app.py

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