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import argparse
import os
import time
import accelerate
import cv2
import numpy as np
import torch
import torchaudio.functional
import torchvision.io
from PIL import Image
from diffusers import AutoencoderKL, DDIMScheduler
from diffusers.utils.import_utils import is_xformers_available
from insightface.app import FaceAnalysis
from omegaconf import OmegaConf
from transformers import Wav2Vec2Model, Wav2Vec2Processor
from modules import UNet2DConditionModel, UNet3DConditionModel, VKpsGuider, AudioProjection
from pipelines import VExpressPipeline
from pipelines.utils import draw_kps_image, save_video
from pipelines.utils import retarget_kps
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--unet_config_path', type=str, default='./model_ckpts/stable-diffusion-v1-5/unet/config.json')
parser.add_argument('--vae_path', type=str, default='./model_ckpts/sd-vae-ft-mse/')
parser.add_argument('--audio_encoder_path', type=str, default='./model_ckpts/wav2vec2-base-960h/')
parser.add_argument('--insightface_model_path', type=str, default='./model_ckpts/insightface_models/')
parser.add_argument('--denoising_unet_path', type=str, default='./model_ckpts/v-express/denoising_unet.bin')
parser.add_argument('--reference_net_path', type=str, default='./model_ckpts/v-express/reference_net.bin')
parser.add_argument('--v_kps_guider_path', type=str, default='./model_ckpts/v-express/v_kps_guider.bin')
parser.add_argument('--audio_projection_path', type=str, default='./model_ckpts/v-express/audio_projection.bin')
parser.add_argument('--motion_module_path', type=str, default='./model_ckpts/v-express/motion_module.bin')
parser.add_argument('--retarget_strategy', type=str, default='fix_face',
help='{fix_face, no_retarget, offset_retarget, naive_retarget}')
parser.add_argument('--dtype', type=str, default='fp16')
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--gpu_id', type=int, default=0)
parser.add_argument('--do_multi_devices_inference', action='store_true')
parser.add_argument('--save_gpu_memory', action='store_true')
parser.add_argument('--num_pad_audio_frames', type=int, default=2)
parser.add_argument('--standard_audio_sampling_rate', type=int, default=16000)
parser.add_argument('--reference_image_path', type=str, default='./test_samples/emo/talk_emotion/ref.jpg')
parser.add_argument('--audio_path', type=str, default='./test_samples/emo/talk_emotion/aud.mp3')
parser.add_argument('--kps_path', type=str, default='./test_samples/emo/talk_emotion/kps.pth')
parser.add_argument('--output_path', type=str, default='./output/emo/talk_emotion.mp4')
parser.add_argument('--image_width', type=int, default=512)
parser.add_argument('--image_height', type=int, default=512)
parser.add_argument('--fps', type=float, default=30.0)
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--num_inference_steps', type=int, default=25)
parser.add_argument('--guidance_scale', type=float, default=3.5)
parser.add_argument('--context_frames', type=int, default=12)
parser.add_argument('--context_overlap', type=int, default=4)
parser.add_argument('--reference_attention_weight', default=0.95, type=float)
parser.add_argument('--audio_attention_weight', default=3., type=float)
args = parser.parse_args()
return args
def load_reference_net(unet_config_path, reference_net_path, dtype, device):
reference_net = UNet2DConditionModel.from_config(unet_config_path).to(dtype=dtype, device=device)
reference_net.load_state_dict(torch.load(reference_net_path, map_location="cpu"), strict=False)
print(f'Loaded weights of Reference Net from {reference_net_path}.')
return reference_net
def load_denoising_unet(inf_config_path, unet_config_path, denoising_unet_path, motion_module_path, dtype, device):
inference_config = OmegaConf.load(inf_config_path)
denoising_unet = UNet3DConditionModel.from_config_2d(
unet_config_path,
unet_additional_kwargs=inference_config.unet_additional_kwargs,
).to(dtype=dtype, device=device)
denoising_unet.load_state_dict(torch.load(denoising_unet_path, map_location="cpu"), strict=False)
print(f'Loaded weights of Denoising U-Net from {denoising_unet_path}.')
