Remove deprecated stubs for video conversion and generation; introduce new weight conversion and generation scripts for Wan2.2 models in MLX.

mlx_video/models/wan/convert.py

@@ -0,0 +1,773 @@
+"""Weight conversion for Wan2.2 models (PyTorch -> MLX)."""
+
+import gc
+import logging
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import mlx.core as mx
+import mlx.utils
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+def load_torch_weights(path: str) -> Dict[str, mx.array]:
+    """Load PyTorch .pth weights and convert to MLX arrays.
+
+    Args:
+        path: Path to .pth file
+
+    Returns:
+        Dictionary of MLX arrays
+    """
+    try:
+        import torch
+    except ImportError:
+        raise ImportError("PyTorch is required to load .pth weights: pip install torch")
+
+    logging.info(f"Loading weights from {path}")
+    state_dict = torch.load(path, map_location="cpu", weights_only=True)
+
+    weights = {}
+    for key, value in state_dict.items():
+        if isinstance(value, torch.Tensor):
+            np_val = value.detach().float().numpy()
+            weights[key] = mx.array(np_val)
+
+    return weights
+
+
+def load_safetensors_weights(path: str) -> Dict[str, mx.array]:
+    """Load safetensors weights as MLX arrays.
+
+    Args:
+        path: Path to directory with safetensors files or single file
+
+    Returns:
+        Dictionary of MLX arrays
+    """
+    path = Path(path)
+    weights = {}
+    if path.is_file():
+        weights = mx.load(str(path))
+    elif path.is_dir():
+        for sf in sorted(path.glob("*.safetensors")):
+            weights.update(mx.load(str(sf)))
+    return weights
+
+
+def sanitize_wan_transformer_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]:
+    """Convert Wan2.2 transformer weight keys to MLX model structure.
+
+    Wan2.2 keys follow the pattern:
+        patch_embedding.weight/bias
+        text_embedding.{0,2}.weight/bias
+        time_embedding.{0,2}.weight/bias
+        time_projection.1.weight/bias
+        blocks.{i}.norm1.weight
+        blocks.{i}.self_attn.{q,k,v,o}.weight/bias
+        blocks.{i}.self_attn.norm_q.weight
+        blocks.{i}.self_attn.norm_k.weight
+        blocks.{i}.norm3.weight/bias (if cross_attn_norm)
+        blocks.{i}.cross_attn.{q,k,v,o}.weight/bias
+        blocks.{i}.cross_attn.norm_q.weight
+        blocks.{i}.cross_attn.norm_k.weight
+        blocks.{i}.norm2.weight
+        blocks.{i}.ffn.{0,2}.weight/bias
+        blocks.{i}.modulation
+        head.norm.weight
+        head.head.weight/bias
+        head.modulation
+        freqs (buffer)
+
+    MLX model uses:
+        patch_embedding_proj.weight/bias (after patchify reshape)
+        text_embedding_0.weight/bias, text_embedding_1.weight/bias
+        time_embedding_0.weight/bias, time_embedding_1.weight/bias
+        time_projection.weight/bias
+        blocks.{i}.norm1.weight
+        blocks.{i}.self_attn.{q,k,v,o}.weight/bias
+        etc.
+    """
+    sanitized = {}
+    consumed = set()
+
+    for key, value in weights.items():
+        new_key = key
+
+        # Patch embedding: Conv3d(16, 5120, (1,2,2)) weight is [O, I, D, H, W]
+        # MLX Linear expects [O, I*D*H*W] after we flatten in patchify
+        if key == "patch_embedding.weight":
+            # Original: [dim, in_dim, 1, 2, 2] -> reshape to [dim, in_dim*1*2*2]
+            value = value.reshape(value.shape[0], -1)
+            new_key = "patch_embedding_proj.weight"
+            sanitized[new_key] = value
+            consumed.add(key)
+            continue
+        if key == "patch_embedding.bias":
+            new_key = "patch_embedding_proj.bias"
+            sanitized[new_key] = value
+            consumed.add(key)
+            continue
+
+        # Text embedding Sequential: 0=Linear, 1=GELU(no params), 2=Linear
+        if key.startswith("text_embedding.0."):
+            new_key = key.replace("text_embedding.0.", "text_embedding_0.")
+            sanitized[new_key] = value
+            consumed.add(key)
+            continue
+        if key.startswith("text_embedding.2."):
+            new_key = key.replace("text_embedding.2.", "text_embedding_1.")
+            sanitized[new_key] = value
+            consumed.add(key)
+            continue
+
+        # Time embedding Sequential: 0=Linear, 1=SiLU(no params), 2=Linear
+        if key.startswith("time_embedding.0."):
+            new_key = key.replace("time_embedding.0.", "time_embedding_0.")
+            sanitized[new_key] = value
+            consumed.add(key)
+            continue
+        if key.startswith("time_embedding.2."):
+            new_key = key.replace("time_embedding.2.", "time_embedding_1.")
+            sanitized[new_key] = value
+            consumed.add(key)
+            continue
+
+        # Time projection Sequential: 0=SiLU(no params), 1=Linear
+        if key.startswith("time_projection.1."):
+            new_key = key.replace("time_projection.1.", "time_projection.")
+            sanitized[new_key] = value
+            consumed.add(key)
+            continue
+
+        # FFN: Sequential(Linear, GELU, Linear) -> ffn.{0,2} -> ffn.fc1, ffn.fc2
+        new_key = new_key.replace(".ffn.0.", ".ffn.fc1.")
+        new_key = new_key.replace(".ffn.2.", ".ffn.fc2.")
+
+        # Skip the freqs buffer (we compute it in the model)
+        if key == "freqs":
+            consumed.add(key)
+            continue
+
+        sanitized[new_key] = value
+        consumed.add(key)
+
+    unconsumed = set(weights.keys()) - consumed
+    if unconsumed:
+        logger.warning("Unconsumed transformer weight keys: %s", sorted(unconsumed))
+
+    return sanitized
+
+
+def sanitize_wan_t5_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]:
+    """Convert Wan2.2 T5 encoder weight keys to MLX T5Encoder structure.
+
+    Wan2.2 T5 keys:
+        token_embedding.weight
+        pos_embedding.embedding.weight (if shared_pos)
+        blocks.{i}.norm1.weight
+        blocks.{i}.attn.{q,k,v,o}.weight
+        blocks.{i}.norm2.weight
+        blocks.{i}.ffn.gate.0.weight  (gate linear)
+        blocks.{i}.ffn.fc1.weight
+        blocks.{i}.ffn.fc2.weight
+        blocks.{i}.pos_embedding.embedding.weight (if not shared_pos)
+        norm.weight
+
+    MLX T5Encoder structure:
+        token_embedding.weight
+        blocks.{i}.norm1.weight
+        blocks.{i}.attn.{q,k,v,o}.weight
+        blocks.{i}.norm2.weight
+        blocks.{i}.ffn.gate_proj.weight  (mapped from gate.0)
+        blocks.{i}.ffn.fc1.weight
+        blocks.{i}.ffn.fc2.weight
+        blocks.{i}.pos_embedding.embedding.weight
+        norm.weight
+    """
+    sanitized = {}
+    consumed = set()
+
+    for key, value in weights.items():
+        new_key = key
+
+        # Map gate.0 -> gate_proj (the GELU is a separate module, not a parameter)
+        new_key = new_key.replace(".ffn.gate.0.", ".ffn.gate_proj.")
+
+        sanitized[new_key] = value
+        consumed.add(key)
+
+    unconsumed = set(weights.keys()) - consumed
+    if unconsumed:
+        logger.warning("Unconsumed T5 weight keys: %s", sorted(unconsumed))
+
+    return sanitized
+
+
+def sanitize_wan_vae_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]:
+    """Convert Wan2.2 VAE weight keys to MLX WanVAE structure.
+
+    Handles Conv3d and Conv2d weight transpositions for MLX format.
