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a342aa8 3581719 a342aa8 3581719 a342aa8 3581719 a342aa8 3581719 a342aa8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | import torch
from diffusers.models import AutoencoderKL # type: ignore
from torch import nn
import os
from diffusers.models import AutoencoderKL
class AutoEncoder(nn.Module):
scale_factor: float = 0.18215
downsample: int = 8
def __init__(self, chunk_size: int | None = None):
super().__init__()
vae_repo = os.getenv("VAE_REPO", "sd2-community/stable-diffusion-2-1")
# 2) 读取 Hugging Face 访问令牌（access token），可选
hf_token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN")
kwargs = dict(
subfolder="vae",
force_download=False,
low_cpu_mem_usage=False,
)
if hf_token:
kwargs["token"] = hf_token # diffusers/hf-hub 会用它做认证（authentication）
self.module = AutoencoderKL.from_pretrained(vae_repo, **kwargs)
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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