829 lines
34 KiB
Python
829 lines
34 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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from mlx_video.models.wan.i2v_utils import build_i2v_mask, preprocess_image
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from mlx_video.models.wan.loading import (
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_clean_text,
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encode_text,
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load_t5_encoder,
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load_vae_decoder,
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load_vae_encoder,
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load_wan_model,
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)
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from mlx_video.models.wan.postprocess import save_video
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class Colors:
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"""ANSI color codes for terminal output."""
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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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# Backward-compat alias (tests and external code may use the old name)
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_build_i2v_mask = build_i2v_mask
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def _best_output_size(w, h, dw, dh, max_area):
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"""Compute the best output resolution that fits within max_area while
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preserving the input aspect ratio and satisfying alignment constraints.
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Matches the reference implementation's best_output_size().
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"""
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ratio = w / h
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ow = (max_area * ratio) ** 0.5
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oh = max_area / ow
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# Option 1: process width first
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ow1 = int(ow // dw * dw)
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oh1 = int(max_area / ow1 // dh * dh)
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ratio1 = ow1 / oh1
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# Option 2: process height first
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oh2 = int(oh // dh * dh)
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ow2 = int(max_area / oh2 // dw * dw)
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ratio2 = ow2 / oh2
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if max(ratio / ratio1, ratio1 / ratio) < max(ratio / ratio2, ratio2 / ratio):
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return ow1, oh1
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return ow2, oh2
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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 | None = None,
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image: str | None = None,
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width: int = 1280,
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height: int = 704,
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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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scheduler: str = "unipc",
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loras: list | None = None,
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loras_high: list | None = None,
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loras_low: list | None = None,
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tiling: str = "auto",
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no_compile: bool = False,
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trim_first_frames: int = 0,
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debug_latents: bool = False,
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):
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"""Generate video using Wan pipeline (supports T2V and I2V).
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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 (None = use config default, "" = no negative prompt)
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image: Path to input image for I2V (None = T2V mode)
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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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scheduler: Solver type: 'euler', 'dpm++', or 'unipc' (default)
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loras: Optional list of (path, strength) tuples applied to all models
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loras_high: Optional list of (path, strength) tuples for high-noise model only
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loras_low: Optional list of (path, strength) tuples for low-noise model only
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tiling: Tiling mode for VAE decoding. Options:
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- "auto": Automatically determine tiling based on video size (default)
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- "none": Disable tiling
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- "default", "aggressive", "conservative": Preset tiling configs
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- "spatial": Spatial tiling only
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- "temporal": Temporal tiling only
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no_compile: If True, skip mx.compile on models (useful for debugging)
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trim_first_frames: Number of temporal latent positions to generate extra
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and discard from the start. Each position = 4 pixel frames. Use 1
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to fix first-frame artifacts on 14B models (generates 4 extra frames,
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discards first 4). Use 2 for more aggressive trimming. Default: 0.
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debug_latents: If True, print per-temporal-position latent statistics
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after denoising for diagnosing first-frame artifacts.
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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 (
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FlowDPMPP2MScheduler,
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FlowMatchEulerScheduler,
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FlowUniPCScheduler,
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)
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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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is_i2v = image is not None
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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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# Detect CFG-disabled mode (guide_scale=1.0 for all models → skip uncond pass for 2x speedup)
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if isinstance(guide_scale, tuple):
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cfg_disabled = all(gs <= 1.0 for gs in guide_scale)
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else:
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cfg_disabled = guide_scale <= 1.0
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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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gen_frames = num_frames
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if trim_first_frames > 0:
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gen_frames = num_frames + trim_first_frames * 4
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print(f"{Colors.DIM} Trim: generating {gen_frames} frames, will discard first {trim_first_frames * 4}{Colors.RESET}")
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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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pipeline_str = "Image-to-Video" if is_i2v else "Text-to-Video"
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# Resolve negative prompt: explicit user value > config default
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# The official Wan2.2 uses a Chinese negative prompt (config.sample_neg_prompt)
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# that prevents oversaturation, artifacts, and comic look. We use it by default.
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# Text cleaning (_clean_text) normalizes fullwidth chars to match official tokenization.
