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

from ..utils import DummyObject, requires_backends

class AdaptiveProjectedGuidance(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 AdaptiveProjectedMixGuidance(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 AutoGuidance(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 BaseGuidance(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 ClassifierFreeGuidance(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 ClassifierFreeZeroStarGuidance(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 FrequencyDecoupledGuidance(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 PerturbedAttentionGuidance(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 SkipLayerGuidance(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 SmoothedEnergyGuidance(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 TangentialClassifierFreeGuidance(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 FasterCacheConfig(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 FirstBlockCacheConfig(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 HookRegistry(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 LayerSkipConfig(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 MagCacheConfig(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 PyramidAttentionBroadcastConfig(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 SmoothedEnergyGuidanceConfig(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 TaylorSeerCacheConfig(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 apply_faster_cache(*args, **kwargs):

requires_backends(apply_faster_cache, ["torch"])

def apply_first_block_cache(*args, **kwargs):

requires_backends(apply_first_block_cache, ["torch"])

def apply_layer_skip(*args, **kwargs):

requires_backends(apply_layer_skip, ["torch"])

def apply_mag_cache(*args, **kwargs):

requires_backends(apply_mag_cache, ["torch"])

def apply_pyramid_attention_broadcast(*args, **kwargs):

requires_backends(apply_pyramid_attention_broadcast, ["torch"])

def apply_taylorseer_cache(*args, **kwargs):

requires_backends(apply_taylorseer_cache, ["torch"])

class AllegroTransformer3DModel(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 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 AttentionBackendName(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 AutoencoderDC(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 AutoencoderKLAllegro(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 AutoencoderKLCosmos(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 AutoencoderKLFlux2(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 AutoencoderKLHunyuanImage(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 AutoencoderKLHunyuanImageRefiner(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 AutoencoderKLHunyuanVideo(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 AutoencoderKLHunyuanVideo15(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 AutoencoderKLKVAE(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 AutoencoderKLKVAEVideo(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 AutoencoderKLLTX2Audio(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 AutoencoderKLLTX2Video(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 AutoencoderKLLTXVideo(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 AutoencoderKLMagvit(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 AutoencoderKLMochi(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 AutoencoderKLQwenImage(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 AutoencoderKLWan(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 AutoencoderRAE(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 AutoencoderVidTok(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 AutoModel(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 BriaFiboTransformer2DModel(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 BriaTransformer2DModel(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 CacheMixin(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 ChromaTransformer2DModel(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 ChronoEditTransformer3DModel(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 CogView3PlusTransformer2DModel(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 CogView4Transformer2DModel(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 ConsisIDTransformer3DModel(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 ContextParallelConfig(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 ControlNetUnionModel(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 CosmosControlNetModel(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 CosmosTransformer3DModel(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):