Ethanfel
6bc3fd6443
Pure PyTorch SelVA source for SelvaModelLoader/FeatureExtractor/Sampler nodes. Imports rewritten from selva.* to selva_core.*. mel_converter.py: replaced librosa.filters.mel with pure-numpy implementation to avoid librosa→numba→NumPy version incompatibility in some ComfyUI environments. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
278 lines
11 KiB
Python
278 lines
11 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 selva_core.data.av_utils import ImageInfo, VideoInfo, read_frames, reencode_with_audio
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from selva_core.model.flow_matching import FlowMatching
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from selva_core.model.networks_video_enc import TextSynch
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from selva_core.model.networks_generator import MMAudio
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from selva_core.model.sequence_config import CONFIG_16K, CONFIG_44K, SequenceConfig
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from selva_core.model.utils.features_utils import FeaturesUtils
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from selva_core.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_video_enc_path: Path
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model_generator_path: Path
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mode: str
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vae_path: Path
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bigvgan_16k_path: Optional[Path]
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synchformer_ckpt: Path = Path('./ext_weights/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_video_enc_path)
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download_model_if_needed(self.model_generator_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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def download_video_enc_if_needed(self):
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download_model_if_needed(self.model_video_enc_path)
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def download_generator_if_needed(self):
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download_model_if_needed(self.model_generator_path)
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def download_external_modules_if_needed(self):
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download_model_if_needed(self.synchformer_ckpt)
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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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small_16k = ModelConfig(model_name='small_16k',
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model_video_enc_path=Path('./weights/video_enc_sup_5.pth'),
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model_generator_path=Path('./weights/generator_small_16k_sup_5.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_video_enc_path=Path('./weights/video_enc_sup_5.pth'),
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model_generator_path=Path('./weights/generator_small_44k_sup_5.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_video_enc_path=Path('./weights/video_enc_sup_5.pth'),
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model_generator_path=Path('./weights/generator_medium_44k_sup_5.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_video_enc_path=Path('./weights/video_enc_sup_5.pth'),
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model_generator_path=Path('./weights/generator_large_44k_sup_5.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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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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}
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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_video_enc: TextSynch,
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net_generator: 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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) -> 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 text is not None:
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text_features_clip = feature_utils.encode_text_clip(text)
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text_features_flant5, text_mask_flant5 = feature_utils.encode_text_t5(text)
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else:
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text_features_clip = net_generator.get_empty_string_sequence(bs)
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text_features_flant5 = net_video_enc.get_empty_string_sequence(bs)
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text_mask_flant5 = torch.zeros_like(text_features_flant5)
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text_mask_flant5[:, 0] = 1
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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_clip = feature_utils.encode_text_clip(negative_text)
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negative_text_features_flant5, negative_text_mask_flant5 = feature_utils.encode_text_t5(negative_text)
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else:
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negative_text_features_clip = None
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negative_text_features_flant5, negative_text_mask_flant5 = None, None
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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_generator.clip_seq_len, -1)
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else:
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clip_features = net_generator.get_empty_clip_sequence(bs)
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if sync_video is not None and not image_input:
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text_features_flant5, text_mask_flant5 = net_video_enc.prepend_sup_text_tokens(text_features_flant5, text_mask_flant5)
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sync_video = sync_video.to(net_video_enc.device, net_video_enc.dtype, non_blocking=True)
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sync_features = net_video_enc.encode_video_with_sync(
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sync_video, text_f=text_features_flant5, text_mask=text_mask_flant5
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)
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else:
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sync_features = net_generator.get_empty_sync_sequence(bs)
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x0 = torch.randn(bs,
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net_generator.latent_seq_len,
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net_generator.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_generator.preprocess_conditions(clip_features, sync_features, text_features_clip)
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empty_conditions = net_generator.get_empty_conditions(
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bs, negative_text_features=negative_text_features_clip
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)
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cfg_ode_wrapper = lambda t, x: net_generator.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_generator.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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logging.root.setLevel(log_level)
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formatter = ColoredFormatter(LOGFORMAT)
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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 = logging.getLogger()
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log.setLevel(log_level)
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log.addHandler(stream)
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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, _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(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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