513 lines
18 KiB
Python
513 lines
18 KiB
Python
"""Wan2.2 Text-to-Video generation pipeline for MLX."""
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import argparse
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import gc
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import math
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import random
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import sys
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import time
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from pathlib import Path
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import mlx.core as mx
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import mlx.nn as nn
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import numpy as np
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from tqdm import tqdm
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class Colors:
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CYAN = "\033[96m"
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BLUE = "\033[94m"
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GREEN = "\033[92m"
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YELLOW = "\033[93m"
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RED = "\033[91m"
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MAGENTA = "\033[95m"
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BOLD = "\033[1m"
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DIM = "\033[2m"
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RESET = "\033[0m"
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def load_wan_model(model_path: Path, config, quantization: dict | None = None):
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"""Load and initialize WanModel, with optional quantization support.
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Args:
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model_path: Path to model safetensors file
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config: WanModelConfig
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quantization: Optional dict with 'bits' and 'group_size' keys.
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If provided, creates QuantizedLinear stubs before loading.
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"""
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from mlx_video.models.wan.model import WanModel
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model = WanModel(config)
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if quantization:
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from mlx_video.convert_wan import _quantize_predicate
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nn.quantize(
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model,
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group_size=quantization["group_size"],
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bits=quantization["bits"],
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class_predicate=lambda path, m: _quantize_predicate(path, m),
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)
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weights = mx.load(str(model_path))
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model.load_weights(list(weights.items()), strict=False)
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mx.eval(model.parameters())
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return model
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def load_t5_encoder(model_path: Path, config):
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"""Load T5 text encoder."""
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from mlx_video.models.wan.text_encoder import T5Encoder
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encoder = T5Encoder(
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vocab_size=config.t5_vocab_size,
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dim=config.t5_dim,
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dim_attn=config.t5_dim_attn,
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dim_ffn=config.t5_dim_ffn,
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num_heads=config.t5_num_heads,
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num_layers=config.t5_num_layers,
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num_buckets=config.t5_num_buckets,
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shared_pos=False,
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)
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weights = mx.load(str(model_path))
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encoder.load_weights(list(weights.items()))
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mx.eval(encoder.parameters())
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return encoder
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def load_vae_decoder(model_path: Path, config=None):
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"""Load VAE decoder (skips encoder weights with strict=False).
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For Wan2.2 (vae_z_dim=48), uses Wan22VAEDecoder.
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For Wan2.1 (vae_z_dim=16), uses WanVAE.
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"""
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is_wan22 = config is not None and config.vae_z_dim == 48
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if is_wan22:
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from mlx_video.models.wan.vae22 import Wan22VAEDecoder
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vae = Wan22VAEDecoder(z_dim=48)
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else:
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from mlx_video.models.wan.vae import WanVAE
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vae = WanVAE(z_dim=16)
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weights = mx.load(str(model_path))
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vae.load_weights(list(weights.items()), strict=False)
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mx.eval(vae.parameters())
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return vae
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def encode_text(
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encoder,
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tokenizer,
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prompt: str,
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text_len: int = 512,
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) -> mx.array:
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"""Encode text prompt using T5 encoder.
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Args:
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encoder: T5Encoder model
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tokenizer: HuggingFace tokenizer
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prompt: Text prompt
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text_len: Maximum text length
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Returns:
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Text embeddings [L, dim]
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"""
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tokens = tokenizer(
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prompt,
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max_length=text_len,
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padding="max_length",
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truncation=True,
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return_tensors="np",
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)
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ids = mx.array(tokens["input_ids"])
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mask = mx.array(tokens["attention_mask"])
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embeddings = encoder(ids, mask=mask)
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# Return only non-padding tokens
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seq_len = int(mask.sum().item())
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return embeddings[0, :seq_len]
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def generate_video(
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model_dir: str,
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prompt: str,
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negative_prompt: str = "",
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width: int = 1280,
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height: int = 720,
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num_frames: int = 81,
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steps: int = None,
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guide_scale: str | float | tuple = None,
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shift: float = None,
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seed: int = -1,
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output_path: str = "output.mp4",
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):
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"""Generate video using Wan T2V pipeline (supports 2.1 and 2.2).
