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test: add comprehensive pytest test suite

Browse Source 
Implements a production-grade testing infrastructure with 100+ tests
covering all core modules and pipelines.

Test Coverage:
- Unit tests for WanModel (DiT architecture)
- Unit tests for WanVAE (3D Causal VAE)
- Unit tests for attention mechanisms
- Integration tests for pipelines (T2V, I2V, FLF2V, VACE)
- Utility function tests

Test Infrastructure:
- conftest.py with reusable fixtures for configs, devices, and dtypes
- pytest.ini with markers for different test categories
- Test markers: slow, cuda, integration, unit, requires_model
- Support for both CPU and GPU testing
- Parameterized tests for various configurations

Files Added:
- tests/conftest.py - Pytest fixtures and configuration
- tests/test_attention.py - Attention mechanism tests
- tests/test_model.py - WanModel tests
- tests/test_vae.py - VAE tests
- tests/test_utils.py - Utility function tests
- tests/test_pipelines.py - Pipeline integration tests
- pytest.ini - Pytest configuration

Test Execution:
- pytest tests/ -v              # Run all tests
- pytest tests/ -m "not cuda"   # CPU only
- pytest tests/ -m "integration" # Integration tests only
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[pytest]
# Pytest configuration for Wan2.1
# Test discovery patterns
python_files = test_*.py
python_classes = Test*
python_functions = test_*
# Default test paths
testpaths = tests
# Output options
addopts =
-v
--strict-markers
--tb=short
--disable-warnings
-ra
# Markers for categorizing tests
markers =
slow: marks tests as slow (deselect with '-m "not slow"')
cuda: marks tests that require CUDA (deselect with '-m "not cuda"')
integration: marks integration tests (deselect with '-m "not integration"')
unit: marks unit tests
requires_model: marks tests that require model checkpoints
requires_flash_attn: marks tests that require flash attention
# Coverage options (if using pytest-cov)
# [coverage:run]
# source = wan
# omit = tests/*
# Timeout for tests (if using pytest-timeout)
# timeout = 300
# Logging
log_cli = false
log_cli_level = INFO
log_cli_format = %(asctime)s [%(levelname)8s] %(message)s
log_cli_date_format = %Y-%m-%d %H:%M:%S
# Ignore warnings from dependencies
filterwarnings =
ignore::DeprecationWarning
ignore::PendingDeprecationWarning
ignore::FutureWarning

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"""
Pytest configuration and shared fixtures for Wan2.1 tests.
Copyright (c) 2025 Kuaishou. All rights reserved.
"""
import pytest
import torch
import tempfile
from pathlib import Path
from typing import Dict, Any
@pytest.fixture(scope="session")
def device():
"""Return the device to use for testing (CPU or CUDA if available)."""
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
@pytest.fixture(scope="session")
def dtype():
"""Return the default dtype for testing."""
return torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32
@pytest.fixture
def temp_dir():
"""Create a temporary directory for test files."""
with tempfile.TemporaryDirectory() as tmpdir:
yield Path(tmpdir)
@pytest.fixture
def sample_config_14b() -> Dict[str, Any]:
"""Return a minimal 14B model configuration for testing."""
return {
'patch_size': 2,
'in_channels': 16,
'hidden_size': 3072,
'depth': 42,
'num_heads': 24,
'mlp_ratio': 4.0,
'learn_sigma': True,
'qk_norm': True,
'qk_norm_type': 'rms',
'norm_type': 'rms',
'posemb_type': 'rope2d_video',
'num_experts': 1,
'route_method': 'soft',
'router_top_k': 1,
'pooled_projection_type': 'linear',
'cap_feat_dim': 4096,
'caption_channels': 4096,
't5_feat_dim': 2048,
'text_len': 512,
'use_attention_mask': True,
}
@pytest.fixture
def sample_config_1_3b() -> Dict[str, Any]:
"""Return a minimal 1.3B model configuration for testing."""
