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inference.py
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inference.py
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import argparse
import glob
import os
import os.path as osp
from pathlib import Path
import soundfile as sf
import torch
import torchvision
from huggingface_hub import snapshot_download
from moviepy.editor import AudioFileClip, VideoFileClip
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from foleycrafter.models.onset import torch_utils
from foleycrafter.models.time_detector.model import VideoOnsetNet
from foleycrafter.pipelines.auffusion_pipeline import Generator, denormalize_spectrogram
from foleycrafter.utils.util import build_foleycrafter, read_frames_with_moviepy
vision_transform_list = [
torchvision.transforms.Resize((128, 128)),
torchvision.transforms.CenterCrop((112, 112)),
torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
video_transform = torchvision.transforms.Compose(vision_transform_list)
def args_parse():
config = argparse.ArgumentParser()
config.add_argument("--prompt", type=str, default="", help="prompt for audio generation")
config.add_argument("--nprompt", type=str, default="", help="negative prompt for audio generation")
config.add_argument("--seed", type=int, default=42, help="ramdom seed")
config.add_argument("--semantic_scale", type=float, default=1.0, help="visual content scale")
config.add_argument("--temporal_scale", type=float, default=0.2, help="temporal align scale")
config.add_argument("--input", type=str, default="examples/sora", help="input video folder path")
config.add_argument("--ckpt", type=str, default="checkpoints/", help="checkpoints folder path")
config.add_argument("--save_dir", type=str, default="output/", help="generation result save path")
config.add_argument(
"--pretrain",
type=str,
default="auffusion/auffusion-full-no-adapter",
help="audio generator pretrained checkpoint path",
)
config.add_argument("--device", type=str, default="cuda")
config = config.parse_args()
return config
def build_models(config):
# download ckpt
pretrained_model_name_or_path = config.pretrain
if not os.path.isdir(pretrained_model_name_or_path):
pretrained_model_name_or_path = snapshot_download(pretrained_model_name_or_path)
fc_ckpt = "ymzhang319/FoleyCrafter"
if not os.path.isdir(fc_ckpt):
fc_ckpt = snapshot_download(fc_ckpt, local_dir=config.ckpt)
# ckpt path
temporal_ckpt_path = osp.join(config.ckpt, "temporal_adapter.ckpt")
# load vocoder
vocoder_config_path = fc_ckpt
vocoder = Generator.from_pretrained(vocoder_config_path, subfolder="vocoder").to(config.device)
# load time_detector
time_detector_ckpt = osp.join(osp.join(config.ckpt, "timestamp_detector.pth.tar"))
time_detector = VideoOnsetNet(False)
time_detector, _ = torch_utils.load_model(time_detector_ckpt, time_detector, device=config.device, strict=True)
# load adapters
pipe = build_foleycrafter().to(config.device)
ckpt = torch.load(temporal_ckpt_path)
# load temporal adapter
if "state_dict" in ckpt.keys():
ckpt = ckpt["state_dict"]
load_gligen_ckpt = {}
for key, value in ckpt.items():
if key.startswith("module."):
load_gligen_ckpt[key[len("module.") :]] = value
else:
load_gligen_ckpt[key] = value
m, u = pipe.controlnet.load_state_dict(load_gligen_ckpt, strict=False)
print(f"### Control Net missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
# load semantic adapter
pipe.load_ip_adapter(
osp.join(config.ckpt, "semantic"), subfolder="", weight_name="semantic_adapter.bin", image_encoder_folder=None
)
ip_adapter_weight = config.semantic_scale
pipe.set_ip_adapter_scale(ip_adapter_weight)
return pipe, vocoder, time_detector
def run_inference(config, pipe, vocoder, time_detector):
controlnet_conditioning_scale = config.temporal_scale
os.makedirs(config.save_dir, exist_ok=True)
input_list = glob.glob(f"{config.input}/*.mp4")
assert len(input_list) != 0, "input list is empty!"
generator = torch.Generator(device=config.device)
generator.manual_seed(config.seed)
image_processor = CLIPImageProcessor()
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter", subfolder="models/image_encoder"
).to(config.device)
input_list.sort()
with torch.no_grad():
for input_video in input_list:
print(f" >>> Begin Inference: {input_video} <<< ")
frames, duration = read_frames_with_moviepy(input_video, max_frame_nums=150)
time_frames = torch.FloatTensor(frames).permute(0, 3, 1, 2)
time_frames = video_transform(time_frames)
time_frames = {"frames": time_frames.unsqueeze(0).permute(0, 2, 1, 3, 4)}
preds = time_detector(time_frames)
preds = torch.sigmoid(preds)
# duration
# import ipdb; ipdb.set_trace()
time_condition = [
-1 if preds[0][int(i / (1024 / 10 * duration) * 150)] < 0.5 else 1
for i in range(int(1024 / 10 * duration))
]
time_condition = time_condition + [-1] * (1024 - len(time_condition))
# w -> b c h w
time_condition = (
torch.FloatTensor(time_condition)
.unsqueeze(0)
.unsqueeze(0)
.unsqueeze(0)
.repeat(1, 1, 256, 1)
.to("cuda")
)
images = image_processor(images=frames, return_tensors="pt").to("cuda")
image_embeddings = image_encoder(**images).image_embeds
image_embeddings = torch.mean(image_embeddings, dim=0, keepdim=True).unsqueeze(0).unsqueeze(0)
neg_image_embeddings = torch.zeros_like(image_embeddings)
image_embeddings = torch.cat([neg_image_embeddings, image_embeddings], dim=1)
name = Path(input_video).stem
name = name.replace("+", " ")
sample = pipe(
prompt=config.prompt,
negative_prompt=config.nprompt,
ip_adapter_image_embeds=image_embeddings,
image=time_condition,
# audio_length_in_s=10,
controlnet_conditioning_scale=controlnet_conditioning_scale,
num_inference_steps=25,
height=256,
width=1024,
output_type="pt",
generator=generator,
# guidance_scale=0,
)
audio_img = sample.images[0]
audio = denormalize_spectrogram(audio_img)
audio = vocoder.inference(audio, lengths=160000)[0]
audio_save_path = osp.join(config.save_dir, "audio")
video_save_path = osp.join(config.save_dir, "video")
os.makedirs(audio_save_path, exist_ok=True)
os.makedirs(video_save_path, exist_ok=True)
audio = audio[: int(duration * 16000)]
save_path = osp.join(audio_save_path, f"{name}.wav")
sf.write(save_path, audio, 16000)
audio = AudioFileClip(osp.join(audio_save_path, f"{name}.wav"))
video = VideoFileClip(input_video)
audio = audio.subclip(0, duration)
video.audio = audio
video = video.subclip(0, duration)
os.makedirs(video_save_path, exist_ok=True)
video.write_videofile(osp.join(video_save_path, f"{name}.mp4"))
if __name__ == "__main__":
config = args_parse()
pipe, vocoder, time_detector = build_models(config)
run_inference(config, pipe, vocoder, time_detector)