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Refactor Wan model structure by renaming and relocating model imports from model.py to wan2.py, enhancing code organization and clarity across the Wan2 module.

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This commit is contained in:
Prince Canuma
2026-03-18 17:57:29 +01:00
parent 6c63163671
commit b029668cd2
13 changed files with 45 additions and 45 deletions
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6
tests/test_wan_generate.py
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@@ -14,7 +14,7 @@ class TestEndToEnd:
def test_tiny_model_denoise_step(self):
"""Simulate one denoising step with tiny model."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
mx.random.seed(42)
@@ -43,7 +43,7 @@ class TestEndToEnd:
def test_tiny_model_full_loop(self):
"""Run a complete (tiny) diffusion loop."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
mx.random.seed(123)
@@ -201,7 +201,7 @@ class TestDimensionAlignment:
def test_patchify_valid_after_alignment(self):
"""After alignment, patchify should succeed without reshape errors."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)

16
tests/test_wan_i2v.py
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@@ -66,7 +66,7 @@ class TestModelYParameter:
def test_forward_without_y(self):
"""Standard T2V forward pass (no y) still works."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -85,7 +85,7 @@ class TestModelYParameter:
def test_forward_with_y(self):
"""I2V forward pass with y channel concatenation."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_i2v_config()
model = WanModel(config)
@@ -108,7 +108,7 @@ class TestModelYParameter:
def test_y_none_is_noop(self):
"""Passing y=None should be identical to not passing y."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -129,7 +129,7 @@ class TestModelYParameter:
def test_batched_cfg_with_y(self):
"""Batched CFG (B=2) with y should work."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_i2v_config()
model = WanModel(config)
@@ -307,7 +307,7 @@ class TestI2VEndToEndPipeline:
def test_full_i2v_pipeline(self):
"""End-to-end I2V: synthetic image → VAE encode → build y → denoise → VAE decode."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
from mlx_video.models.wan2.vae import WanVAE
@@ -410,7 +410,7 @@ class TestDualModelSwitching:
def test_model_selection_by_timestep(self):
"""Verify high_noise model used for timesteps >= boundary, low_noise otherwise."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
mx.random.seed(1)
@@ -485,7 +485,7 @@ class TestDualModelSwitching:
def test_guide_scale_tuple_applied_per_model(self):
"""Verify (low_gs, high_gs) tuple applies different scales per model."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
mx.random.seed(2)
@@ -545,7 +545,7 @@ class TestDualModelSwitching:
def test_single_model_fallback_with_tuple_guide_scale(self):
"""When dual_model=False, guide_scale tuple should use first element."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
mx.random.seed(3)

36
tests/test_wan_model.py
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@@ -12,7 +12,7 @@ from wan_test_helpers import _make_tiny_config
class TestSinusoidalEmbedding:
def test_output_shape(self):
from mlx_video.models.wan2.model import sinusoidal_embedding_1d
from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
pos = mx.arange(10).astype(mx.float32)
emb = sinusoidal_embedding_1d(256, pos)
@@ -21,7 +21,7 @@ class TestSinusoidalEmbedding:
def test_position_zero(self):
"""Position 0 should have cos=1 for all dims and sin=0."""
from mlx_video.models.wan2.model import sinusoidal_embedding_1d
from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
pos = mx.array([0.0])
emb = sinusoidal_embedding_1d(64, pos)
@@ -33,7 +33,7 @@ class TestSinusoidalEmbedding:
np.testing.assert_allclose(emb_np[32:], 0.0, atol=1e-5)
def test_different_positions_differ(self):
from mlx_video.models.wan2.model import sinusoidal_embedding_1d
from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
pos = mx.array([0.0, 100.0, 999.0])
emb = sinusoidal_embedding_1d(128, pos)
@@ -50,7 +50,7 @@ class TestSinusoidalEmbedding:
class TestHead:
def test_output_shape(self):
from mlx_video.models.wan2.model import Head
from mlx_video.models.wan2.wan2 import Head
head = Head(dim=64, out_dim=16, patch_size=(1, 2, 2))
B, L = 1, 24
@@ -62,7 +62,7 @@ class TestHead:
assert out.shape == (B, L, expected_proj_dim)
def test_modulation_shape(self):
from mlx_video.models.wan2.model import Head
from mlx_video.models.wan2.wan2 import Head
head = Head(dim=64, out_dim=16, patch_size=(1, 2, 2))
assert head.modulation.shape == (1, 2, 64)
@@ -78,7 +78,7 @@ class TestWanModel:
mx.random.seed(42)
def test_instantiation(self):
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -86,7 +86,7 @@ class TestWanModel:
assert num_params > 0
def test_patchify_shape(self):
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -99,7 +99,7 @@ class TestWanModel:
assert patches.shape == (1, 1 * 2 * 2, config.dim)
def test_patchify_various_sizes(self):
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -115,7 +115,7 @@ class TestWanModel:
def test_unpatchify_inverse(self):
"""Patchify then unpatchify should reconstruct original spatial dims."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -131,7 +131,7 @@ class TestWanModel:
assert out[0].shape == (config.out_dim, F, H, W)
def test_forward_pass(self):
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -149,7 +149,7 @@ class TestWanModel:
assert out[0].shape == (C, F, H, W)
def test_forward_batch(self):
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -171,7 +171,7 @@ class TestWanModel:
assert o.shape == (C, F, H, W)
def test_output_is_float32(self):
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -234,7 +234,7 @@ class TestWan21Model:
def test_wan21_tiny_model_forward(self):
"""Forward pass with Wan2.1 tiny config."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = self._make_tiny_wan21_config()
model = WanModel(config)
@@ -252,7 +252,7 @@ class TestWan21Model:
def test_wan21_1_3b_tiny_model_forward(self):
"""Forward pass with Wan2.1 1.3B tiny config."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = self._make_tiny_wan21_1_3b_config()
model = WanModel(config)
@@ -270,7 +270,7 @@ class TestWan21Model:
def test_wan21_single_model_loop(self):
"""Full diffusion loop with single model (Wan2.1 style)."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
config = self._make_tiny_wan21_config()
@@ -333,21 +333,21 @@ class TestPerTokenTimestep:
"""Tests for per-token sinusoidal embedding."""
def test_1d_unchanged(self):
from mlx_video.models.wan2.model import sinusoidal_embedding_1d
from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
pos = mx.array([0.0, 100.0, 500.0])
emb = sinusoidal_embedding_1d(256, pos)
assert emb.shape == (3, 256)
def test_2d_per_token(self):
from mlx_video.models.wan2.model import sinusoidal_embedding_1d
from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
pos = mx.array([[0.0, 100.0, 100.0], [50.0, 50.0, 50.0]])
emb = sinusoidal_embedding_1d(256, pos)
assert emb.shape == (2, 3, 256)
def test_consistency(self):
from mlx_video.models.wan2.model import sinusoidal_embedding_1d
from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
pos_1d = mx.array([0.0, 100.0])
emb_1d = sinusoidal_embedding_1d(256, pos_1d)

