| from typing import Dict, List, Any |
| import torch |
| from base64 import b64decode |
| 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 = "cpu" |
| self.dtype = torch.float32 |
| 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 video |
|
|