+    """
+    sanitized = {}
+    consumed = set()
+
+    for key, value in weights.items():
+        new_key = key
+
+        # Handle Conv3d: PyTorch [O, I, D, H, W] -> MLX CausalConv3d weight [O, D, H, W, I]
+        if "weight" in key and value.ndim == 5:
+            value = mx.transpose(value, (0, 2, 3, 4, 1))
+
+        # Handle Conv2d: PyTorch [O, I, H, W] -> MLX [O, H, W, I]
+        if "weight" in key and value.ndim == 4:
+            value = mx.transpose(value, (0, 2, 3, 1))
+
+        # Map decoder keys to MLX decoder structure
+        # Wan2.2 uses encoder/decoder with downsamples/upsamples
+        # Need to adapt naming for our simplified structure
+
+        sanitized[new_key] = value
+        consumed.add(key)
+
+    unconsumed = set(weights.keys()) - consumed
+    if unconsumed:
+        logger.warning("Unconsumed VAE weight keys: %s", sorted(unconsumed))
+
+    return sanitized
+
+
+def _load_lora_configs(
+    lora_configs: List[Tuple[str, float]],
+) -> Dict[str, list]:
+    """Load LoRA weights from config tuples, returning module_to_loras dict.
+
+    Shared between weight-merging and runtime-wrapping paths.
+    """
+    from mlx_video.lora import LoRAConfig, load_multiple_loras
+    from mlx_video.generate_wan import Colors
+
+    print(f"\n{Colors.CYAN}Loading {len(lora_configs)} LoRA(s)...{Colors.RESET}")
+
+    configs = []
+    for lora_path, strength in lora_configs:
+        try:
+            config = LoRAConfig(path=lora_path, strength=strength)
+            configs.append(config)
+            print(f"  - {Path(lora_path).name} (strength: {strength})")
+        except Exception as e:
+            print(f"{Colors.RED}Error loading LoRA {lora_path}: {e}{Colors.RESET}")
+            raise
+
+    module_to_loras = load_multiple_loras(configs)
+
+    if not module_to_loras:
+        print(f"{Colors.YELLOW}Warning: No LoRA weights matched model layers{Colors.RESET}")
+
+    return module_to_loras
+
+
+def load_and_apply_loras(
+    model_weights: Dict[str, mx.array],
+    lora_configs: Optional[List[Tuple[str, float]]] = None,
+    verbose: bool = False,
+    quantization_bits: int = 0,
+) -> Dict[str, mx.array]:
+    """Load and apply LoRA weights to model weights by merging into weight dict.
+
+    For non-quantized (bf16) models. For quantized models, use apply_loras_to_model().
+    """
+    from mlx_video.lora import apply_loras_to_weights
+    from mlx_video.generate_wan import Colors
+
+    if not lora_configs:
+        return model_weights
+
+    module_to_loras = _load_lora_configs(lora_configs)
+    if not module_to_loras:
+        return model_weights
+
+    print(f"{Colors.GREEN}Applying LoRAs to {len(module_to_loras)} modules...{Colors.RESET}")
+    if verbose:
+        print(f"  Model has {len(model_weights)} weight keys")
+
+    modified_weights = apply_loras_to_weights(
+        model_weights, module_to_loras, verbose=verbose, quantization_bits=quantization_bits
+    )
+
+    print(f"{Colors.GREEN}✓ LoRAs applied successfully{Colors.RESET}")
+
+    return modified_weights
+
+
+def convert_wan_checkpoint(
+    checkpoint_dir: str,
+    output_dir: str,
+    dtype: str = "bfloat16",
+    model_version: str = "auto",
+    quantize: bool = False,
+    bits: int = 4,
+    group_size: int = 64,
+):
+    """Convert a Wan2.1 or Wan2.2 checkpoint directory to MLX format.
+
+    Wan2.2 expected structure:
+        checkpoint_dir/
+            models_t5_umt5-xxl-enc-bf16.pth
+            Wan2.1_VAE.pth
+            low_noise_model/   (safetensors)
+            high_noise_model/  (safetensors)
+
+    Wan2.1 expected structure:
+        checkpoint_dir/
+            models_t5_umt5-xxl-enc-bf16.pth
+            Wan2.1_VAE.pth
+            diffusion_pytorch_model*.safetensors  (single model)
+
+    Args:
+        checkpoint_dir: Path to Wan checkpoint directory
+        output_dir: Path to output MLX model directory
+        dtype: Target dtype
+        model_version: "2.1", "2.2", or "auto" (detect from directory)
+        quantize: Whether to quantize the transformer weights
+        bits: Quantization bits (4 or 8)
+        group_size: Quantization group size (32, 64, or 128)
+    """
+    import json
+
+    checkpoint_dir = Path(checkpoint_dir)
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    dtype_map = {
+        "float16": mx.float16,
+        "float32": mx.float32,
+        "bfloat16": mx.bfloat16,
+    }
+    target_dtype = dtype_map.get(dtype, mx.bfloat16)
+
+    # Auto-detect version
+    if model_version == "auto":
+        if (checkpoint_dir / "low_noise_model").exists():
+            model_version = "2.2"
+        elif (checkpoint_dir / "Wan2.2_VAE.pth").exists():
+            model_version = "2.2"
+        else:
+            model_version = "2.1"
+        print(f"Auto-detected Wan{model_version} checkpoint")
+
+    is_dual = (checkpoint_dir / "low_noise_model").exists()
+
+    if is_dual:
+        # Wan2.2: Convert dual transformer models
+        low_noise_path = checkpoint_dir / "low_noise_model"
+        if low_noise_path.exists():
+            print("Converting low-noise transformer...")
+            weights = load_safetensors_weights(str(low_noise_path))
+            weights = sanitize_wan_transformer_weights(weights)
+            weights = {k: v.astype(target_dtype) for k, v in weights.items()}
+            out_path = output_dir / "low_noise_model.safetensors"
+            mx.save_safetensors(str(out_path), weights)
+            print(f"  Saved {len(weights)} weight tensors to {out_path}")
+
+        high_noise_path = checkpoint_dir / "high_noise_model"
+        if high_noise_path.exists():
+            print("Converting high-noise transformer...")
+            weights = load_safetensors_weights(str(high_noise_path))
+            weights = sanitize_wan_transformer_weights(weights)
+            weights = {k: v.astype(target_dtype) for k, v in weights.items()}
+            out_path = output_dir / "high_noise_model.safetensors"
+            mx.save_safetensors(str(out_path), weights)
+            print(f"  Saved {len(weights)} weight tensors to {out_path}")
+    else:
+        # Wan2.1: Convert single transformer model
+        # Try safetensors in the checkpoint dir itself
+        print("Converting transformer (single model)...")
+        weights = load_safetensors_weights(str(checkpoint_dir))
+        if not weights:
+            # Fallback: look for .pth files
+            for pth in sorted(checkpoint_dir.glob("*.pth")):
+                if "t5" not in pth.name.lower() and "vae" not in pth.name.lower():
+                    print(f"  Loading from {pth.name}...")
+                    weights = load_torch_weights(str(pth))
+                    break
+        if weights:
+            weights = sanitize_wan_transformer_weights(weights)
+            weights = {k: v.astype(target_dtype) for k, v in weights.items()}
+            out_path = output_dir / "model.safetensors"
+            mx.save_safetensors(str(out_path), weights)
+            print(f"  Saved {len(weights)} weight tensors to {out_path}")
+        else:
+            print("  Warning: No transformer weights found!")
+
+    # Save config — detect model size from source config.json or transformer weights
+    from mlx_video.models.wan.config import WanModelConfig
+
+    def _detect_config():
+        """Detect config from source config.json or transformer weight shapes."""
