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import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
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=16): # Adjusted to match expected input channels
return nn.Sequential(
Clamp(),
conv(latent_channels, 48), # Reduced from 64 to 48 channels
nn.ReLU(),
Block(48, 48), Block(48, 48), # Reduced number of blocks
nn.Upsample(scale_factor=2), conv(48, 48, bias=False),
Block(48, 48), Block(48, 48), # Reduced number of blocks
nn.Upsample(scale_factor=2), conv(48, 48, bias=False),
Block(48, 48), # Further reduction in blocks
nn.Upsample(scale_factor=2), conv(48, 48, bias=False),
Block(48, 48),
conv(48, 3), # Final convolution to output channels
)
class Model(nn.Module):
latent_magnitude = 3
latent_shift = 0.5
def __init__(self, encoder_path="encoder.pth", decoder_path="decoder.pth", latent_channels=None):
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:
encoder_state_dict = torch.load(encoder_path, map_location="cpu", weights_only=True)
filtered_state_dict = {k.strip('encoder.'): v for k, v in encoder_state_dict.items() if k.strip('encoder.') in self.encoder.state_dict() and v.size() == self.encoder.state_dict()[k.strip('encoder.')].size()}
print(f" num of keys in filtered: {len(filtered_state_dict)} and in decoder: {len(self.encoder.state_dict())}")
self.encoder.load_state_dict(filtered_state_dict, strict=False)
if decoder_path is not None:
decoder_state_dict = torch.load(decoder_path, map_location="cpu", weights_only=True)
filtered_state_dict = {k.strip('decoder.'): v for k, v in decoder_state_dict.items() if k.strip('decoder.') in self.decoder.state_dict() and v.size() == self.decoder.state_dict()[k.strip('decoder.')].size()}
print(f" num of keys in filtered: {len(filtered_state_dict)} and in decoder: {len(self.decoder.state_dict())}")
self.decoder.load_state_dict(filtered_state_dict, strict=False)
self.encoder.requires_grad_(False)
self.decoder.requires_grad_(False)
def guess_latent_channels(self, encoder_path):
if "taef1" in encoder_path:return 16
if "taesd3" in encoder_path:return 16
return 4
@staticmethod
def scale_latents(x):
return x.div(2 * Model.latent_magnitude).add(Model.latent_shift).clamp(0, 1)
@staticmethod
def unscale_latents(x):
return x.sub(Model.latent_shift).mul(2 * Model.latent_magnitude)
def forward(self, x, return_latent=False):
latent = self.encoder(x)
out = self.decoder(latent)
if return_latent:
return out.clamp(0, 1), latent
return out.clamp(0, 1)
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