Refactor and remove Wan2.1/2.2 model files; update README.md to include new model features and usage instructions for LTX-2 and Wan2 models.

mlx_video/models/wan2/transformer.py

@@ -0,0 +1,97 @@
+import mlx.core as mx
+import mlx.nn as nn
+
+from .attention import WanCrossAttention, WanLayerNorm, WanSelfAttention, _linear_dtype
+
+
+class WanAttentionBlock(nn.Module):
+    """Wan transformer block with learned modulation, self-attn, cross-attn, and FFN."""
+
+    def __init__(
+        self,
+        dim: int,
+        ffn_dim: int,
+        num_heads: int,
+        window_size: tuple = (-1, -1),
+        qk_norm: bool = True,
+        cross_attn_norm: bool = False,
+        eps: float = 1e-6,
+    ):
+        super().__init__()
+
+        # Self-attention
+        self.norm1 = WanLayerNorm(dim, eps)
+        self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, eps)
+
+        # Cross-attention (with optional norm on context)
+        self.norm3 = (
+            WanLayerNorm(dim, eps, elementwise_affine=True)
+            if cross_attn_norm
+            else None
+        )
+        self.cross_attn = WanCrossAttention(dim, num_heads, qk_norm, eps)
+
+        # Feed-forward
+        self.norm2 = WanLayerNorm(dim, eps)
+        self.ffn = WanFFN(dim, ffn_dim)
+
+        # Learned modulation: 6 vectors for scale/shift/gate (kept in float32 for precision)
+        self.modulation = (mx.random.normal((1, 6, dim)) * (dim**-0.5)).astype(mx.float32)
+
+    def __call__(
+        self,
+        x: mx.array,
+        e: mx.array,
+        seq_lens: list,
+        grid_sizes: list,
+        freqs: mx.array,
+        context: mx.array,
+        context_lens: list | None = None,
+        cross_kv_cache: tuple | None = None,
+        rope_cos_sin: tuple | None = None,
+        attn_mask: mx.array | None = None,
+    ) -> mx.array:
+        # Modulation: compute in float32 for precision, matching the reference
+        # which keeps residual x in float32 via torch.amp.autocast(dtype=float32).
+        # By keeping modulation in float32, type promotion ensures the residual
+        # stream stays float32 throughout all 30 layers (gate * output + x → float32).
+        mod = self.modulation + e  # float32
+        e0, e1, e2, e3, e4, e5 = (
+            mod[:, :, 0, :],  # shift for self-attn
+            mod[:, :, 1, :],  # scale for self-attn
+            mod[:, :, 2, :],  # gate for self-attn
+            mod[:, :, 3, :],  # shift for ffn
+            mod[:, :, 4, :],  # scale for ffn
+            mod[:, :, 5, :],  # gate for ffn
+        )
+
+        # Self-attention with modulation (hidden state stays in w_dtype)
+        x_mod = self.norm1(x) * (1 + e1) + e0
+        y = self.self_attn(x_mod, seq_lens, grid_sizes, freqs, rope_cos_sin=rope_cos_sin, attn_mask=attn_mask)
+        x = x + y * e2
+
+        # Cross-attention (no modulation, just norm)
+        x_cross = self.norm3(x) if self.norm3 is not None else x
+        x = x + self.cross_attn(x_cross, context, context_lens, kv_cache=cross_kv_cache)
+
+        # FFN with modulation
+        x_mod = self.norm2(x) * (1 + e4) + e3
+        y = self.ffn(x_mod)
+        x = x + y * e5
+
+        return x
+
+
+class WanFFN(nn.Module):
+    """Gated feed-forward network with GELU(tanh) activation."""
+
+    def __init__(self, dim: int, ffn_dim: int):
+        super().__init__()
+        self.fc1 = nn.Linear(dim, ffn_dim)
+        self.act = nn.GELU(approx="tanh")
+        self.fc2 = nn.Linear(ffn_dim, dim)
+
+    def __call__(self, x: mx.array) -> mx.array:
+        # Cast to compute dtype for efficient matmul (bfloat16 matching official autocast)
+        x_w = x.astype(_linear_dtype(self.fc1))
+        return self.fc2(self.act(self.fc1(x_w)))