move test num_images_per_prompt to pipeline mixin (#2488)

* attend and excite batch test causing timeouts

* move test num_images_per_prompt to pipeline mixin

* style

* prompt_key -> self.batch_params

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py

@@ -517,8 +517,30 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline):
                     f" {negative_prompt_embeds.shape}."
                 )
 
-        if (indices is None) or (indices is not None and not isinstance(indices, List)):
+        indices_is_list_ints = isinstance(indices, list) and isinstance(indices[0], int)
-            raise ValueError(f"`indices` has to be a list but is {type(indices)}")
+        indices_is_list_list_ints = (
+            isinstance(indices, list) and isinstance(indices[0], list) and isinstance(indices[0][0], int)
+        )
+
+        if not indices_is_list_ints and not indices_is_list_list_ints:
+            raise TypeError("`indices` must be a list of ints or a list of a list of ints")
+
+        if indices_is_list_ints:
+            indices_batch_size = 1
+        elif indices_is_list_list_ints:
+            indices_batch_size = len(indices)
+
+        if prompt is not None and isinstance(prompt, str):
+            prompt_batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            prompt_batch_size = len(prompt)
+        elif prompt_embeds is not None:
+            prompt_batch_size = prompt_embeds.shape[0]
+
+        if indices_batch_size != prompt_batch_size:
+            raise ValueError(
+                f"indices batch size must be same as prompt batch size. indices batch size: {indices_batch_size}, prompt batch size: {prompt_batch_size}"
+            )
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
@@ -675,7 +697,7 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline):
     def __call__(
         self,
         prompt: Union[str, List[str]],
-        token_indices: List[int],
+        token_indices: Union[List[int], List[List[int]]],
         height: Optional[int] = None,
         width: Optional[int] = None,
         num_inference_steps: int = 50,
@@ -851,7 +873,9 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline):
 
         if isinstance(token_indices[0], int):
             token_indices = [token_indices]
+
         indices = []
+
         for ind in token_indices:
             indices = indices + [ind] * num_images_per_prompt
 

tests/pipelines/paint_by_example/test_paint_by_example.py

@@ -160,19 +160,6 @@ class PaintByExamplePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         assert out_1.shape == (1, 64, 64, 3)
         assert np.abs(out_1.flatten() - out_2.flatten()).max() < 5e-2
 
-    def test_paint_by_example_inpaint_with_num_images_per_prompt(self):
-        device = "cpu"
-        pipe = PaintByExamplePipeline(**self.get_dummy_components())
-        pipe = pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs()
-
-        images = pipe(**inputs, num_images_per_prompt=2).images
-
-        # check if the output is a list of 2 images
-        assert len(images) == 2
-
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_cycle_diffusion.py

@@ -41,7 +41,7 @@ class CycleDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         "negative_prompt_embeds",
     }
     required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
-    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
+    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"})
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -477,43 +477,6 @@ class StableDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
-    def test_stable_diffusion_num_images_per_prompt(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-        components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
-        sd_pipe = StableDiffusionPipeline(**components)
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-
-        # test num_images_per_prompt=1 (default)
-        images = sd_pipe(prompt, num_inference_steps=2, output_type="np").images
-
-        assert images.shape == (1, 64, 64, 3)
-
-        # test num_images_per_prompt=1 (default) for batch of prompts
-        batch_size = 2
-        images = sd_pipe([prompt] * batch_size, num_inference_steps=2, output_type="np").images
-
-        assert images.shape == (batch_size, 64, 64, 3)
-
-        # test num_images_per_prompt for single prompt
-        num_images_per_prompt = 2
-        images = sd_pipe(
-            prompt, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
-        ).images
-
-        assert images.shape == (num_images_per_prompt, 64, 64, 3)
-
-        # test num_images_per_prompt for batch of prompts
-        batch_size = 2
-        images = sd_pipe(
-            [prompt] * batch_size, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
-        ).images
-
-        assert images.shape == (batch_size * num_images_per_prompt, 64, 64, 3)
-
     def test_stable_diffusion_long_prompt(self):
         components = self.get_dummy_components()
         components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)

tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py

@@ -143,42 +143,6 @@ class StableDiffusionImageVariationPipelineFastTests(PipelineTesterMixin, unitte
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
-    def test_stable_diffusion_img_variation_num_images_per_prompt(self):
-        device = "cpu"
-        components = self.get_dummy_components()
-        sd_pipe = StableDiffusionImageVariationPipeline(**components)
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        # test num_images_per_prompt=1 (default)
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs).images
-
-        assert images.shape == (1, 64, 64, 3)
-
-        # test num_images_per_prompt=1 (default) for batch of images
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["image"] = batch_size * [inputs["image"]]
-        images = sd_pipe(**inputs).images
-
-        assert images.shape == (batch_size, 64, 64, 3)
-
-        # test num_images_per_prompt for single prompt
-        num_images_per_prompt = 2
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (num_images_per_prompt, 64, 64, 3)
-
-        # test num_images_per_prompt for batch of prompts
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["image"] = batch_size * [inputs["image"]]
-        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (batch_size * num_images_per_prompt, 64, 64, 3)
-
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -181,42 +181,6 @@ class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.Test
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
-    def test_stable_diffusion_img2img_num_images_per_prompt(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-        sd_pipe = StableDiffusionImg2ImgPipeline(**components)
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        # test num_images_per_prompt=1 (default)
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs).images
-
-        assert images.shape == (1, 32, 32, 3)
-
-        # test num_images_per_prompt=1 (default) for batch of prompts
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = [inputs["prompt"]] * batch_size
-        images = sd_pipe(**inputs).images
-
-        assert images.shape == (batch_size, 32, 32, 3)
-
-        # test num_images_per_prompt for single prompt
-        num_images_per_prompt = 2
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (num_images_per_prompt, 32, 32, 3)
-
-        # test num_images_per_prompt for batch of prompts
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = [inputs["prompt"]] * batch_size
-        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
-
     @skip_mps
     def test_save_load_local(self):
         return super().test_save_load_local()

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -151,19 +151,6 @@ class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.Test
         assert out_pil.shape == (1, 64, 64, 3)
         assert np.abs(out_pil.flatten() - out_tensor.flatten()).max() < 5e-2
 
-    def test_stable_diffusion_inpaint_with_num_images_per_prompt(self):
-        device = "cpu"
-        components = self.get_dummy_components()
-        sd_pipe = StableDiffusionInpaintPipeline(**components)
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs, num_images_per_prompt=2).images
-
-        # check if the output is a list of 2 images
-        assert len(images) == 2
-
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py

@@ -191,42 +191,6 @@ class StableDiffusionInstructPix2PixPipelineFastTests(PipelineTesterMixin, unitt
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
-    def test_stable_diffusion_pix2pix_num_images_per_prompt(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-        sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        # test num_images_per_prompt=1 (default)
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs).images
-
-        assert images.shape == (1, 32, 32, 3)
-
-        # test num_images_per_prompt=1 (default) for batch of prompts
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = [inputs["prompt"]] * batch_size
-        images = sd_pipe(**inputs).images
-
-        assert images.shape == (batch_size, 32, 32, 3)
-
-        # test num_images_per_prompt for single prompt
-        num_images_per_prompt = 2
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (num_images_per_prompt, 32, 32, 3)
-
-        # test num_images_per_prompt for batch of prompts
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = [inputs["prompt"]] * batch_size
-        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
-
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_stable_diffusion_panorama.py

@@ -177,42 +177,6 @@ class StableDiffusionPanoramaPipelineFastTests(PipelineTesterMixin, unittest.Tes
         with self.assertRaises(ValueError):
             _ = sd_pipe(**inputs).images
 
-    def test_stable_diffusion_panorama_num_images_per_prompt(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-        sd_pipe = StableDiffusionPanoramaPipeline(**components)
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        # test num_images_per_prompt=1 (default)
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs).images
-
-        assert images.shape == (1, 64, 64, 3)
-
-        # test num_images_per_prompt=1 (default) for batch of prompts
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = [inputs["prompt"]] * batch_size
-        images = sd_pipe(**inputs).images
-
-        assert images.shape == (batch_size, 64, 64, 3)
-
-        # test num_images_per_prompt for single prompt
-        num_images_per_prompt = 2
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (num_images_per_prompt, 64, 64, 3)
-
-        # test num_images_per_prompt for batch of prompts
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = [inputs["prompt"]] * batch_size
-        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (batch_size * num_images_per_prompt, 64, 64, 3)
-
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_stable_diffusion_pix2pix_zero.py