+        if is_dual:
+            # Check source config.json for model_type (I2V vs T2V)
+            src_cfg_path = checkpoint_dir / "high_noise_model" / "config.json"
+            if src_cfg_path.exists():
+                with open(src_cfg_path) as f:
+                    src_config = json.load(f)
+                src_model_type = src_config.get("model_type", "t2v")
+                if src_model_type == "i2v" or src_config.get("in_dim") == 36:
+                    return WanModelConfig.wan22_i2v_14b()
+            return WanModelConfig.wan22_t2v_14b()
+
+        # Try reading source config.json first (most reliable)
+        src_cfg_path = checkpoint_dir / "config.json"
+        src_config = None
+        if src_cfg_path.exists():
+            with open(src_cfg_path) as f:
+                src_config = json.load(f)
+
+        if src_config and "dim" in src_config:
+            src_dim = src_config.get("dim", 5120)
+            src_in_dim = src_config.get("in_dim", 16)
+            src_out_dim = src_config.get("out_dim", 16)
+            src_ffn_dim = src_config.get("ffn_dim", 13824)
+            src_num_heads = src_config.get("num_heads", 40)
+            src_num_layers = src_config.get("num_layers", 40)
+            src_model_type = src_config.get("model_type", "t2v")
+            src_text_len = src_config.get("text_len", 512)
+
+            print(f"  Source config: dim={src_dim}, layers={src_num_layers}, "
+                  f"heads={src_num_heads}, type={src_model_type}")
+
+            # Use preset for known TI2V 5B configuration
+            if src_model_type == "ti2v" and src_dim == 3072:
+                return WanModelConfig.wan22_ti2v_5b()
+
+            is_22 = model_version == "2.2"
+
+            # Wan2.2 uses different VAE with z_dim=48 and stride (4,16,16)
+            vae_z = 48 if is_22 else 16
+            vae_s = (4, 16, 16) if is_22 else (4, 8, 8)
+            fps = 24 if is_22 else 16
+
+            return WanModelConfig(
+                model_type=src_model_type,
+                model_version=model_version,
+                dim=src_dim,
+                ffn_dim=src_ffn_dim,
+                in_dim=src_in_dim,
+                out_dim=src_out_dim,
+                num_heads=src_num_heads,
+                num_layers=src_num_layers,
+                text_len=src_text_len,
+                vae_z_dim=vae_z,
+                vae_stride=vae_s,
+                dual_model=False,
+                boundary=0.0,
+                sample_shift=5.0,
+                sample_steps=50,
+                sample_guide_scale=5.0,
+                sample_fps=fps,
+            )
+
+        # Fallback: detect from saved transformer weight shapes
+        saved_model = output_dir / "model.safetensors"
+        if saved_model.exists():
+            det_weights = mx.load(str(saved_model))
+            dim = None
+            for k, v in det_weights.items():
+                if "patch_embedding_proj.weight" in k:
+                    dim = v.shape[0]
+                    break
+            del det_weights
+            if dim is not None and dim <= 2048:
+                print(f"  Auto-detected 1.3B model (dim={dim})")
+                return WanModelConfig.wan21_t2v_1_3b()
+
+        return WanModelConfig.wan21_t2v_14b()
+
+    config = _detect_config()
+    config_path = output_dir / "config.json"
+    with open(config_path, "w") as f:
+        json.dump(config.to_dict(), f, indent=2)
+    print(f"  Saved config to {config_path}")
+
+    # Convert T5 encoder
+    t5_path = checkpoint_dir / "models_t5_umt5-xxl-enc-bf16.pth"
+    if t5_path.exists():
+        print("Converting T5 encoder...")
+        weights = load_torch_weights(str(t5_path))
+        weights = sanitize_wan_t5_weights(weights)
+        weights = {k: v.astype(target_dtype) for k, v in weights.items()}
+        out_path = output_dir / "t5_encoder.safetensors"
+        mx.save_safetensors(str(out_path), weights)
+        print(f"  Saved {len(weights)} weight tensors to {out_path}")
+
+    # Convert VAE (check both naming conventions)
+    vae_path = checkpoint_dir / "Wan2.1_VAE.pth"
+    is_wan22_vae = False
+    if not vae_path.exists():
+        vae_path = checkpoint_dir / "Wan2.2_VAE.pth"
+        is_wan22_vae = True
+    if vae_path.exists():
+        print(f"Converting VAE ({'Wan2.2' if is_wan22_vae else 'Wan2.1'})...")
+        weights = load_torch_weights(str(vae_path))
+        if is_wan22_vae:
+            from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights
+            include_encoder = config.model_type in ("ti2v", "i2v")
+            weights = sanitize_wan22_vae_weights(weights, include_encoder=include_encoder)
+        else:
+            weights = sanitize_wan_vae_weights(weights)
+        # Always save VAE in float32 — official Wan2.2 runs VAE decode in
+        # float32 (dtype=torch.float).  Saving in bfloat16 loses precision
+        # that cannot be recovered by upcasting at load time.
+        weights = {k: v.astype(mx.float32) for k, v in weights.items()}
+        out_path = output_dir / "vae.safetensors"
+        mx.save_safetensors(str(out_path), weights)
+        print(f"  Saved {len(weights)} weight tensors to {out_path} (float32)")
+
+    # Quantize transformer weights if requested
+    if quantize:
+        print(f"\nQuantizing transformer weights ({bits}-bit, group_size={group_size})...")
+        _quantize_saved_model(output_dir, config, is_dual, bits, group_size)
+
+    print(f"\nConversion complete! Output: {output_dir}")
+
+
+def _quantize_predicate(path: str, module) -> bool:
+    """Return True for layers that should be quantized.
+
+    Targets heavyweight Linear layers in attention and FFN blocks.
+    Skips embeddings, norms, head, and modulation (small, precision-sensitive).
+    """
+    if not hasattr(module, "to_quantized"):
+        return False
+    # Quantize attention Q/K/V/O and FFN fc1/fc2
+    quantize_patterns = (
+        ".self_attn.q", ".self_attn.k", ".self_attn.v", ".self_attn.o",
+        ".cross_attn.q", ".cross_attn.k", ".cross_attn.v", ".cross_attn.o",
+        ".ffn.fc1", ".ffn.fc2",
+    )
+    return any(path.endswith(p) for p in quantize_patterns)
+
+
+def _quantize_saved_model(
+    output_dir: Path,
+    config,
+    is_dual: bool,
+    bits: int,
+    group_size: int,
+    source_dir: Path = None,
+):
+    """Load saved bf16 model, quantize, and re-save.
+
+    Args:
+        output_dir: Directory to write quantized weights to.
+        config: WanModelConfig for creating the model.
+        is_dual: Whether this is a dual-expert model.
+        bits: Quantization bits.
+        group_size: Quantization group size.
+        source_dir: Directory to read bf16 weights from. Defaults to output_dir.
+    """
+    import json
+
+    import mlx.nn as nn
+
+    from mlx_video.models.wan.model import WanModel
+
+    if source_dir is None:
+        source_dir = output_dir
+
+    model_names = []
+    if is_dual:
+        for name in ["low_noise_model.safetensors", "high_noise_model.safetensors"]:
+            if (source_dir / name).exists():
+                model_names.append(name)
+    else:
+        if (source_dir / "model.safetensors").exists():
+            model_names.append("model.safetensors")
+
+    for name in model_names:
+        print(f"  Quantizing {name}...")
+        model = WanModel(config)
+        weights = mx.load(str(source_dir / name))
+        model.load_weights(list(weights.items()), strict=False)
+        mx.eval(model.parameters())
+        del weights
+        gc.collect()
+        mx.clear_cache()
+
+        # Apply quantization to targeted layers
+        nn.quantize(
+            model,
+            group_size=group_size,
+            bits=bits,
+            class_predicate=lambda path, m: _quantize_predicate(path, m),
+        )
+
+        # Save quantized weights
+        weights_dict = dict(mlx.utils.tree_flatten(model.parameters()))
+
+        # Validate: check for NaN/Inf in bias tensors (corruption canary)
+        bad_keys = []
+        for k, v in weights_dict.items():
+            if k.endswith(".bias") and not k.endswith(".biases"):
+                mx.eval(v)
+                if mx.any(mx.isnan(v)).item() or mx.any(mx.isinf(v)).item():
+                    bad_keys.append(k)
+        if bad_keys:
+            raise RuntimeError(
+                f"Quantization produced corrupted weights in {model_path.name}: "
+                f"{len(bad_keys)} bias tensors contain NaN/Inf "
+                f"(e.g. {bad_keys[0]}). Try re-running with more available memory."
+            )
+
+        mx.save_safetensors(str(output_dir / name), weights_dict)
+        n_quantized = sum(1 for k in weights_dict if ".scales" in k)
+        print(f"    {n_quantized} layers quantized, {len(weights_dict)} tensors saved")
+
+        # Free model before processing next file
+        del model, weights_dict
+        gc.collect()
+        mx.clear_cache()
+
+    # Update config.json with quantization metadata
+    config_path = output_dir / "config.json"
+    with open(config_path) as f:
+        cfg = json.load(f)
+    cfg["quantization"] = {
+        "group_size": group_size,
+        "bits": bits,
+    }
+    with open(config_path, "w") as f:
+        json.dump(cfg, f, indent=2)
+    print(f"  Updated config.json with quantization metadata")
+
+
+def quantize_mlx_model(
+    mlx_model_dir: str,
+    output_dir: str,
+    bits: int = 4,
+    group_size: int = 64,
+):
+    """Quantize an already-converted MLX model (skips PyTorch conversion).
