[Stable Diffusion Inpaint] Allow tensor as input image & mask

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -630,11 +630,7 @@ def __call__(
         )
 
         # 4. Preprocess mask and image
-        if isinstance(image, PIL.Image.Image) and isinstance(mask_image, PIL.Image.Image):
-            mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
-        else:
-            mask = mask_image
-            masked_image = image * (mask < 0.5)
+        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
 
         # 5. set timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -218,6 +218,64 @@ def test_stable_diffusion_inpaint(self):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
 
+    def test_stable_diffusion_inpaint_image_tensor(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet_inpaint
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        image = self.dummy_image.repeat(1, 1, 2, 2)
+        mask_image = image / 2
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionInpaintPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            feature_extractor=None,
+        )
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "A painting of a squirrel eating a burger"
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            image=image,
+            mask_image=mask_image[:, 0],
+        )
+        out_1 = output.images
+
+        image = image.cpu().permute(0, 2, 3, 1)[0]
+        mask_image = mask_image.cpu().permute(0, 2, 3, 1)[0]
+
+        image = Image.fromarray(np.uint8(image)).convert("RGB")
+        mask_image = Image.fromarray(np.uint8(mask_image)).convert("RGB")
+
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            image=image,
+            mask_image=mask_image,
+        )
+        out_2 = output.images
+
+        assert out_1.shape == (1, 64, 64, 3)
+        assert np.abs(out_1.flatten() - out_2.flatten()).max() < 5e-2
+
     def test_stable_diffusion_inpaint_with_num_images_per_prompt(self):
         device = "cpu"
         unet = self.dummy_cond_unet_inpaint