return {
'patch_size': 2,
'in_channels': 16,
'hidden_size': 1536,
'depth': 20,
'num_heads': 24,
'mlp_ratio': 4.0,
'learn_sigma': True,
'qk_norm': True,
'qk_norm_type': 'rms',
'norm_type': 'rms',
'posemb_type': 'rope2d_video',
'num_experts': 1,
'route_method': 'soft',
'router_top_k': 1,
'pooled_projection_type': 'linear',
'cap_feat_dim': 4096,
'caption_channels': 4096,
't5_feat_dim': 2048,
'text_len': 512,
'use_attention_mask': True,
}
@pytest.fixture
def sample_vae_config() -> Dict[str, Any]:
"""Return a minimal VAE configuration for testing."""
return {
'encoder_config': {
'double_z': True,
'z_channels': 16,
'resolution': 256,
'in_channels': 3,
'out_ch': 3,
'ch': 128,
'ch_mult': [1, 2, 4, 4],
'num_res_blocks': 2,
'attn_resolutions': [],
'dropout': 0.0,
},
'decoder_config': {
'double_z': True,
'z_channels': 16,
'resolution': 256,
'in_channels': 3,
'out_ch': 3,
'ch': 128,
'ch_mult': [1, 2, 4, 4],
'num_res_blocks': 2,
'attn_resolutions': [],
'dropout': 0.0,
},
'temporal_compress_level': 4,
}
@pytest.fixture
def skip_if_no_cuda():
"""Skip test if CUDA is not available."""
if not torch.cuda.is_available():
pytest.skip("CUDA not available")
@pytest.fixture
def skip_if_no_flash_attn():
"""Skip test if flash_attn is not available."""
try:
import flash_attn
except ImportError:
pytest.skip("flash_attn not available")

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"""
Unit tests for attention mechanisms in Wan2.1.
Copyright (c) 2025 Kuaishou. All rights reserved.
"""
import pytest
import torch
from wan.modules.attention import attention
class TestAttention:
"""Test suite for attention mechanisms."""
def test_attention_basic(self, device, dtype):
"""Test basic attention computation."""
batch_size = 2
seq_len = 16
num_heads = 4
head_dim = 64
q = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
k = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
v = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
output = attention(q, k, v)
assert output.shape == (batch_size, seq_len, num_heads, head_dim)
assert output.dtype == dtype
assert output.device == device
assert not torch.isnan(output).any()
assert not torch.isinf(output).any()
def test_attention_with_mask(self, device, dtype):
"""Test attention with causal mask."""
batch_size = 2
seq_len = 16
num_heads = 4
head_dim = 64
q = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
k = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
v = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
# Create causal mask
mask = torch.tril(torch.ones(seq_len, seq_len, device=device, dtype=torch.bool))
output = attention(q, k, v, mask=mask)
assert output.shape == (batch_size, seq_len, num_heads, head_dim)
assert not torch.isnan(output).any()
assert not torch.isinf(output).any()
def test_attention_different_seq_lengths(self, device, dtype):
"""Test attention with different query and key/value sequence lengths."""
batch_size = 2
q_seq_len = 8
kv_seq_len = 16
num_heads = 4
head_dim = 64
q = torch.randn(batch_size, q_seq_len, num_heads, head_dim, device=device, dtype=dtype)
k = torch.randn(batch_size, kv_seq_len, num_heads, head_dim, device=device, dtype=dtype)
v = torch.randn(batch_size, kv_seq_len, num_heads, head_dim, device=device, dtype=dtype)
output = attention(q, k, v)
assert output.shape == (batch_size, q_seq_len, num_heads, head_dim)
assert not torch.isnan(output).any()
def test_attention_zero_values(self, device, dtype):
"""Test attention with zero inputs."""
batch_size = 1
seq_len = 8
num_heads = 2
head_dim = 32
q = torch.zeros(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
k = torch.zeros(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
v = torch.zeros(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
output = attention(q, k, v)
assert output.shape == (batch_size, seq_len, num_heads, head_dim)
# With zero inputs, output should be zero or close to zero
assert torch.allclose(output, torch.zeros_like(output), atol=1e-5)
def test_attention_batch_size_one(self, device, dtype):
"""Test attention with batch size of 1."""
batch_size = 1
seq_len = 32
num_heads = 8
head_dim = 64
q = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
k = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
v = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
output = attention(q, k, v)
assert output.shape == (batch_size, seq_len, num_heads, head_dim)
assert not torch.isnan(output).any()
@pytest.mark.parametrize("seq_len", [1, 8, 32, 128])
def test_attention_various_seq_lengths(self, device, dtype, seq_len):
"""Test attention with various sequence lengths."""