+
+    Args:
+        mlx_model_dir: Path to existing MLX model directory (bf16/fp16).
+        output_dir: Path to output quantized model directory.
+        bits: Quantization bits (4 or 8).
+        group_size: Quantization group size (32, 64, or 128).
+    """
+    import json
+    import shutil
+
+    src = Path(mlx_model_dir)
+    dst = Path(output_dir)
+
+    config_path = src / "config.json"
+    if not config_path.exists():
+        raise FileNotFoundError(f"No config.json found in {src}")
+
+    with open(config_path) as f:
+        cfg = json.load(f)
+
+    if cfg.get("quantization"):
+        raise ValueError(
+            f"Model at {src} is already quantized "
+            f"({cfg['quantization']['bits']}-bit). Use a bf16/fp16 source."
+        )
+
+    # Detect dual vs single expert
+    is_dual = (src / "low_noise_model.safetensors").exists() and (
+        src / "high_noise_model.safetensors"
+    ).exists()
+
+    # Build model config
+    from mlx_video.models.wan.config import WanModelConfig
+
+    config_dict = {k: v for k, v in cfg.items() if k in WanModelConfig.__dataclass_fields__}
+    for key in ("patch_size", "vae_stride", "window_size", "sample_guide_scale"):
+        if key in config_dict and isinstance(config_dict[key], list):
+            config_dict[key] = tuple(config_dict[key])
+    config = WanModelConfig(**config_dict)
+
+    # Copy non-transformer files to output dir (skip large model weights)
+    transformer_files = {"low_noise_model.safetensors", "high_noise_model.safetensors", "model.safetensors"}
+    if dst.resolve() != src.resolve():
+        dst.mkdir(parents=True, exist_ok=True)
+        for f in src.iterdir():
+            if f.is_file() and f.name not in transformer_files:
+                shutil.copy2(f, dst / f.name)
+        print(f"Copied non-transformer files from {src} to {dst}")
+
+    print(f"Quantizing transformer weights ({bits}-bit, group_size={group_size})...")
+    _quantize_saved_model(dst, config, is_dual, bits, group_size, source_dir=src)
+
+    print(f"\nQuantization complete! Output: {dst}")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Convert Wan model to MLX format")
+    parser.add_argument(
+        "--checkpoint-dir",
+        type=str,
+        required=True,
+        help="Path to Wan checkpoint directory",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="wan_mlx_model",
+        help="Output path for MLX model",
+    )
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        choices=["float16", "float32", "bfloat16"],
+        default="bfloat16",
+        help="Target dtype",
+    )
+    parser.add_argument(
+        "--model-version",
+        type=str,
+        choices=["2.1", "2.2", "auto"],
+        default="auto",
+        help="Wan model version (auto-detect by default)",
+    )
+    parser.add_argument(
+        "--quantize",
+        action="store_true",
+        help="Quantize transformer weights for faster inference",
+    )
+    parser.add_argument(
+        "--quantize-only",
+        action="store_true",
+        help="Quantize an already-converted MLX model (skips PyTorch conversion)",
+    )
+    parser.add_argument(
+        "--bits",
+        type=int,
+        choices=[4, 8],
+        default=4,
+        help="Quantization bits (default: 4)",
+    )
+    parser.add_argument(
+        "--group-size",
+        type=int,
+        choices=[32, 64, 128],
+        default=64,
+        help="Quantization group size (default: 64)",
+    )
+    args = parser.parse_args()
+
+    if args.quantize_only:
+        quantize_mlx_model(
+            args.checkpoint_dir, args.output_dir,
+            bits=args.bits, group_size=args.group_size,
+        )
+    else:
+        convert_wan_checkpoint(
+            args.checkpoint_dir, args.output_dir, args.dtype, args.model_version,
+            quantize=args.quantize, bits=args.bits, group_size=args.group_size,
+        )

mlx_video/models/wan/generate.py

@@ -0,0 +1,828 @@
+"""Wan2.2 Text-to-Video generation pipeline for MLX."""
+
+import argparse
+import gc
+import math
+import random
+import sys
+import time
+from pathlib import Path
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+from tqdm import tqdm
+
+from mlx_video.models.wan.i2v_utils import build_i2v_mask, preprocess_image
+from mlx_video.models.wan.loading import (
+    _clean_text,
+    encode_text,
+    load_t5_encoder,
+    load_vae_decoder,
+    load_vae_encoder,
+    load_wan_model,
+)
+from mlx_video.models.wan.postprocess import save_video
+
+class Colors:
+    """ANSI color codes for terminal output."""
+
+    CYAN = "\033[96m"
+    BLUE = "\033[94m"
+    GREEN = "\033[92m"
+    YELLOW = "\033[93m"
+    RED = "\033[91m"
+    MAGENTA = "\033[95m"
+    BOLD = "\033[1m"
+    DIM = "\033[2m"
+    RESET = "\033[0m"
+
+# Backward-compat alias (tests and external code may use the old name)
+_build_i2v_mask = build_i2v_mask
+
+
+def _best_output_size(w, h, dw, dh, max_area):
+    """Compute the best output resolution that fits within max_area while
+    preserving the input aspect ratio and satisfying alignment constraints.
+    Matches the reference implementation's best_output_size().
+    """
+    ratio = w / h
+    ow = (max_area * ratio) ** 0.5
+    oh = max_area / ow
+
+    # Option 1: process width first
+    ow1 = int(ow // dw * dw)
+    oh1 = int(max_area / ow1 // dh * dh)
+    ratio1 = ow1 / oh1
+
+    # Option 2: process height first
+    oh2 = int(oh // dh * dh)
+    ow2 = int(max_area / oh2 // dw * dw)
+    ratio2 = ow2 / oh2
+
+    if max(ratio / ratio1, ratio1 / ratio) < max(ratio / ratio2, ratio2 / ratio):
+        return ow1, oh1
+    return ow2, oh2
+
+
+def generate_video(
+    model_dir: str,
+    prompt: str,
+    negative_prompt: str | None = None,
+    image: str | None = None,
+    width: int = 1280,
+    height: int = 704,
+    num_frames: int = 81,
+    steps: int = None,
+    guide_scale: str | float | tuple = None,
+    shift: float = None,
+    seed: int = -1,
+    output_path: str = "output.mp4",
+    scheduler: str = "unipc",
+    loras: list | None = None,
+    loras_high: list | None = None,
+    loras_low: list | None = None,
+    tiling: str = "auto",
+    no_compile: bool = False,
+    trim_first_frames: int = 0,
+    debug_latents: bool = False,
+):
+    """Generate video using Wan pipeline (supports T2V and I2V).
+
+    Args:
+        model_dir: Path to converted MLX model directory
+        prompt: Text prompt
+        negative_prompt: Negative prompt (None = use config default, "" = no negative prompt)
+        image: Path to input image for I2V (None = T2V mode)
+        width: Video width
+        height: Video height
+        num_frames: Number of frames (must be 4n+1)
+        steps: Number of diffusion steps (None = use config default)
+        guide_scale: Guidance scale: float for single, (low,high) for dual (None = config default)
+        shift: Noise schedule shift (None = use config default)
+        seed: Random seed (-1 for random)
+        output_path: Output video path
+        scheduler: Solver type: 'euler', 'dpm++', or 'unipc' (default)
+        loras: Optional list of (path, strength) tuples applied to all models
+        loras_high: Optional list of (path, strength) tuples for high-noise model only
+        loras_low: Optional list of (path, strength) tuples for low-noise model only
+        tiling: Tiling mode for VAE decoding. Options:
+            - "auto": Automatically determine tiling based on video size (default)
+            - "none": Disable tiling
+            - "default", "aggressive", "conservative": Preset tiling configs
+            - "spatial": Spatial tiling only
+            - "temporal": Temporal tiling only
+        no_compile: If True, skip mx.compile on models (useful for debugging)
+        trim_first_frames: Number of temporal latent positions to generate extra
+            and discard from the start. Each position = 4 pixel frames. Use 1
+            to fix first-frame artifacts on 14B models (generates 4 extra frames,
+            discards first 4). Use 2 for more aggressive trimming. Default: 0.
