make fix-copies

Author: a-r-r-o-w

src/diffusers/pipelines/__init__.py

@@ -155,6 +155,7 @@
     ]
     _import_structure["cogview3"] = ["CogView3PlusPipeline"]
     _import_structure["cogview4"] = ["CogView4Pipeline"]
+    _import_structure["consisid"] = ["ConsisIDPipeline"]
     _import_structure["controlnet"].extend(
         [
             "BlipDiffusionControlNetPipeline",

src/diffusers/pipelines/cogview4/pipeline_cogview4.py

@@ -237,11 +237,11 @@ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype
             latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
         else:
             latents = latents.to(device)
+
         # scale the initial noise by the standard deviation required by the scheduler
         latents = latents * self.scheduler.init_noise_sigma
         return latents
 
-    # Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs
     def check_inputs(
         self,
         prompt,

src/diffusers/schedulers/scheduling_ddim_cogview4.py

@@ -28,7 +28,6 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
 
 
-# Copied from diffusers.schedulers.scheduling_flow_match_heun_discrete.FlowMatchHeunDiscreteSchedulerOutput
 @dataclass
 class CogView4DDIMSchedulerOutput(BaseOutput):
     """

src/diffusers/utils/dummy_pt_objects.py

@@ -276,6 +276,21 @@ 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"]
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -362,6 +362,21 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class CogView4Pipeline(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 ConsisIDPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]