[tests] test `encode_prompt()` in isolation (#10438)

* poc encode_prompt() tests

* fix

* updates.

* fixes

* fixes

* updates

* updates

* updates

* revert

* updates

* updates

* updates

* updates

* remove SDXLOptionalComponentsTesterMixin.

* remove tests that directly leveraged encode_prompt() in some way or the other.

* fix imports.

* remove _save_load

* fixes

* fixes

* fixes

* fixes

src/diffusers/pipelines/pag/pipeline_pag_sana.py

@@ -268,7 +268,8 @@ class SanaPAGPipeline(DiffusionPipeline, PAGMixin):
         else:
             batch_size = prompt_embeds.shape[0]
 
-        self.tokenizer.padding_side = "right"
+        if getattr(self, "tokenizer", None) is not None:
+            self.tokenizer.padding_side = "right"
 
         # See Section 3.1. of the paper.
         max_length = max_sequence_length

src/diffusers/pipelines/sana/pipeline_sana.py

@@ -312,7 +312,8 @@ class SanaPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
         else:
             batch_size = prompt_embeds.shape[0]
 
-        self.tokenizer.padding_side = "right"
+        if getattr(self, "tokenizer", None) is not None:
+            self.tokenizer.padding_side = "right"
 
         # See Section 3.1. of the paper.
         max_length = max_sequence_length

src/diffusers/utils/source_code_parsing_utils.py

+import ast
+import importlib
+import inspect
+import textwrap
+
+
+class ReturnNameVisitor(ast.NodeVisitor):
+    """Thanks to ChatGPT for pairing."""
+
+    def __init__(self):
+        self.return_names = []
+
+    def visit_Return(self, node):
+        # Check if the return value is a tuple.
+        if isinstance(node.value, ast.Tuple):
+            for elt in node.value.elts:
+                if isinstance(elt, ast.Name):
+                    self.return_names.append(elt.id)
+                else:
+                    try:
+                        self.return_names.append(ast.unparse(elt))
+                    except Exception:
+                        self.return_names.append(str(elt))
+        else:
+            if isinstance(node.value, ast.Name):
+                self.return_names.append(node.value.id)
+            else:
+                try:
+                    self.return_names.append(ast.unparse(node.value))
+                except Exception:
+                    self.return_names.append(str(node.value))
+        self.generic_visit(node)
+
+    def _determine_parent_module(self, cls):
+        from diffusers import DiffusionPipeline
+        from diffusers.models.modeling_utils import ModelMixin
+
+        if issubclass(cls, DiffusionPipeline):
+            return "pipelines"
+        elif issubclass(cls, ModelMixin):
+            return "models"
+        else:
+            raise NotImplementedError
+
+    def get_ast_tree(self, cls, attribute_name="encode_prompt"):
+        parent_module_name = self._determine_parent_module(cls)
+        main_module = importlib.import_module(f"diffusers.{parent_module_name}")
+        current_cls_module = getattr(main_module, cls.__name__)
+        source_code = inspect.getsource(getattr(current_cls_module, attribute_name))
+        source_code = textwrap.dedent(source_code)
+        tree = ast.parse(source_code)
+        return tree

tests/pipelines/animatediff/test_animatediff.py

@@ -548,6 +548,14 @@ class AnimateDiffPipelineFastTests(
     def test_vae_slicing(self):
         return super().test_vae_slicing(image_count=2)
 
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "num_images_per_prompt": 1,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
+
 
 @slow
 @require_torch_accelerator

tests/pipelines/animatediff/test_animatediff_controlnet.py

@@ -517,3 +517,11 @@ class AnimateDiffControlNetPipelineFastTests(
         output_2 = pipe(**inputs)
 
         assert np.abs(output_2[0].flatten() - output_1[0].flatten()).max() < 1e-2
+
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "num_images_per_prompt": 1,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)

tests/pipelines/animatediff/test_animatediff_sdxl.py

@@ -21,7 +21,6 @@ from ..test_pipelines_common import (
     IPAdapterTesterMixin,
     PipelineTesterMixin,
     SDFunctionTesterMixin,
-    SDXLOptionalComponentsTesterMixin,
 )
 