+        debug_latents: If True, print per-temporal-position latent statistics
+            after denoising for diagnosing first-frame artifacts.
+    """
+    import json
+
+    from mlx_video.models.wan.config import WanModelConfig
+    from mlx_video.models.wan.scheduler import (
+        FlowDPMPP2MScheduler,
+        FlowMatchEulerScheduler,
+        FlowUniPCScheduler,
+    )
+
+    model_dir = Path(model_dir)
+
+    # Load config from model dir if available, otherwise auto-detect
+    config_path = model_dir / "config.json"
+    quantization = None
+    if config_path.exists():
+        with open(config_path) as f:
+            config_dict = json.load(f)
+        # Extract quantization config (not a model config field)
+        quantization = config_dict.pop("quantization", None)
+        # Handle tuple fields stored as lists in JSON
+        for key in ("patch_size", "vae_stride", "window_size", "sample_guide_scale"):
+            if key in config_dict and isinstance(config_dict[key], list):
+                config_dict[key] = tuple(config_dict[key])
+        config = WanModelConfig(**{
+            k: v for k, v in config_dict.items()
+            if k in WanModelConfig.__dataclass_fields__
+        })
+    else:
+        # Auto-detect: dual model files → 2.2, single model → 2.1
+        if (model_dir / "low_noise_model.safetensors").exists():
+            config = WanModelConfig.wan22_t2v_14b()
+        else:
+            # Detect 1.3B vs 14B from weight shapes
+            model_path = model_dir / "model.safetensors"
+            if model_path.exists():
+                probe = mx.load(str(model_path), return_metadata=False)
+                for k, v in probe.items():
+                    if "patch_embedding_proj.weight" in k:
+                        dim = v.shape[0]
+                        if dim <= 2048:
+                            config = WanModelConfig.wan21_t2v_1_3b()
+                        else:
+                            config = WanModelConfig.wan21_t2v_14b()
+                        break
+                else:
+                    config = WanModelConfig.wan21_t2v_14b()
+                del probe
+            else:
+                config = WanModelConfig.wan21_t2v_14b()
+
+    is_dual = config.dual_model
+    is_i2v = image is not None
+
+    # Validate config against actual weights (handles mismatched config.json)
+    if not is_dual:
+        model_path = model_dir / "model.safetensors"
+        if model_path.exists():
+            probe = mx.load(str(model_path), return_metadata=False)
+            for k, v in probe.items():
+                if "patch_embedding_proj.weight" in k:
+                    actual_dim = v.shape[0]
+                    if actual_dim != config.dim:
+                        print(f"{Colors.YELLOW}  Config dim={config.dim} doesn't match weights dim={actual_dim}, auto-correcting...{Colors.RESET}")
+                        if actual_dim <= 2048:
+                            config = WanModelConfig.wan21_t2v_1_3b()
+                        else:
+                            config = WanModelConfig.wan21_t2v_14b()
+                    break
+            del probe
+
+    # Auto-correct Wan2.2 VAE params from stale configs
+    if config.in_dim == 48 and config.vae_z_dim != 48:
+        print(f"{Colors.YELLOW}  Auto-correcting Wan2.2 VAE params (in_dim=48 but vae_z_dim={config.vae_z_dim}){Colors.RESET}")
+        config = WanModelConfig(**{
+            **{f.name: getattr(config, f.name) for f in config.__dataclass_fields__.values()},
+            "vae_z_dim": 48,
+            "vae_stride": (4, 16, 16),
+            "sample_fps": 24,
+        })
+
+    # Apply defaults from config if not overridden
+    if steps is None:
+        steps = config.sample_steps
+    if shift is None:
+        shift = config.sample_shift
+    if guide_scale is None:
+        guide_scale = config.sample_guide_scale
+
+    # Normalize guide_scale
+    if isinstance(guide_scale, (int, float)):
+        guide_scale = float(guide_scale)
+    elif isinstance(guide_scale, str):
+        parts = [float(x) for x in guide_scale.split(",")]
+        guide_scale = tuple(parts) if len(parts) > 1 else parts[0]
+
+    # Detect CFG-disabled mode (guide_scale=1.0 for all models → skip uncond pass for 2x speedup)
+    if isinstance(guide_scale, tuple):
+        cfg_disabled = all(gs <= 1.0 for gs in guide_scale)
+    else:
+        cfg_disabled = guide_scale <= 1.0
+
+    # Validate frame count
+    assert (num_frames - 1) % 4 == 0, f"num_frames must be 4n+1, got {num_frames}"
+
+    gen_frames = num_frames
+    if trim_first_frames > 0:
+        gen_frames = num_frames + trim_first_frames * 4
+        print(f"{Colors.DIM}  Trim: generating {gen_frames} frames, will discard first {trim_first_frames * 4}{Colors.RESET}")
+
+    version_str = f"Wan{config.model_version}"
+    mode_str = "dual-model" if is_dual else "single-model"
+    pipeline_str = "Image-to-Video" if is_i2v else "Text-to-Video"
+    # Resolve negative prompt: explicit user value > config default
+    # The official Wan2.2 uses a Chinese negative prompt (config.sample_neg_prompt)
+    # that prevents oversaturation, artifacts, and comic look. We use it by default.
+    # Text cleaning (_clean_text) normalizes fullwidth chars to match official tokenization.
+    if negative_prompt is None:
+        neg_prompt_resolved = config.sample_neg_prompt
+    else:
+        neg_prompt_resolved = negative_prompt
+    print(f"{Colors.CYAN}{'='*60}")
+    print(f"  {version_str} {pipeline_str} Generation (MLX, {mode_str})")
+    print(f"{'='*60}{Colors.RESET}")
+    print(f"{Colors.DIM}  Prompt: {prompt}")
+    if is_i2v:
+        print(f"  Image: {image}")
+    if neg_prompt_resolved and neg_prompt_resolved.strip():
+        neg_display = neg_prompt_resolved[:60] + "..." if len(neg_prompt_resolved) > 60 else neg_prompt_resolved
+        print(f"  Neg prompt: {neg_display}")
+    print(f"  Size: {width}x{height}, Frames: {num_frames}")
+    print(f"  Steps: {steps}, Guide: {guide_scale}, Shift: {shift}, Solver: {scheduler}")
+    if cfg_disabled:
+        print(f"  CFG: disabled (guide_scale≤1 → B=1 fast path, 2x denoising speedup)")
+    print(f"{Colors.RESET}")
+
+    # Seed
+    if seed < 0:
+        seed = random.randint(0, 2**32 - 1)
+    mx.random.seed(seed)
+    np.random.seed(seed)
+    print(f"{Colors.DIM}  Seed: {seed}{Colors.RESET}")
+
+    # Align dimensions to patch_size * vae_stride (required for patchify)
+    vae_stride = config.vae_stride
+    patch_size = config.patch_size
+    align_h = patch_size[1] * vae_stride[1]  # e.g. 2*16=32
+    align_w = patch_size[2] * vae_stride[2]
+    if height % align_h != 0 or width % align_w != 0:
+        old_h, old_w = height, width
+        height = (height // align_h) * align_h
+        width = (width // align_w) * align_w
+        if height == 0:
+            height = align_h
+        if width == 0:
+            width = align_w
+        print(f"{Colors.DIM}  Aligned {old_w}x{old_h} → {width}x{height} (must be divisible by {align_w}x{align_h}){Colors.RESET}")
+
+    # Enforce max_area constraint (model-specific resolution limit)
+    if config.max_area > 0 and height * width > config.max_area:
+        old_h, old_w = height, width
+        width, height = _best_output_size(width, height, align_w, align_h, config.max_area)
+        print(
+            f"{Colors.YELLOW}  ⚠ Resolution {old_w}x{old_h} exceeds model's max area "
+            f"({config.max_area:,}px). Adjusted → {width}x{height}{Colors.RESET}"
+        )
+
+    # Compute target latent shape
+    z_dim = config.vae_z_dim
+    t_latent = (gen_frames - 1) // vae_stride[0] + 1
+    h_latent = height // vae_stride[1]
+    w_latent = width // vae_stride[2]