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if negative_prompt is None:
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neg_prompt_resolved = config.sample_neg_prompt
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else:
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neg_prompt_resolved = negative_prompt
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print(f"{Colors.CYAN}{'='*60}")
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print(f" {version_str} {pipeline_str} 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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if is_i2v:
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print(f" Image: {image}")
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if neg_prompt_resolved and neg_prompt_resolved.strip():
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neg_display = neg_prompt_resolved[:60] + "..." if len(neg_prompt_resolved) > 60 else neg_prompt_resolved
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print(f" Neg prompt: {neg_display}")
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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}, Solver: {scheduler}")
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if cfg_disabled:
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print(f" CFG: disabled (guide_scale≤1 → B=1 fast path, 2x denoising speedup)")
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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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# Align dimensions to patch_size * vae_stride (required for patchify)
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vae_stride = config.vae_stride
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patch_size = config.patch_size
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align_h = patch_size[1] * vae_stride[1] # e.g. 2*16=32
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align_w = patch_size[2] * vae_stride[2]
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if height % align_h != 0 or width % align_w != 0:
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old_h, old_w = height, width
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height = (height // align_h) * align_h
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width = (width // align_w) * align_w
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if height == 0:
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height = align_h
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if width == 0:
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width = align_w
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print(f"{Colors.DIM} Aligned {old_w}x{old_h} → {width}x{height} (must be divisible by {align_w}x{align_h}){Colors.RESET}")
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# Enforce max_area constraint (model-specific resolution limit)
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if config.max_area > 0 and height * width > config.max_area:
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old_h, old_w = height, width
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width, height = _best_output_size(width, height, align_w, align_h, config.max_area)
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print(
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f"{Colors.YELLOW} ⚠ Resolution {old_w}x{old_h} exceeds model's max area "
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f"({config.max_area:,}px). Adjusted → {width}x{height}{Colors.RESET}"
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)
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# Compute target latent shape
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z_dim = config.vae_z_dim
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t_latent = (gen_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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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 cfg_disabled:
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context_null = None
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mx.eval(context)
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else:
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context_null = encode_text(t5_encoder, tokenizer, neg_prompt_resolved, 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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# I2V: encode image to latent space
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z_img = None
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i2v_mask = None
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i2v_mask_tokens = None
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y_i2v = None
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is_i2v_channel_concat = is_i2v and config.model_type == "i2v"
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is_i2v_mask_blend = is_i2v and config.model_type != "i2v"
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if is_i2v:
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print(f"\n{Colors.BLUE}Encoding input image...{Colors.RESET}")
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t_img = time.time()
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vae_path = model_dir / "vae.safetensors"
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if is_i2v_channel_concat:
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# I2V-14B: encode full video (first frame = image, rest = zeros)
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# and construct y tensor with mask + encoded latents
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from PIL import Image
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img = Image.open(image).convert("RGB")
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scale = max(width / img.width, height / img.height)
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img = img.resize((round(img.width * scale), round(img.height * scale)), Image.LANCZOS)
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x1, y1 = (img.width - width) // 2, (img.height - height) // 2
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img = img.crop((x1, y1, x1 + width, y1 + height))
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img_arr = mx.array(np.array(img, dtype=np.float32) / 255.0 * 2.0 - 1.0) # [H, W, 3]
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img_chw = img_arr.transpose(2, 0, 1) # [3, H, W]
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# Build video: first frame = image, rest = zeros -> [3, F, H, W]
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# Chunked encoding processes 1-frame + 4-frame chunks with temporal caching
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video = mx.concatenate([
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img_chw[:, None, :, :],
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mx.zeros((3, num_frames - 1, height, width)),
|
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], axis=1)
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# Encode through Wan2.1 VAE -> [1, z_dim, T_lat, H_lat, W_lat]
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vae_enc = load_vae_encoder(vae_path, config)
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z_video = vae_enc.encode(video[None]) # [1, 16, T_lat, H_lat, W_lat]
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mx.eval(z_video)
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z_video = z_video[0] # [16, T_lat, H_lat, W_lat]
|
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# Build mask: 1 for first frame, 0 for rest -> rearrange to [4, T_lat, H, W]
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msk = mx.ones((1, num_frames, h_latent, w_latent))
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msk = mx.concatenate([msk[:, :1], mx.zeros((1, num_frames - 1, h_latent, w_latent))], axis=1)
|
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# Repeat first frame 4x, concat rest: [1, 4 + (F-1), H_lat, W_lat]
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msk = mx.concatenate([
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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]
|
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msk = msk.reshape(1, msk.shape[1] // 4, 4, h_latent, w_latent)
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msk = msk.transpose(0, 2, 1, 3, 4)[0] # [4, T_lat, H_lat, W_lat]
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# y = concat([mask, encoded_video]) -> [20, T_lat, H_lat, W_lat]
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y_i2v = mx.concatenate([msk, z_video], axis=0)
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mx.eval(y_i2v)
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del vae_enc, img_arr, img_chw, video, z_video, msk
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else:
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# TI2V-5B: encode single image, blend with noise via mask
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img_tensor = preprocess_image(image, width, height)
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mx.eval(img_tensor)
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vae_enc = load_vae_encoder(vae_path, config)
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z_img = vae_enc.encode(img_tensor) # [1, 1, H_lat, W_lat, z_dim]
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mx.eval(z_img)
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z_img = z_img[0].transpose(3, 0, 1, 2) # [z_dim, 1, H_lat, W_lat]
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i2v_mask, i2v_mask_tokens = build_i2v_mask(target_shape, config.patch_size)
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del vae_enc, img_tensor
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gc.collect(); mx.clear_cache()
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print(f"{Colors.DIM} Image encoding: {time.time() - t_img:.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}")
|
||
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
|
||
|
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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, 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()
|