diff --git a/src/diffusers/pipelines/pndm/pipeline_pndm.py b/src/diffusers/pipelines/pndm/pipeline_pndm.py
index 32ddbd8c1711..632e31777a53 100644
--- a/src/diffusers/pipelines/pndm/pipeline_pndm.py
+++ b/src/diffusers/pipelines/pndm/pipeline_pndm.py
@@ -15,20 +15,33 @@
 
 
 import warnings
+from typing import Optional
 
 import torch
 
+from ...models import UNet2DModel
 from ...pipeline_utils import DiffusionPipeline
+from ...schedulers import PNDMScheduler
 
 
 class PNDMPipeline(DiffusionPipeline):
-    def __init__(self, unet, scheduler):
+    unet: UNet2DModel
+    scheduler: PNDMScheduler
+
+    def __init__(self, unet: UNet2DModel, scheduler: PNDMScheduler):
         super().__init__()
         scheduler = scheduler.set_format("pt")
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, batch_size=1, generator=None, num_inference_steps=50, 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,
+    ):
         # For more information on the sampling method you can take a look at Algorithm 2 of
         # the official paper: https://arxiv.org/pdf/2202.09778.pdf