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L40S
File size: 1,734 Bytes
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import torch
from diffusers.models import AutoencoderKL # type: ignore
from torch import nn
class AutoEncoder(nn.Module):
scale_factor: float = 0.18215
downsample: int = 8
def __init__(self, chunk_size: int | None = None):
super().__init__()
self.module = AutoencoderKL.from_pretrained(
"stabilityai/stable-diffusion-2-1-base",
subfolder="vae",
force_download=False,
low_cpu_mem_usage=False,
)
self.module.eval().requires_grad_(False) # type: ignore
self.chunk_size = chunk_size
def _encode(self, x: torch.Tensor) -> torch.Tensor:
return (
self.module.encode(x).latent_dist.mean # type: ignore
* self.scale_factor
)
def encode(self, x: torch.Tensor, chunk_size: int | None = None) -> torch.Tensor:
chunk_size = chunk_size or self.chunk_size
if chunk_size is not None:
return torch.cat(
[self._encode(x_chunk) for x_chunk in x.split(chunk_size)],
dim=0,
)
else:
return self._encode(x)
def _decode(self, z: torch.Tensor) -> torch.Tensor:
return self.module.decode(z / self.scale_factor).sample # type: ignore
def decode(self, z: torch.Tensor, chunk_size: int | None = None) -> torch.Tensor:
chunk_size = chunk_size or self.chunk_size
if chunk_size is not None:
return torch.cat(
[self._decode(z_chunk) for z_chunk in z.split(chunk_size)],
dim=0,
)
else:
return self._decode(z)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.decode(self.encode(x))
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