infer_face_swap.py

import torch
from data.video import save_video
from wan_loader import load_wan_pipe
from models.set_condition_branch import set_stand_in
from preprocessor import FaceProcessor, VideoMaskGenerator
import argparse

parser = argparse.ArgumentParser()

parser.add_argument(
    "--ip_image",
    type=str,
    default="test/input/ruonan.jpg",
    help="Input face image path or URL",
)
parser.add_argument(
    "--input_video",
    type=str,
    default="test/input/woman.mp4",
    help="Input video path",
)
parser.add_argument(
    "--denoising_strength",
    type=float,
    default=0.85,
    help="The lower denoising strength represents a higher similarity to the original video.",
)
parser.add_argument(
    "--prompt",
    type=str,
    default="The video features a woman standing in front of a large screen displaying the words "
    "Tech Minute"
    " and the logo for CNET. She is wearing a purple top and appears to be presenting or speaking about technology-related topics. The background includes a cityscape with tall buildings, suggesting an urban setting. The woman seems to be engaged in a discussion or providing information on technology news or trends. The overall atmosphere is professional and informative, likely aimed at educating viewers about the latest developments in the tech industry.",
    help="Text prompt for video generation",
)
parser.add_argument(
    "--output",
    type=str,
    default="test/output/ruonan.mp4",
    help="Output video file path",
)
parser.add_argument(
    "--seed", type=int, default=0, help="Random seed for reproducibility"
)
parser.add_argument(
    "--num_inference_steps", type=int, default=20, help="Number of inference steps"
)
parser.add_argument(
    "--force_background_consistency",
    type=bool,
    default=False,
    help="Set to True to force background consistency across generated frames.",
)

parser.add_argument(
    "--negative_prompt",
    type=str,
    default="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",
    help="Negative prompt to avoid unwanted features",
)
parser.add_argument("--tiled", action="store_true", help="Enable tiled mode")
parser.add_argument(
    "--fps", type=int, default=25, help="Frames per second for output video"
)
parser.add_argument(
    "--quality", type=int, default=9, help="Output video quality (1-9)"
)
parser.add_argument(
    "--base_path",
    type=str,
    default="checkpoints/base_model/",
    help="Path to base model checkpoint",
)
parser.add_argument(
    "--stand_in_path",
    type=str,
    default="checkpoints/Stand-In/Stand-In_wan2.1_T2V_14B_ver1.0.ckpt",
    help="Path to LoRA weights checkpoint",
)
parser.add_argument(
    "--antelopv2_path",
    type=str,
    default="checkpoints/antelopev2",
    help="Path to AntelopeV2 model checkpoint",
)

args = parser.parse_args()

face_processor = FaceProcessor(antelopv2_path=args.antelopv2_path)
videomask_generator = VideoMaskGenerator(antelopv2_path=args.antelopv2_path)

ip_image, ip_image_rgba = face_processor.process(args.ip_image, extra_input=True)
input_video, face_mask, width, height, num_frames = videomask_generator.process(args.input_video, ip_image_rgba, random_horizontal_flip_chance=0.05, dilation_kernel_size=10)

pipe = load_wan_pipe(
    base_path=args.base_path, face_swap=True, torch_dtype=torch.bfloat16
)

set_stand_in(
    pipe,
    model_path=args.stand_in_path,
)

video = pipe(
    prompt=args.prompt,
    negative_prompt=args.negative_prompt,
    seed=args.seed,
    width=width,
    height=height,
    num_frames=num_frames,
    denoising_strength=args.denoising_strength,
    ip_image=ip_image,
    face_mask=face_mask,
    input_video=input_video,
    num_inference_steps=args.num_inference_steps,
    tiled=args.tiled,
    force_background_consistency=args.force_background_consistency
)
save_video(video, args.output, fps=args.fps, quality=args.quality)