipeline(metaclass=DummyObject):349    _backends = ["torch"]350 351    def __init__(self, *args, **kwargs):352        requires_backends(self, ["torch"])353 354    @classmethod355    def from_config(cls, *args, **kwargs):356        requires_backends(cls, ["torch"])357 358    @classmethod359    def from_pretrained(cls, *args, **kwargs):360        requires_backends(cls, ["torch"])361 362 363class RePaintPipeline(metaclass=DummyObject):364    _backends = ["torch"]365 366    def __init__(self, *args, **kwargs):367        requires_backends(self, ["torch"])368 369    @classmethod370    def from_config(cls, *args, **kwargs):371        requires_backends(cls, ["torch"])372 373    @classmethod374    def from_pretrained(cls, *args, **kwargs):375        requires_backends(cls, ["torch"])376 377 378class ScoreSdeVePipeline(metaclass=DummyObject):379    _backends = ["torch"]380 381    def __init__(self, *args, **kwargs):382        requires_backends(self, ["torch"])383 384    @classmethod385    def from_config(cls, *args, **kwargs):386        requires_backends(cls, ["torch"])387 388    @classmethod389    def from_pretrained(cls, *args, **kwargs):390        requires_backends(cls, ["torch"])391 392 393class DDIMInverseScheduler(metaclass=DummyObject):394    _backends = ["torch"]395 396    def __init__(self, *args, **kwargs):397        requires_backends(self, ["torch"])398 399    @classmethod400    def from_config(cls, *args, **kwargs):401        requires_backends(cls, ["torch"])402 403    @classmethod404    def from_pretrained(cls, *args, **kwargs):405        requires_backends(cls, ["torch"])406 407 408class DDIMScheduler(metaclass=DummyObject):409    _backends = ["torch"]410 411    def __init__(self, *args, **kwargs):412        requires_backends(self, ["torch"])413 414    @classmethod415    def from_config(cls, *args, **kwargs):416        requires_backends(cls, ["torch"])417 418    @classmethod419    def from_pretrained(cls, *args, **kwargs):420        requires_backends(cls, ["torch"])421 422 423class DDPMScheduler(metaclass=DummyObject):424    _backends = ["torch"]425 426    def __init__(self, *args, **kwargs):427        requires_backends(self, ["torch"])428 429    @classmethod430    def from_config(cls, *args, **kwargs):431        requires_backends(cls, ["torch"])432 433    @classmethod434    def from_pretrained(cls, *args, **kwargs):435        requires_backends(cls, ["torch"])436 437 438class DEISMultistepScheduler(metaclass=DummyObject):439    _backends = ["torch"]440 441    def __init__(self, *args, **kwargs):442        requires_backends(self, ["torch"])443 444    @classmethod445    def from_config(cls, *args, **kwargs):446        requires_backends(cls, ["torch"])447 448    @classmethod449    def from_pretrained(cls, *args, **kwargs):450        requires_backends(cls, ["torch"])451 452 453class DPMSolverMultistepScheduler(metaclass=DummyObject):454    _backends = ["torch"]455 456    def __init__(self, *args, **kwargs):457        requires_backends(self, ["torch"])458 459    @classmethod460    def from_config(cls, *args, **kwargs):461        requires_backends(cls, ["torch"])462 463    @classmethod464    def from_pretrained(cls, *args, **kwargs):465        requires_backends(cls, ["torch"])466 467 468class DPMSolverSinglestepScheduler(metaclass=DummyObject):469    _backends = ["torch"]470 471    def __init__(self, *args, **kwargs):472        requires_backends(self, ["torch"])473 474    @classmethod475    def from_config(cls, *args, **kwargs):476        requires_backends(cls, ["torch"])477 478    @classmethod479    def from_pretrained(cls, *args, **kwargs):480        requires_backends(cls, ["torch"])481 482 483class EulerAncestralDiscreteScheduler(metaclass=DummyObject):484    _backends = ["torch"]485 486    def __init__(self, *args, **kwargs):487        requires_backends(self, ["torch"])488 489    @classmethod490    def from_config(cls, *args, **kwargs):491        requires_backends(cls, ["torch"])492 493    @classmethod494    def from_pretrained(cls, *args, **kwargs):495        requires_backends(cls, ["torch"])496 497 498class EulerDiscreteScheduler(metaclass=DummyObject):499    _backends = ["torch"]500 501    def __init__(self, *args, **kwargs):502        requires_backends(self, ["torch"])503 504    @classmethod505    def from_config(cls, *args, **kwargs):506        requires_backends(cls, ["torch"])507 508    @classmethod509    def from_pretrained(cls, *args, **kwargs):510        requires_backends(cls, ["torch"])511 512 513class HeunDiscreteScheduler(metaclass=DummyObject):514    _backends = ["torch"]515 516    def __init__(self, *args, **kwargs):517        requires_backends(self, ["torch"])518 519    @classmethod520    def from_config(cls, *args, **kwargs):521        requires_backends(cls, ["torch"])522 523    @classmethod524    def from_pretrained(cls, *args, **kwargs):525        requires_backends(cls, ["torch"])526 