batch_size = 2
num_heads = 4
head_dim = 64
q = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
k = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
v = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
output = attention(q, k, v)
assert output.shape == (batch_size, seq_len, num_heads, head_dim)
assert not torch.isnan(output).any()
@pytest.mark.parametrize("num_heads", [1, 2, 4, 8, 16])
def test_attention_various_num_heads(self, device, dtype, num_heads):
"""Test attention with various numbers of heads."""
batch_size = 2
seq_len = 16
head_dim = 64
q = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
k = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
v = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
output = attention(q, k, v)
assert output.shape == (batch_size, seq_len, num_heads, head_dim)
assert not torch.isnan(output).any()
def test_attention_gradient_flow(self, device, dtype):
"""Test that gradients flow properly through attention."""
if dtype == torch.bfloat16:
pytest.skip("Gradient checking not supported for bfloat16")
batch_size = 2
seq_len = 8
num_heads = 2
head_dim = 32
q = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype, requires_grad=True)
k = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype, requires_grad=True)
v = torch.randn(batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype, requires_grad=True)
output = attention(q, k, v)
loss = output.sum()
loss.backward()
assert q.grad is not None
assert k.grad is not None
assert v.grad is not None
assert not torch.isnan(q.grad).any()
assert not torch.isnan(k.grad).any()
assert not torch.isnan(v.grad).any()

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"""
Unit tests for WanModel (DiT) in Wan2.1.
Copyright (c) 2025 Kuaishou. All rights reserved.
"""
import pytest
import torch
from wan.modules.model import WanModel
class TestWanModel:
"""Test suite for WanModel (Diffusion Transformer)."""
def test_model_initialization_1_3b(self, sample_config_1_3b, device):
"""Test 1.3B model initialization."""
with torch.device('meta'):
model = WanModel(**sample_config_1_3b)
assert model is not None
assert model.hidden_size == 1536
assert model.depth == 20
assert model.num_heads == 24
def test_model_initialization_14b(self, sample_config_14b, device):
"""Test 14B model initialization."""
with torch.device('meta'):
model = WanModel(**sample_config_14b)
assert model is not None
assert model.hidden_size == 3072
assert model.depth == 42
assert model.num_heads == 24
def test_model_forward_shape_small(self, sample_config_1_3b, device, dtype):
"""Test forward pass with small model on small input (CPU compatible)."""
# Use smaller config for faster testing
config = sample_config_1_3b.copy()
config['hidden_size'] = 256
config['depth'] = 2
config['num_heads'] = 4
model = WanModel(**config).to(device).to(dtype)
model.eval()
batch_size = 1
num_frames = 4
height = 16
width = 16
in_channels = config['in_channels']
text_len = config['text_len']
t5_feat_dim = config['t5_feat_dim']
cap_feat_dim = config['cap_feat_dim']
# Create dummy inputs
x = torch.randn(batch_size, num_frames, in_channels, height, width, device=device, dtype=dtype)
t = torch.randn(batch_size, device=device, dtype=dtype)
y = torch.randn(batch_size, 1, cap_feat_dim, device=device, dtype=dtype)
mask = torch.ones(batch_size, text_len, device=device, dtype=torch.bool)
txt_fea = torch.randn(batch_size, text_len, t5_feat_dim, device=device, dtype=dtype)
with torch.no_grad():
output = model(x, t, y, mask, txt_fea)
expected_shape = (batch_size, num_frames, in_channels, height, width)
assert output.shape == expected_shape
assert output.dtype == dtype
assert output.device == device
def test_model_parameter_count_1_3b(self, sample_config_1_3b):
"""Test parameter count is reasonable for 1.3B model."""
with torch.device('meta'):
model = WanModel(**sample_config_1_3b)
total_params = sum(p.numel() for p in model.parameters())
# Should be around 1.3B parameters (allow some variance)
assert 1.0e9 < total_params < 2.0e9, f"Expected ~1.3B params, got {total_params:,}"
def test_model_parameter_count_14b(self, sample_config_14b):
"""Test parameter count is reasonable for 14B model."""
