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75da08b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | # 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.
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(video_path, audio_path, video_out_path="./outputs/outvideo.mp4",unet_ckpt_path="./checkpoints/latentsync/latentsync_unet.pt",vae_path="./checkpoints/sd-vae-ft-mse",unet_config_path="configs/unet/second_stage.yaml", guidance_scale=1.0, seed=1247):
print(f"Input video path: {video_path}")
print(f"Input audio path: {audio_path}")
print(f"Loaded unet checkpoint path: {unet_ckpt_path}")
config = OmegaConf.load(unet_config_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(vae_path, torch_dtype=torch.float16)
vae.config.scaling_factor = 0.18215
vae.config.shift_factor = 0
unet, _ = UNet3DConditionModel.from_pretrained(
OmegaConf.to_container(config.model),
unet_ckpt_path, # load checkpoint
device="cpu",
)
unet = unet.to(dtype=torch.float16)
pipeline = LipsyncPipeline(
vae=vae,
audio_encoder=audio_encoder,
unet=unet,
scheduler=scheduler,
).to("cuda")
if seed != -1:
set_seed(seed)
else:
torch.seed()
print(f"Initial seed: {torch.initial_seed()}")
pipeline(
video_path=video_path,
audio_path=audio_path,
video_out_path=video_out_path,
video_mask_path=video_out_path.replace(".mp4", "_mask.mp4"),
num_frames=config.data.num_frames,
num_inference_steps=config.run.inference_steps,
guidance_scale=guidance_scale,
weight_dtype=torch.float16,
width=config.data.resolution,
height=config.data.resolution,
)
import os
def get_videos_from_path(path):
"""Get all video files from a path, returns only filenames without extension"""
video_names = []
try:
# List all files in the directory
files = os.listdir(path)
# Filter for mp4 files
for file in files:
if file.lower().endswith('.mp4'):
# Remove the extension
name_without_ext = os.path.splitext(file)[0]
video_names.append(name_without_ext)
except FileNotFoundError:
print(f"Directory {path} not found")
return []
return video_names
def get_audios_from_path(path):
"""Get all audio files from a path, returns only filenames without extension"""
audio_names = []
try:
# List all files in the directory
files = os.listdir(path)
# Filter for wav files
for file in files:
if file.lower().endswith('.wav'):
# Remove the extension
name_without_ext = os.path.splitext(file)[0]
audio_names.append(name_without_ext)
except FileNotFoundError:
print(f"Directory {path} not found")
return []
return audio_names
if __name__ == "__main__":
file_path = "./assets/edge_cases"
videos = get_videos_from_path(file_path) # all with extension .mp4 returns only the name without extension
audios = get_audios_from_path(file_path) # all with extension .wav returns only the name without extension
for audio in audios:
for video in videos:
print(video,audio)
output_path = "./outputs/" + video + "_" + audio + ".mp4"
try:
main(f"./assets/edge_cases/{video}.mp4", f"./assets/edge_cases/{audio}.wav", output_path)
except:
print("Couldn't detect faces")
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