527 528class IPNDMScheduler(metaclass=DummyObject):529    _backends = ["torch"]530 531    def __init__(self, *args, **kwargs):532        requires_backends(self, ["torch"])533 534    @classmethod535    def from_config(cls, *args, **kwargs):536        requires_backends(cls, ["torch"])537 538    @classmethod539    def from_pretrained(cls, *args, **kwargs):540        requires_backends(cls, ["torch"])541 542 543class KarrasVeScheduler(metaclass=DummyObject):544    _backends = ["torch"]545 546    def __init__(self, *args, **kwargs):547        requires_backends(self, ["torch"])548 549    @classmethod550    def from_config(cls, *args, **kwargs):551        requires_backends(cls, ["torch"])552 553    @classmethod554    def from_pretrained(cls, *args, **kwargs):555        requires_backends(cls, ["torch"])556 557 558class KDPM2AncestralDiscreteScheduler(metaclass=DummyObject):559    _backends = ["torch"]560 561    def __init__(self, *args, **kwargs):562        requires_backends(self, ["torch"])563 564    @classmethod565    def from_config(cls, *args, **kwargs):566        requires_backends(cls, ["torch"])567 568    @classmethod569    def from_pretrained(cls, *args, **kwargs):570        requires_backends(cls, ["torch"])571 572 573class KDPM2DiscreteScheduler(metaclass=DummyObject):574    _backends = ["torch"]575 576    def __init__(self, *args, **kwargs):577        requires_backends(self, ["torch"])578 579    @classmethod580    def from_config(cls, *args, **kwargs):581        requires_backends(cls, ["torch"])582 583    @classmethod584    def from_pretrained(cls, *args, **kwargs):585        requires_backends(cls, ["torch"])586 587 588class PNDMScheduler(metaclass=DummyObject):589    _backends = ["torch"]590 591    def __init__(self, *args, **kwargs):592        requires_backends(self, ["torch"])593 594    @classmethod595    def from_config(cls, *args, **kwargs):596        requires_backends(cls, ["torch"])597 598    @classmethod599    def from_pretrained(cls, *args, **kwargs):600        requires_backends(cls, ["torch"])601 602 603class RePaintScheduler(metaclass=DummyObject):604    _backends = ["torch"]605 606    def __init__(self, *args, **kwargs):607        requires_backends(self, ["torch"])608 609    @classmethod610    def from_config(cls, *args, **kwargs):611        requires_backends(cls, ["torch"])612 613    @classmethod614    def from_pretrained(cls, *args, **kwargs):615        requires_backends(cls, ["torch"])616 617 618class SchedulerMixin(metaclass=DummyObject):619    _backends = ["torch"]620 621    def __init__(self, *args, **kwargs):622        requires_backends(self, ["torch"])623 624    @classmethod625    def from_config(cls, *args, **kwargs):626        requires_backends(cls, ["torch"])627 628    @classmethod629    def from_pretrained(cls, *args, **kwargs):630        requires_backends(cls, ["torch"])631 632 633class ScoreSdeVeScheduler(metaclass=DummyObject):634    _backends = ["torch"]635 636    def __init__(self, *args, **kwargs):637        requires_backends(self, ["torch"])638 639    @classmethod640    def from_config(cls, *args, **kwargs):641        requires_backends(cls, ["torch"])642 643    @classmethod644    def from_pretrained(cls, *args, **kwargs):645        requires_backends(cls, ["torch"])646 647 648class UnCLIPScheduler(metaclass=DummyObject):649    _backends = ["torch"]650 651    def __init__(self, *args, **kwargs):652        requires_backends(self, ["torch"])653 654    @classmethod655    def from_config(cls, *args, **kwargs):656        requires_backends(cls, ["torch"])657 658    @classmethod659    def from_pretrained(cls, *args, **kwargs):660        requires_backends(cls, ["torch"])661 662 663class UniPCMultistepScheduler(metaclass=DummyObject):664    _backends = ["torch"]665 666    def __init__(self, *args, **kwargs):667        requires_backends(self, ["torch"])668 669    @classmethod670    def from_config(cls, *args, **kwargs):671        requires_backends(cls, ["torch"])672 673    @classmethod674    def from_pretrained(cls, *args, **kwargs):675        requires_backends(cls, ["torch"])676 677 678class VQDiffusionScheduler(metaclass=DummyObject):679    _backends = ["torch"]680 681    def __init__(self, *args, **kwargs):682        requires_backends(self, ["torch"])683 684    @classmethod685    def from_config(cls, *args, **kwargs):686        requires_backends(cls, ["torch"])687 688    @classmethod689    def from_pretrained(cls, *args, **kwargs):690        requires_backends(cls, ["torch"])691 692 693class EMAModel(metaclass=DummyObject):694    _backends = ["torch"]695 696    def __init__(self, *args, **kwargs):697        requires_backends(self, ["torch"])698 699    @classmethod700    def from_config(cls, *args, **kwargs):701        requires_backends(cls, ["torch"])702 703    @classmethod704    def from_pretrained(cls, *args, **kwargs):705        requires_backends(cls, ["torch"])706