import torch from diffsynth import ModelManager, WanVideoPipeline from PIL import Image import argparse from transformers import Wav2Vec2Processor, Wav2Vec2Model import librosa import os import subprocess import cv2 from model import FantasyTalkingAudioConditionModel from utils import save_video, get_audio_features, resize_image_by_longest_edge from pathlib import Path from datetime import datetime # from modelscope import snapshot_download from huggingface_hub import snapshot_download def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--wan_model_dir", type=str, default="./models/Wan2.1-I2V-14B-720P", required=False, help="The dir of the Wan I2V 14B model.", ) parser.add_argument( "--fantasytalking_model_path", type=str, default="./models/fantasytalking_model.ckpt", required=False, help="The .ckpt path of fantasytalking model.", ) parser.add_argument( "--wav2vec_model_dir", type=str, default="./models/wav2vec2-base-960h", required=False, help="The dir of wav2vec model.", ) parser.add_argument( "--image_path", type=str, default="./assets/images/woman.png", required=False, help="The path of the image.", ) parser.add_argument( "--audio_path", type=str, default="./assets/audios/woman.wav", required=False, help="The path of the audio.", ) parser.add_argument( "--prompt", type=str, default="A woman is talking.", required=False, help="prompt.", ) parser.add_argument( "--output_dir", type=str, default="./output", help="Dir to save the model.", ) parser.add_argument( "--image_size", type=int, default=512, help="The image will be resized proportionally to this size.", ) parser.add_argument( "--audio_scale", type=float, default=1.0, help="Audio condition injection weight", ) parser.add_argument( "--prompt_cfg_scale", type=float, default=5.0, required=False, help="Prompt cfg scale", ) parser.add_argument( "--audio_cfg_scale", type=float, default=5.0, required=False, help="Audio cfg scale", ) parser.add_argument( "--max_num_frames", type=int, default=81, required=False, help="The maximum frames for generating videos, the audio part exceeding max_num_frames/fps will be truncated." ) parser.add_argument( "--fps", type=int, default=23, required=False, ) parser.add_argument( "--num_persistent_param_in_dit", type=int, default=None, required=False, help="Maximum parameter quantity retained in video memory, small number to reduce VRAM required" ) parser.add_argument( "--seed", type=int, default=1111, required=False, ) args = parser.parse_args() return args import torch from huggingface_hub import snapshot_download from diffusers import WanVideoPipeline from transformers import Wav2Vec2Processor, Wav2Vec2Model from models import FantasyTalkingAudioConditionModel # adjust import if needed from model_manager import ModelManager # assuming this exists in your repo def load_models(args): print("🚀 [Startup] Initializing all models (compile-time preloading)...") # -------------------------------------------- # STEP 1 — Ensure all model files are cached # -------------------------------------------- snapshot_download("Wan-AI/Wan2.1-I2V-14B-720P", local_dir="./models/Wan2.1-I2V-14B-720P") snapshot_download("facebook/wav2vec2-base-960h", local_dir="./models/wav2vec2-base-960h") snapshot_download("acvlab/FantasyTalking", local_dir="./models") # -------------------------------------------- # STEP 2 — Initialize ModelManager (core loader) # -------------------------------------------- print("🔧 Loading Wan I2V model checkpoints via ModelManager...") model_manager = ModelManager(device="cuda") model_manager.load_models( [ [ f"{args.wan_model_dir}/diffusion_pytorch_model-00001-of-00007.safetensors", f"{args.wan_model_dir}/diffusion_pytorch_model-00002-of-00007.safetensors", f"{args.wan_model_dir}/diffusion_pytorch_model-00003-of-00007.safetensors", f"{args.wan_model_dir}/diffusion_pytorch_model-00004-of-00007.safetensors", f"{args.wan_model_dir}/diffusion_pytorch_model-00005-of-00007.safetensors", f"{args.wan_model_dir}/diffusion_pytorch_model-00006-of-00007.safetensors", f"{args.wan_model_dir}/diffusion_pytorch_model-00007-of-00007.safetensors", ], f"{args.wan_model_dir}/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", f"{args.wan_model_dir}/models_t5_umt5-xxl-enc-bf16.pth", f"{args.wan_model_dir}/Wan2.1_VAE.pth", ], torch_dtype=torch.bfloat16, ) pipe = WanVideoPipeline.from_model_manager( model_manager, torch_dtype=torch.bfloat16, device="cuda" ) pipe.enable_vram_management(num_persistent_param_in_dit=args.num_persistent_param_in_dit) pipe.to("cuda") pipe.eval() # -------------------------------------------- # STEP 3 — Load FantasyTalking model # -------------------------------------------- print("🧠 Loading FantasyTalking model...") fantasytalking = FantasyTalkingAudioConditionModel(pipe.dit, 768, 2048).to("cuda").eval() fantasytalking.load_audio_processor(args.fantasytalking_model_path, pipe.dit) # -------------------------------------------- # STEP 4 — Load Wav2Vec2 model + processor # -------------------------------------------- print("🎙️ Loading Wav2Vec2 model...") wav2vec_processor = Wav2Vec2Processor.from_pretrained(args.wav2vec_model_dir) wav2vec = Wav2Vec2Model.from_pretrained(args.wav2vec_model_dir).to("cuda").eval() # -------------------------------------------- # STEP 5 — FORCE preload (compile-time warmup) # -------------------------------------------- print("🔥 Preloading all models into GPU memory (forcing weight instantiation)...") with torch.no_grad(), torch.autocast("cuda", dtype=torch.bfloat16): # Wav2Vec2 warmup dummy_audio = torch.zeros(1, 16000).to("cuda") _ = wav2vec(dummy_audio) # Diffusion UNet warmup dummy_latent = torch.randn(1, pipe.unet.in_channels, 64, 64, device="cuda", dtype=torch.bfloat16) _ = pipe.unet(dummy_latent, 0.5) # FantasyTalking warmup try: dummy_feat = torch.randn(1, 256).to("cuda") _ = fantasytalking(dummy_feat) except Exception as e: print(f"⚠️ FantasyTalking warmup skipped: {e}") torch.cuda.synchronize() print("✅ [Ready] All models fully loaded and warmed up in GPU memory.") return pipe, fantasytalking, wav2vec_processor, wav2vec def main(args,pipe,fantasytalking,wav2vec_processor,wav2vec): os.makedirs(args.output_dir,exist_ok=True) duration = librosa.get_duration(filename=args.audio_path) num_frames = min(int(args.fps*duration//4)*4+5,args.max_num_frames) audio_wav2vec_fea = get_audio_features(wav2vec,wav2vec_processor,args.audio_path,args.fps,num_frames) image = resize_image_by_longest_edge(args.image_path,args.image_size) width, height = image.size audio_proj_fea = fantasytalking.get_proj_fea(audio_wav2vec_fea) pos_idx_ranges = fantasytalking.split_audio_sequence(audio_proj_fea.size(1),num_frames=num_frames) audio_proj_split,audio_context_lens = fantasytalking.split_tensor_with_padding(audio_proj_fea,pos_idx_ranges,expand_length=4) # [b,21,9+8,768] # Image-to-video video_audio = pipe( prompt=args.prompt, negative_prompt="人物静止不动，静止，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", input_image=image, width=width, height=height, num_frames=num_frames, num_inference_steps=30, seed=args.seed, tiled=True, audio_scale=args.audio_scale, cfg_scale = args.prompt_cfg_scale, audio_cfg_scale=args.audio_cfg_scale, audio_proj=audio_proj_split, audio_context_lens=audio_context_lens, latents_num_frames=(num_frames-1)//4+1 ) current_time = datetime.now().strftime("%Y%m%d_%H%M%S") save_path_tmp = f"{args.output_dir}/tmp_{Path(args.image_path).stem}_{Path(args.audio_path).stem}_{current_time}.mp4" save_video(video_audio, save_path_tmp, fps=args.fps, quality=5) save_path = f"{args.output_dir}/{Path(args.image_path).stem}_{Path(args.audio_path).stem}_{current_time}.mp4" final_command = [ "ffmpeg", "-y", "-i", save_path_tmp, "-i", args.audio_path, "-c:v", "libx264", "-c:a", "aac", "-shortest", save_path ] subprocess.run(final_command, check=True) os.remove(save_path_tmp) return save_path if __name__ == "__main__": args = parse_args() pipe,fantasytalking,wav2vec_processor,wav2vec = load_models(args) main(args,pipe,fantasytalking,wav2vec_processor,wav2vec)