 
@@ -36,7 +35,6 @@ class AnimateDiffPipelineSDXLFastTests(
     IPAdapterTesterMixin,
     SDFunctionTesterMixin,
     PipelineTesterMixin,
-    SDXLOptionalComponentsTesterMixin,
     unittest.TestCase,
 ):
     pipeline_class = AnimateDiffSDXLPipeline
@@ -250,33 +248,6 @@ class AnimateDiffPipelineSDXLFastTests(
         model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, "dtype")]
         self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))
 
-    def test_prompt_embeds(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.to(torch_device)
-
-        inputs = self.get_dummy_inputs(torch_device)
-        prompt = inputs.pop("prompt")
-
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = pipe.encode_prompt(prompt)
-
-        pipe(
-            **inputs,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-        )
-
-    def test_save_load_optional_components(self):
-        self._test_save_load_optional_components()
-
     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available(),
         reason="XFormers attention is only available with CUDA and `xformers` installed",
@@ -305,3 +276,11 @@ class AnimateDiffPipelineSDXLFastTests(
 
         max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
         self.assertLess(max_diff, 1e-4, "XFormers attention should not affect the inference results")
+
+    @unittest.skip("Test currently not supported.")
+    def test_encode_prompt_works_in_isolation(self):
+        pass
+
+    @unittest.skip("Functionality is tested elsewhere.")
+    def test_save_load_optional_components(self):
+        pass

tests/pipelines/animatediff/test_animatediff_sparsectrl.py

@@ -484,3 +484,11 @@ class AnimateDiffSparseControlNetPipelineFastTests(
 
     def test_vae_slicing(self):
         return super().test_vae_slicing(image_count=2)
+
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "num_images_per_prompt": 1,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)

tests/pipelines/animatediff/test_animatediff_video2video.py

@@ -544,3 +544,11 @@ class AnimateDiffVideoToVideoPipelineFastTests(
             inputs["strength"] = 0.5
             inputs["prompt"] = {0: "Caterpillar on a leaf", 10: "Butterfly on a leaf", 42: "Error on a leaf"}
             pipe(**inputs).frames[0]
+
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "num_images_per_prompt": 1,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)

tests/pipelines/animatediff/test_animatediff_video2video_controlnet.py

@@ -533,3 +533,11 @@ class AnimateDiffVideoToVideoControlNetPipelineFastTests(
             inputs["strength"] = 0.5
             inputs["prompt"] = {0: "Caterpillar on a leaf", 10: "Butterfly on a leaf", 42: "Error on a leaf"}
             pipe(**inputs).frames[0]
+
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "num_images_per_prompt": 1,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)

tests/pipelines/audioldm2/test_audioldm2.py

@@ -508,9 +508,14 @@ class AudioLDM2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, "dtype")}
         self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes.values()))
 
+    @unittest.skip("Test not supported.")
     def test_sequential_cpu_offload_forward_pass(self):
         pass
 
+    @unittest.skip("Test not supported for now because of the use of `projection_model` in `encode_prompt()`.")
+    def test_encode_prompt_works_in_isolation(self):
+        pass
+
 
 @nightly
 class AudioLDM2PipelineSlowTests(unittest.TestCase):

tests/pipelines/aura_flow/test_pipeline_aura_flow.py

@@ -5,9 +5,6 @@ import torch
 from transformers import AutoTokenizer, UMT5EncoderModel
 
 from diffusers import AuraFlowPipeline, AuraFlowTransformer2DModel, AutoencoderKL, FlowMatchEulerDiscreteScheduler
-from diffusers.utils.testing_utils import (
-    torch_device,
-)
 
 from ..test_pipelines_common import (
     PipelineTesterMixin,
@@ -90,37 +87,6 @@ class AuraFlowPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
         }
         return inputs
 
-    def test_aura_flow_prompt_embeds(self):
-        pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
-        inputs = self.get_dummy_inputs(torch_device)
-
-        output_with_prompt = pipe(**inputs).images[0]
-
-        inputs = self.get_dummy_inputs(torch_device)
-        prompt = inputs.pop("prompt")
-
-        do_classifier_free_guidance = inputs["guidance_scale"] > 1
-        (
-            prompt_embeds,
-            prompt_attention_mask,
-            negative_prompt_embeds,
-            negative_prompt_attention_mask,
-        ) = pipe.encode_prompt(
-            prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            device=torch_device,
-        )
-        output_with_embeds = pipe(
-            prompt_embeds=prompt_embeds,
-            prompt_attention_mask=prompt_attention_mask,
-            negative_prompt_embeds=negative_prompt_embeds,
-            negative_prompt_attention_mask=negative_prompt_attention_mask,
-            **inputs,
-        ).images[0]
-
-        max_diff = np.abs(output_with_prompt - output_with_embeds).max()
-        assert max_diff < 1e-4
-
     def test_attention_slicing_forward_pass(self):
         # Attention slicing needs to implemented differently for this because how single DiT and MMDiT
         # blocks interfere with each other.

