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# This file is autogenerated by the command `make fix-copies`, do not edit. | |
from ..utils import DummyObject, requires_backends | |
class AsymmetricAutoencoderKL(metaclass=DummyObject): | |
_backends = ["torch"] | |
def __init__(self, *args, **kwargs): | |
requires_backends(self, ["torch"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
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"]) | |
def from_config(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |
def from_pretrained(cls, *args, **kwargs): | |
requires_backends(cls, ["torch"]) | |