""" sonic.py – 2025-05 hot-fix 주요 수정 • config.pretrained_model_name_or_path 가 실제 폴더인지 확인 • 없다면 huggingface_hub.snapshot_download 로 자동 다운로드 • 경로가 준비된 뒤 모델 로드 진행 """ import os, math, torch, cv2 from PIL import Image from omegaconf import OmegaConf from tqdm.auto import tqdm from diffusers import AutoencoderKLTemporalDecoder, EulerDiscreteScheduler from transformers import WhisperModel, CLIPVisionModelWithProjection, AutoFeatureExtractor from huggingface_hub import snapshot_download, hf_hub_download 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__)) HF_STABLE_REPO = "stabilityai/stable-video-diffusion-img2vid-xt" LOCAL_STABLE_DIR = os.path.join(BASE_DIR, "checkpoints", "stable-video-diffusion-img2vid-xt") # ------------------------------------------------------------------ # single image + speech → video tensor # ------------------------------------------------------------------ def test(pipe, cfg, wav_enc, audio_pe, audio2bucket, img_enc, width, height, batch): # --- batch 차원 맞추기 ------------------------------------------ for k, v in batch.items(): if isinstance(v, torch.Tensor): batch[k] = v.unsqueeze(0).float().to(pipe.device) ref_img = batch['ref_img'] clip_img = batch['clip_images'] face_mask = batch['face_mask'] img_emb = img_enc(clip_img).image_embeds # (1,1024) audio_feat = batch['audio_feature'] # (1,80,T) audio_len = int(batch['audio_len']) step = max(1, int(cfg.step)) # 안전 보정 window = 16_000 # 1-초 chunk prompt_list, last_list = [], [] for i in range(0, audio_feat.shape[-1], window): chunk = audio_feat[:, :, i:i+window] hs = wav_enc.encoder(chunk, output_hidden_states=True).hidden_states prompt_list.append(torch.stack(hs, 2)) # (1,80,L,384) last = wav_enc.encoder(chunk).last_hidden_state.unsqueeze(-2) last_list.append(last) # (1,80,1,384) if not prompt_list: raise ValueError("❌ No speech recognised in audio.") audio_prompts = torch.cat(prompt_list, 1) # (1,80,*L,384) last_prompts = torch.cat(last_list, 1) # (1,80,*1,384) # pad 규칙 (모델 원 논문과 동일) audio_prompts = torch.cat([ torch.zeros_like(audio_prompts[:,:4]), audio_prompts, torch.zeros_like(audio_prompts[:,:6]) ], 1) last_prompts = torch.cat([ torch.zeros_like(last_prompts[:,:24]), last_prompts, torch.zeros_like(last_prompts[:,:26]) ], 1) # -------------------------------------------------------------- total_tok = audio_prompts.shape[1] n_chunks = max(1, math.ceil(total_tok / (2*step))) ref_L, aud_L, uncond_L, buckets = [], [], [], [] for i in tqdm(range(n_chunks), ncols=0): st = i * 2 * step # ① 조건 오디오 토큰(pad → 10×5×384) cond = audio_prompts[:, st:st+10] # (1,80,10,384) → (1,10,8,384)? cond = cond[:, :10] # f = 10 cond = cond.permute(0,2,1,3) # (1,10,80,384) cond = cond.reshape(1, 10, 10, 5, 384) # ★ w=10, b=5 (zero-pad auto) # ② bucket 추정용 토큰 buck = last_prompts[:, st:st+50] # (1,80,50,384) if buck.shape[1] < 50: pad = torch.zeros(1, 50-buck.shape[1], *buck.shape[2:], device=buck.device) buck = torch.cat([buck, pad], 1) buck = buck[:, :50].permute(0,2,1,3).reshape(1, 50, 10, 5, 384) motion = audio2bucket(buck, img_emb) * 16 + 16 ref_L.append(ref_img[0]) aud_L.append(audio_pe(cond).squeeze(0)) # (10,1024) uncond_L.append(audio_pe(torch.zeros_like(cond)).squeeze(0)) buckets.append(motion[0]) # -------------- diffusion ------------------------------------------------- vid = pipe( ref_img, clip_img, face_mask, aud_L, uncond_L, buckets, height=height, width=width, num_frames=len(aud_L), decode_chunk_size=cfg.decode_chunk_size, motion_bucket_scale=cfg.motion_bucket_scale, fps=cfg.fps, noise_aug_strength=cfg.noise_aug_strength, min_guidance_scale1=cfg.min_appearance_guidance_scale, max_guidance_scale1=cfg.max_appearance_guidance_scale, min_guidance_scale2=cfg.audio_guidance_scale, max_guidance_scale2=cfg.audio_guidance_scale, overlap=cfg.overlap, shift_offset=cfg.shift_offset, frames_per_batch=cfg.n_sample_frames, num_inference_steps=cfg.num_inference_steps, i2i_noise_strength=cfg.i2i_noise_strength, ).frames return (vid*0.5+0.5).clamp(0,1).to(pipe.device).unsqueeze(0).cpu() # ------------------------------------------------------------------ # Sonic wrapper # ------------------------------------------------------------------ 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 torch.cuda.is_available() and device_id >= 0 else "cpu" # ----------- ✨ [NEW] pretrained 모델 폴더 확보 ---------------------- if not os.path.isdir(LOCAL_STABLE_DIR) or not os.path.isfile(os.path.join(LOCAL_STABLE_DIR, "vae", "config.json")): print("[INFO] 1st-run – downloading base model (~2 GB)…") snapshot_download(repo_id=HF_STABLE_REPO, local_dir=LOCAL_STABLE_DIR, resume_download=True, local_dir_use_symlinks=False) cfg.pretrained_model_name_or_path = LOCAL_STABLE_DIR # ------------------------------------------------------------------ self._load_models(cfg) print("Sonic init done") # model-loader (unchanged, but with tiny clean-ups) ------------------------ 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_enc = 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]) self.audio2token = AudioProjModel(10, 5, 384, 1024, 1024, 32).to(self.device) self.audio2bucket = 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")) self.audio2token.load_state_dict (torch.load(os.path.join(BASE_DIR, cfg.audio2token_checkpoint_path), map_location="cpu")) self.audio2bucket.load_state_dict(torch.load(os.path.join(BASE_DIR, cfg.audio2bucket_checkpoint_path), map_location="cpu")) self.whisper = WhisperModel.from_pretrained(os.path.join(BASE_DIR, "checkpoints/whisper-tiny")).to(self.device).eval() self.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/")) for m in (img_enc, vae, unet): m.to(dtype) self.pipe = SonicPipeline(unet=unet, image_encoder=img_enc, vae=vae, scheduler=sched).to(device=self.device, dtype=dtype) self.image_encoder = img_enc # ------------------------------------------------------------------ def preprocess(self, img_path, expand_ratio=1.0): img = cv2.imread(img_path) _, _, boxes = self.face_det(img, maxface=True) if boxes: x,y,w,h = boxes[0]; return {"face_num":1,"crop_bbox":process_bbox((x,y,x+w,y+h),expand_ratio,*img.shape[:2])} return {"face_num":0,"crop_bbox":None} # ------------------------------------------------------------------ @torch.no_grad() def process(self, img_path, wav_path, out_path, min_resolution=512, inference_steps=25, dynamic_scale=1.0, keep_resolution=False, seed=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, wav_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: # RIFE interpolation 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) frames += [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 '{wav_path}' -s {resolution} " f"-vcodec libx264 -acodec aac -crf 18 -shortest '{out_path}' -y -loglevel error") os.remove(tmp); return 0