[Type hint] Karras VE pipeline (#288)

* [Type hint] Karras VE pipeline

* Apply suggestions from code review

Co-authored-by: Anton Lozhkov <anton@huggingface.co>

Co-authored-by: Anton Lozhkov <anton@huggingface.co>

Author: 2 people

src/diffusers/pipelines/stochatic_karras_ve/pipeline_stochastic_karras_ve.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 import warnings
+from typing import Optional
 
 import torch
 
@@ -21,13 +22,20 @@ class KarrasVePipeline(DiffusionPipeline):
     unet: UNet2DModel
     scheduler: KarrasVeScheduler
 
-    def __init__(self, unet, scheduler):
+    def __init__(self, unet: UNet2DModel, scheduler: KarrasVeScheduler):
         super().__init__()
         scheduler = scheduler.set_format("pt")
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, batch_size=1, num_inference_steps=50, generator=None, output_type="pil", **kwargs):
+    def __call__(
+        self,
+        batch_size: int = 1,
+        num_inference_steps: int = 50,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        **kwargs,
+    ):
         if "torch_device" in kwargs:
             device = kwargs.pop("torch_device")
             warnings.warn(