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Args:
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model_dir: Path to converted MLX model directory
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prompt: Text prompt
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negative_prompt: Negative prompt
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width: Video width
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height: Video height
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num_frames: Number of frames (must be 4n+1)
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steps: Number of diffusion steps (None = use config default)
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guide_scale: Guidance scale: float for single, (low,high) for dual (None = config default)
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shift: Noise schedule shift (None = use config default)
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seed: Random seed (-1 for random)
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output_path: Output video path
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"""
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import json
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from mlx_video.models.wan.config import WanModelConfig
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from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler
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model_dir = Path(model_dir)
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# Load config from model dir if available, otherwise auto-detect
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config_path = model_dir / "config.json"
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quantization = None
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if config_path.exists():
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with open(config_path) as f:
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config_dict = json.load(f)
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# Extract quantization config (not a model config field)
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quantization = config_dict.pop("quantization", None)
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# Handle tuple fields stored as lists in JSON
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for key in ("patch_size", "vae_stride", "window_size", "sample_guide_scale"):
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if key in config_dict and isinstance(config_dict[key], list):
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config_dict[key] = tuple(config_dict[key])
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config = WanModelConfig(**{
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k: v for k, v in config_dict.items()
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if k in WanModelConfig.__dataclass_fields__
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})
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else:
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# Auto-detect: dual model files → 2.2, single model → 2.1
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if (model_dir / "low_noise_model.safetensors").exists():
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config = WanModelConfig.wan22_t2v_14b()
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else:
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# Detect 1.3B vs 14B from weight shapes
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model_path = model_dir / "model.safetensors"
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if model_path.exists():
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probe = mx.load(str(model_path), return_metadata=False)
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for k, v in probe.items():
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if "patch_embedding_proj.weight" in k:
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dim = v.shape[0]
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if dim <= 2048:
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config = WanModelConfig.wan21_t2v_1_3b()
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else:
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config = WanModelConfig.wan21_t2v_14b()
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break
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else:
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config = WanModelConfig.wan21_t2v_14b()
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del probe
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else:
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config = WanModelConfig.wan21_t2v_14b()
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is_dual = config.dual_model
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# Validate config against actual weights (handles mismatched config.json)
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if not is_dual:
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model_path = model_dir / "model.safetensors"
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if model_path.exists():
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probe = mx.load(str(model_path), return_metadata=False)
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for k, v in probe.items():
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if "patch_embedding_proj.weight" in k:
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actual_dim = v.shape[0]
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if actual_dim != config.dim:
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print(f"{Colors.YELLOW} Config dim={config.dim} doesn't match weights dim={actual_dim}, auto-correcting...{Colors.RESET}")
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if actual_dim <= 2048:
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config = WanModelConfig.wan21_t2v_1_3b()
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else:
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config = WanModelConfig.wan21_t2v_14b()
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break
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del probe
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# Auto-correct Wan2.2 VAE params from stale configs
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if config.in_dim == 48 and config.vae_z_dim != 48:
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print(f"{Colors.YELLOW} Auto-correcting Wan2.2 VAE params (in_dim=48 but vae_z_dim={config.vae_z_dim}){Colors.RESET}")
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config = WanModelConfig(**{
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**{f.name: getattr(config, f.name) for f in config.__dataclass_fields__.values()},
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"vae_z_dim": 48,
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"vae_stride": (4, 16, 16),
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"sample_fps": 24,
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})
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# Apply defaults from config if not overridden
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if steps is None:
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steps = config.sample_steps
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if shift is None:
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shift = config.sample_shift
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if guide_scale is None:
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guide_scale = config.sample_guide_scale
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# Normalize guide_scale
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if isinstance(guide_scale, (int, float)):
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guide_scale = float(guide_scale)
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elif isinstance(guide_scale, str):
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parts = [float(x) for x in guide_scale.split(",")]
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guide_scale = tuple(parts) if len(parts) > 1 else parts[0]
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# Validate frame count
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assert (num_frames - 1) % 4 == 0, f"num_frames must be 4n+1, got {num_frames}"
