| |
| 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 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 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): |
| requires_backends(cls, ["torch"]) |
|
|
|
|
| class EasyAnimateTransformer3DModel(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 Flux2Transformer2DModel(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 FluxMultiControlNetModel(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 GlmImageTransformer2DModel(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 HeliosTransformer3DModel(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 HiDreamImageTransformer2DModel(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 HunyuanImageTransformer2DModel(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 HunyuanVideo15Transformer3DModel(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 HunyuanVideoFramepackTransformer3DModel(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 HunyuanVideoTransformer3DModel(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 Kandinsky5Transformer3DModel(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 LongCatImageTransformer2DModel(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 LTX2VideoTransformer3DModel(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 LTXVideoTransformer3DModel(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 Lumina2Transformer2DModel(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 MochiTransformer3DModel(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 MultiControlNetModel(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 OmniGenTransformer2DModel(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 OvisImageTransformer2DModel(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 ParallelConfig(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 PRXTransformer2DModel(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 QwenImageControlNetModel(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 QwenImageMultiControlNetModel(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 QwenImageTransformer2DModel(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 SanaControlNetModel(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 SanaTransformer2DModel(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 SanaVideoTransformer3DModel(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 SkyReelsV2Transformer3DModel(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 TransformerTemporalModel(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"]) |
|
|
|
|
| class WanAnimateTransformer3DModel(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 WanTransformer3DModel(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 WanVACETransformer3DModel(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 ZImageControlNetModel(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 ZImageTransformer2DModel(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 attention_backend(*args, **kwargs): |
| requires_backends(attention_backend, ["torch"]) |
|
|
|
|
| class AutoPipelineBlocks(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 ComponentsManager(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 ComponentSpec(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 ConditionalPipelineBlocks(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 ConfigSpec(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 InputParam(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 LoopSequentialPipelineBlocks(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 ModularPipeline(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 ModularPipelineBlocks(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 OutputParam(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 SequentialPipelineBlocks(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 DiffusersQuantizer(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): |
| 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 CogVideoXDDIMScheduler(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 CogVideoXDPMScheduler(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 DDPMWuerstchenScheduler(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 EDMDPMSolverMultistepScheduler(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 EDMEulerScheduler(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 FlowMatchEulerDiscreteScheduler(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 FlowMatchHeunDiscreteScheduler(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 FlowMatchLCMScheduler(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 HeliosDMDScheduler(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 HeliosScheduler(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 LCMScheduler(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 LTXEulerAncestralRFScheduler(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 SASolverScheduler(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 SCMScheduler(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 TCDScheduler(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"]) |
|
|