@@ -191,34 +191,6 @@ class StableDiffusionPix2PixZeroPipelineFastTests(PipelineTesterMixin, unittest.
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
-    def test_stable_diffusion_pix2pix_zero_num_images_per_prompt(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-        sd_pipe = StableDiffusionPix2PixZeroPipeline(**components)
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        # test num_images_per_prompt=1 (default)
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs).images
-
-        assert images.shape == (1, 64, 64, 3)
-
-        # test num_images_per_prompt=2 for a single prompt
-        num_images_per_prompt = 2
-        inputs = self.get_dummy_inputs(device)
-        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (num_images_per_prompt, 64, 64, 3)
-
-        # test num_images_per_prompt for batch of prompts
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = [inputs["prompt"]] * batch_size
-        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (batch_size * num_images_per_prompt, 64, 64, 3)
-
     # Non-determinism caused by the scheduler optimizing the latent inputs during inference
     @unittest.skip("non-deterministic pipeline")
     def test_inference_batch_single_identical(self):

tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py

@@ -64,7 +64,7 @@ class StableDiffusionDepth2ImgPipelineFastTests(PipelineTesterMixin, unittest.Te
     test_save_load_optional_components = False
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
-    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
+    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS - {"image"}
 
     def get_dummy_components(self):
         torch.manual_seed(0)
@@ -340,42 +340,6 @@ class StableDiffusionDepth2ImgPipelineFastTests(PipelineTesterMixin, unittest.Te
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
-    def test_stable_diffusion_depth2img_num_images_per_prompt(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-        pipe = StableDiffusionDepth2ImgPipeline(**components)
-        pipe = pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        # test num_images_per_prompt=1 (default)
-        inputs = self.get_dummy_inputs(device)
-        images = pipe(**inputs).images
-
-        assert images.shape == (1, 32, 32, 3)
-
-        # test num_images_per_prompt=1 (default) for batch of prompts
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = [inputs["prompt"]] * batch_size
-        images = pipe(**inputs).images
-
-        assert images.shape == (batch_size, 32, 32, 3)
-
-        # test num_images_per_prompt for single prompt
-        num_images_per_prompt = 2
-        inputs = self.get_dummy_inputs(device)
-        images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (num_images_per_prompt, 32, 32, 3)
-
-        # test num_images_per_prompt for batch of prompts
-        batch_size = 2
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = [inputs["prompt"]] * batch_size
-        images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
-
-        assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
-
     def test_stable_diffusion_depth2img_pil(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()

tests/pipelines/unclip/test_unclip_image_variation.py

@@ -361,59 +361,6 @@ class UnCLIPImageVariationPipelineFastTests(PipelineTesterMixin, unittest.TestCa
         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_unclip_image_variation_input_num_images_per_prompt(self):
-        device = "cpu"
-
-        components = self.get_dummy_components()
-
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(device)
-
-        pipe.set_progress_bar_config(disable=None)
-
-        pipeline_inputs = self.get_dummy_inputs(device, pil_image=True)
-        pipeline_inputs["image"] = [
-            pipeline_inputs["image"],
-            pipeline_inputs["image"],
-        ]
-
-        output = pipe(**pipeline_inputs, num_images_per_prompt=2)
-        image = output.images
-
-        tuple_pipeline_inputs = self.get_dummy_inputs(device, pil_image=True)
-        tuple_pipeline_inputs["image"] = [
-            tuple_pipeline_inputs["image"],
-            tuple_pipeline_inputs["image"],
-        ]
-
-        image_from_tuple = pipe(
-            **tuple_pipeline_inputs,
-            num_images_per_prompt=2,
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (4, 64, 64, 3)
-
-        expected_slice = np.array(
-            [
-                0.9980,
-                0.9997,
-                0.0023,
-                0.0029,
-                0.9997,
-                0.9985,
-                0.9997,
-                0.0010,
-                0.9995,
-            ]
-        )
-
-        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_unclip_passed_image_embed(self):
         device = torch.device("cpu")
 

tests/test_pipelines_common.py

@@ -550,6 +550,32 @@ class PipelineTesterMixin:
             _ = pipe(**inputs)
             self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled")
 
+    def test_num_images_per_prompt(self):
+        sig = inspect.signature(self.pipeline_class.__call__)
+
+        if "num_images_per_prompt" not in sig.parameters:
+            return
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        batch_sizes = [1, 2]
+        num_images_per_prompts = [1, 2]
+
+        for batch_size in batch_sizes:
+            for num_images_per_prompt in num_images_per_prompts:
+                inputs = self.get_dummy_inputs(torch_device)
+
+                for key in inputs.keys():
+                    if key in self.batch_params:
+                        inputs[key] = batch_size * [inputs[key]]
+
+                images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
+
+                assert images.shape[0] == batch_size * num_images_per_prompt
+
 
 # Some models (e.g. unCLIP) are extremely likely to significantly deviate depending on which hardware is used.
 # This helper function is used to check that the image doesn't deviate on average more than 10 pixels from a