+    target_shape = (z_dim, t_latent, h_latent, w_latent)
+
+    # Sequence length for transformer
+    seq_len = math.ceil(
+        (h_latent * w_latent) / (patch_size[1] * patch_size[2]) * t_latent
+    )
+
+    print(f"{Colors.DIM}  Latent shape: {target_shape}")
+    print(f"  Sequence length: {seq_len}{Colors.RESET}")
+
+    # Load T5 encoder
+    t1 = time.time()
+    print(f"\n{Colors.BLUE}Loading T5 encoder...{Colors.RESET}")
+    t5_path = model_dir / "t5_encoder.safetensors"
+    t5_encoder = load_t5_encoder(t5_path, config)
+
+    # Load tokenizer
+    from transformers import AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl")
+
+    # Encode prompts
+    print(f"{Colors.BLUE}Encoding text...{Colors.RESET}")
+    context = encode_text(t5_encoder, tokenizer, prompt, config.text_len)
+    if cfg_disabled:
+        context_null = None
+        mx.eval(context)
+    else:
+        context_null = encode_text(t5_encoder, tokenizer, neg_prompt_resolved, config.text_len)
+        mx.eval(context, context_null)
+
+    # Free T5 from memory
+    del t5_encoder
+    gc.collect(); mx.clear_cache()
+    print(f"{Colors.DIM}  T5 encoding: {time.time() - t1:.1f}s{Colors.RESET}")
+
+    # I2V: encode image to latent space
+    z_img = None
+    i2v_mask = None
+    i2v_mask_tokens = None
+    y_i2v = None
+    is_i2v_channel_concat = is_i2v and config.model_type == "i2v"
+    is_i2v_mask_blend = is_i2v and config.model_type != "i2v"
+    if is_i2v:
+        print(f"\n{Colors.BLUE}Encoding input image...{Colors.RESET}")
+        t_img = time.time()
+
+        vae_path = model_dir / "vae.safetensors"
+
+        if is_i2v_channel_concat:
+            # I2V-14B: encode full video (first frame = image, rest = zeros)
+            # and construct y tensor with mask + encoded latents
+            from PIL import Image
+
+            img = Image.open(image).convert("RGB")
+            scale = max(width / img.width, height / img.height)
+            img = img.resize((round(img.width * scale), round(img.height * scale)), Image.LANCZOS)
+            x1, y1 = (img.width - width) // 2, (img.height - height) // 2
+            img = img.crop((x1, y1, x1 + width, y1 + height))
+            img_arr = mx.array(np.array(img, dtype=np.float32) / 255.0 * 2.0 - 1.0)  # [H, W, 3]
+            img_chw = img_arr.transpose(2, 0, 1)  # [3, H, W]
+
+            # Build video: first frame = image, rest = zeros -> [3, F, H, W]
+            # Chunked encoding processes 1-frame + 4-frame chunks with temporal caching
+            video = mx.concatenate([
+                img_chw[:, None, :, :],
+                mx.zeros((3, num_frames - 1, height, width)),
+            ], axis=1)
+
+            # Encode through Wan2.1 VAE -> [1, z_dim, T_lat, H_lat, W_lat]
+            vae_enc = load_vae_encoder(vae_path, config)
+            z_video = vae_enc.encode(video[None])  # [1, 16, T_lat, H_lat, W_lat]
+            mx.eval(z_video)
+            z_video = z_video[0]  # [16, T_lat, H_lat, W_lat]
+
+            # Build mask: 1 for first frame, 0 for rest -> rearrange to [4, T_lat, H, W]
+            msk = mx.ones((1, num_frames, h_latent, w_latent))
+            msk = mx.concatenate([msk[:, :1], mx.zeros((1, num_frames - 1, h_latent, w_latent))], axis=1)
+            # Repeat first frame 4x, concat rest: [1, 4 + (F-1), H_lat, W_lat]
+            msk = mx.concatenate([
+                mx.repeat(msk[:, :1], 4, axis=1),
+                msk[:, 1:],
+            ], axis=1)
+            # Reshape to [1, T_lat, 4, H_lat, W_lat] then transpose -> [4, T_lat, H_lat, W_lat]
+            msk = msk.reshape(1, msk.shape[1] // 4, 4, h_latent, w_latent)
+            msk = msk.transpose(0, 2, 1, 3, 4)[0]  # [4, T_lat, H_lat, W_lat]
+
+            # y = concat([mask, encoded_video]) -> [20, T_lat, H_lat, W_lat]
+            y_i2v = mx.concatenate([msk, z_video], axis=0)
+            mx.eval(y_i2v)
+
+            del vae_enc, img_arr, img_chw, video, z_video, msk
+        else:
+            # TI2V-5B: encode single image, blend with noise via mask
+            img_tensor = preprocess_image(image, width, height)
+            mx.eval(img_tensor)
+
+            vae_enc = load_vae_encoder(vae_path, config)
+            z_img = vae_enc.encode(img_tensor)  # [1, 1, H_lat, W_lat, z_dim]
+            mx.eval(z_img)
+            z_img = z_img[0].transpose(3, 0, 1, 2)  # [z_dim, 1, H_lat, W_lat]
+            i2v_mask, i2v_mask_tokens = build_i2v_mask(target_shape, config.patch_size)
+
+            del vae_enc, img_tensor
+
+        gc.collect(); mx.clear_cache()
+        print(f"{Colors.DIM}  Image encoding: {time.time() - t_img:.1f}s{Colors.RESET}")
+
+    # Load transformer models
+    print(f"\n{Colors.BLUE}Loading transformer model(s)...{Colors.RESET}")
+    if quantization:
+        print(f"{Colors.DIM}  Using {quantization['bits']}-bit quantized weights (group_size={quantization['group_size']}){Colors.RESET}")
+    t2 = time.time()
+
+    # Merge per-model LoRAs with shared LoRAs
+    _loras_low = (loras or []) + (loras_low or []) or None
+    _loras_high = (loras or []) + (loras_high or []) or None
+    _loras_single = loras
+
+    if is_dual:
+        low_noise_path = model_dir / "low_noise_model.safetensors"
+        high_noise_path = model_dir / "high_noise_model.safetensors"
+        low_noise_model = load_wan_model(low_noise_path, config, quantization, loras=_loras_low)
+        high_noise_model = load_wan_model(high_noise_path, config, quantization, loras=_loras_high)
+    else:
+        single_model = load_wan_model(model_dir / "model.safetensors", config, quantization, loras=_loras_single)
+    print(f"{Colors.DIM}  Models loaded: {time.time() - t2:.1f}s{Colors.RESET}")
+
+    # Precompute text embeddings once (avoids redundant MLP in every step)
+    # Each model has its own text_embedding weights, so dual models need separate embeddings
+    if cfg_disabled:
+        # No CFG: only compute cond embeddings (B=1 forward pass, 2x faster)
+        if is_dual:
+            context_emb_low = low_noise_model.embed_text([context])
+            context_emb_high = high_noise_model.embed_text([context])
+            mx.eval(context_emb_low, context_emb_high)
+            context_cond_low = context_emb_low[0:1]
+            context_cond_high = context_emb_high[0:1]
+        else:
+            context_emb = single_model.embed_text([context])
+            mx.eval(context_emb)
+            context_cond = context_emb[0:1]
+    else:
+        if is_dual:
+            context_emb_low = low_noise_model.embed_text([context, context_null])
+            context_emb_high = high_noise_model.embed_text([context, context_null])
+            mx.eval(context_emb_low, context_emb_high)
+            context_cfg_low = mx.concatenate([context_emb_low[0:1], context_emb_low[1:2]], axis=0)
+            context_cfg_high = mx.concatenate([context_emb_high[0:1], context_emb_high[1:2]], axis=0)
+        else:
+            context_emb = single_model.embed_text([context, context_null])
+            mx.eval(context_emb)
+            context_cfg = mx.concatenate([context_emb[0:1], context_emb[1:2]], axis=0)
+
+    # Precompute cross-attention K/V caches (constant across all steps)
+    if cfg_disabled:
+        if is_dual:
+            cross_kv_low = low_noise_model.prepare_cross_kv(context_cond_low)
+            cross_kv_high = high_noise_model.prepare_cross_kv(context_cond_high)
+            mx.eval(cross_kv_low, cross_kv_high)
+        else:
+            cross_kv = single_model.prepare_cross_kv(context_cond)
+            mx.eval(cross_kv)
+    else:
+        if is_dual:
+            cross_kv_low = low_noise_model.prepare_cross_kv(context_cfg_low)
+            cross_kv_high = high_noise_model.prepare_cross_kv(context_cfg_high)
+            mx.eval(cross_kv_low, cross_kv_high)