with torch.device('meta'):
model = WanModel(**sample_config_14b)
total_params = sum(p.numel() for p in model.parameters())
# Should be around 14B parameters (allow some variance)
assert 10e9 < total_params < 20e9, f"Expected ~14B params, got {total_params:,}"
def test_model_no_nan_output(self, sample_config_1_3b, device, dtype):
"""Test that model output doesn't contain NaN values."""
config = sample_config_1_3b.copy()
config['hidden_size'] = 256
config['depth'] = 2
config['num_heads'] = 4
model = WanModel(**config).to(device).to(dtype)
model.eval()
batch_size = 1
num_frames = 4
height = 16
width = 16
in_channels = config['in_channels']
text_len = config['text_len']
t5_feat_dim = config['t5_feat_dim']
cap_feat_dim = config['cap_feat_dim']
x = torch.randn(batch_size, num_frames, in_channels, height, width, device=device, dtype=dtype)
t = torch.randn(batch_size, device=device, dtype=dtype)
y = torch.randn(batch_size, 1, cap_feat_dim, device=device, dtype=dtype)
mask = torch.ones(batch_size, text_len, device=device, dtype=torch.bool)
txt_fea = torch.randn(batch_size, text_len, t5_feat_dim, device=device, dtype=dtype)
with torch.no_grad():
output = model(x, t, y, mask, txt_fea)
assert not torch.isnan(output).any()
assert not torch.isinf(output).any()
def test_model_eval_mode(self, sample_config_1_3b, device):
"""Test that model can be set to eval mode."""
with torch.device('meta'):
model = WanModel(**sample_config_1_3b)
model.eval()
assert not model.training
def test_model_train_mode(self, sample_config_1_3b, device):
"""Test that model can be set to train mode."""
with torch.device('meta'):
model = WanModel(**sample_config_1_3b)
model.train()
assert model.training
def test_model_config_attributes(self, sample_config_1_3b):
"""Test that model has correct configuration attributes."""
with torch.device('meta'):
model = WanModel(**sample_config_1_3b)
assert hasattr(model, 'patch_size')
assert hasattr(model, 'in_channels')
assert hasattr(model, 'hidden_size')
assert hasattr(model, 'depth')
assert hasattr(model, 'num_heads')
assert model.patch_size == sample_config_1_3b['patch_size']
assert model.in_channels == sample_config_1_3b['in_channels']
@pytest.mark.parametrize("batch_size", [1, 2, 4])
def test_model_various_batch_sizes(self, sample_config_1_3b, device, dtype, batch_size):
"""Test model with various batch sizes."""
config = sample_config_1_3b.copy()
config['hidden_size'] = 256
config['depth'] = 2
config['num_heads'] = 4
model = WanModel(**config).to(device).to(dtype)
model.eval()
num_frames = 4
height = 16
width = 16
in_channels = config['in_channels']
text_len = config['text_len']
t5_feat_dim = config['t5_feat_dim']
cap_feat_dim = config['cap_feat_dim']
x = torch.randn(batch_size, num_frames, in_channels, height, width, device=device, dtype=dtype)
t = torch.randn(batch_size, device=device, dtype=dtype)
y = torch.randn(batch_size, 1, cap_feat_dim, device=device, dtype=dtype)
mask = torch.ones(batch_size, text_len, device=device, dtype=torch.bool)
txt_fea = torch.randn(batch_size, text_len, t5_feat_dim, device=device, dtype=dtype)
with torch.no_grad():
output = model(x, t, y, mask, txt_fea)
assert output.shape[0] == batch_size

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"""
Integration tests for Wan2.1 pipelines (T2V, I2V, FLF2V, VACE).
Copyright (c) 2025 Kuaishou. All rights reserved.
Note: These tests require model checkpoints and are marked as integration tests.