14
tests/test_wan_quantization.py
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@@ -91,7 +91,7 @@ class TestQuantizeRoundTrip:
def _quantize_and_save(self, config, tmp_path, bits=4, group_size=64):
"""Helper: create model, quantize, save to tmp_path."""
from mlx_video.models.wan2.convert import _quantize_predicate
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
model = WanModel(config)
nn.quantize(
@@ -164,7 +164,7 @@ class TestQuantizeRoundTrip:
def test_loading_without_quantization_flag(self, tmp_path):
"""Loading a non-quantized model should have standard Linear layers."""
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -188,7 +188,7 @@ class TestQuantizeRoundTrip:
class TestQuantizedInference:
def _make_quantized_model(self, config, bits=4):
from mlx_video.models.wan2.convert import _quantize_predicate
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
model = WanModel(config)
nn.quantize(
@@ -239,7 +239,7 @@ class TestQuantizedInference:
def test_quantized_output_differs_from_unquantized(self):
"""Sanity check: quantization should change the weights."""
from mlx_video.models.wan2.convert import _quantize_predicate
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
mx.random.seed(42)
@@ -272,7 +272,7 @@ class TestQuantizationConfig:
def test_config_metadata_written(self, tmp_path):
"""Verify _quantize_saved_model writes quantization metadata to config.json."""
from mlx_video.models.wan2.convert import _quantize_saved_model
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -296,7 +296,7 @@ class TestQuantizationConfig:
def test_config_metadata_8bit(self, tmp_path):
from mlx_video.models.wan2.convert import _quantize_saved_model
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()
model = WanModel(config)
@@ -317,7 +317,7 @@ class TestQuantizationConfig:
def test_dual_model_quantization(self, tmp_path):
"""Verify dual-model quantization writes both model files."""
from mlx_video.models.wan2.convert import _quantize_saved_model
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = _make_tiny_config()

2
tests/test_wan_rope_freqs.py
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@@ -28,7 +28,7 @@ class TestRoPEFrequencyConstruction:
def _get_model_freqs(self, dim=64, num_heads=4):
"""Instantiate a tiny WanModel and return its .freqs tensor."""
from mlx_video.models.wan2.config import WanModelConfig
from mlx_video.models.wan2.model import WanModel
from mlx_video.models.wan2.wan2 import WanModel
config = WanModelConfig()
config.dim = dim

6
tests/test_wan_transformer.py
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@@ -153,7 +153,7 @@ class TestFloat32Modulation:
def test_head_modulation_float32(self):
"""Head modulation should be float32 even with bf16 e input."""
from mlx_video.models.wan2.model import Head
from mlx_video.models.wan2.wan2 import Head
head = Head(self.dim, 4, (1, 2, 2))
x = mx.random.normal((1, 8, self.dim))
@@ -164,7 +164,7 @@ class TestFloat32Modulation:
def test_model_time_embedding_float32(self):
"""sinusoidal_embedding_1d output must be float32."""
from mlx_video.models.wan2.model import sinusoidal_embedding_1d
from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
t = mx.array([500.0])
emb = sinusoidal_embedding_1d(256, t)
@@ -173,7 +173,7 @@ class TestFloat32Modulation:
def test_model_per_token_time_embedding_float32(self):
"""Per-token time embeddings (I2V) should also be float32."""
from mlx_video.models.wan2.model import sinusoidal_embedding_1d
from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
t = mx.array([[0.0, 100.0, 200.0, 300.0]]) # [B=1, L=4]
emb = sinusoidal_embedding_1d(256, t)