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refactor wan animate pipeline tests to the new mixin structure #14239

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refactor wan animate pipeline tests to the new mixin structure #14239

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f462802
refactor wan animate pipeline tests to the new mixin structure
akshan-main Jul 19, 2026
b19c4cc
assert against real value slice in wan animate test_inference
akshan-main Jul 20, 2026
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127 changes: 38 additions & 89 deletions tests/pipelines/wan/test_wan_animate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,10 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import gc
import unittest

import numpy as np
import pytest
import torch
from PIL import Image
from transformers import (
Expand All @@ -34,37 +31,19 @@
WanAnimateTransformer3DModel,
)

from ...testing_utils import (
backend_empty_cache,
enable_full_determinism,
require_torch_accelerator,
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


enable_full_determinism()
from ..testing_utils import BasePipelineTesterConfig, MemoryTesterMixin, PipelineTesterMixin


class WanAnimatePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
class WanAnimatePipelineTesterConfig(BasePipelineTesterConfig):
pipeline_class = WanAnimatePipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
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",
]
required_input_params_in_call_signature = frozenset(
["prompt", "negative_prompt", "height", "width", "guidance_scale", "prompt_embeds", "negative_prompt_embeds"]
)
batch_input_params = frozenset(["prompt"])
# Wan is a video pipeline: it exposes `num_videos_per_prompt`, not the base default `num_images_per_prompt`.
optional_input_params = frozenset(
["num_inference_steps", "num_videos_per_prompt", "generator", "latents", "output_type", "return_dict"]
)
test_xformers_attention = False

def get_dummy_components(self):
torch.manual_seed(0)
Expand Down Expand Up @@ -124,7 +103,7 @@ def get_dummy_components(self):
torch.manual_seed(0)
image_processor = CLIPImageProcessor(crop_size=4, size=4)

components = {
return {
"transformer": transformer,
"vae": vae,
"scheduler": scheduler,
Expand All @@ -133,14 +112,8 @@ def get_dummy_components(self):
"image_encoder": image_encoder,
"image_processor": image_processor,
}
return components

def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)

def get_dummy_inputs(self):
num_frames = 17
height = 16
width = 16
Expand All @@ -151,7 +124,7 @@ def get_dummy_inputs(self, device, seed=0):
pose_video = [Image.new("RGB", (height, width))] * num_frames
face_video = [Image.new("RGB", (face_height, face_width))] * num_frames

inputs = {
return {
"image": image,
"pose_video": pose_video,
"face_video": face_video,
Expand All @@ -163,40 +136,36 @@ def get_dummy_inputs(self, device, seed=0):
"num_inference_steps": 2,
"mode": "animate",
"prev_segment_conditioning_frames": 1,
"generator": generator,
"generator": self.get_generator(0),
"guidance_scale": 1.0,
# Request torch outputs so tests compare torch tensors directly (see `BasePipelineTesterConfig`).
"output_type": "pt",
"max_sequence_length": 16,
}
return inputs

def test_inference(self):
"""Test basic inference in animation mode."""
device = "cpu"

components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
class TestWanAnimatePipeline(WanAnimatePipelineTesterConfig, PipelineTesterMixin):
def test_inference(self):
# Basic inference in animation mode. Run on CPU.
pipe = self.get_pipeline()

inputs = self.get_dummy_inputs(device)
inputs = self.get_dummy_inputs()
video = pipe(**inputs).frames[0]
self.assertEqual(video.shape, (17, 3, 16, 16))
assert video.shape == (17, 3, 16, 16)

expected_video = torch.randn(17, 3, 16, 16)
max_diff = np.abs(video - expected_video).max()
self.assertLessEqual(max_diff, 1e10)
# fmt: off
expected_slice = torch.tensor([0.4525, 0.4521, 0.4486, 0.4534, 0.4523, 0.4529, 0.454, 0.4533, 0.5055, 0.5203, 0.5363, 0.4827, 0.5057, 0.5176, 0.5117, 0.5139])
# fmt: on

def test_inference_replacement(self):
"""Test the pipeline in replacement mode with background and mask videos."""
device = "cpu"
generated_slice = video.flatten()
generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
assert torch.allclose(generated_slice, expected_slice, atol=1e-3)

components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
def test_inference_replacement(self):
# Replacement mode with background and mask videos. Run on CPU.
pipe = self.get_pipeline()

inputs = self.get_dummy_inputs(device)
inputs = self.get_dummy_inputs()
inputs["mode"] = "replace"
num_frames = 17
height = 16
Expand All @@ -205,36 +174,16 @@ def test_inference_replacement(self):
inputs["mask_video"] = [Image.new("L", (height, width))] * num_frames

video = pipe(**inputs).frames[0]
self.assertEqual(video.shape, (17, 3, 16, 16))

@unittest.skip("Test not supported")
def test_attention_slicing_forward_pass(self):
pass
assert video.shape == (17, 3, 16, 16)

@unittest.skip(
"Setting the Wan Animate latents to zero at the last denoising step does not guarantee that the output will be"
" zero. I believe this is because the latents are further processed in the outer loop where we loop over"
" inference segments."
@pytest.mark.skip(
reason="Setting the Wan Animate latents to zero at the last denoising step does not guarantee that the output"
" will be zero. I believe this is because the latents are further processed in the outer loop where we loop"
" over inference segments."
)
def test_callback_inputs(self):
pass


@slow
@require_torch_accelerator
class WanAnimatePipelineIntegrationTests(unittest.TestCase):
prompt = "A painting of a squirrel eating a burger."

def setUp(self):
super().setUp()
gc.collect()
backend_empty_cache(torch_device)

def tearDown(self):
super().tearDown()
gc.collect()
backend_empty_cache(torch_device)

@unittest.skip("TODO: test needs to be implemented")
def test_wan_animate(self):
pass
class TestWanAnimatePipelineMemory(WanAnimatePipelineTesterConfig, MemoryTesterMixin):
pass
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