+        else:
+            cross_kv = single_model.prepare_cross_kv(context_cfg)
+            mx.eval(cross_kv)
+
+    # Precompute RoPE frequencies (grid sizes are constant across all steps)
+    f_grid = t_latent // patch_size[0]
+    h_grid = h_latent // patch_size[1]
+    w_grid = w_latent // patch_size[2]
+    if cfg_disabled:
+        rope_grid_sizes = [(f_grid, h_grid, w_grid)]
+    else:
+        rope_grid_sizes = [(f_grid, h_grid, w_grid), (f_grid, h_grid, w_grid)]
+    if is_dual:
+        rope_cos_sin_low = low_noise_model.prepare_rope(rope_grid_sizes)
+        rope_cos_sin_high = high_noise_model.prepare_rope(rope_grid_sizes)
+        mx.eval(rope_cos_sin_low, rope_cos_sin_high)
+    else:
+        rope_cos_sin = single_model.prepare_rope(rope_grid_sizes)
+        mx.eval(rope_cos_sin)
+
+    # Setup scheduler
+    _schedulers = {
+        "euler": FlowMatchEulerScheduler,
+        "dpm++": FlowDPMPP2MScheduler,
+        "unipc": FlowUniPCScheduler,
+    }
+    sched_cls = _schedulers.get(scheduler, FlowUniPCScheduler)
+    sched = sched_cls(num_train_timesteps=config.num_train_timesteps)
+    sched.set_timesteps(steps, shift=shift)
+
+    # Generate initial noise
+    noise = mx.random.normal(target_shape)
+
+    # I2V initialization: TI2V-5B blends image with noise, I2V-14B uses pure noise
+    if is_i2v_mask_blend:
+        latents = (1.0 - i2v_mask) * z_img + i2v_mask * noise
+    else:
+        latents = noise
+
+    # Boundary for model switching (dual model only)
+    boundary = (config.boundary * config.num_train_timesteps) if is_dual else None
+
+    # Diffusion loop
+    print(f"\n{Colors.GREEN}Denoising ({steps} steps)...{Colors.RESET}")
+    t3 = time.time()
+
+    # Compile model forward for faster denoising
+    if not no_compile:
+        models_to_compile = (
+            [high_noise_model, low_noise_model] if is_dual else [single_model]
+        )
+        for m in models_to_compile:
+            m._compiled = mx.compile(m)
+
+    # Pre-convert timesteps to Python list to avoid .item() sync each step
+    timestep_list = sched.timesteps.tolist()
+
+    for i, t in enumerate(tqdm(range(steps), desc="Diffusion")):
+        timestep_val = timestep_list[i]
+
+        # Select model, cached K/V, and precomputed RoPE
+        if is_dual:
+            if timestep_val >= boundary:
+                model = high_noise_model
+                kv = cross_kv_high
+                rcs = rope_cos_sin_high
+            else:
+                model = low_noise_model
+                kv = cross_kv_low
+                rcs = rope_cos_sin_low
+        else:
+            model = single_model
+            kv = cross_kv
+            rcs = rope_cos_sin
+
+        # Use compiled forward when available (faster after first trace)
+        _call = getattr(model, '_compiled', model)
+
+        if cfg_disabled:
+            # No CFG: B=1 forward pass (2x faster than B=2 CFG batch)
+            if is_i2v_mask_blend:
+                t_tokens = i2v_mask_tokens * timestep_val
+                pad_len = seq_len - t_tokens.shape[1]
+                if pad_len > 0:
+                    t_tokens = mx.concatenate(
+                        [t_tokens, mx.full((1, pad_len), timestep_val)], axis=1
+                    )
+                t_batch = t_tokens  # [1, L]
+            else:
+                t_batch = mx.array([timestep_val])
+
+            y_arg = [y_i2v] if is_i2v_channel_concat else None
+
+            if is_dual:
+                ctx = context_cond_high if timestep_val >= boundary else context_cond_low
+            else:
+                ctx = context_cond
+            preds = _call(
+                [latents],
+                t=t_batch,
+                context=ctx,
+                seq_len=seq_len,
+                cross_kv_caches=kv,
+                y=y_arg,
+                rope_cos_sin=rcs,
+            )
+            noise_pred = preds[0]
+            del preds
+        else:
+            # CFG: batch cond + uncond into single B=2 forward pass
+            if is_dual:
+                gs = guide_scale[1] if timestep_val >= boundary else guide_scale[0]
+            else:
+                gs = guide_scale if isinstance(guide_scale, (int, float)) else guide_scale[0]
+
+            if is_i2v_mask_blend:
+                t_tokens = i2v_mask_tokens * timestep_val
+                pad_len = seq_len - t_tokens.shape[1]
+                if pad_len > 0:
+                    t_tokens = mx.concatenate(
+                        [t_tokens, mx.full((1, pad_len), timestep_val)], axis=1
+                    )
+                t_batch = mx.concatenate([t_tokens, t_tokens], axis=0)
+            else:
+                t_batch = mx.array([timestep_val, timestep_val])
+
+            y_arg = [y_i2v, y_i2v] if is_i2v_channel_concat else None
+
+            ctx = context_cfg if not is_dual else (
+                context_cfg_high if timestep_val >= boundary else context_cfg_low
+            )
+            preds = _call(
+                [latents, latents],
+                t=t_batch,
+                context=ctx,
+                seq_len=seq_len,
+                cross_kv_caches=kv,
+                y=y_arg,
+                rope_cos_sin=rcs,
+            )
+            noise_pred_cond, noise_pred_uncond = preds[0], preds[1]
+            noise_pred = noise_pred_uncond + gs * (noise_pred_cond - noise_pred_uncond)
+            del noise_pred_cond, noise_pred_uncond, preds
+
+        latents = sched.step(noise_pred[None], timestep_val, latents[None]).squeeze(0)
+
+        # TI2V-5B: re-apply mask to keep first frame frozen
+        if is_i2v_mask_blend:
+            latents = (1.0 - i2v_mask) * z_img + i2v_mask * latents
+
+        # Release temporaries before eval to free memory for graph execution
+        del noise_pred
+        mx.eval(latents)
+
+    print(f"{Colors.DIM}  Denoising: {time.time() - t3:.1f}s{Colors.RESET}")
+
+    # Diagnostic: per-temporal-position latent statistics
+    if debug_latents:
+        lat_np = np.array(latents)  # [C, T, H, W]
+        n_t = lat_np.shape[1]
+        print(f"\n{Colors.CYAN}  Latent diagnostics (shape {lat_np.shape}):{Colors.RESET}")
+        print(f"  {'Pos':>4s}  {'Mean':>8s}  {'Std':>8s}  {'Min':>8s}  {'Max':>8s}  {'AbsMean':>8s}")
+        for t_pos in range(min(n_t, 8)):
+            frame = lat_np[:, t_pos, :, :]
+            print(f"  {t_pos:4d}  {frame.mean():8.4f}  {frame.std():8.4f}  "
+                  f"{frame.min():8.4f}  {frame.max():8.4f}  {np.abs(frame).mean():8.4f}")
+        if n_t > 8:
+            interior = lat_np[:, 4:, :, :]
+            print(f"  {'4+':>4s}  {interior.mean():8.4f}  {interior.std():8.4f}  "
+                  f"{interior.min():8.4f}  {interior.max():8.4f}  {np.abs(interior).mean():8.4f}")
+        print()
+
+    # Free transformer models and text embeddings
+    if is_dual:
+        del low_noise_model, high_noise_model, cross_kv_low, cross_kv_high
+        if cfg_disabled:
+            del context_cond_low, context_cond_high
+        else:
+            del context_cfg_low, context_cfg_high
+    else:
+        del single_model, cross_kv
+        if cfg_disabled:
+            del context_cond
+        else:
+            del context_cfg
+    del model, kv, context
+    if context_null is not None:
+        del context_null
+    gc.collect(); mx.clear_cache()
+
+    # Load VAE and decode
+    print(f"\n{Colors.BLUE}Decoding with VAE...{Colors.RESET}")
+    t4 = time.time()
+    vae_path = model_dir / "vae.safetensors"
+    vae = load_vae_decoder(vae_path, config)
+
+    is_wan22_vae = config.vae_z_dim == 48
+
+    # Temporal extend: prepend reflected latent frames to the VAE input so that
+    # the CausalConv3d zero-padding artifacts fall on the prefix (which we crop).
