# 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 AuraFlowTransformer2DModel(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 AutoencoderKLCogVideoX(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 AutoencoderKLTemporalDecoder(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 AutoencoderOobleck(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 CogVideoXTransformer3DModel(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 ConsistencyDecoderVAE(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 ControlNetXSAdapter(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 DiTTransformer2DModel(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 FluxControlNetModel(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 FluxTransformer2DModel(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 HunyuanDiT2DControlNetModel(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 HunyuanDiT2DModel(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 HunyuanDiT2DMultiControlNetModel(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 I2VGenXLUNet(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 Kandinsky3UNet(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 LatteTransformer3DModel(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 LuminaNextDiT2DModel(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 MotionAdapter(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 PixArtTransformer2DModel(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 SD3ControlNetModel(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 SD3MultiControlNetModel(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 SD3Transformer2DModel(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 SparseControlNetModel(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 StableAudioDiTModel(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 UNetControlNetXSModel(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 UNetMotionModel(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 UNetSpatioTemporalConditionModel(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 UVit2DModel(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 BlipDiffusionControlNetPipeline(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 BlipDiffusionPipeline(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 CLIPImageProjection(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 StableDiffusionMixin(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 AmusedScheduler(metaclass=DummyObject):

_backends = ["torch"]

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

requires_backends(self, ["torch"])

@classmethod

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