|
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 |
|
|