denoising_unet.load_state_dict(torch.load(motion_module_path, map_location="cpu"), strict=False)
print(f'Loaded weights of Denoising U-Net Motion Module from {motion_module_path}.')
return denoising_unet
def load_v_kps_guider(v_kps_guider_path, dtype, device):
v_kps_guider = VKpsGuider(320, block_out_channels=(16, 32, 96, 256)).to(dtype=dtype, device=device)
v_kps_guider.load_state_dict(torch.load(v_kps_guider_path, map_location="cpu"))
print(f'Loaded weights of V-Kps Guider from {v_kps_guider_path}.')
return v_kps_guider
def load_audio_projection(
audio_projection_path,
dtype,
device,
inp_dim: int,
mid_dim: int,
out_dim: int,
inp_seq_len: int,
out_seq_len: int,
):
audio_projection = AudioProjection(
dim=mid_dim,
depth=4,
dim_head=64,
heads=12,
num_queries=out_seq_len,
embedding_dim=inp_dim,
output_dim=out_dim,
ff_mult=4,
max_seq_len=inp_seq_len,
).to(dtype=dtype, device=device)
audio_projection.load_state_dict(torch.load(audio_projection_path, map_location='cpu'))
print(f'Loaded weights of Audio Projection from {audio_projection_path}.')
return audio_projection
def get_scheduler(inference_config_path):
inference_config = OmegaConf.load(inference_config_path)
scheduler_kwargs = OmegaConf.to_container(inference_config.noise_scheduler_kwargs)
scheduler = DDIMScheduler(**scheduler_kwargs)
return scheduler
def main():
args = parse_args()
start_time = time.time()
if not args.do_multi_devices_inference:
# TODO
accelerator = None
device = torch.device(f'{args.device}:{args.gpu_id}' if args.device == 'cuda' else args.device)
else:
accelerator = accelerate.Accelerator()
device = torch.device(f'cuda:{accelerator.process_index}')
dtype = torch.float16 if args.dtype == 'fp16' else torch.float32
vae_path = args.vae_path
audio_encoder_path = args.audio_encoder_path
vae = AutoencoderKL.from_pretrained(vae_path).to(dtype=dtype, device=device)
audio_encoder = Wav2Vec2Model.from_pretrained(audio_encoder_path).to(dtype=dtype, device=device)
audio_processor = Wav2Vec2Processor.from_pretrained(audio_encoder_path)
unet_config_path = args.unet_config_path
reference_net_path = args.reference_net_path
denoising_unet_path = args.denoising_unet_path
v_kps_guider_path = args.v_kps_guider_path
audio_projection_path = args.audio_projection_path
motion_module_path = args.motion_module_path
inference_config_path = './inference_v2.yaml'
scheduler = get_scheduler(inference_config_path)
reference_net = load_reference_net(unet_config_path, reference_net_path, dtype, device)
denoising_unet = load_denoising_unet(
inference_config_path, unet_config_path, denoising_unet_path, motion_module_path,
dtype, device
)
v_kps_guider = load_v_kps_guider(v_kps_guider_path, dtype, device)
audio_projection = load_audio_projection(
audio_projection_path,
dtype,
device,
inp_dim=denoising_unet.config.cross_attention_dim,
mid_dim=denoising_unet.config.cross_attention_dim,
out_dim=denoising_unet.config.cross_attention_dim,
inp_seq_len=2 * (2 * args.num_pad_audio_frames + 1),
out_seq_len=2 * args.num_pad_audio_frames + 1,
)
if is_xformers_available():
reference_net.enable_xformers_memory_efficient_attention()
denoising_unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
generator = torch.manual_seed(args.seed)
pipeline = VExpressPipeline(
vae=vae,
reference_net=reference_net,
denoising_unet=denoising_unet,
v_kps_guider=v_kps_guider,
audio_processor=audio_processor,
audio_encoder=audio_encoder,
audio_projection=audio_projection,
scheduler=scheduler,
).to(dtype=dtype, device=device)
app = FaceAnalysis(
providers=['CUDAExecutionProvider' if args.device == 'cuda' else 'CPUExecutionProvider'],
provider_options=[{'device_id': args.gpu_id}] if args.device == 'cuda' else [],
root=args.insightface_model_path,
)
app.prepare(ctx_id=0, det_size=(args.image_height, args.image_width))
reference_image = Image.open(args.reference_image_path).convert('RGB')
reference_image = reference_image.resize((args.image_height, args.image_width))
reference_image_for_kps = cv2.imread(args.reference_image_path)
reference_image_for_kps = cv2.resize(reference_image_for_kps, (args.image_width, args.image_height))
reference_kps = app.get(reference_image_for_kps)[0].kps[:3]
if args.save_gpu_memory:
del app
torch.cuda.empty_cache()
_, audio_waveform, meta_info = torchvision.io.read_video(os.path.join(os.path.dirname(args.audio_path), os.path.basename(args.audio_path)), pts_unit='sec')
audio_sampling_rate = meta_info['audio_fps']
print(f'Length of audio is {audio_waveform.shape[1]} with the sampling rate of {audio_sampling_rate}.')