tests/pipelines/blipdiffusion/test_blipdiffusion.py

@@ -198,3 +198,7 @@ class BlipDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         assert (
             np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         ), f" expected_slice {image_slice.flatten()}, but got {image_slice.flatten()}"
+
+    @unittest.skip("Test not supported because of complexities in deriving query_embeds.")
+    def test_encode_prompt_works_in_isolation(self):
+        pass

tests/pipelines/cogview3/test_cogview3plus.py

@@ -232,6 +232,9 @@ class CogView3PlusPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
                 "Attention slicing should not affect the inference results",
             )
 
+    def test_encode_prompt_works_in_isolation(self):
+        return super().test_encode_prompt_works_in_isolation(atol=1e-3, rtol=1e-3)
+
 
 @slow
 @require_torch_accelerator

tests/pipelines/controlnet/test_controlnet.py

@@ -288,6 +288,13 @@ class ControlNetPipelineFastTests(
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
+
 
 class StableDiffusionMultiControlNetPipelineFastTests(
     IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
@@ -522,6 +529,13 @@ class StableDiffusionMultiControlNetPipelineFastTests(
 
         assert image.shape == (4, 64, 64, 3)
 
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
+
 
 class StableDiffusionMultiControlNetOneModelPipelineFastTests(
     IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
@@ -707,6 +721,13 @@ class StableDiffusionMultiControlNetOneModelPipelineFastTests(
             except NotImplementedError:
                 pass
 
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
+
 
 @slow
 @require_torch_accelerator

tests/pipelines/controlnet/test_controlnet_blip_diffusion.py

@@ -222,3 +222,7 @@ class BlipDiffusionControlNetPipelineFastTests(PipelineTesterMixin, unittest.Tes
         assert (
             np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
+
+    @unittest.skip("Test not supported because of complexities in deriving query_embeds.")
+    def test_encode_prompt_works_in_isolation(self):
+        pass

tests/pipelines/controlnet/test_controlnet_img2img.py

@@ -189,6 +189,13 @@ class ControlNetImg2ImgPipelineFastTests(
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
+
 
 class StableDiffusionMultiControlNetPipelineFastTests(
     IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
@@ -391,6 +398,13 @@ class StableDiffusionMultiControlNetPipelineFastTests(
             except NotImplementedError:
                 pass
 
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
+
 
 @slow
 @require_torch_accelerator

tests/pipelines/controlnet/test_controlnet_inpaint.py

@@ -176,6 +176,13 @@ class ControlNetInpaintPipelineFastTests(
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
+
 
 class ControlNetSimpleInpaintPipelineFastTests(ControlNetInpaintPipelineFastTests):
     pipeline_class = StableDiffusionControlNetInpaintPipeline
@@ -443,6 +450,13 @@ class MultiControlNetInpaintPipelineFastTests(
             except NotImplementedError:
                 pass
 
+    def test_encode_prompt_works_in_isolation(self):
+        extra_required_param_value_dict = {
+            "device": torch.device(torch_device).type,
+            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
+        }
+        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
+
 
 @slow
 @require_torch_accelerator

tests/pipelines/controlnet/test_controlnet_sdxl.py

@@ -55,7 +55,6 @@ from ..test_pipelines_common import (
     PipelineKarrasSchedulerTesterMixin,
     PipelineLatentTesterMixin,
     PipelineTesterMixin,
-    SDXLOptionalComponentsTesterMixin,
 )
 
 
@@ -67,7 +66,6 @@ class StableDiffusionXLControlNetPipelineFastTests(
     PipelineLatentTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineTesterMixin,
-    SDXLOptionalComponentsTesterMixin,
     unittest.TestCase,
 ):
     pipeline_class = StableDiffusionXLControlNetPipeline
@@ -212,8 +210,9 @@ class StableDiffusionXLControlNetPipelineFastTests(
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
+    @unittest.skip("We test this functionality elsewhere already.")
     def test_save_load_optional_components(self):
-        self._test_save_load_optional_components()
+        pass
 