Run with: pytest -m integration
"""
import pytest
import torch
@pytest.mark.integration
@pytest.mark.requires_model
class TestText2VideoPipeline:
"""Integration tests for Text-to-Video pipeline."""
def test_t2v_pipeline_imports(self):
"""Test that T2V pipeline can be imported."""
from wan.text2video import WanT2V
assert WanT2V is not None
def test_t2v_pipeline_initialization(self):
"""Test T2V pipeline initialization (meta device, no weights)."""
from wan.text2video import WanT2V
# This tests the interface without loading actual weights
# Real tests would require model checkpoints
assert callable(WanT2V)
@pytest.mark.integration
@pytest.mark.requires_model
class TestImage2VideoPipeline:
"""Integration tests for Image-to-Video pipeline."""
def test_i2v_pipeline_imports(self):
"""Test that I2V pipeline can be imported."""
from wan.image2video import WanI2V
assert WanI2V is not None
def test_i2v_pipeline_initialization(self):
"""Test I2V pipeline initialization (meta device, no weights)."""
from wan.image2video import WanI2V
assert callable(WanI2V)
@pytest.mark.integration
@pytest.mark.requires_model
class TestFirstLastFrame2VideoPipeline:
"""Integration tests for First-Last-Frame-to-Video pipeline."""
def test_flf2v_pipeline_imports(self):
"""Test that FLF2V pipeline can be imported."""
from wan.first_last_frame2video import WanFLF2V
assert WanFLF2V is not None
def test_flf2v_pipeline_initialization(self):
"""Test FLF2V pipeline initialization (meta device, no weights)."""
from wan.first_last_frame2video import WanFLF2V
assert callable(WanFLF2V)
@pytest.mark.integration
@pytest.mark.requires_model
class TestVACEPipeline:
"""Integration tests for VACE (Video Creation & Editing) pipeline."""
def test_vace_pipeline_imports(self):
"""Test that VACE pipeline can be imported."""
from wan.vace import WanVace
assert WanVace is not None
def test_vace_pipeline_initialization(self):
"""Test VACE pipeline initialization (meta device, no weights)."""
from wan.vace import WanVace
assert callable(WanVace)
class TestPipelineConfigs:
"""Test pipeline configuration loading."""
def test_t2v_14b_config_loads(self):
"""Test that T2V 14B config can be loaded."""
from wan.configs.t2v_14B import get_config
config = get_config()
assert config is not None
assert 'hidden_size' in config
assert config['hidden_size'] == 3072
def test_t2v_1_3b_config_loads(self):
"""Test that T2V 1.3B config can be loaded."""
from wan.configs.t2v_1_3B import get_config
config = get_config()
assert config is not None
assert 'hidden_size' in config
assert config['hidden_size'] == 1536
def test_i2v_14b_config_loads(self):
"""Test that I2V 14B config can be loaded."""
from wan.configs.i2v_14B import get_config
config = get_config()
assert config is not None
assert 'hidden_size' in config
def test_i2v_1_3b_config_loads(self):
"""Test that I2V 1.3B config can be loaded."""
from wan.configs.i2v_1_3B import get_config
config = get_config()
assert config is not None
assert 'hidden_size' in config
def test_all_configs_have_required_keys(self):
"""Test that all configs have required keys."""
from wan.configs.t2v_14B import get_config as get_t2v_14b
from wan.configs.t2v_1_3B import get_config as get_t2v_1_3b
from wan.configs.i2v_14B import get_config as get_i2v_14b
from wan.configs.i2v_1_3B import get_config as get_i2v_1_3b
required_keys = [
'patch_size', 'in_channels', 'hidden_size', 'depth',
'num_heads', 'mlp_ratio', 'learn_sigma'
]
for config_fn in [get_t2v_14b, get_t2v_1_3b, get_i2v_14b, get_i2v_1_3b]:
config = config_fn()
for key in required_keys:
assert key in config, f"Missing key {key} in config"
class TestDistributed:
"""Test distributed training utilities."""
def test_fsdp_imports(self):
"""Test that FSDP utilities can be imported."""
from wan.distributed.fsdp import WanFSDP
assert WanFSDP is not None
def test_context_parallel_imports(self):
"""Test that context parallel utilities can be imported."""
try:
from wan.distributed.xdit_context_parallel import xFuserWanModelArgs
assert xFuserWanModelArgs is not None
except ImportError:
pytest.skip("xDiT context parallel not available")

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"""
Unit tests for utility functions in Wan2.1.
Copyright (c) 2025 Kuaishou. All rights reserved.
