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# Copyright 2025 The HuggingFace Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import AutoTokenizer, T5EncoderModel
from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler, WanVACEPipeline, WanVACETransformer3DModel
from diffusers.utils.testing_utils import enable_full_determinism
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()
class WanVACEPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = WanVACEPipeline
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",
]
)
test_xformers_attention = False
supports_dduf = False
def get_dummy_components(self):
torch.manual_seed(0)
vae = AutoencoderKLWan(
base_dim=3,
z_dim=16,
dim_mult=[1, 1, 1, 1],
num_res_blocks=1,
temperal_downsample=[False, True, True],
)
torch.manual_seed(0)
scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)
text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
transformer = WanVACETransformer3DModel(
patch_size=(1, 2, 2),
num_attention_heads=2,
attention_head_dim=12,
in_channels=16,
out_channels=16,
text_dim=32,
freq_dim=256,
ffn_dim=32,
num_layers=3,
cross_attn_norm=True,
qk_norm="rms_norm_across_heads",
rope_max_seq_len=32,
vace_layers=[0, 2],
vace_in_channels=96,
)
components = {
"transformer": transformer,
"vae": vae,
"scheduler": scheduler,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
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)
num_frames = 17
height = 16
width = 16
video = [Image.new("RGB", (height, width))] * num_frames
mask = [Image.new("L", (height, width), 0)] * num_frames
inputs = {
"video": video,
"mask": mask,
"prompt": "dance monkey",
"negative_prompt": "negative",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"height": 16,
"width": 16,
"num_frames": num_frames,
"max_sequence_length": 16,
"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[0]
self.assertEqual(video.shape, (17, 3, 16, 16))
# fmt: off
expected_slice = [0.4523, 0.45198, 0.44872, 0.45326, 0.45211, 0.45258, 0.45344, 0.453, 0.52431, 0.52572, 0.50701, 0.5118, 0.53717, 0.53093, 0.50557, 0.51402]
# fmt: on
video_slice = video.flatten()
video_slice = torch.cat([video_slice[:8], video_slice[-8:]])
video_slice = [round(x, 5) for x in video_slice.tolist()]
self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3))
def test_inference_with_single_reference_image(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)
inputs["reference_images"] = Image.new("RGB", (16, 16))
video = pipe(**inputs).frames[0]
self.assertEqual(video.shape, (17, 3, 16, 16))
# fmt: off
expected_slice = [0.45247, 0.45214, 0.44874, 0.45314, 0.45171, 0.45299, 0.45428, 0.45317, 0.51378, 0.52658, 0.53361, 0.52303, 0.46204, 0.50435, 0.52555, 0.51342]
# fmt: on
video_slice = video.flatten()
video_slice = torch.cat([video_slice[:8], video_slice[-8:]])
video_slice = [round(x, 5) for x in video_slice.tolist()]
self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3))
def test_inference_with_multiple_reference_image(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)
inputs["reference_images"] = [[Image.new("RGB", (16, 16))] * 2]
video = pipe(**inputs).frames[0]
self.assertEqual(video.shape, (17, 3, 16, 16))
# fmt: off
expected_slice = [0.45321, 0.45221, 0.44818, 0.45375, 0.45268, 0.4519, 0.45271, 0.45253, 0.51244, 0.52223, 0.51253, 0.51321, 0.50743, 0.51177, 0.51626, 0.50983]
# fmt: on
video_slice = video.flatten()
video_slice = torch.cat([video_slice[:8], video_slice[-8:]])
video_slice = [round(x, 5) for x in video_slice.tolist()]
self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3))
@unittest.skip("Test not supported")
def test_attention_slicing_forward_pass(self):
pass
@unittest.skip("Errors out because passing multiple prompts at once is not yet supported by this pipeline.")
def test_encode_prompt_works_in_isolation(self):
pass
@unittest.skip("Batching is not yet supported with this pipeline")
def test_inference_batch_consistent(self):
pass
@unittest.skip("Batching is not yet supported with this pipeline")
def test_inference_batch_single_identical(self):
return super().test_inference_batch_single_identical()
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