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version_str = f"Wan{config.model_version}"
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mode_str = "dual-model" if is_dual else "single-model"
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print(f"{Colors.CYAN}{'='*60}")
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print(f" {version_str} Text-to-Video Generation (MLX, {mode_str})")
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print(f"{'='*60}{Colors.RESET}")
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print(f"{Colors.DIM} Prompt: {prompt}")
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print(f" Size: {width}x{height}, Frames: {num_frames}")
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print(f" Steps: {steps}, Guide: {guide_scale}, Shift: {shift}")
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print(f"{Colors.RESET}")
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# Seed
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if seed < 0:
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seed = random.randint(0, 2**32 - 1)
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mx.random.seed(seed)
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np.random.seed(seed)
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print(f"{Colors.DIM} Seed: {seed}{Colors.RESET}")
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# Compute target latent shape
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vae_stride = config.vae_stride
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z_dim = config.vae_z_dim
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t_latent = (num_frames - 1) // vae_stride[0] + 1
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h_latent = height // vae_stride[1]
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w_latent = width // vae_stride[2]
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target_shape = (z_dim, t_latent, h_latent, w_latent)
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# Sequence length for transformer
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patch_size = config.patch_size
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seq_len = math.ceil(
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(h_latent * w_latent) / (patch_size[1] * patch_size[2]) * t_latent
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)
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print(f"{Colors.DIM} Latent shape: {target_shape}")
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print(f" Sequence length: {seq_len}{Colors.RESET}")
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# Load T5 encoder
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t1 = time.time()
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print(f"\n{Colors.BLUE}Loading T5 encoder...{Colors.RESET}")
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t5_path = model_dir / "t5_encoder.safetensors"
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t5_encoder = load_t5_encoder(t5_path, config)
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# Load tokenizer
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl")
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# Encode prompts
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print(f"{Colors.BLUE}Encoding text...{Colors.RESET}")
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context = encode_text(t5_encoder, tokenizer, prompt, config.text_len)
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if negative_prompt:
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context_null = encode_text(t5_encoder, tokenizer, negative_prompt, config.text_len)
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else:
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context_null = encode_text(t5_encoder, tokenizer, "", config.text_len)
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mx.eval(context, context_null)
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# Free T5 from memory
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del t5_encoder
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gc.collect(); mx.clear_cache()
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print(f"{Colors.DIM} T5 encoding: {time.time() - t1:.1f}s{Colors.RESET}")
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# Load transformer models
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print(f"\n{Colors.BLUE}Loading transformer model(s)...{Colors.RESET}")
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if quantization:
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print(f"{Colors.DIM} Using {quantization['bits']}-bit quantized weights (group_size={quantization['group_size']}){Colors.RESET}")
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t2 = time.time()
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if is_dual:
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low_noise_path = model_dir / "low_noise_model.safetensors"
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high_noise_path = model_dir / "high_noise_model.safetensors"
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low_noise_model = load_wan_model(low_noise_path, config, quantization)
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high_noise_model = load_wan_model(high_noise_path, config, quantization)
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else:
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single_model = load_wan_model(model_dir / "model.safetensors", config, quantization)
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print(f"{Colors.DIM} Models loaded: {time.time() - t2:.1f}s{Colors.RESET}")
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# Precompute text embeddings once (avoids redundant MLP in every step)
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ref_model = single_model if not is_dual else low_noise_model
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context_emb = ref_model.embed_text([context, context_null])
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mx.eval(context_emb)
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context_cond = context_emb[0:1] # [1, text_len, dim]
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context_uncond = context_emb[1:2] # [1, text_len, dim]
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# Stack for batched CFG: [2, text_len, dim]
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context_cfg = mx.concatenate([context_cond, context_uncond], axis=0)
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# Precompute cross-attention K/V caches (constant across all steps)
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if is_dual:
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cross_kv_low = low_noise_model.prepare_cross_kv(context_cfg)
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cross_kv_high = high_noise_model.prepare_cross_kv(context_cfg)
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mx.eval(cross_kv_low, cross_kv_high)
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else:
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cross_kv = single_model.prepare_cross_kv(context_cfg)
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mx.eval(cross_kv)
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# Setup scheduler
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scheduler = FlowMatchEulerScheduler(num_train_timesteps=config.num_train_timesteps)
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scheduler.set_timesteps(steps, shift=shift)
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# Generate initial noise
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noise = mx.random.normal(target_shape)
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# Boundary for model switching (dual model only)
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boundary = (config.boundary * config.num_train_timesteps) if is_dual else None
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# Diffusion loop
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print(f"\n{Colors.GREEN}Denoising ({steps} steps)...{Colors.RESET}")
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latents = noise
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t3 = time.time()
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for i, t in enumerate(tqdm(range(steps), desc="Diffusion")):