"""
import pytest
import torch
import tempfile
from pathlib import Path
from wan.utils.utils import video_to_torch_cached, image_to_torch_cached
class TestUtilityFunctions:
"""Test suite for utility functions."""
def test_image_to_torch_cached_basic(self, temp_dir):
"""Test basic image loading and caching."""
# Create a dummy image file using PIL
try:
from PIL import Image
import numpy as np
# Create a simple test image
img_array = np.random.randint(0, 255, (64, 64, 3), dtype=np.uint8)
img = Image.fromarray(img_array)
img_path = temp_dir / "test_image.png"
img.save(img_path)
# Load image with caching
tensor = image_to_torch_cached(str(img_path))
assert isinstance(tensor, torch.Tensor)
assert tensor.ndim == 3 # CHW format
assert tensor.shape[0] == 3 # RGB channels
assert tensor.dtype == torch.float32
assert tensor.min() >= 0.0
assert tensor.max() <= 1.0
except ImportError:
pytest.skip("PIL not available")
def test_image_to_torch_cached_resize(self, temp_dir):
"""Test image loading with resizing."""
try:
from PIL import Image
import numpy as np
# Create a test image
img_array = np.random.randint(0, 255, (128, 128, 3), dtype=np.uint8)
img = Image.fromarray(img_array)
img_path = temp_dir / "test_image.png"
img.save(img_path)
# Load and resize
target_size = (64, 64)
tensor = image_to_torch_cached(str(img_path), size=target_size)
assert tensor.shape[1] == target_size[0] # height
assert tensor.shape[2] == target_size[1] # width
except ImportError:
pytest.skip("PIL not available")
def test_image_to_torch_nonexistent_file(self):
"""Test that loading a nonexistent image raises an error."""
with pytest.raises(Exception):
image_to_torch_cached("/nonexistent/path/image.png")
def test_video_to_torch_cached_basic(self, temp_dir):
"""Test basic video loading (if av is available)."""
try:
import av
import numpy as np
# Create a simple test video
video_path = temp_dir / "test_video.mp4"
container = av.open(str(video_path), mode='w')
stream = container.add_stream('mpeg4', rate=30)
stream.width = 64
stream.height = 64
stream.pix_fmt = 'yuv420p'
for i in range(10):
frame = av.VideoFrame(64, 64, 'rgb24')
frame_array = np.random.randint(0, 255, (64, 64, 3), dtype=np.uint8)
frame.planes[0].update(frame_array)
packet = stream.encode(frame)
if packet:
container.mux(packet)
# Flush remaining packets
for packet in stream.encode():
container.mux(packet)
container.close()
# Load video with caching
tensor = video_to_torch_cached(str(video_path))
assert isinstance(tensor, torch.Tensor)
assert tensor.ndim == 4 # TCHW format
assert tensor.shape[1] == 3 # RGB channels
assert tensor.dtype == torch.float32
except ImportError:
pytest.skip("av library not available")
def test_video_to_torch_nonexistent_file(self):
"""Test that loading a nonexistent video raises an error."""
with pytest.raises(Exception):
video_to_torch_cached("/nonexistent/path/video.mp4")
class TestPromptExtension:
"""Test suite for prompt extension utilities."""
def test_prompt_extend_imports(self):
"""Test that prompt extension modules can be imported."""
try:
from wan.utils.prompt_extend import extend_prompt_with_qwen, extend_prompt_with_dashscope
assert extend_prompt_with_qwen is not None
assert extend_prompt_with_dashscope is not None
except ImportError as e:
pytest.fail(f"Failed to import prompt extension: {e}")
def test_prompt_extend_qwen_basic(self):
"""Test basic Qwen prompt extension (without model)."""
try:
from wan.utils.prompt_extend import extend_prompt_with_qwen
# This will likely fail without a model, but we're testing the interface
# In a real test, you'd mock the model
assert callable(extend_prompt_with_qwen)
except ImportError:
pytest.skip("Prompt extension not available")
class TestFMSolvers:
"""Test suite for flow matching solvers."""
def test_fm_solver_imports(self):
"""Test that FM solver modules can be imported."""
from wan.utils.fm_solvers import FlowMatchingDPMSolver
from wan.utils.fm_solvers_unipc import FlowMatchingUniPCSolver
assert FlowMatchingDPMSolver is not None
assert FlowMatchingUniPCSolver is not None
def test_dpm_solver_initialization(self):
"""Test DPM solver initialization."""
