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from typing import cast, Union
import PIL.Image
import torch
from diffusers import AutoencoderKL
from diffusers.image_processor import VaeImageProcessor
class EndpointHandler:
def __init__(self, path=""):
self.device = "cuda"
self.dtype = torch.float16
self.vae = cast(AutoencoderKL, AutoencoderKL.from_pretrained(path, torch_dtype=self.dtype).to(self.device, self.dtype).eval())
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
@torch.no_grad()
def __call__(self, data) -> Union[torch.Tensor, PIL.Image.Image]:
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
"""
tensor = cast(torch.Tensor, data["inputs"])
parameters = cast(dict, data.get("parameters", {}))
do_scaling = cast(bool, parameters.get("do_scaling", True))
output_type = cast(str, parameters.get("output_type", "pil"))
partial_postprocess = cast(bool, parameters.get("partial_postprocess", False))
if partial_postprocess and output_type != "pt":
output_type = "pt"
tensor = tensor.to(self.device, self.dtype)
if do_scaling:
has_latents_mean = (
hasattr(self.vae.config, "latents_mean")
and self.vae.config.latents_mean is not None
)
has_latents_std = (
hasattr(self.vae.config, "latents_std")
and self.vae.config.latents_std is not None
)
if has_latents_mean and has_latents_std:
latents_mean = (
torch.tensor(self.vae.config.latents_mean)
.view(1, 4, 1, 1)
.to(tensor.device, tensor.dtype)
)
latents_std = (
torch.tensor(self.vae.config.latents_std)
.view(1, 4, 1, 1)
.to(tensor.device, tensor.dtype)
)
tensor = (
tensor * latents_std / self.vae.config.scaling_factor + latents_mean
)
else:
tensor = tensor / self.vae.config.scaling_factor
with torch.no_grad():
image = cast(torch.Tensor, self.vae.decode(tensor, return_dict=False)[0])
if partial_postprocess:
image = (image * 0.5 + 0.5).clamp(0, 1)
image = image.permute(0, 2, 3, 1).contiguous().float()
image = (image * 255).round().to(torch.uint8)
elif output_type == "pil":
image = cast(PIL.Image.Image, self.image_processor.postprocess(image, output_type="pil")[0])
return image
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