[Upscaling] Fix batch size

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

@@ -459,8 +459,10 @@ def __call__(
         else:
             noise = torch.randn(image.shape, generator=generator, device=device, dtype=text_embeddings.dtype)
         image = self.low_res_scheduler.add_noise(image, noise, noise_level)
-        image = torch.cat([image] * 2) if do_classifier_free_guidance else image
-        noise_level = torch.cat([noise_level] * 2) if do_classifier_free_guidance else noise_level
+
+        batch_multiplier = 2 if do_classifier_free_guidance else 1
+        image = torch.cat([image] * batch_multiplier * num_images_per_prompt)
+        noise_level = torch.cat([noise_level] * image.shape[0])
 
         # 6. Prepare latent variables
         height, width = image.shape[2:]
@@ -515,7 +517,7 @@ def __call__(
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py

@@ -161,6 +161,57 @@ def test_stable_diffusion_upscale(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_upscale_batch(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"
+        output = sd_pipe(
+            2 * [prompt],
+            image=2 * [low_res_image],
+            guidance_scale=6.0,
+            noise_level=20,
+            num_inference_steps=2,
+            output_type="np",
+        )
+        image = output.images
+        assert image.shape[0] == 2
+
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe(
+            [prompt],
+            image=low_res_image,
+            generator=generator,
+            num_images_per_prompt=2,
+            guidance_scale=6.0,
+            noise_level=20,
+            num_inference_steps=2,
+            output_type="np",
+        )
+        image = output.images
+        assert image.shape[0] == 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"""