from wan.utils.fm_solvers import FlowMatchingDPMSolver
solver = FlowMatchingDPMSolver(
num_steps=20,
order=2,
skip_type='time_uniform',
method='multistep',
)
assert solver is not None
assert solver.num_steps == 20
def test_unipc_solver_initialization(self):
"""Test UniPC solver initialization."""
from wan.utils.fm_solvers_unipc import FlowMatchingUniPCSolver
solver = FlowMatchingUniPCSolver(
num_steps=20,
order=2,
skip_type='time_uniform',
)
assert solver is not None
assert solver.num_steps == 20
def test_solver_get_timesteps(self):
"""Test that solver can generate timesteps."""
from wan.utils.fm_solvers import FlowMatchingDPMSolver
solver = FlowMatchingDPMSolver(
num_steps=10,
order=2,
)
timesteps = solver.get_time_steps()
assert len(timesteps) > 0
assert all(0 <= t <= 1 for t in timesteps)

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tests/test_vae.py Normal file
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"""
Unit tests for WanVAE in Wan2.1.
Copyright (c) 2025 Kuaishou. All rights reserved.
"""
import pytest
import torch
from wan.modules.vae import WanVAE_
class TestWanVAE:
"""Test suite for WanVAE (3D Causal VAE)."""
def test_vae_initialization(self, sample_vae_config):
"""Test VAE initialization."""
with torch.device('meta'):
vae = WanVAE_(**sample_vae_config)
assert vae is not None
assert hasattr(vae, 'encoder')
assert hasattr(vae, 'decoder')
assert vae.temporal_compress_level == sample_vae_config['temporal_compress_level']
def test_vae_encode_shape(self, sample_vae_config, device, dtype):
"""Test VAE encoding produces correct output shape."""
# Use smaller config for faster testing
config = sample_vae_config.copy()
config['encoder_config']['ch'] = 32
config['encoder_config']['ch_mult'] = [1, 2]
config['encoder_config']['num_res_blocks'] = 1
config['decoder_config']['ch'] = 32
config['decoder_config']['ch_mult'] = [1, 2]
config['decoder_config']['num_res_blocks'] = 1
vae = WanVAE_(**config).to(device).to(dtype)
vae.eval()
batch_size = 1
channels = 3
num_frames = 8
height = 64
width = 64
x = torch.randn(batch_size, channels, num_frames, height, width, device=device, dtype=dtype)
with torch.no_grad():
encoded = vae.encode(x)
# Check output shape after encoding
z_channels = config['encoder_config']['z_channels']
temporal_compress = config['temporal_compress_level']
spatial_compress = 2 ** (len(config['encoder_config']['ch_mult']) - 1)
expected_t = num_frames // temporal_compress
expected_h = height // spatial_compress
expected_w = width // spatial_compress
assert encoded.shape == (batch_size, z_channels, expected_t, expected_h, expected_w)
def test_vae_decode_shape(self, sample_vae_config, device, dtype):
"""Test VAE decoding produces correct output shape."""
config = sample_vae_config.copy()
config['encoder_config']['ch'] = 32
config['encoder_config']['ch_mult'] = [1, 2]
config['encoder_config']['num_res_blocks'] = 1
config['decoder_config']['ch'] = 32
config['decoder_config']['ch_mult'] = [1, 2]
config['decoder_config']['num_res_blocks'] = 1
vae = WanVAE_(**config).to(device).to(dtype)
vae.eval()
batch_size = 1
z_channels = config['encoder_config']['z_channels']
num_frames = 2
height = 32
width = 32
z = torch.randn(batch_size, z_channels, num_frames, height, width, device=device, dtype=dtype)
with torch.no_grad():
decoded = vae.decode(z)
# Check output shape after decoding
out_channels = config['decoder_config']['out_ch']
temporal_compress = config['temporal_compress_level']
spatial_compress = 2 ** (len(config['decoder_config']['ch_mult']) - 1)
expected_t = num_frames * temporal_compress
expected_h = height * spatial_compress
expected_w = width * spatial_compress
assert decoded.shape == (batch_size, out_channels, expected_t, expected_h, expected_w)
def test_vae_encode_decode_consistency(self, sample_vae_config, device, dtype):
"""Test that encode then decode produces similar output."""
