Added LucyEditPipeline (#12340)

* Added LucyEditPipeline

* add import & stype

missing copied from

* Fix example doc string

---------
Co-authored-by: yiyixuxu <yixu310@gmail.com>

src/diffusers/__init__.py

@@ -495,6 +495,7 @@ else:
             "LTXImageToVideoPipeline",
             "LTXLatentUpsamplePipeline",
             "LTXPipeline",
+            "LucyEditPipeline",
             "Lumina2Pipeline",
             "Lumina2Text2ImgPipeline",
             "LuminaPipeline",
@@ -1149,6 +1150,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LTXImageToVideoPipeline,
             LTXLatentUpsamplePipeline,
             LTXPipeline,
+            LucyEditPipeline,
             Lumina2Pipeline,
             Lumina2Text2ImgPipeline,
             LuminaPipeline,

src/diffusers/pipelines/__init__.py

@@ -285,6 +285,7 @@ else:
     ]
     _import_structure["lumina"] = ["LuminaPipeline", "LuminaText2ImgPipeline"]
     _import_structure["lumina2"] = ["Lumina2Pipeline", "Lumina2Text2ImgPipeline"]
+    _import_structure["lucy"] = ["LucyEditPipeline"]
     _import_structure["marigold"].extend(
         [
             "MarigoldDepthPipeline",
@@ -682,6 +683,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LEditsPPPipelineStableDiffusionXL,
         )
         from .ltx import LTXConditionPipeline, LTXImageToVideoPipeline, LTXLatentUpsamplePipeline, LTXPipeline
+        from .lucy import LucyEditPipeline
         from .lumina import LuminaPipeline, LuminaText2ImgPipeline
         from .lumina2 import Lumina2Pipeline, Lumina2Text2ImgPipeline
         from .marigold import (

src/diffusers/pipelines/lucy/__init__.py

+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_lucy_edit"] = ["LucyEditPipeline"]
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_lucy_edit import LucyEditPipeline
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/lucy/pipeline_output.py

+from dataclasses import dataclass
+
+import torch
+
+from diffusers.utils import BaseOutput
+
+
+@dataclass
+class LucyPipelineOutput(BaseOutput):
+    r"""
+    Output class for Lucy pipelines.
+
+    Args:
+        frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
+            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
+            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
+            `(batch_size, num_frames, channels, height, width)`.
+    """
+
+    frames: torch.Tensor

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -1592,6 +1592,21 @@ class LTXPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class LucyEditPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class Lumina2Pipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]