diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
index 0f309625ae..9db646af09 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
@@ -28,6 +28,8 @@ class StableDiffusionPipeline(DiffusionPipeline):
     def __call__(
         self,
         prompt: Union[str, List[str]],
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
         num_inference_steps: Optional[int] = 50,
         guidance_scale: Optional[float] = 1.0,
         eta: Optional[float] = 0.0,
@@ -45,6 +47,9 @@ class StableDiffusionPipeline(DiffusionPipeline):
         else:
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
 
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
         self.unet.to(torch_device)
         self.vae.to(torch_device)
         self.text_encoder.to(torch_device)
@@ -72,7 +77,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
 
         # get the intial random noise
         latents = torch.randn(
-            (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+            (batch_size, self.unet.in_channels, height // 8, width // 8),
             generator=generator,
         )
         latents = latents.to(torch_device)