import os, math, torch, cv2 from PIL import Image from omegaconf import OmegaConf from tqdm import tqdm from diffusers import AutoencoderKLTemporalDecoder from diffusers.schedulers import EulerDiscreteScheduler from transformers import WhisperModel, CLIPVisionModelWithProjection, AutoFeatureExtractor from src.utils.util import save_videos_grid, seed_everything from src.dataset.test_preprocess import process_bbox, image_audio_to_tensor from src.models.base.unet_spatio_temporal_condition import ( UNetSpatioTemporalConditionModel, add_ip_adapters, ) from src.pipelines.pipeline_sonic import SonicPipeline from src.models.audio_adapter.audio_proj import AudioProjModel from src.models.audio_adapter.audio_to_bucket import Audio2bucketModel from src.utils.RIFE.RIFE_HDv3 import RIFEModel from src.dataset.face_align.align import AlignImage BASE_DIR = os.path.dirname(os.path.abspath(__file__)) # ------------------------------------------------------------------ # single image + speech → video-tensor generator # ------------------------------------------------------------------ def test( pipe, config, wav_enc, audio_pe, audio2bucket, image_encoder, width, height, batch, ): # ---- 배치 차원 맞추기 ----------------------------------------- for k, v in batch.items(): if isinstance(v, torch.Tensor): batch[k] = v.unsqueeze(0).to(pipe.device).float() ref_img = batch["ref_img"] clip_img = batch["clip_images"] face_mask = batch["face_mask"] image_embeds = image_encoder(clip_img).image_embeds # (1,1024) audio_feature = batch["audio_feature"] # (1, 80, T) audio_len = int(batch["audio_len"]) step = int(config.step) window = 16_000 # 1-sec chunks audio_prompts, last_prompts = [], [] for i in range(0, audio_feature.shape[-1], window): chunk = audio_feature[:, :, i : i + window] # (1, 80, win) layers = wav_enc.encoder(chunk, output_hidden_states=True).hidden_states last = wav_enc.encoder(chunk).last_hidden_state.unsqueeze(-2) audio_prompts.append(torch.stack(layers, dim=2)) # (1, w, L, 384) last_prompts.append(last) if not audio_prompts: raise ValueError("[ERROR] No speech recognised in the provided audio.") audio_prompts = torch.cat(audio_prompts, dim=1) last_prompts = torch.cat(last_prompts, dim=1) # padding 규칙 audio_prompts = torch.cat( [torch.zeros_like(audio_prompts[:, :4]), audio_prompts, torch.zeros_like(audio_prompts[:, :6])], dim=1) last_prompts = torch.cat( [torch.zeros_like(last_prompts[:, :24]), last_prompts, torch.zeros_like(last_prompts[:, :26])], dim=1) total_tokens = audio_prompts.shape[1] num_chunks = max(1, math.ceil(total_tokens / (2 * step))) ref_list, audio_list, uncond_list, motion_buckets = [], [], [], [] for i in tqdm(range(num_chunks)): start = i * 2 * step # ------------ cond_clip : (1,1,10,5,384) ------------------ clip_raw = audio_prompts[:, start : start + 10] # (1, ≤10, L, 384) # ★ W-padding은 dim=1 이어야 함! if clip_raw.shape[1] < 10: pad_w = torch.zeros_like(clip_raw[:, : 10 - clip_raw.shape[1]]) clip_raw = torch.cat([clip_raw, pad_w], dim=1) # ★ L-padding은 dim=2 while clip_raw.shape[2] < 5: clip_raw = torch.cat([clip_raw, clip_raw[:, :, -1:]], dim=2) clip_raw = clip_raw[:, :, :5] # (1,10,5,384) cond_clip = clip_raw.unsqueeze(1) # (1,1,10,5,384) # ------------ bucket_clip : (1,1,50,1,384) ----------------- bucket_raw = last_prompts[:, start : start + 50] if bucket_raw.shape[1] < 50: # ★ dim=1 pad_w = torch.zeros_like(bucket_raw[:, : 50 - bucket_raw.shape[1]]) bucket_raw = torch.cat([bucket_raw, pad_w], dim=1) bucket_clip = bucket_raw.unsqueeze(1) # (1,1,50,1,384) motion = audio2bucket(bucket_clip, image_embeds) * 16 + 16 ref_list.append(ref_img[0]) audio_list.append(audio_pe(cond_clip).squeeze(0)) # (50,1024) uncond_list.append(audio_pe(torch.zeros_like(cond_clip)).squeeze(0)) motion_buckets.append(motion[0]) # ---- Stable-Video-Diffusion 호출 ------------------------------ video = pipe( ref_img, clip_img, face_mask, audio_list, uncond_list, motion_buckets, height=height, width=width, num_frames=len(audio_list), decode_chunk_size=config.decode_chunk_size, motion_bucket_scale=config.motion_bucket_scale, fps=config.fps, noise_aug_strength=config.noise_aug_strength, min_guidance_scale1=config.min_appearance_guidance_scale, max_guidance_scale1=config.max_appearance_guidance_scale, min_guidance_scale2=config.audio_guidance_scale, max_guidance_scale2=config.audio_guidance_scale, overlap=config.overlap, shift_offset=config.shift_offset, frames_per_batch=config.n_sample_frames, num_inference_steps=config.num_inference_steps, i2i_noise_strength=config.i2i_noise_strength, ).frames video = (video * 0.5 + 0.5).clamp(0, 1) return video.to(pipe.device).unsqueeze(0).cpu() # ------------------------------------------------------------------ # Sonic 클래스 # ------------------------------------------------------------------ class Sonic: config_file = os.path.join(BASE_DIR, "config/inference/sonic.yaml") config = OmegaConf.load(config_file) def __init__(self, device_id: int = 0, enable_interpolate_frame: bool = True): cfg = self.config cfg.use_interframe = enable_interpolate_frame self.device = f"cuda:{device_id}" if device_id >= 0 and torch.cuda.is_available() else "cpu" cfg.pretrained_model_name_or_path = os.path.join(BASE_DIR, cfg.pretrained_model_name_or_path) self._load_models(cfg) print("Sonic init done") # -------------------------------------------------------------- def _load_models(self, cfg): dtype = {"fp16": torch.float16, "fp32": torch.float32, "bf16": torch.bfloat16}[cfg.weight_dtype] vae = AutoencoderKLTemporalDecoder.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="vae", variant="fp16") sched = EulerDiscreteScheduler.from_pretrained (cfg.pretrained_model_name_or_path, subfolder="scheduler") img_e = CLIPVisionModelWithProjection.from_pretrained (cfg.pretrained_model_name_or_path, subfolder="image_encoder", variant="fp16") unet = UNetSpatioTemporalConditionModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="unet", variant="fp16") add_ip_adapters(unet, [32], [cfg.ip_audio_scale]) a2t = AudioProjModel(10, 5, 384, 1024, 1024, 32).to(self.device) a2b = Audio2bucketModel(50, 1, 384, 1024, 1024, 1, 2).to(self.device) unet.load_state_dict(torch.load(os.path.join(BASE_DIR, cfg.unet_checkpoint_path), map_location="cpu")) a2t.load_state_dict(torch.load(os.path.join(BASE_DIR, cfg.audio2token_checkpoint_path), map_location="cpu")) a2b.load_state_dict(torch.load(os.path.join(BASE_DIR, cfg.audio2bucket_checkpoint_path), map_location="cpu")) whisper = WhisperModel.from_pretrained(os.path.join(BASE_DIR, "checkpoints/whisper-tiny")).to(self.device).eval() whisper.requires_grad_(False) self.feature_extractor = AutoFeatureExtractor.from_pretrained(os.path.join(BASE_DIR, "checkpoints/whisper-tiny")) self.face_det = AlignImage(self.device, det_path=os.path.join(BASE_DIR, "checkpoints/yoloface_v5m.pt")) if cfg.use_interframe: self.rife = RIFEModel(device=self.device) self.rife.load_model(os.path.join(BASE_DIR, "checkpoints/RIFE/")) img_e.to(dtype); vae.to(dtype); unet.to(dtype) self.pipe = SonicPipeline(unet=unet, image_encoder=img_e, vae=vae, scheduler=sched).to(device=self.device, dtype=dtype) self.image_encoder = img_e self.audio2token = a2t self.audio2bucket = a2b self.whisper = whisper # -------------------------------------------------------------- def preprocess(self, img_path: str, expand_ratio: float = 1.0): img = cv2.imread(img_path) h, w = img.shape[:2] _, _, faces = self.face_det(img, maxface=True) if faces: x1, y1, ww, hh = faces[0] return {"face_num": 1, "crop_bbox": process_bbox((x1, y1, x1 + ww, y1 + hh), expand_ratio, h, w)} return {"face_num": 0, "crop_bbox": None} # -------------------------------------------------------------- @torch.no_grad() def process( self, img_path: str, audio_path:str, out_path: str, min_resolution: int = 512, inference_steps:int = 25, dynamic_scale: float = 1.0, keep_resolution: bool = False, seed: int | None = None, ): cfg = self.config if seed is not None: cfg.seed = seed cfg.num_inference_steps = inference_steps cfg.motion_bucket_scale = dynamic_scale seed_everything(cfg.seed) sample = image_audio_to_tensor( self.face_det, self.feature_extractor, img_path, audio_path, limit=-1, image_size=min_resolution, area=cfg.area, ) if sample is None: return -1 h, w = sample["ref_img"].shape[-2:] resolution = (f"{(Image.open(img_path).width //2)*2}x{(Image.open(img_path).height//2)*2}" if keep_resolution else f"{w}x{h}") video = test( self.pipe, cfg, self.whisper, self.audio2token, self.audio2bucket, self.image_encoder, w, h, sample, ) if cfg.use_interframe: out = video.to(self.device) frames = [] for i in tqdm(range(out.shape[2] - 1), ncols=0): mid = self.rife.inference(out[:, :, i], out[:, :, i + 1]).clamp(0, 1).detach() frames.extend([out[:, :, i], mid]) frames.append(out[:, :, -1]) video = torch.stack(frames, 2).cpu() tmp = out_path.replace(".mp4", "_noaudio.mp4") save_videos_grid(video, tmp, n_rows=video.shape[0], fps=cfg.fps * (2 if cfg.use_interframe else 1)) os.system( f"ffmpeg -i '{tmp}' -i '{audio_path}' -s {resolution} " f"-vcodec libx264 -acodec aac -crf 18 -shortest '{out_path}' -y -loglevel error" ) os.remove(tmp) return 0