if audio_sampling_rate != args.standard_audio_sampling_rate:
audio_waveform = torchaudio.functional.resample(
audio_waveform,
orig_freq=audio_sampling_rate,
new_freq=args.standard_audio_sampling_rate,
)
audio_waveform = audio_waveform.mean(dim=0)
duration = audio_waveform.shape[0] / args.standard_audio_sampling_rate
init_video_length = int(duration * args.fps)
num_contexts = np.around((init_video_length + args.context_overlap) / args.context_frames)
video_length = int(num_contexts * args.context_frames - args.context_overlap)
fps = video_length / duration
print(f'The corresponding video length is {video_length}.')
kps_sequence = None
if args.kps_path != "":
assert os.path.exists(args.kps_path), f'{args.kps_path} does not exist'
kps_sequence = torch.tensor(torch.load(args.kps_path)) # [len, 3, 2]
print(f'The original length of kps sequence is {kps_sequence.shape[0]}.')
if kps_sequence.shape[0] > video_length:
kps_sequence = kps_sequence[:video_length, :, :]
kps_sequence = torch.nn.functional.interpolate(kps_sequence.permute(1, 2, 0), size=video_length, mode='linear')
kps_sequence = kps_sequence.permute(2, 0, 1)
print(f'The interpolated length of kps sequence is {kps_sequence.shape[0]}.')
retarget_strategy = args.retarget_strategy
if retarget_strategy == 'fix_face':
kps_sequence = torch.tensor([reference_kps] * video_length)
elif retarget_strategy == 'no_retarget':
kps_sequence = kps_sequence
elif retarget_strategy == 'offset_retarget':
kps_sequence = retarget_kps(reference_kps, kps_sequence, only_offset=True)
elif retarget_strategy == 'naive_retarget':
kps_sequence = retarget_kps(reference_kps, kps_sequence, only_offset=False)
else:
raise ValueError(f'The retarget strategy {retarget_strategy} is not supported.')
kps_images = []
for i in range(video_length):
kps_image = draw_kps_image(args.image_height, args.image_width, kps_sequence[i])
kps_images.append(Image.fromarray(kps_image))
video_tensor = pipeline(
reference_image=reference_image,
kps_images=kps_images,
audio_waveform=audio_waveform,
width=args.image_width,
height=args.image_height,
video_length=video_length,
num_inference_steps=args.num_inference_steps,
guidance_scale=args.guidance_scale,
context_frames=args.context_frames,
context_overlap=args.context_overlap,
reference_attention_weight=args.reference_attention_weight,
audio_attention_weight=args.audio_attention_weight,
num_pad_audio_frames=args.num_pad_audio_frames,
generator=generator,
do_multi_devices_inference=args.do_multi_devices_inference,
save_gpu_memory=args.save_gpu_memory,
)
if accelerator is None or accelerator.is_main_process:
save_video(video_tensor, args.audio_path, args.output_path, device, fps)
consumed_time = time.time() - start_time
generation_fps = video_tensor.shape[2] / consumed_time
print(f'The generated video has been saved at {args.output_path}. '
f'The generation time is {consumed_time:.1f} seconds. '
f'The generation FPS is {generation_fps:.2f}.')
if __name__ == '__main__':
main()
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