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# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from omegaconf import OmegaConf
import torch
from diffusers import AutoencoderKL, DDIMScheduler
from latentsync.models.unet import UNet3DConditionModel
from latentsync.pipelines.lipsync_pipeline import LipsyncPipeline
from diffusers.utils.import_utils import is_xformers_available
from accelerate.utils import set_seed
from latentsync.whisper.audio2feature import Audio2Feature
def main(config, args):
print(f"Input video path: {args.video_path}")
print(f"Input audio path: {args.audio_path}")
print(f"Loaded checkpoint path: {args.inference_ckpt_path}")
scheduler = DDIMScheduler.from_pretrained("configs")
if config.model.cross_attention_dim == 768:
whisper_model_path = "checkpoints/whisper/small.pt"
elif config.model.cross_attention_dim == 384:
whisper_model_path = "checkpoints/whisper/tiny.pt"
else:
raise NotImplementedError("cross_attention_dim must be 768 or 384")
audio_encoder = Audio2Feature(model_path=whisper_model_path, device="cuda", num_frames=config.data.num_frames)
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
vae.config.scaling_factor = 0.18215
vae.config.shift_factor = 0
unet, _ = UNet3DConditionModel.from_pretrained(
OmegaConf.to_container(config.model),
args.inference_ckpt_path, # load checkpoint
device="cpu",
)
unet = unet.to(dtype=torch.float16)
# set xformers
if is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
pipeline = LipsyncPipeline(
vae=vae,
audio_encoder=audio_encoder,
unet=unet,
scheduler=scheduler,
).to("cuda")
if args.seed != -1:
set_seed(args.seed)
else:
torch.seed()
print(f"Initial seed: {torch.initial_seed()}")
pipeline(
video_path=args.video_path,
audio_path=args.audio_path,
video_out_path=args.video_out_path,
video_mask_path=args.video_out_path.replace(".mp4", "_mask.mp4"),
num_frames=config.data.num_frames,
num_inference_steps=config.run.inference_steps,
guidance_scale=args.guidance_scale,
weight_dtype=torch.float16,
width=config.data.resolution,
height=config.data.resolution,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--unet_config_path", type=str, default="configs/unet.yaml")
parser.add_argument("--inference_ckpt_path", type=str, required=True)
parser.add_argument("--video_path", type=str, required=True)
parser.add_argument("--audio_path", type=str, required=True)
parser.add_argument("--video_out_path", type=str, required=True)
parser.add_argument("--guidance_scale", type=float, default=1.0)
parser.add_argument("--seed", type=int, default=1247)
args = parser.parse_args()
config = OmegaConf.load(args.unet_config_path)
main(config, args)
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