Create output.py

src/output.py

@@ -0,0 +1,70 @@
+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)