[PixArt-Alpha] fix mask_feature so that precomputed embeddings work with a batch size > 1 (#5677)

* fix embeds

* remove todo

* add: test

* better name

src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py

@@ -253,7 +253,7 @@ class PixArtAlphaPipeline(DiffusionPipeline):
             negative_prompt_embeds = None
 
         # Perform additional masking.
-        if mask_feature:
+        if mask_feature and prompt_embeds is None and negative_prompt_embeds is None:
             prompt_embeds = prompt_embeds.unsqueeze(1)
             masked_prompt_embeds, keep_indices = self.mask_text_embeddings(prompt_embeds, prompt_embeds_attention_mask)
             masked_prompt_embeds = masked_prompt_embeds.squeeze(1)

tests/pipelines/pixart/test_pixart.py

@@ -181,11 +181,76 @@ class PixArtAlphaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         max_diff = np.abs(image_slice.flatten() - expected_slice).max()
         self.assertLessEqual(max_diff, 1e-3)
 
+    def test_inference_with_embeddings_and_multiple_images(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        prompt = inputs["prompt"]
+        generator = inputs["generator"]
+        num_inference_steps = inputs["num_inference_steps"]
+        output_type = inputs["output_type"]
+
+        prompt_embeds, negative_prompt_embeds = pipe.encode_prompt(prompt)
+
+        # inputs with prompt converted to embeddings
+        inputs = {
+            "prompt_embeds": prompt_embeds,
+            "negative_prompt": None,
+            "negative_prompt_embeds": negative_prompt_embeds,
+            "generator": generator,
+            "num_inference_steps": num_inference_steps,
+            "output_type": output_type,
+            "num_images_per_prompt": 2,
+        }
+
+        # set all optional components to None
+        for optional_component in pipe._optional_components:
+            setattr(pipe, optional_component, None)
+
+        output = pipe(**inputs)[0]
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            pipe.save_pretrained(tmpdir)
+            pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
+            pipe_loaded.to(torch_device)
+            pipe_loaded.set_progress_bar_config(disable=None)
+
+        for optional_component in pipe._optional_components:
+            self.assertTrue(
+                getattr(pipe_loaded, optional_component) is None,
+                f"`{optional_component}` did not stay set to None after loading.",
+            )
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        generator = inputs["generator"]
+        num_inference_steps = inputs["num_inference_steps"]
+        output_type = inputs["output_type"]
+
+        # inputs with prompt converted to embeddings
+        inputs = {
+            "prompt_embeds": prompt_embeds,
+            "negative_prompt": None,
+            "negative_prompt_embeds": negative_prompt_embeds,
+            "generator": generator,
+            "num_inference_steps": num_inference_steps,
+            "output_type": output_type,
+            "num_images_per_prompt": 2,
+        }
+
+        output_loaded = pipe_loaded(**inputs)[0]
+
+        max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
+        self.assertLess(max_diff, 1e-4)
+
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=1e-3)
 
 
-# TODO: needs to be updated.
 @slow
 @require_torch_gpu
 class PixArtAlphaPipelineIntegrationTests(unittest.TestCase):