mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 22:26:36 +00:00
260 lines
9.5 KiB
Python
260 lines
9.5 KiB
Python
import dataclasses
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import logging
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from pathlib import Path
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from typing import Optional
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import numpy as np
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import torch
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# from colorlog import ColoredFormatter
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from PIL import Image
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from torchvision.transforms import v2
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from .data.av_utils import ImageInfo, VideoInfo, read_frames, reencode_with_audio, remux_with_audio
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from .model.flow_matching import FlowMatching
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from .model.networks import MMAudio
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from .model.sequence_config import CONFIG_16K, CONFIG_44K, SequenceConfig
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from .model.utils.features_utils import FeaturesUtils
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from .utils.download_utils import download_model_if_needed
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log = logging.getLogger()
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@dataclasses.dataclass
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class ModelConfig:
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model_name: str
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model_path: Path
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vae_path: Path
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bigvgan_16k_path: Optional[Path]
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mode: str
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synchformer_ckpt: Path = Path('ckpts/mmaudio/synchformer_state_dict.pth')
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@property
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def seq_cfg(self) -> SequenceConfig:
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if self.mode == '16k':
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return CONFIG_16K
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elif self.mode == '44k':
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return CONFIG_44K
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def download_if_needed(self):
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download_model_if_needed(self.model_path)
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download_model_if_needed(self.vae_path)
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if self.bigvgan_16k_path is not None:
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download_model_if_needed(self.bigvgan_16k_path)
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download_model_if_needed(self.synchformer_ckpt)
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small_16k = ModelConfig(model_name='small_16k',
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model_path=Path('./weights/mmaudio_small_16k.pth'),
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vae_path=Path('./ext_weights/v1-16.pth'),
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bigvgan_16k_path=Path('./ext_weights/best_netG.pt'),
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mode='16k')
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small_44k = ModelConfig(model_name='small_44k',
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model_path=Path('./weights/mmaudio_small_44k.pth'),
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vae_path=Path('./ext_weights/v1-44.pth'),
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bigvgan_16k_path=None,
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mode='44k')
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medium_44k = ModelConfig(model_name='medium_44k',
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model_path=Path('./weights/mmaudio_medium_44k.pth'),
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vae_path=Path('./ext_weights/v1-44.pth'),
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bigvgan_16k_path=None,
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mode='44k')
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large_44k = ModelConfig(model_name='large_44k',
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model_path=Path('./weights/mmaudio_large_44k.pth'),
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vae_path=Path('./ext_weights/v1-44.pth'),
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bigvgan_16k_path=None,
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mode='44k')
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large_44k_v2 = ModelConfig(model_name='large_44k_v2',
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model_path=Path('ckpts/mmaudio/mmaudio_large_44k_v2.pth'),
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vae_path=Path('ckpts/mmaudio/v1-44.pth'),
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bigvgan_16k_path=None,
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mode='44k')
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all_model_cfg: dict[str, ModelConfig] = {
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'small_16k': small_16k,
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'small_44k': small_44k,
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'medium_44k': medium_44k,
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'large_44k': large_44k,
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'large_44k_v2': large_44k_v2,
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}
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def generate(
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clip_video: Optional[torch.Tensor],
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sync_video: Optional[torch.Tensor],
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text: Optional[list[str]],
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*,
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negative_text: Optional[list[str]] = None,
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feature_utils: FeaturesUtils,
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net: MMAudio,
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fm: FlowMatching,
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rng: torch.Generator,
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cfg_strength: float,
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clip_batch_size_multiplier: int = 40,
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sync_batch_size_multiplier: int = 40,
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image_input: bool = False,
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offloadobj = None
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) -> torch.Tensor:
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device = feature_utils.device
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dtype = feature_utils.dtype
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bs = len(text)
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if clip_video is not None:
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clip_video = clip_video.to(device, dtype, non_blocking=True)
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clip_features = feature_utils.encode_video_with_clip(clip_video,
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batch_size=bs *
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clip_batch_size_multiplier)
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if image_input:
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clip_features = clip_features.expand(-1, net.clip_seq_len, -1)
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else:
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clip_features = net.get_empty_clip_sequence(bs)
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if sync_video is not None and not image_input:
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sync_video = sync_video.to(device, dtype, non_blocking=True)
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sync_features = feature_utils.encode_video_with_sync(sync_video,
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batch_size=bs *
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sync_batch_size_multiplier)
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else:
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sync_features = net.get_empty_sync_sequence(bs)
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if text is not None:
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text_features = feature_utils.encode_text(text)
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else:
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text_features = net.get_empty_string_sequence(bs)
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if negative_text is not None:
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assert len(negative_text) == bs
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negative_text_features = feature_utils.encode_text(negative_text)
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else:
