mirror of
https://github.com/huggingface/diffusers.git
synced 2026-02-14 14:55:26 +08:00
a1816166a5
* initial conversion script * cosmos control net block * CosmosAttention * base model conversion * wip * pipeline updates * convert controlnet * pipeline: working without controls * wip * debugging * Almost working * temp * control working * cleanup + detail on neg_encoder_hidden_states * convert edge * pos emb for control latents * convert all chkpts * resolve TODOs * remove prints * Docs * add siglip image reference encoder * Add unit tests * controlnet: add duplicate layers * Additional tests * skip less * skip less * remove image_ref * minor * docs * remove skipped test in transfer * Don't crash process * formatting * revert some changes * remove skipped test * make style * Address comment + fix example * CosmosAttnProcessor2_0 revert + CosmosAttnProcessor2_5 changes * make style * make fix-copies
2909 lines
71 KiB
Python
2909 lines
71 KiB
Python
# This file is autogenerated by the command `make fix-copies`, do not edit.
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from ..utils import DummyObject, requires_backends
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class AdaptiveProjectedGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AdaptiveProjectedMixGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class BaseGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class ClassifierFreeGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class ClassifierFreeZeroStarGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class FrequencyDecoupledGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class PerturbedAttentionGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class SkipLayerGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class SmoothedEnergyGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class TangentialClassifierFreeGuidance(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class FasterCacheConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class FirstBlockCacheConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class HookRegistry(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class LayerSkipConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class MagCacheConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class PyramidAttentionBroadcastConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class SmoothedEnergyGuidanceConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class TaylorSeerCacheConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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def apply_faster_cache(*args, **kwargs):
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requires_backends(apply_faster_cache, ["torch"])
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def apply_first_block_cache(*args, **kwargs):
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requires_backends(apply_first_block_cache, ["torch"])
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def apply_layer_skip(*args, **kwargs):
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requires_backends(apply_layer_skip, ["torch"])
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def apply_mag_cache(*args, **kwargs):
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requires_backends(apply_mag_cache, ["torch"])
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def apply_pyramid_attention_broadcast(*args, **kwargs):
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requires_backends(apply_pyramid_attention_broadcast, ["torch"])
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def apply_taylorseer_cache(*args, **kwargs):
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requires_backends(apply_taylorseer_cache, ["torch"])
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class AllegroTransformer3DModel(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AsymmetricAutoencoderKL(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AttentionBackendName(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AuraFlowTransformer2DModel(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderDC(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKL(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLAllegro(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLCogVideoX(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLCosmos(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLFlux2(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLHunyuanImage(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLHunyuanImageRefiner(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLHunyuanVideo(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLHunyuanVideo15(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLLTX2Audio(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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|
def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLLTX2Video(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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class AutoencoderKLLTXVideo(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@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 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 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 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 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"])
|
|
|
|
|
|
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 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"])
|