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