from typing import Dict, List, Any import torch from base64 import b64decode, b64encode from diffusers import AutoencoderKLHunyuanVideo from diffusers.video_processor import VideoProcessor from diffusers.utils import export_to_video class EndpointHandler: def __init__(self, path=""): self.device = "cuda" self.dtype = torch.float16 self.vae = ( AutoencoderKLHunyuanVideo.from_pretrained( path, subfolder="vae", torch_dtype=self.dtype ) .to(self.device, self.dtype) .eval() ) self.vae_scale_factor_spatial = self.vae.spatial_compression_ratio self.video_processor = VideoProcessor( vae_scale_factor=self.vae_scale_factor_spatial ) @torch.no_grad() def __call__(self, data: Any) -> List[List[Dict[str, float]]]: """ Args: data (:obj:): includes the input data and the parameters for the inference. """ tensor = data["inputs"] tensor = b64decode(tensor.encode("utf-8")) parameters = data.get("parameters", {}) if "shape" not in parameters: raise ValueError("Expected `shape` in parameters.") if "dtype" not in parameters: raise ValueError("Expected `dtype` in parameters.") DTYPE_MAP = { "float16": torch.float16, "float32": torch.float32, "bfloat16": torch.bfloat16, } shape = parameters.get("shape") dtype = DTYPE_MAP.get(parameters.get("dtype")) tensor = torch.frombuffer(bytearray(tensor), dtype=dtype).reshape(shape) tensor = tensor.to(self.device, self.dtype) tensor = tensor / self.vae.config.scaling_factor with torch.no_grad(): frames = self.vae.decode(tensor, return_dict=False)[0] frames = self.video_processor.postprocess_video(frames, output_type="pil")[0] path = export_to_video(frames, fps=15) with open(path, "rb") as f: video = f.read() return {'bytes': b64encode(video).decode("utf-8")}