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negative_text_features = net.get_empty_string_sequence(bs)
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if offloadobj != None:
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offloadobj.ensure_model_loaded("net")
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x0 = torch.randn(bs,
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net.latent_seq_len,
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net.latent_dim,
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device=device,
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dtype=dtype,
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generator=rng)
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preprocessed_conditions = net.preprocess_conditions(clip_features, sync_features, text_features)
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empty_conditions = net.get_empty_conditions(
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bs, negative_text_features=negative_text_features if negative_text is not None else None)
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cfg_ode_wrapper = lambda t, x: net.ode_wrapper(t, x, preprocessed_conditions, empty_conditions,
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cfg_strength)
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x1 = fm.to_data(cfg_ode_wrapper, x0)
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x1 = net.unnormalize(x1)
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spec = feature_utils.decode(x1)
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audio = feature_utils.vocode(spec)
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return audio
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LOGFORMAT = "[%(log_color)s%(levelname)-8s%(reset)s]: %(log_color)s%(message)s%(reset)s"
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def setup_eval_logging(log_level: int = logging.INFO):
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log = logging.getLogger(__name__)
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if not log.handlers:
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formatter = None # or your ColoredFormatter
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stream = logging.StreamHandler()
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stream.setLevel(log_level)
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stream.setFormatter(formatter)
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log.addHandler(stream)
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log.setLevel(log_level)
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log.propagate = False # Prevent propagation to root logger
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return log
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_CLIP_SIZE = 384
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_CLIP_FPS = 8.0
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_SYNC_SIZE = 224
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_SYNC_FPS = 25.0
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def load_video(video_path: Path, duration_sec: float, load_all_frames: bool = True) -> VideoInfo:
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clip_transform = v2.Compose([
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v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
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v2.ToImage(),
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v2.ToDtype(torch.float32, scale=True),
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])
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sync_transform = v2.Compose([
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v2.Resize(_SYNC_SIZE, interpolation=v2.InterpolationMode.BICUBIC),
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v2.CenterCrop(_SYNC_SIZE),
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v2.ToImage(),
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v2.ToDtype(torch.float32, scale=True),
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v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
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])
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output_frames, all_frames, orig_fps = read_frames(video_path,
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list_of_fps=[_CLIP_FPS, _SYNC_FPS],
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start_sec=0,
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end_sec=duration_sec,
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need_all_frames=load_all_frames)
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clip_chunk, sync_chunk = output_frames
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clip_chunk = torch.from_numpy(clip_chunk).permute(0, 3, 1, 2)
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sync_chunk = torch.from_numpy(sync_chunk).permute(0, 3, 1, 2)
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clip_frames = clip_transform(clip_chunk)
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sync_frames = sync_transform(sync_chunk)
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clip_length_sec = clip_frames.shape[0] / _CLIP_FPS
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sync_length_sec = sync_frames.shape[0] / _SYNC_FPS
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if clip_length_sec < duration_sec:
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log.warning(f'Clip video is too short: {clip_length_sec:.2f} < {duration_sec:.2f}')
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log.warning(f'Truncating to {clip_length_sec:.2f} sec')
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duration_sec = clip_length_sec
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if sync_length_sec < duration_sec:
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log.warning(f'Sync video is too short: {sync_length_sec:.2f} < {duration_sec:.2f}')
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log.warning(f'Truncating to {sync_length_sec:.2f} sec')
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duration_sec = sync_length_sec
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clip_frames = clip_frames[:int(_CLIP_FPS * duration_sec)]
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sync_frames = sync_frames[:int(_SYNC_FPS * duration_sec)]
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video_info = VideoInfo(
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duration_sec=duration_sec,
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fps=orig_fps,
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clip_frames=clip_frames,
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sync_frames=sync_frames,
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all_frames=all_frames if load_all_frames else None,
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)
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return video_info
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def load_image(image_path: Path) -> VideoInfo:
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clip_transform = v2.Compose([
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v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
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v2.ToImage(),
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v2.ToDtype(torch.float32, scale=True),
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])
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sync_transform = v2.Compose([
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v2.Resize(_SYNC_SIZE, interpolation=v2.InterpolationMode.BICUBIC),
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v2.CenterCrop(_SYNC_SIZE),
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v2.ToImage(),
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v2.ToDtype(torch.float32, scale=True),
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v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
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])
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frame = np.array(Image.open(image_path))
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clip_chunk = torch.from_numpy(frame).unsqueeze(0).permute(0, 3, 1, 2)
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sync_chunk = torch.from_numpy(frame).unsqueeze(0).permute(0, 3, 1, 2)
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clip_frames = clip_transform(clip_chunk)
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sync_frames = sync_transform(sync_chunk)
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video_info = ImageInfo(
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clip_frames=clip_frames,
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sync_frames=sync_frames,
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original_frame=frame,
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)
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return video_info
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def make_video(source_path, video_info: VideoInfo, output_path: Path, audio: torch.Tensor, sampling_rate: int):
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# reencode_with_audio(video_info, output_path, audio, sampling_rate)
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remux_with_audio(source_path, output_path, audio, sampling_rate) |