diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
index 05ea84ae0326..806135f2f131 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
@@ -145,8 +145,9 @@ def __call__(
                 process. This is the image whose masked region will be inpainted.
             mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
                 `Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
-                replaced by noise and therefore repainted, while black pixels will be preserved. The mask image will be
-                converted to a single channel (luminance) before use.
+                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
+                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
+                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
             strength (`float`, *optional*, defaults to 0.8):
                 Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
                 is 1, the denoising process will be run on the masked area for the full number of iterations specified
@@ -202,10 +203,12 @@ def __call__(
         self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
 
         # preprocess image
-        init_image = preprocess_image(init_image).to(self.device)
+        if not isinstance(init_image, torch.FloatTensor):
+            init_image = preprocess_image(init_image)
+        init_image.to(self.device)
 
         # encode the init image into latents and scale the latents
-        init_latent_dist = self.vae.encode(init_image.to(self.device)).latent_dist
+        init_latent_dist = self.vae.encode(init_image).latent_dist
         init_latents = init_latent_dist.sample(generator=generator)
 
         init_latents = 0.18215 * init_latents
@@ -215,8 +218,10 @@ def __call__(
         init_latents_orig = init_latents
 
         # preprocess mask
-        mask = preprocess_mask(mask_image).to(self.device)
-        mask = torch.cat([mask] * batch_size)
+        if not isinstance(mask_image, torch.FloatTensor):
+            mask_image = preprocess_mask(mask_image)
+        mask_image.to(self.device)
+        mask = torch.cat([mask_image] * batch_size)
 
         # check sizes
         if not mask.shape == init_latents.shape: