ldm/models/autoencoder.py

import torch

from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution


class AutoencoderKL(torch.nn.Module):
    def __init__(
            self,
            ddconfig,
            embed_dim
    ):
        super().__init__()
        self.encoder = Encoder(**ddconfig)
        self.decoder = Decoder(**ddconfig)
        assert ddconfig["double_z"]
        self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
        self.embed_dim = embed_dim

    def encode(self, x):
        h = self.encoder(x)
        moments = self.quant_conv(h)
        posterior = DiagonalGaussianDistribution(moments)
        return posterior

    def decode(self, z):
        z = self.post_quant_conv(z)
        dec = self.decoder(z)
        return dec

    def get_last_layer(self):
        return self.decoder.conv_out.weight

    @property
    def dtype(self):
        return self.decoder.conv_out.weight.dtype