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[tests] Add test slices for Wan (#11920)

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* update

* fix wan vace test slice

* test

* fix
This commit is contained in:
Aryan
2025-07-23 17:23:52 +05:30
committed by GitHub
parent ef1e628729
commit 178d32dedd
3 changed files with 73 additions and 19 deletions
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17
tests/pipelines/wan/test_wan.py
View File

@@ -15,7 +15,6 @@
import gc
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, T5EncoderModel
@@ -29,9 +28,7 @@ from diffusers.utils.testing_utils import (
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineTesterMixin,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@@ -127,11 +124,15 @@ class WanPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
inputs = self.get_dummy_inputs(device)
video = pipe(**inputs).frames
generated_video = video[0]
self.assertEqual(generated_video.shape, (9, 3, 16, 16))
expected_video = torch.randn(9, 3, 16, 16)
max_diff = np.abs(generated_video - expected_video).max()
self.assertLessEqual(max_diff, 1e10)
# fmt: off
expected_slice = torch.tensor([0.4525, 0.452, 0.4485, 0.4534, 0.4524, 0.4529, 0.454, 0.453, 0.5127, 0.5326, 0.5204, 0.5253, 0.5439, 0.5424, 0.5133, 0.5078])
# fmt: on
generated_slice = generated_video.flatten()
generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3))
@unittest.skip("Test not supported")
def test_attention_slicing_forward_pass(self):

62
tests/pipelines/wan/test_wan_image_to_video.py
View File

@@ -14,7 +14,6 @@
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import (
@@ -147,11 +146,15 @@ class WanImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
inputs = self.get_dummy_inputs(device)
video = pipe(**inputs).frames
generated_video = video[0]
self.assertEqual(generated_video.shape, (9, 3, 16, 16))
expected_video = torch.randn(9, 3, 16, 16)
max_diff = np.abs(generated_video - expected_video).max()
self.assertLessEqual(max_diff, 1e10)
# fmt: off
expected_slice = torch.tensor([0.4525, 0.4525, 0.4497, 0.4536, 0.452, 0.4529, 0.454, 0.4535, 0.5072, 0.5527, 0.5165, 0.5244, 0.5481, 0.5282, 0.5208, 0.5214])
# fmt: on
generated_slice = generated_video.flatten()
generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3))
@unittest.skip("Test not supported")
def test_attention_slicing_forward_pass(self):
@@ -162,7 +165,25 @@ class WanImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pass
class WanFLFToVideoPipelineFastTests(WanImageToVideoPipelineFastTests):
class WanFLFToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = WanImageToVideoPipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs", "height", "width"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
required_optional_params = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback_on_step_end",
"callback_on_step_end_tensor_inputs",
]
)
test_xformers_attention = False
supports_dduf = False
def get_dummy_components(self):
torch.manual_seed(0)
vae = AutoencoderKLWan(
@@ -247,3 +268,32 @@ class WanFLFToVideoPipelineFastTests(WanImageToVideoPipelineFastTests):
"output_type": "pt",
}
return inputs
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
video = pipe(**inputs).frames
generated_video = video[0]
self.assertEqual(generated_video.shape, (9, 3, 16, 16))
# fmt: off
expected_slice = torch.tensor([0.4531, 0.4527, 0.4498, 0.4542, 0.4526, 0.4527, 0.4534, 0.4534, 0.5061, 0.5185, 0.5283, 0.5181, 0.5309, 0.5365, 0.5113, 0.5244])
# fmt: on
generated_slice = generated_video.flatten()
generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3))
@unittest.skip("Test not supported")
def test_attention_slicing_forward_pass(self):
pass
@unittest.skip("TODO: revisit failing as it requires a very high threshold to pass")
def test_inference_batch_single_identical(self):
pass

13
tests/pipelines/wan/test_wan_video_to_video.py
View File

@@ -14,7 +14,6 @@
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import AutoTokenizer, T5EncoderModel
@@ -123,11 +122,15 @@ class WanVideoToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
inputs = self.get_dummy_inputs(device)
video = pipe(**inputs).frames
generated_video = video[0]
self.assertEqual(generated_video.shape, (17, 3, 16, 16))
expected_video = torch.randn(17, 3, 16, 16)
max_diff = np.abs(generated_video - expected_video).max()
self.assertLessEqual(max_diff, 1e10)
# fmt: off
expected_slice = torch.tensor([0.4522, 0.4534, 0.4532, 0.4553, 0.4526, 0.4538, 0.4533, 0.4547, 0.513, 0.5176, 0.5286, 0.4958, 0.4955, 0.5381, 0.5154, 0.5195])
# fmt:on
generated_slice = generated_video.flatten()
generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3))
@unittest.skip("Test not supported")
def test_attention_slicing_forward_pass(self):