[StableDiffusionInpaintPipeline] fix batch_size for mask and masked latents

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -536,7 +536,7 @@ def __call__(
         mask, masked_image_latents = self.prepare_mask_latents(
             mask,
             masked_image,
-            batch_size,
+            batch_size * num_images_per_prompt,
             height,
             width,
             text_embeddings.dtype,

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -215,6 +215,47 @@ 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_with_num_images_per_prompt(self):
+        device = "cpu"
+        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.cpu().permute(0, 2, 3, 1)[0]
+        init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
+        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
+
+        # 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)
+        images = sd_pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            image=init_image,
+            mask_image=mask_image,
+            num_images_per_prompt=2,
+        ).images
+
+        # check if the output is a list of 2 images
+        assert len(images) == 2
+
     @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
     def test_stable_diffusion_inpaint_fp16(self):
         """Test that stable diffusion inpaint_legacy works with fp16"""