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import argparse
import os
import random
from datetime import datetime
from pathlib import Path
from typing import List
import av
import cv2
import numpy as np
import torch
import torchvision
from diffusers import AutoencoderKL, DDIMScheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline
from einops import repeat
from omegaconf import OmegaConf
from PIL import Image
from torchvision import transforms
from transformers import CLIPVisionModelWithProjection
from src.models.unet_2d_condition import UNet2DConditionModel
from src.models.unet_3d_echo import EchoUNet3DConditionModel
from src.models.whisper.audio2feature import load_audio_model
from src.pipelines.pipeline_echo_mimic_pose_acc import AudioPose2VideoPipeline
from src.utils.util import get_fps, read_frames, save_videos_grid, crop_and_pad
import sys
from src.models.face_locator import FaceLocator
from moviepy.editor import VideoFileClip, AudioFileClip
from facenet_pytorch import MTCNN
from src.utils.draw_utils import FaceMeshVisualizer
import pickle
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/prompts/animation_pose_acc.yaml")
parser.add_argument("-W", type=int, default=512)
parser.add_argument("-H", type=int, default=512)
parser.add_argument("-L", type=int, default=160)
parser.add_argument("--seed", type=int, default=420)
parser.add_argument("--facemusk_dilation_ratio", type=float, default=0.1)
parser.add_argument("--facecrop_dilation_ratio", type=float, default=0.5)
parser.add_argument("--context_frames", type=int, default=12)
parser.add_argument("--context_overlap", type=int, default=3)
parser.add_argument("--cfg", type=float, default=1.0)
parser.add_argument("--steps", type=int, default=6)
parser.add_argument("--sample_rate", type=int, default=16000)
parser.add_argument("--fps", type=int, default=24)
parser.add_argument("--device", type=str, default="cuda")
args = parser.parse_args()
return args
def select_face(det_bboxes, probs):
## max face from faces that the prob is above 0.8
## box: xyxy
filtered_bboxes = []
for bbox_i in range(len(det_bboxes)):
if probs[bbox_i] > 0.8:
filtered_bboxes.append(det_bboxes[bbox_i])
if len(filtered_bboxes) == 0:
return None
sorted_bboxes = sorted(filtered_bboxes, key=lambda x:(x[3]-x[1]) * (x[2] - x[0]), reverse=True)
return sorted_bboxes[0]
def main():
args = parse_args()
config = OmegaConf.load(args.config)
if config.weight_dtype == "fp16":
weight_dtype = torch.float16
else:
weight_dtype = torch.float32
device = args.device
if device.__contains__("cuda") and not torch.cuda.is_available():
device = "cpu"
inference_config_path = config.inference_config
infer_config = OmegaConf.load(inference_config_path)
############# model_init started #############
## vae init
vae = AutoencoderKL.from_pretrained(
config.pretrained_vae_path,
).to("cuda", dtype=weight_dtype)
## reference net init
reference_unet = UNet2DConditionModel.from_pretrained(
config.pretrained_base_model_path,
subfolder="unet",
).to(dtype=weight_dtype, device=device)
reference_unet.load_state_dict(
torch.load(config.reference_unet_path, map_location="cpu"),
)
## denoising net init
if os.path.exists(config.motion_module_path):
### stage1 + stage2
denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
config.pretrained_base_model_path,
config.motion_module_path,
subfolder="unet",
unet_additional_kwargs=infer_config.unet_additional_kwargs,
).to(dtype=weight_dtype, device=device)
else:
### only stage1
denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
config.pretrained_base_model_path,
"",
subfolder="unet",
unet_additional_kwargs={
"use_motion_module": False,
"unet_use_temporal_attention": False,
"cross_attention_dim": infer_config.unet_additional_kwargs.cross_attention_dim
}
).to(dtype=weight_dtype, device=device)
denoising_unet.load_state_dict(
torch.load(config.denoising_unet_path, map_location="cpu"),
strict=False
)
## face locator init
face_locator = FaceLocator(320, conditioning_channels=3, block_out_channels=(16, 32, 96, 256)).to(
dtype=weight_dtype, device="cuda"
)
face_locator.load_state_dict(torch.load(config.face_locator_path))
visualizer = FaceMeshVisualizer(draw_iris=False, draw_mouse=False)
### load audio processor params
audio_processor = load_audio_model(model_path=config.audio_model_path, device=device)
### load face detector params
face_detector = MTCNN(image_size=320, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, device=device)
############# model_init finished #############
width, height = args.W, args.H
sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
scheduler = DDIMScheduler(**sched_kwargs)
pipe = AudioPose2VideoPipeline(
vae=vae,
reference_unet=reference_unet,
denoising_unet=denoising_unet,
audio_guider=audio_processor,
face_locator=face_locator,
scheduler=scheduler,
)
pipe = pipe.to("cuda", dtype=weight_dtype)
date_str = datetime.now().strftime("%Y%m%d")
time_str = datetime.now().strftime("%H%M")
save_dir_name = f"{time_str}--seed_{args.seed}-{args.W}x{args.H}"
save_dir = Path(f"output/{date_str}/{save_dir_name}")
save_dir.mkdir(exist_ok=True, parents=True)
for ref_image_path in config["test_cases"].keys():
for file_path in config["test_cases"][ref_image_path]:
if ".wav" in file_path:
audio_path = file_path
else:
pose_dir = file_path
if args.seed is not None and args.seed > -1:
generator = torch.manual_seed(args.seed)
else:
generator = torch.manual_seed(random.randint(100, 1000000))
ref_name = Path(ref_image_path).stem
audio_name = Path(audio_path).stem
final_fps = args.fps
ref_image_pil = Image.open(ref_image_path).convert("RGB")
# ==================== face_locator =====================
pose_list = []
for index in range(len(os.listdir(pose_dir))):
tgt_musk_path = os.path.join(pose_dir, f"{index}.pkl")
with open(tgt_musk_path, "rb") as f:
tgt_kpts = pickle.load(f)
tgt_musk = visualizer.draw_landmarks((args.W, args.H), tgt_kpts)
tgt_musk_pil = Image.fromarray(np.array(tgt_musk).astype(np.uint8)).convert('RGB')
pose_list.append(torch.Tensor(np.array(tgt_musk_pil)).to(dtype=weight_dtype, device="cuda").permute(2,0,1) / 255.0)
face_mask_tensor = torch.stack(pose_list, dim=1).unsqueeze(0)
video = pipe(
ref_image_pil,
audio_path,
face_mask_tensor,
width,
height,
args.L,
args.steps,
args.cfg,
generator=generator,
audio_sample_rate=args.sample_rate,
context_frames=12,
fps=final_fps,
context_overlap=3
).videos
final_length = min(video.shape[2], face_mask_tensor.shape[2])
video = torch.cat([video[:, :, :final_length, :, :], face_mask_tensor[:, :, :final_length, :, :].detach().cpu()], dim=-1)
save_videos_grid(
video,
f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4",
n_rows=2,
fps=final_fps,
)
from moviepy.editor import VideoFileClip, AudioFileClip
video_clip = VideoFileClip(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4")
audio_clip = AudioFileClip(audio_path)
video_clip = video_clip.set_audio(audio_clip)
video_clip.write_videofile(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4", codec="libx264", audio_codec="aac")
print(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4")
if __name__ == "__main__":
main()
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