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Fix Pixart Slow Tests (#6962)

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* update

* update
This commit is contained in:
Dhruv Nair
2024-02-19 09:50:18 +05:30
committed by GitHub
parent eba7e7a6d7
commit d5b8d1ca04
1 changed files with 19 additions and 15 deletions
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34
tests/pipelines/pixart_alpha/test_pixart.py
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@@ -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 = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)
self.assertLessEqual(max_diff, 1e-4)
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 = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)
self.assertLessEqual(max_diff, 1e-4)
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,