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timestep_val = scheduler.timesteps[i].item()
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# Select model, guide scale, and cached K/V
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if is_dual:
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if timestep_val >= boundary:
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model = high_noise_model
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gs = guide_scale[1]
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kv = cross_kv_high
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else:
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model = low_noise_model
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gs = guide_scale[0]
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kv = cross_kv_low
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else:
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model = single_model
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gs = guide_scale if isinstance(guide_scale, (int, float)) else guide_scale[0]
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kv = cross_kv
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# CFG: batch cond + uncond into single B=2 forward pass
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preds = model(
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[latents, latents],
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t=mx.array([timestep_val, timestep_val]),
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context=context_cfg,
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seq_len=seq_len,
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cross_kv_caches=kv,
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)
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noise_pred_cond, noise_pred_uncond = preds[0], preds[1]
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# Classifier-free guidance + scheduler step
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noise_pred = noise_pred_uncond + gs * (noise_pred_cond - noise_pred_uncond)
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latents = scheduler.step(noise_pred[None], timestep_val, latents[None]).squeeze(0)
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# Release temporaries before eval to free memory for graph execution
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del noise_pred_cond, noise_pred_uncond, noise_pred, preds
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mx.eval(latents)
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print(f"{Colors.DIM} Denoising: {time.time() - t3:.1f}s{Colors.RESET}")
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# Free transformer models and text embeddings
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if is_dual:
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del low_noise_model, high_noise_model, cross_kv_low, cross_kv_high
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else:
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del single_model, cross_kv
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del model, kv, context, context_null, context_cfg
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gc.collect(); mx.clear_cache()
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# Load VAE and decode
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print(f"\n{Colors.BLUE}Decoding with VAE...{Colors.RESET}")
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t4 = time.time()
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vae_path = model_dir / "vae.safetensors"
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vae = load_vae_decoder(vae_path, config)
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is_wan22_vae = config.vae_z_dim == 48
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if is_wan22_vae:
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from mlx_video.models.wan.vae22 import denormalize_latents
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# latents: [C, T, H, W] → [1, T, H, W, C] (channels-last for Wan2.2 VAE)
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z = latents.transpose(1, 2, 3, 0)[None] # [1, T, H, W, C]
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z = denormalize_latents(z)
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video = vae(z) # [1, T', H', W', 3]
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mx.eval(video)
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print(f"{Colors.DIM} VAE decode: {time.time() - t4:.1f}s{Colors.RESET}")
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video = np.array(video[0]) # [T', H', W', 3]
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video = (video + 1.0) / 2.0
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video = np.clip(video * 255.0, 0, 255).astype(np.uint8)
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else:
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video = vae.decode(latents[None]) # [1, 3, T, H, W]
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mx.eval(video)
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print(f"{Colors.DIM} VAE decode: {time.time() - t4:.1f}s{Colors.RESET}")
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video = np.array(video[0]) # [3, T, H, W]
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video = (video + 1.0) / 2.0
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video = np.clip(video * 255.0, 0, 255).astype(np.uint8)
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video = video.transpose(1, 2, 3, 0) # [T, H, W, 3]
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save_video(video, output_path, fps=config.sample_fps)
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print(f"\n{Colors.GREEN}✓ Video saved to {output_path}{Colors.RESET}")
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print(f"{Colors.DIM} Total time: {time.time() - t1:.1f}s{Colors.RESET}")
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def save_video(frames: np.ndarray, output_path: str, fps: int = 16):
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"""Save video frames to MP4.
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Args:
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frames: Video frames [T, H, W, 3] uint8
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output_path: Output file path
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fps: Frames per second
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"""
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try:
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import imageio
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writer = imageio.get_writer(output_path, fps=fps, codec="libx264", quality=8)
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for frame in frames:
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writer.append_data(frame)
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writer.close()
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except ImportError:
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try:
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import cv2
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h, w = frames.shape[1], frames.shape[2]
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fourcc = cv2.VideoWriter_fourcc(*"avc1")
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writer = cv2.VideoWriter(output_path, fourcc, fps, (w, h))
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for frame in frames:
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writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
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|
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}/)")
|
|
|
|
|
|
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("--negative-prompt", type=str, default="", help="Negative prompt")
|
|
parser.add_argument("--width", type=int, default=1280, help="Video width")
|
|
parser.add_argument("--height", type=int, default=720, help="Video height")
|
|
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")
|
|
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]
|
|
|
|
generate_video(
|
|
model_dir=args.model_dir,
|
|
prompt=args.prompt,
|
|
negative_prompt=args.negative_prompt,
|
|
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,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|