Face Generation with Generative Models

Module: Generative AI | Difficulty: Advanced

StyleGAN3 Aliasing Fix

Identity Preservation

Face Reenactment

Metrics

| Metric | StyleGAN2 | StyleGAN3 | EG3D | |--------|-----------|-----------|------| | FID | 2.84 | 2.41 | 4.12 | | LPIPS | 0.12 | 0.11 | 0.15 | | ArcFace | 0.62 | 0.65 | 0.71 |

class StyleGAN3Face(nn.Module):
    def __init__(self, style_dim=512, n_layers=14):
        super().__init__()
        self.mapping = nn.Sequential(*[nn.Linear(style_dim, style_dim) for _ in range(8)])
        self.synthesis = StyleSynthesis(style_dim, n_layers)
    def forward(self, z, truncation=0.7):
        w = self.mapping(z)
        w_avg = self.mapping(torch.randn(1, z.size(1))).mean(0)
        w = w_avg + truncation * (w - w_avg)
        return self.synthesis(w)

Research Insight: StyleGAN3 solved the texture sticking problem of StyleGAN2 by using continuous signal processing. The key insight is that aliasing in upsampling operations causes spatial information to leak into style codes.