fix(wan): Fix scheduler sigma schedule and add debug flags

mlx_video/models/wan/model.py

@@ -118,14 +118,12 @@ class WanModel(nn.Module):
             rope_params(1024, 2 * (d // 6)),
         ], axis=1)
 
-        # Precompute sinusoidal inv_freq for time embedding
-        # Use numpy float64 for precision (matches reference torch.float64),
-        # then store as float32 since MLX GPU doesn't support float64.
+        # Precompute sinusoidal inv_freq for time embedding.
         half = config.freq_dim // 2
-        inv_freq_np = np.power(
-            10000.0, -np.arange(half, dtype=np.float64) / half
+        self._inv_freq = mx.array(
+            np.power(10000.0, -np.arange(half, dtype=np.float64) / half
+            ).astype(np.float32)
         )
-        self._inv_freq = mx.array(inv_freq_np.astype(np.float32))
 
 
     def _patchify(self, x: mx.array) -> tuple:
@@ -311,13 +309,16 @@ class WanModel(nn.Module):
                 axis=0,
             )  # [B, seq_len, dim]
 
-        # Time embedding (use cached inv_freq to avoid recomputing each step)
+        # Time embedding: sinusoidal from precomputed inv_freq.
+        # inv_freq was computed in float64 for precision, stored as float32.
+        # With integer timesteps (matching reference), float32 sin/cos is fine.
         if t.ndim == 0:
             t = t[None]
 
-        pos = t.astype(mx.float32)
-        sinusoid = pos[..., None] * self._inv_freq
-        sin_emb = mx.concatenate([mx.cos(sinusoid), mx.sin(sinusoid)], axis=-1)
+        sinusoid = t[..., None].astype(mx.float32) * self._inv_freq
+        sin_emb = mx.concatenate(
+            [mx.cos(sinusoid), mx.sin(sinusoid)], axis=-1
+        )
 
         if t.ndim == 1:
             # Standard T2V: scalar timestep per batch element [B]