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75da08b 2795099 75da08b 2795099 75da08b 2795099 75da08b 2795099 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 | """
This script runs inference of LatentSync using Modal.
To run you must first install modal.
Then you should run the download of the
"""
import modal
#Shared volume with models
volume = modal.Volume.from_name("openlipsync-volume", create_if_missing=True)
model_volume = modal.Volume.from_name("hf-hub-cache", create_if_missing=True)
MODEL_PATH = "/models" # where the Volume will appear on our Functions' filesystems
#Lipsync image
lipsync_image = (
modal.Image.from_registry("nvidia/cuda:12.8.0-devel-ubuntu22.04", add_python="3.11")
.uv_pip_install(
[
"fastapi[standard]",
"torch",
"torchvision",
"xformers",
"triton",
"diffusers",
"transformers",
"huggingface-hub",
"imageio==2.27.0",
"decord==0.6.0",
"accelerate",
"einops==0.7.0",
"omegaconf==2.3.0",
"safetensors>=0.4.3",
"opencv-python==4.9.0.80",
"mediapipe==0.10.11",
"av==11.0.0",
"torch-fidelity==0.3.0",
"torchmetrics==1.3.1",
"python_speech_features==0.6",
"librosa==0.10.1",
"scenedetect==0.6.1",
"ffmpeg-python==0.2.0",
"lpips==0.1.4",
"face-alignment==1.4.1",
"ninja==1.11.1.1",
"pandas==2.0.3",
"numpy<2",
"pydub==0.25.1",
"moviepy==1.0.3",
"hf-xet==1.1.8"
]
)
.apt_install([
"libgl1",
"curl",
"git",
"wget",
"ffmpeg",
])
.env(
{
"HF_HUB_ENABLE_HF_TRANSFER": "1", # faster downloads
"HF_HUB_CACHE": MODEL_PATH
}
)
.run_commands(
"mkdir -p ~/.cache/torch/hub/checkpoints"
)
.run_commands(
"ln -s /data/data/checkpoints/auxiliary/2DFAN4-cd938726ad.zip ~/.cache/torch/hub/checkpoints/2DFAN4-cd938726ad.zip"
)
.run_commands(
"ln -s /data/data/checkpoints/auxiliary/vgg16-397923af.pth ~/.cache/torch/hub/checkpoints/vgg16-397923af.pth"
)
.run_commands(
"ln -s /data/data/checkpoints/auxiliary/s3fd-619a316812.pth ~/.cache/torch/hub/checkpoints/s3fd-619a316812.pth"
)
.entrypoint([]
)
.add_local_python_source("latentsync")# remove NVIDIA base container entrypoint
)
with lipsync_image.imports():
import time
# Create the Modal app
app = modal.App("lipsync-dummy",image=lipsync_image)
@app.function(
image=lipsync_image,
#gpu="A100",
volumes={"/data": volume,MODEL_PATH:model_volume},
timeout=300
)
def volume_search(some_path="/data"):
"""Generates a lipsynced video"""
import os
print("Files in volume:")
def list_directory(path):
try:
for item in os.listdir(path):
item_path = os.path.join(path, item)
abs_path = os.path.abspath(item_path)
if os.path.isdir(item_path):
print(f" {abs_path}/")
list_directory(item_path)
else:
print(f" {abs_path}")
except Exception as e:
print(f"Error accessing {path}: {e}")
# List files in the volume
list_directory(some_path)
@app.function(
image=lipsync_image,
gpu="H100",
volumes={"/data": volume,MODEL_PATH:model_volume},
timeout=300
)
def inference(video_uri, audio_uri, unet_ckpt_path="./checkpoints/latentsync/latentsync_unet.pt", vae_path="./checkpoints/sd-vae-ft-mse", unet_config_path="configs/unet/second_stage.yaml", scheduler_path="configs/scheduler_config.json",whisper_model_path="./checkpoints/whisper",guidance_scale=1.0, seed=1247):
"""Generates a lipsynced video"""
from omegaconf import OmegaConf
import torch
import time
from diffusers import AutoencoderKL, DDIMScheduler
from latentsync.models.unet import UNet3DConditionModel
from latentsync.pipelines.lipsync_pipeline import LipsyncPipeline
from accelerate.utils import set_seed
from latentsync.whisper.audio2feature import Audio2Feature
import torch
import requests
from PIL import Image
import io
# Download video and audio files
video_response = requests.get(video_uri)
audio_response = requests.get(audio_uri)
# Save video and audio files
video_path = "./temp_video.mp4"
audio_path = "./temp_audio.wav"
with open(video_path, "wb") as video_file:
video_file.write(video_response.content)
with open(audio_path, "wb") as audio_file:
audio_file.write(audio_response.content)
video_out_path = "./outvideo.mp4"
config = OmegaConf.load(unet_config_path)
scheduler = DDIMScheduler.from_pretrained(scheduler_path)
if config.model.cross_attention_dim == 768:
whisper_model_path = whisper_model_path+"/small.pt"
elif config.model.cross_attention_dim == 384:
whisper_model_path = whisper_model_path+"/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()}")
# Start timing
start_time = time.time()
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,
)
# Calculate execution time
end_time = time.time()
execution_time = end_time - start_time
# Read the processed video as bytes and return it
with open(video_out_path, "rb") as video_file:
video_bytes = video_file.read()
return video_bytes, execution_time
@app.local_entrypoint()
def main():
#run the function locally
# Example video and audio URIs (replace with actual URLs)
video_uri = "https://huggingface.co/miguelamendez/openlipsync/resolve/main/assets/demo3_video.mp4"
audio_uri = "https://huggingface.co/miguelamendez/openlipsync/resolve/main/assets/demo1_audio.wav"
# Call the inference function
#print(volume_search.remote())
print("Local inference")
try:
video_bytes,exec_time = inference.local(
video_uri=video_uri,
audio_uri=audio_uri,
unet_ckpt_path="./checkpoints/latentsync/latentsync_unet.pt",
vae_path="./checkpoints/sd-vae-ft-mse",
unet_config_path="./configs/unet/second_stage.yaml",
whisper_model_path="./checkpoints/whisper",
scheduler_path="./configs/scheduler_config.json",
guidance_scale=1.0,
seed=1247
)
print(f"Inference time:{exec_time}")
# Save the video bytes to a file in the current path
output_filename = "local_video.mp4"
with open(output_filename, "wb") as output_file:
output_file.write(video_bytes)
print(f"Video saved successfully as {output_filename}")
except Exception as e:
print(f"Error during inference: {e}")
"""
print("remote inference")
try:
video_bytes,exec_time = inference.remote(
video_uri=video_uri,
audio_uri=audio_uri,
unet_ckpt_path="/data/data/checkpoints/latentsync/latentsync_unet.pt",
vae_path="/data/data/checkpoints/sd-vae-ft-mse",
unet_config_path="/data/data/configs/unet/second_stage.yaml",
whisper_model_path="/data/data/checkpoints/whisper",
scheduler_path="/data/data/configs/scheduler_config.json",
guidance_scale=1.0,
seed=1247
)
print(f"Inference time:{exec_time}")
# Save the video bytes to a file in the current path
output_filename = "remote_video.mp4"
with open(output_filename, "wb") as output_file:
output_file.write(video_bytes)
print(f"Video saved successfully as {output_filename}")
except Exception as e:
print(f"Error during inference: {e}")
"""
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