[Type hint] Latent Diffusion Uncond pipeline (#333)

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

 import inspect
 import warnings
+from typing import Optional
 
 import torch
 
+from ...models import UNet2DModel, VQModel
 from ...pipeline_utils import DiffusionPipeline
+from ...schedulers import DDIMScheduler
 
 
 class LDMPipeline(DiffusionPipeline):
-    def __init__(self, vqvae, unet, scheduler):
+
+    vqvae: VQModel
+    unet: UNet2DModel
+    scheduler: DDIMScheduler
+
+    def __init__(self, vqvae: VQModel, unet: UNet2DModel, scheduler: DDIMScheduler):
         super().__init__()
         scheduler = scheduler.set_format("pt")
         self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, batch_size=1, generator=None, eta=0.0, num_inference_steps=50, output_type="pil", **kwargs):
+    def __call__(
+        self,
+        batch_size: int = 1,
+        generator: Optional[torch.Generator] = None,
+        eta: float = 0.0,
+        num_inference_steps: int = 50,
+        output_type: Optional[str] = "pil",
+        **kwargs,
+    ):
         # eta corresponds to η in paper and should be between [0, 1]
 
         if "torch_device" in kwargs: