make fix-copies

src/diffusers/pipelines/qwenimage/pipeline_qwenimage_layered.py

@@ -393,7 +393,6 @@ def check_inputs(
             raise ValueError(f"`max_sequence_length` cannot be greater than 1024 but is {max_sequence_length}")
 
     @staticmethod
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._pack_latents
     def _pack_latents(latents, batch_size, num_channels_latents, height, width, layers):
         latents = latents.view(batch_size, layers, num_channels_latents, height // 2, 2, width // 2, 2)
         latents = latents.permute(0, 1, 3, 5, 2, 4, 6)
@@ -402,7 +401,6 @@ def _pack_latents(latents, batch_size, num_channels_latents, height, width, laye
         return latents
 
     @staticmethod
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._unpack_latents
     def _unpack_latents(latents, height, width, layers, vae_scale_factor):
         batch_size, num_patches, channels = latents.shape
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -2297,6 +2297,21 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class QwenImageLayeredPipeline(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 QwenImagePipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]