+    # This gives the first real frame a full temporal receptive field of real data.
+    # Select tiling configuration
+    from mlx_video.models.ltx.video_vae.tiling import TilingConfig
+
+    if tiling == "none":
+        tiling_config = None
+    elif tiling == "auto":
+        tiling_config = TilingConfig.auto(height, width, num_frames)
+    elif tiling == "default":
+        tiling_config = TilingConfig.default()
+    elif tiling == "aggressive":
+        tiling_config = TilingConfig.aggressive()
+    elif tiling == "conservative":
+        tiling_config = TilingConfig.conservative()
+    elif tiling == "spatial":
+        tiling_config = TilingConfig.spatial_only()
+    elif tiling == "temporal":
+        tiling_config = TilingConfig.temporal_only()
+    else:
+        print(f"{Colors.YELLOW}  Unknown tiling mode '{tiling}', using auto{Colors.RESET}")
+        tiling_config = TilingConfig.auto(height, width, num_frames)
+
+    if tiling_config is not None:
+        spatial_info = f"{tiling_config.spatial_config.tile_size_in_pixels}px" if tiling_config.spatial_config else "none"
+        temporal_info = f"{tiling_config.temporal_config.tile_size_in_frames}f" if tiling_config.temporal_config else "none"
+        print(f"{Colors.DIM}  Tiling ({tiling}): spatial={spatial_info}, temporal={temporal_info}{Colors.RESET}")
+
+    if is_wan22_vae:
+        from mlx_video.models.wan.vae22 import denormalize_latents
+
+        # latents: [C, T, H, W] → [1, T, H, W, C] (channels-last for Wan2.2 VAE)
+        z = latents.transpose(1, 2, 3, 0)[None]
+        z = denormalize_latents(z)
+        if tiling_config is not None:
+            video = vae.decode_tiled(z, tiling_config)
+        else:
+            video = vae(z)
+        mx.eval(video)
+        print(f"{Colors.DIM}  VAE decode: {time.time() - t4:.1f}s{Colors.RESET}")
+
+        video = np.array(video[0])  # [T', H', W', 3]
+        video = (video + 1.0) / 2.0
+        video = np.clip(video * 255.0, 0, 255).astype(np.uint8)
+    else:
+        if tiling_config is not None:
+            video = vae.decode_tiled(latents[None], tiling_config)
+        else:
+            video = vae.decode(latents[None])
+        mx.eval(video)
+        print(f"{Colors.DIM}  VAE decode: {time.time() - t4:.1f}s{Colors.RESET}")
+
+        video = np.array(video[0])  # [3, T', H, W]
+        video = (video + 1.0) / 2.0
+        video = np.clip(video * 255.0, 0, 255).astype(np.uint8)
+        video = video.transpose(1, 2, 3, 0)  # [T, H, W, 3]
+
+    # Trim first N temporal chunks if requested (avoids first-frame artifacts)
+    if trim_first_frames > 0:
+        trim_pixels = trim_first_frames * 4
+        video = video[trim_pixels:]
+        print(f"{Colors.DIM}  Trimmed first {trim_pixels} frames ({video.shape[0]} remaining){Colors.RESET}")
+
+    save_video(video, output_path, fps=config.sample_fps)
+    print(f"\n{Colors.GREEN}✓ Video saved to {output_path}{Colors.RESET}")
+    print(f"{Colors.DIM}  Total time: {time.time() - t1:.1f}s{Colors.RESET}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Wan Text-to-Video Generation (MLX)")
+    parser.add_argument("--model-dir", type=str, required=True, help="Path to converted MLX model directory")
+    parser.add_argument("--prompt", type=str, required=True, help="Text prompt")
+    parser.add_argument("--image", type=str, default=None,
+                        help="Path to input image for I2V (omit for T2V mode)")
+    parser.add_argument("--negative-prompt", type=str, default=None,
+                        help="Negative prompt for CFG (default: official Chinese prompt from config)")
+    parser.add_argument("--no-negative-prompt", action="store_true",
+                        help="Disable negative prompt (use empty string instead of config default)")
+    parser.add_argument("--width", type=int, default=1280, help="Video width (default: 1280)")
+    parser.add_argument("--height", type=int, default=704, help="Video height (default: 704; 720p models use 704)")
+    parser.add_argument("--num-frames", type=int, default=81, help="Number of frames (must be 4n+1)")
+    parser.add_argument("--steps", type=int, default=None, help="Number of diffusion steps (default: from config)")
+    parser.add_argument("--guide-scale", type=str, default=None, help="Guidance scale: single float or low,high pair")
+    parser.add_argument("--shift", type=float, default=None, help="Noise schedule shift (default: from config)")
+    parser.add_argument("--seed", type=int, default=-1, help="Random seed")
+    parser.add_argument("--output-path", type=str, default="output.mp4", help="Output video path")
+    parser.add_argument(
+        "--scheduler", type=str, default="unipc",
+        choices=["euler", "dpm++", "unipc"],
+        help="Diffusion solver: euler (1st order), dpm++ (2nd order), unipc (2nd order PC, default/official)",
+    )
+    parser.add_argument(
+        "--lora", nargs=2, action="append", metavar=("PATH", "STRENGTH"),
+        help="Apply a LoRA to all models (repeatable). Format: --lora path.safetensors 0.8",
+    )
+    parser.add_argument(
+        "--lora-high", nargs=2, action="append", metavar=("PATH", "STRENGTH"),
+        help="Apply a LoRA to high-noise model only (dual-model, repeatable)",
+    )
+    parser.add_argument(
+        "--lora-low", nargs=2, action="append", metavar=("PATH", "STRENGTH"),
+        help="Apply a LoRA to low-noise model only (dual-model, repeatable)",
+    )
+    parser.add_argument(
+        "--tiling",
+        type=str,
+        default="auto",
+        choices=["auto", "none", "default", "aggressive", "conservative", "spatial", "temporal"],
+        help="VAE tiling mode to reduce memory during decoding (default: auto)",
+    )
+    parser.add_argument(
+        "--no-compile", action="store_true",
+        help="Disable mx.compile on models (for debugging)",
+    )
+    parser.add_argument(
+        "--trim-first-frames", type=int, default=0, metavar="N",
+        help="Generate N extra temporal chunks (N×4 frames) and discard them from the start. "
+             "Fixes first-frame color/lighting artifacts on 14B models. Try 1 first (4 frames). "
+             "Default: 0 (disabled)",
+    )
+    parser.add_argument(
+        "--debug-latents", action="store_true",
+        help="Print per-temporal-position latent statistics after denoising (diagnostic)",
+    )
+    args = parser.parse_args()
+
+    # Parse guide scale
+    guide_scale = None
+    if args.guide_scale is not None:
+        parts = [float(x) for x in args.guide_scale.split(",")]
+        guide_scale = tuple(parts) if len(parts) > 1 else parts[0]
+
+    # Handle negative prompt: --no-negative-prompt forces empty, otherwise pass through
+    neg_prompt = args.negative_prompt
+    if args.no_negative_prompt:
+        neg_prompt = ""
+
+    # Parse LoRA configs: convert [path, strength_str] → (path, float)
+    def _parse_lora_args(lora_list):
+        if not lora_list:
+            return None
+        return [(path, float(strength)) for path, strength in lora_list]
+
+    generate_video(
+        model_dir=args.model_dir,
+        prompt=args.prompt,
+        negative_prompt=neg_prompt,
+        image=args.image,
+        width=args.width,
+        height=args.height,
+        num_frames=args.num_frames,
+        steps=args.steps,
+        guide_scale=guide_scale,
+        shift=args.shift,
+        seed=args.seed,
+        output_path=args.output_path,
+        scheduler=args.scheduler,
+        loras=_parse_lora_args(args.lora),
+        loras_high=_parse_lora_args(args.lora_high),
+        loras_low=_parse_lora_args(args.lora_low),
+        tiling=args.tiling,
+        no_compile=args.no_compile,
+        trim_first_frames=args.trim_first_frames,
+        debug_latents=args.debug_latents,
+    )
+
+
+if __name__ == "__main__":
+    main()