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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 AutoencoderKL |
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from diffusers.image_processor import VaeImageProcessor |
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class EndpointHandler: |
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def __init__(self, path=""): |
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self.device = "cuda" |
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self.dtype = torch.float16 |
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self.vae = AutoencoderKL.from_pretrained(path, torch_dtype=self.dtype).to(self.device, self.dtype).eval() |
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) |
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) |
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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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needs_upcasting = ( |
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self.vae.dtype == torch.float16 and self.vae.config.force_upcast |
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) |
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if needs_upcasting: |
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self.vae = self.vae.to(torch.float32) |
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tensor = tensor.to(self.device, torch.float32) |
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else: |
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tensor = tensor.to(self.device, self.dtype) |
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has_latents_mean = ( |
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hasattr(self.vae.config, "latents_mean") |
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and self.vae.config.latents_mean is not None |
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) |
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has_latents_std = ( |
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hasattr(self.vae.config, "latents_std") |
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and self.vae.config.latents_std is not None |
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) |
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if has_latents_mean and has_latents_std: |
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latents_mean = ( |
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torch.tensor(self.vae.config.latents_mean) |
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.view(1, 4, 1, 1) |
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.to(tensor.device, tensor.dtype) |
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) |
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latents_std = ( |
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torch.tensor(self.vae.config.latents_std) |
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.view(1, 4, 1, 1) |
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.to(tensor.device, tensor.dtype) |
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) |
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tensor = ( |
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tensor * latents_std / self.vae.config.scaling_factor + latents_mean |
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) |
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else: |
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tensor = tensor / self.vae.config.scaling_factor |
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with torch.no_grad(): |
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image = self.vae.decode(tensor, return_dict=False)[0] |
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if needs_upcasting: |
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self.vae.to(dtype=torch.float16) |
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image = self.image_processor.postprocess(image, output_type="pil") |
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return image[0] |
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