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from typing import cast, Union |
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import PIL.Image |
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import torch |
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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.bfloat16 |
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self.vae = cast(AutoencoderKL, 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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@staticmethod |
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def _unpack_latents(latents, height, width, vae_scale_factor): |
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batch_size, num_patches, channels = latents.shape |
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height = 2 * (int(height) // (vae_scale_factor * 2)) |
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width = 2 * (int(width) // (vae_scale_factor * 2)) |
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latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2) |
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latents = latents.permute(0, 3, 1, 4, 2, 5) |
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latents = latents.reshape(batch_size, channels // (2 * 2), height, width) |
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return latents |
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@torch.no_grad() |
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def __call__(self, data) -> Union[torch.Tensor, PIL.Image.Image]: |
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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 = cast(torch.Tensor, data["inputs"]) |
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parameters = cast(dict, data.get("parameters", {})) |
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if tensor.ndim == 3 and ("height" not in parameters or "width" not in parameters): |
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raise ValueError("Expected `height` and `width` in parameters.") |
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height = cast(int, parameters.get("height", 0)) |
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width = cast(int, parameters.get("width", 0)) |
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do_scaling = cast(bool, parameters.get("do_scaling", True)) |
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output_type = cast(str, parameters.get("output_type", "pil")) |
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partial_postprocess = cast(bool, parameters.get("partial_postprocess", False)) |
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if partial_postprocess and output_type != "pt": |
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output_type = "pt" |
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tensor = tensor.to(self.device, self.dtype) |
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if tensor.ndim == 3: |
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tensor = self._unpack_latents(tensor, height, width, self.vae_scale_factor) |
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if do_scaling: |
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tensor = ( |
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tensor / self.vae.config.scaling_factor |
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) + self.vae.config.shift_factor |
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with torch.no_grad(): |
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image = cast(torch.Tensor, self.vae.decode(tensor, return_dict=False)[0]) |
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if partial_postprocess: |
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image = (image * 0.5 + 0.5).clamp(0, 1) |
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image = image.permute(0, 2, 3, 1).contiguous().float() |
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image = (image * 255).round().to(torch.uint8) |
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elif output_type == "pil": |
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image = cast(PIL.Image.Image, self.image_processor.postprocess(image, output_type="pil")[0]) |
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return image |
