Add training code

upscaler.py

@@ -0,0 +1,25 @@
+import torch
+import torch.nn as nn
+import numpy as np
+
+class LatentUpscaler(nn.Module):
+	def __init__(self, fac):
+		super().__init__()
+
+		module_list = [
+			nn.Conv2d(4, 64, kernel_size=5, padding=2),
+			nn.ReLU(),
+			nn.Upsample(scale_factor=fac, mode="nearest"), # bicubic was blurry
+			nn.ReLU(),
+			nn.Conv2d(64, 64, kernel_size=7, padding=3),
+			nn.ReLU(),
+			nn.Conv2d(64, 64, kernel_size=7, padding=3),
+			nn.ReLU(),
+			nn.Conv2d(64, 32, kernel_size=7, padding=3),
+			nn.ReLU(),
+			nn.Conv2d(32, 4, kernel_size=5, padding=2),
+		]
+		self.sequential = nn.Sequential(*module_list)
+
+	def forward(self, x: torch.Tensor) -> torch.Tensor:
+		return self.sequential(x)