Update sonic.py

sonic.py

@@ -1,20 +1,23 @@
+# ---------------------------------------------------------
+# sonic.py  (2025-05 rev – fix AudioProjModel tensor shape)
+# ---------------------------------------------------------
 import os, math, torch, cv2
+import torch.utils.checkpoint
 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.dataset.test_preprocess import image_audio_to_tensor, process_bbox
 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.pipelines.pipeline_sonic import SonicPipeline
 from src.utils.RIFE.RIFE_HDv3 import RIFEModel
 from src.dataset.face_align.align import AlignImage
 
@@ -22,223 +25,189 @@ BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 
 
 # ------------------------------------------------------------------
-#            single image + speech  →  video-tensor generator
+#                single image + speech  →  video tensor
 # ------------------------------------------------------------------
-def test(
-    pipe, config, wav_enc, audio_pe, audio2bucket, image_encoder,
-    width, height, batch,
-):
-    # ---- 배치 차원 맞추기 -----------------------------------------
+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).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)))
+            batch[k] = v.unsqueeze(0).float().to(pipe.device)
 
-    ref_list, audio_list, uncond_list, motion_buckets = [], [], [], []
+    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)
 
-    for i in tqdm(range(num_chunks)):
-        start = i * 2 * step
+    audio_feat = batch['audio_feature']   # (1,80,T)
+    audio_len  = int(batch['audio_len'])
+    step       = max(1, int(cfg.step))    # 안전 보정
 
-        # ------------ cond_clip : (1,1,10,5,384) ------------------
-        clip_raw = audio_prompts[:, start : start + 10]              # (1, ≤10, L, 384)
+    window = 16_000                       # 1-초 chunk
+    prompt_list, last_list = [], []
 
-        # ★ 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)
+    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)
 
-        # ★ 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)
+    if not prompt_list:
+        raise ValueError("❌ No speech recognised in audio.")
 
-        cond_clip = clip_raw.unsqueeze(1)                            # (1,1,10,5,384)
+    audio_prompts = torch.cat(prompt_list, 1)   # (1,80,*L,384)
+    last_prompts  = torch.cat(last_list, 1)     # (1,80,*1,384)
 
-        # ------------ 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)
+    # 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)
 
-        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(
+    # --------------------------------------------------------------
+    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,
-        audio_list, uncond_list, motion_buckets,
+        aud_L, uncond_L, 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,
+        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
 
-    video = (video * 0.5 + 0.5).clamp(0, 1)
-    return video.to(pipe.device).unsqueeze(0).cpu()
+    return (vid*0.5+0.5).clamp(0,1).to(pipe.device).unsqueeze(0).cpu()
 
 
 # ------------------------------------------------------------------
-#                        Sonic  클래스
+#                       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):
+    def __init__(self, device_id=0, enable_interpolate_frame=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"
+        self.device        = f"cuda:{device_id}" if torch.cuda.is_available() and device_id>=0 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")
 
-    # --------------------------------------------------------------
+    # 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_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")
+        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])
 
-        a2t = AudioProjModel(10, 5, 384, 1024, 1024, 32).to(self.device)
-        a2b = Audio2bucketModel(50, 1, 384, 1024, 1024, 1, 2).to(self.device)
+        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"))
-        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"))
+        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"))
 
-        whisper = WhisperModel.from_pretrained(os.path.join(BASE_DIR, "checkpoints/whisper-tiny")).to(self.device).eval()
-        whisper.requires_grad_(False)
+        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/"))
-
-        img_e.to(dtype); vae.to(dtype); unet.to(dtype)
+            self.rife = RIFEModel(device=self.device); self.rife.load_model(os.path.join(BASE_DIR, "checkpoints/RIFE/"))
 
-        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
+        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: str, expand_ratio: float = 1.0):
+    # ------------------------------------------------------------------
+    def preprocess(self, img_path, expand_ratio=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}
+        _, _, 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:  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,
-    ):
+    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
+        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,
+            img_path, wav_path, limit=-1,
+            image_size=min_resolution, area=cfg.area,
         )
-        if sample is None:
-            return -1
+        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}"
+        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
+        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