| import torch |
| import torch.nn as nn |
|
|
| def conv(n_in, n_out, **kwargs): |
| return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs) |
|
|
| class Clamp(nn.Module): |
| def forward(self, x): |
| return torch.tanh(x / 3) * 3 |
|
|
| class Block(nn.Module): |
| def __init__(self, n_in, n_out): |
| super().__init__() |
| self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) |
| self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() |
| self.fuse = nn.ReLU() |
| def forward(self, x): |
| return self.fuse(self.conv(x) + self.skip(x)) |
|
|
| def Encoder(latent_channels=4): |
| return nn.Sequential( |
| conv(3, 64), Block(64, 64), |
| conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), |
| conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), |
| conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), |
| conv(64, latent_channels), |
| ) |
|
|
| def Decoder(latent_channels=4): |
| return nn.Sequential( |
| Clamp(), conv(latent_channels, 64), nn.ReLU(), |
| Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), |
| Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), |
| Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), |
| Block(64, 64), conv(64, 3), |
| ) |
|
|
| class TAESD(nn.Module): |
| latent_magnitude = 3 |
| latent_shift = 0.5 |
|
|
| def __init__(self, encoder_path="taesd_encoder.pth", decoder_path="taesd_decoder.pth", latent_channels=None): |
| """Initialize pretrained TAESD on the given device from the given checkpoints.""" |
| super().__init__() |
| if latent_channels is None: |
| latent_channels = self.guess_latent_channels(str(encoder_path)) |
| self.encoder = Encoder(latent_channels) |
| self.decoder = Decoder(latent_channels) |
| if encoder_path is not None: |
| self.encoder.load_state_dict(torch.load(encoder_path, map_location="cpu", weights_only=True)) |
| if decoder_path is not None: |
| self.decoder.load_state_dict(torch.load(decoder_path, map_location="cpu", weights_only=True)) |
|
|
| def guess_latent_channels(self, encoder_path): |
| """guess latent channel count based on encoder filename""" |
| if "taef1" in encoder_path: |
| return 16 |
| if "taesd3" in encoder_path: |
| return 16 |
| return 4 |
|
|
| @staticmethod |
| def scale_latents(x): |
| """raw latents -> [0, 1]""" |
| return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) |
|
|
| @staticmethod |
| def unscale_latents(x): |
| """[0, 1] -> raw latents""" |
| return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) |
|
|
