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import torch from torch import nn, optim from torchvision import transforms # Simple example of improving image generation clarity class SimpleGenerator(nn.Module): def __init__(self): super().__init__() self.layers = nn.Sequential( nn.Linear(100, 256), nn.ReLU(), nn.Linear(256, 512), nn.ReLU(), nn.Linear(512, 1024), nn.ReLU(), nn.Linear(1024, 28*28), nn.Tanh() ) def forward(self, x): return self.layers(x).view(-1, 1, 28, 28) # Training loop with added sharpening loss component def sharpening_loss(output, target): # Simple edge detection filter to encourage sharpness edge_filter = torch.tensor([[[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]], dtype=torch.float32).unsqueeze(0) edge_filter = edge_filter.to(output.device) output_edges = nn.functional.conv2d(output, edge_filter, padding=1) target_edges = nn.functional.conv2d(target, edge_filter, padding=1) return nn.functional.mse_loss(output_edges, target_edges) # Assume we have data_loader providing (noise, real_images) # optimizer and model defined model = SimpleGenerator() optimizer = optim.Adam(model.parameters(), lr=0.0001) # Lower learning rate for epoch in range(50): # Increased epochs for noise, real_images in data_loader: optimizer.zero_grad() generated = model(noise) loss_mse = nn.functional.mse_loss(generated, real_images) loss_sharp = sharpening_loss(generated, real_images) loss = loss_mse + 0.1 * loss_sharp # Combine losses loss.backward() optimizer.step()
Before: Image clarity score was 60/100 and user satisfaction was 55%.
After: Image clarity score improved to 85/100 and user satisfaction rose to 78%.
Draw a cat sitting on a red chair describes a cat on a red chair, which is a clear visual prompt.Draw a cat sitting on a red chair [OK]'A sunny beach with palm trees and blue water''A red apple on a table' but outputs a blue apple. What is the likely cause?