fixes

Author: sayakpaul

tests/pipelines/controlnet/test_controlnet_sdxl.py

@@ -210,6 +210,10 @@ def test_xformers_attention_forwardGenerator_pass(self):
     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):
+        pass
+
     @require_torch_accelerator
     def test_stable_diffusion_xl_offloads(self):
         pipes = []
@@ -641,9 +645,6 @@ def test_xformers_attention_forwardGenerator_pass(self):
     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):
-        return self._test_save_load_optional_components()
-
 
 class StableDiffusionXLMultiControlNetOneModelPipelineFastTests(
     PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
@@ -818,6 +819,10 @@ def test_control_guidance_switch(self):
     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",

tests/pipelines/controlnet_xs/test_controlnetxs_sdxl.py

@@ -199,6 +199,10 @@ def test_xformers_attention_forwardGenerator_pass(self):
     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):
+        pass
+
     @require_torch_accelerator
     # Copied from test_controlnet_sdxl.py
     def test_stable_diffusion_xl_offloads(self):

tests/pipelines/lumina2/test_pipeline_lumina2.py

@@ -1,6 +1,5 @@
 import unittest
 
-import numpy as np
 import torch
 from transformers import AutoTokenizer, GemmaConfig, GemmaForCausalLM
 
@@ -10,7 +9,6 @@
     Lumina2Text2ImgPipeline,
     Lumina2Transformer2DModel,
 )
-from diffusers.utils.testing_utils import torch_device
 
 from ..test_pipelines_common import PipelineTesterMixin
 
@@ -116,32 +114,3 @@ def get_dummy_inputs(self, device, seed=0):
             "output_type": "np",
         }
         return inputs
-
-    def test_lumina_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,
-            **inputs,
-        ).images[0]
-
-        max_diff = np.abs(output_with_prompt - output_with_embeds).max()
-        assert max_diff < 1e-4

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py

@@ -294,6 +294,10 @@ def test_stable_diffusion_xl_offloads(self):
         assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
         assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
 
+    @unittest.skip("We test this functionality elsewhere already.")
+    def test_save_load_optional_components(self):
+        pass
+
     def test_stable_diffusion_two_xl_mixture_of_denoiser_fast(self):
         components = self.get_dummy_components()
         pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device)

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py

@@ -370,9 +370,6 @@ def test_total_downscale_factor(self, adapter_type):
             expected_out_image_size,
         )
 
-    def test_save_load_optional_components(self):
-        return self._test_save_load_optional_components()
-
     def test_adapter_sdxl_lcm(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
 
@@ -512,6 +509,10 @@ def test_inference_batch_consistent(
 
         logger.setLevel(level=diffusers.logging.WARNING)
 
+    @unittest.skip("We test this functionality elsewhere already.")
+    def test_save_load_optional_components(self):
+        pass
+
     def test_num_images_per_prompt(self):
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)