update

tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py

@@ -35,7 +35,6 @@ from diffusers.models.attention_processor import AttnProcessor, AttnProcessor2_0
 from diffusers.utils import load_image
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
-    is_flaky,
     numpy_cosine_similarity_distance,
     require_torch_gpu,
     slow,
@@ -260,7 +259,6 @@ class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
         ]
         assert processors == [True] * len(processors)
 
-    @is_flaky
     def test_multi(self):
         image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = StableDiffusionPipeline.from_pretrained(
@@ -277,7 +275,7 @@ class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
         inputs["ip_adapter_image"] = [ip_adapter_image, [ip_adapter_image] * 2]
         images = pipeline(**inputs).images
         image_slice = images[0, :3, :3, -1].flatten()
-        expected_slice = np.array([0.5234, 0.5352, 0.5625, 0.5713, 0.5947, 0.6206, 0.5786, 0.6187, 0.6494])
+        expected_slice = np.array([0.1704, 0.1296, 0.1272, 0.2212, 0.1514, 0.1479, 0.4172, 0.4263, 0.4360])
 
         max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)
         assert max_diff < 5e-4

tests/pipelines/pixart_alpha/test_pixart.py

@@ -27,7 +27,13 @@ from diffusers import (
     PixArtAlphaPipeline,
     Transformer2DModel,
 )
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    numpy_cosine_similarity_distance,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin, to_np
@@ -332,37 +338,35 @@ class PixArtAlphaPipelineIntegrationTests(unittest.TestCase):
         torch.cuda.empty_cache()
 
     def test_pixart_1024(self):
-        generator = torch.manual_seed(0)
+        generator = torch.Generator("cpu").manual_seed(0)
 
         pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16)
         pipe.enable_model_cpu_offload()
         prompt = self.prompt
 
-        image = pipe(prompt, generator=generator, output_type="np").images
+        image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
+        expected_slice = np.array([0.0742, 0.0835, 0.2114, 0.0295, 0.0784, 0.2361, 0.1738, 0.2251, 0.3589])
 
-        expected_slice = np.array([0.1941, 0.2117, 0.2188, 0.1946, 0.218, 0.2124, 0.199, 0.2437, 0.2583])
+        max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)
-
+        self.assertLessEqual(max_diff, 1e-4)
-        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
-        self.assertLessEqual(max_diff, 1e-3)
 
     def test_pixart_512(self):
-        generator = torch.manual_seed(0)
+        generator = torch.Generator("cpu").manual_seed(0)
 
         pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16)
         pipe.enable_model_cpu_offload()
 
         prompt = self.prompt
 
-        image = pipe(prompt, generator=generator, output_type="np").images
+        image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
+        expected_slice = np.array([0.3477, 0.3882, 0.4541, 0.3413, 0.3821, 0.4463, 0.4001, 0.4409, 0.4958])
 
-        expected_slice = np.array([0.2637, 0.291, 0.2939, 0.207, 0.2512, 0.2783, 0.2168, 0.2324, 0.2817])
+        max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)
-
+        self.assertLessEqual(max_diff, 1e-4)
-        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
-        self.assertLessEqual(max_diff, 1e-3)
 
     def test_pixart_1024_without_resolution_binning(self):
         generator = torch.manual_seed(0)
@@ -372,7 +376,7 @@ class PixArtAlphaPipelineIntegrationTests(unittest.TestCase):
 
         prompt = self.prompt
         height, width = 1024, 768
-        num_inference_steps = 10
+        num_inference_steps = 2
 
         image = pipe(
             prompt,
@@ -406,7 +410,7 @@ class PixArtAlphaPipelineIntegrationTests(unittest.TestCase):
 
         prompt = self.prompt
         height, width = 512, 768
-        num_inference_steps = 10
+        num_inference_steps = 2
 
         image = pipe(
             prompt,