Super-Resolution with Diffusion Models

Module: Generative AI | Difficulty: Advanced

SR3

Real-World Degradation

Blind Super-Resolution

Estimate degradation kernel + SR jointly:

Metrics

class SR3(nn.Module):
    def __init__(self, unet, vae):
        super().__init__()
        self.unet = unet
        self.vae = vae
    def forward(self, x_hr, x_lr, t):
        z_hr = self.vae.encode(x_hr)
        z_lr = nn.functional.interpolate(self.vae.encode(x_lr), size=z_hr.shape[2:])
        noise = torch.randn_like(z_hr)
        z_t = scheduler.add_noise(z_hr, noise, t)
        return ((noise - self.unet(z_t, t, z_lr))**2).mean()