config = sample_vae_config.copy()
config['encoder_config']['ch'] = 32
config['encoder_config']['ch_mult'] = [1, 2]
config['encoder_config']['num_res_blocks'] = 1
config['decoder_config']['ch'] = 32
config['decoder_config']['ch_mult'] = [1, 2]
config['decoder_config']['num_res_blocks'] = 1
vae = WanVAE_(**config).to(device).to(dtype)
vae.eval()
batch_size = 1
channels = 3
num_frames = 8
height = 64
width = 64
x = torch.randn(batch_size, channels, num_frames, height, width, device=device, dtype=dtype)
with torch.no_grad():
encoded = vae.encode(x)
decoded = vae.decode(encoded)
# Decoded output should have same shape as input
assert decoded.shape == x.shape
def test_vae_no_nan_encode(self, sample_vae_config, device, dtype):
"""Test that VAE encoding doesn't produce NaN values."""
config = sample_vae_config.copy()
config['encoder_config']['ch'] = 32
config['encoder_config']['ch_mult'] = [1, 2]
config['encoder_config']['num_res_blocks'] = 1
config['decoder_config']['ch'] = 32
config['decoder_config']['ch_mult'] = [1, 2]
config['decoder_config']['num_res_blocks'] = 1
vae = WanVAE_(**config).to(device).to(dtype)
vae.eval()
batch_size = 1
channels = 3
num_frames = 8
height = 64
width = 64
x = torch.randn(batch_size, channels, num_frames, height, width, device=device, dtype=dtype)
with torch.no_grad():
encoded = vae.encode(x)
assert not torch.isnan(encoded).any()
assert not torch.isinf(encoded).any()
def test_vae_no_nan_decode(self, sample_vae_config, device, dtype):
"""Test that VAE decoding doesn't produce NaN values."""
config = sample_vae_config.copy()
config['encoder_config']['ch'] = 32
config['encoder_config']['ch_mult'] = [1, 2]
config['encoder_config']['num_res_blocks'] = 1
config['decoder_config']['ch'] = 32
config['decoder_config']['ch_mult'] = [1, 2]
config['decoder_config']['num_res_blocks'] = 1
vae = WanVAE_(**config).to(device).to(dtype)
vae.eval()
batch_size = 1
z_channels = config['encoder_config']['z_channels']
num_frames = 2
height = 32
width = 32
z = torch.randn(batch_size, z_channels, num_frames, height, width, device=device, dtype=dtype)
with torch.no_grad():
decoded = vae.decode(z)
assert not torch.isnan(decoded).any()
assert not torch.isinf(decoded).any()
@pytest.mark.parametrize("num_frames", [4, 8, 16])
def test_vae_various_frame_counts(self, sample_vae_config, device, dtype, num_frames):
"""Test VAE with various frame counts."""
config = sample_vae_config.copy()
config['encoder_config']['ch'] = 32
config['encoder_config']['ch_mult'] = [1, 2]
config['encoder_config']['num_res_blocks'] = 1
config['decoder_config']['ch'] = 32
config['decoder_config']['ch_mult'] = [1, 2]
config['decoder_config']['num_res_blocks'] = 1
vae = WanVAE_(**config).to(device).to(dtype)
vae.eval()
batch_size = 1
channels = 3
height = 64
width = 64
x = torch.randn(batch_size, channels, num_frames, height, width, device=device, dtype=dtype)
with torch.no_grad():
encoded = vae.encode(x)
decoded = vae.decode(encoded)
assert decoded.shape == x.shape
assert not torch.isnan(decoded).any()
def test_vae_eval_mode(self, sample_vae_config):
"""Test that VAE can be set to eval mode."""
with torch.device('meta'):
vae = WanVAE_(**sample_vae_config)
vae.eval()
assert not vae.training
def test_vae_config_attributes(self, sample_vae_config):
"""Test that VAE has correct configuration attributes."""
with torch.device('meta'):
vae = WanVAE_(**sample_vae_config)
assert hasattr(vae, 'temporal_compress_level')
assert vae.temporal_compress_level == sample_vae_config['temporal_compress_level']