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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)
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