# This file is autogenerated by the command `make fix-copies`, do not edit.

from ..utils import DummyObject, requires_backends

class AsymmetricAutoencoderKL(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class AutoencoderKL(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class AutoencoderTiny(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class ControlNetModel(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class ModelMixin(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class MultiAdapter(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class PriorTransformer(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class T2IAdapter(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class T5FilmDecoder(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class Transformer2DModel(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class UNet1DModel(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class UNet2DConditionModel(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class UNet2DModel(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class UNet3DConditionModel(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class VQModel(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

def get_constant_schedule(*args, **kwargs):

requires_backends(get_constant_schedule, ["torch"])

def get_constant_schedule_with_warmup(*args, **kwargs):

requires_backends(get_constant_schedule_with_warmup, ["torch"])

def get_cosine_schedule_with_warmup(*args, **kwargs):

requires_backends(get_cosine_schedule_with_warmup, ["torch"])

def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):

requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"])

def get_linear_schedule_with_warmup(*args, **kwargs):

requires_backends(get_linear_schedule_with_warmup, ["torch"])

def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):

requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"])

def get_scheduler(*args, **kwargs):

requires_backends(get_scheduler, ["torch"])

class AudioPipelineOutput(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class AutoPipelineForImage2Image(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class AutoPipelineForInpainting(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class AutoPipelineForText2Image(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class ConsistencyModelPipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DanceDiffusionPipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DDIMPipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DDPMPipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DiffusionPipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DiTPipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class ImagePipelineOutput(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class KarrasVePipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class LDMPipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class LDMSuperResolutionPipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class PNDMPipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class RePaintPipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class ScoreSdeVePipeline(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class CMStochasticIterativeScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DDIMInverseScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DDIMParallelScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DDIMScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DDPMParallelScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DDPMScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DEISMultistepScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DPMSolverMultistepInverseScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DPMSolverMultistepScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class DPMSolverSinglestepScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class EulerAncestralDiscreteScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class EulerDiscreteScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class HeunDiscreteScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class IPNDMScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class KarrasVeScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class KDPM2AncestralDiscreteScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class KDPM2DiscreteScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class PNDMScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class RePaintScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class SchedulerMixin(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class ScoreSdeVeScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class UnCLIPScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class UniPCMultistepScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class VQDiffusionScheduler(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

class EMAModel(metaclass=DummyObject):

_backends = ["torch"]

def __init__(self, *args, **kwargs):

requires_backends(self, ["torch"])

@classmethod

def from_config(cls, *args, **kwargs):

requires_backends(cls, ["torch"])

@classmethod

def from_pretrained(cls, *args, **kwargs):

requires_backends(cls, ["torch"])