diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
index 07918074a8..d551712d70 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
@@ -424,10 +424,13 @@ class StableDiffusionUpscalePipeline(DiffusionPipeline, TextualInversionLoaderMi
 
         # verify batch size of prompt and image are same if image is a list or tensor or numpy array
         if isinstance(image, list) or isinstance(image, torch.Tensor) or isinstance(image, np.ndarray):
-            if isinstance(prompt, str):
+            if prompt is not None and isinstance(prompt, str):
                 batch_size = 1
-            else:
+            elif prompt is not None and isinstance(prompt, list):
                 batch_size = len(prompt)
+            else:
+                batch_size = prompt_embeds.shape[0]
+
             if isinstance(image, list):
                 image_batch_size = len(image)
             else:
diff --git a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py
index 7100e5023a..1905af6c69 100644
--- a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py
+++ b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py
@@ -210,6 +210,68 @@ class StableDiffusionUpscalePipelineFastTests(unittest.TestCase):
         image = output.images
         assert image.shape[0] == 2
 
+    def test_stable_diffusion_upscale_prompt_embeds(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet_upscale
+        low_res_scheduler = DDPMScheduler()
+        scheduler = DDIMScheduler(prediction_type="v_prediction")
+        vae = self.dummy_vae
+        text_encoder = 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]
+        low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionUpscalePipeline(
+            unet=unet,
+            low_res_scheduler=low_res_scheduler,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            max_noise_level=350,
+        )
+        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],
+            image=low_res_image,
+            generator=generator,
+            guidance_scale=6.0,
+            noise_level=20,
+            num_inference_steps=2,
+            output_type="np",
+        )
+
+        image = output.images
+
+        generator = torch.Generator(device=device).manual_seed(0)
+        prompt_embeds = sd_pipe._encode_prompt(prompt, device, 1, False)
+        image_from_prompt_embeds = sd_pipe(
+            prompt_embeds=prompt_embeds,
+            image=[low_res_image],
+            generator=generator,
+            guidance_scale=6.0,
+            noise_level=20,
+            num_inference_steps=2,
+            output_type="np",
+            return_dict=False,
+        )[0]
+
+        image_slice = image[0, -3:, -3:, -1]
+        image_from_prompt_embeds_slice = image_from_prompt_embeds[0, -3:, -3:, -1]
+
+        expected_height_width = low_res_image.size[0] * 4
+        assert image.shape == (1, expected_height_width, expected_height_width, 3)
+        expected_slice = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+        assert np.abs(image_from_prompt_embeds_slice.flatten() - expected_slice).max() < 1e-2
+
     @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
     def test_stable_diffusion_upscale_fp16(self):
         """Test that stable diffusion upscale works with fp16"""