     @require_torch_accelerator
     def test_stable_diffusion_xl_offloads(self):
@@ -297,45 +296,6 @@ class StableDiffusionXLControlNetPipelineFastTests(
         # ensure the results are not equal
         assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
 
-    # Copied from test_stable_diffusion_xl.py
-    def test_stable_diffusion_xl_prompt_embeds(self):
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components)
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        # forward without prompt embeds
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["prompt"] = 2 * [inputs["prompt"]]
-        inputs["num_images_per_prompt"] = 2
-
-        output = sd_pipe(**inputs)
-        image_slice_1 = output.images[0, -3:, -3:, -1]
-
-        # forward with prompt embeds
-        inputs = self.get_dummy_inputs(torch_device)
-        prompt = 2 * [inputs.pop("prompt")]
-
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = sd_pipe.encode_prompt(prompt)
-
-        output = sd_pipe(
-            **inputs,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-        )
-        image_slice_2 = output.images[0, -3:, -3:, -1]
-
-        # make sure that it's equal
-        assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
-
     def test_controlnet_sdxl_guess(self):
         device = "cpu"
 
@@ -483,7 +443,7 @@ class StableDiffusionXLControlNetPipelineFastTests(
 
 
 class StableDiffusionXLMultiControlNetPipelineFastTests(
-    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
+    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionXLControlNetPipeline
     params = TEXT_TO_IMAGE_PARAMS
@@ -685,12 +645,13 @@ class StableDiffusionXLMultiControlNetPipelineFastTests(
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
+    @unittest.skip("We test this functionality elsewhere already.")
     def test_save_load_optional_components(self):
-        return self._test_save_load_optional_components()
+        pass
 
 
 class StableDiffusionXLMultiControlNetOneModelPipelineFastTests(
-    PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
+    PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionXLControlNetPipeline
     params = TEXT_TO_IMAGE_PARAMS
@@ -862,6 +823,10 @@ class StableDiffusionXLMultiControlNetOneModelPipelineFastTests(
     def test_attention_slicing_forward_pass(self):
         return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
 
+    @unittest.skip("We test this functionality elsewhere already.")
+    def test_save_load_optional_components(self):
+        pass
+
     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available(),
         reason="XFormers attention is only available with CUDA and `xformers` installed",
@@ -872,9 +837,6 @@ class StableDiffusionXLMultiControlNetOneModelPipelineFastTests(
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
-    def test_save_load_optional_components(self):
-        self._test_save_load_optional_components()
-
     def test_negative_conditions(self):
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)

tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py

@@ -327,42 +327,3 @@ class ControlNetPipelineSDXLImg2ImgFastTests(
 
         # ensure the results are not equal
         assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
-
-    # Copied from test_stable_diffusion_xl.py
-    def test_stable_diffusion_xl_prompt_embeds(self):
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components)
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        # forward without prompt embeds
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["prompt"] = 2 * [inputs["prompt"]]
-        inputs["num_images_per_prompt"] = 2
-
-        output = sd_pipe(**inputs)
-        image_slice_1 = output.images[0, -3:, -3:, -1]
-
-        # forward with prompt embeds
-        inputs = self.get_dummy_inputs(torch_device)
-        prompt = 2 * [inputs.pop("prompt")]
-
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = sd_pipe.encode_prompt(prompt)
-
-        output = sd_pipe(
-            **inputs,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-        )
-        image_slice_2 = output.images[0, -3:, -3:, -1]
-
-        # make sure that it's equal
-        assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4

tests/pipelines/controlnet_hunyuandit/test_controlnet_hunyuandit.py

@@ -178,6 +178,12 @@ class HunyuanDiTControlNetPipelineFastTests(unittest.TestCase, PipelineTesterMix
         # TODO(YiYi) need to fix later
         pass
 
+    @unittest.skip(
+        "Test not supported as `encode_prompt` is called two times separately which deivates from about 99% of the pipelines we have."
+    )
+    def test_encode_prompt_works_in_isolation(self):
+        pass
+
 
 @slow
 @require_torch_accelerator