| import torch | |
| import torch.nn as nn | |
| from crs_core.modules.diffusionmodules.model import Encoder, Decoder | |
| from crs_core.modules.distributions.distributions import DiagonalGaussianDistribution | |
| class AutoencoderKL(nn.Module): | |
| def __init__(self, ddconfig, lossconfig=None, embed_dim=4, **kwargs): | |
| super().__init__() | |
| del lossconfig, kwargs | |
| self.encoder = Encoder(**ddconfig) | |
| self.decoder = Decoder(**ddconfig) | |
| assert ddconfig["double_z"] | |
| self.quant_conv = nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1) | |
| self.post_quant_conv = 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) | |
| return DiagonalGaussianDistribution(moments) | |
| def decode(self, z): | |
| z = self.post_quant_conv(z) | |
| return self.decoder(z) | |