Remove Wan2 model files, including configuration, attention mechanisms, and utility functions, to streamline the codebase and eliminate unused components. This cleanup enhances maintainability and focuses on the core functionality of the Wan2 module.

mlx_video/models/wan_2/postprocess.py

@@ -0,0 +1,41 @@
+from pathlib import Path
+
+import numpy as np
+
+
+def save_video(frames: np.ndarray, output_path: str, fps: int = 16):
+    """Save video frames to MP4.
+
+    Args:
+        frames: Video frames [T, H, W, 3] uint8
+        output_path: Output file path
+        fps: Frames per second
+    """
+    try:
+        import imageio
+
+        writer = imageio.get_writer(output_path, fps=fps, codec="libx264", quality=8)
+        for frame in frames:
+            writer.append_data(frame)
+        writer.close()
+    except ImportError:
+        try:
+            import cv2
+
+            h, w = frames.shape[1], frames.shape[2]
+            fourcc = cv2.VideoWriter_fourcc(*"avc1")
+            writer = cv2.VideoWriter(output_path, fourcc, fps, (w, h))
+            for frame in frames:
+                writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
+            writer.release()
+        except (ImportError, Exception):
+            # Last resort: save as individual PNGs
+            from PIL import Image
+
+            out_dir = Path(output_path).parent / Path(output_path).stem
+            out_dir.mkdir(parents=True, exist_ok=True)
+            for i, frame in enumerate(frames):
+                Image.fromarray(frame).save(out_dir / f"frame_{i:04d}.png")
+            print(
+                f"  (no video encoder available, saved {len(frames)} frames to {out_dir}/)"
+            )