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Author: stevhliu

src/diffusers/__init__.py

@@ -160,15 +160,6 @@
             "apply_pyramid_attention_broadcast",
         ]
     )
-    _import_structure["image_processor"] = [
-        "IPAdapterMaskProcessor",
-        "PixArtImageProcessor",
-        "VaeImageProcessor",
-        "VaeImageProcessorLDM3D",
-    ]
-    _import_structure["video_processor"] = [
-        "VideoProcessor",
-    ]
     _import_structure["models"].extend(
         [
             "AllegroTransformer3DModel",
@@ -840,12 +831,6 @@
             apply_layer_skip,
             apply_pyramid_attention_broadcast,
         )
-        from .image_processor import (
-            IPAdapterMaskProcessor,
-            PixArtImageProcessor,
-            VaeImageProcessor,
-            VaeImageProcessorLDM3D,
-        )
         from .models import (
             AllegroTransformer3DModel,
             AsymmetricAutoencoderKL,
@@ -1005,8 +990,6 @@
             VQDiffusionScheduler,
         )
         from .training_utils import EMAModel
-        from .video_processor import VideoProcessor
-
     try:
         if not (is_torch_available() and is_scipy_available()):
             raise OptionalDependencyNotAvailable()

src/diffusers/utils/dummy_pt_objects.py

@@ -242,67 +242,6 @@ def apply_layer_skip(*args, **kwargs):
 def apply_pyramid_attention_broadcast(*args, **kwargs):
     requires_backends(apply_pyramid_attention_broadcast, ["torch"])
 
-
-class IPAdapterMaskProcessor(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class PixArtImageProcessor(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class VaeImageProcessor(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class VaeImageProcessorLDM3D(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
 class AllegroTransformer3DModel(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -2523,18 +2462,3 @@ def from_config(cls, *args, **kwargs):
     @classmethod
     def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch"])
-
-
-class VideoProcessor(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])