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To use the gpen-bfr-2048.pth model, you will need to have PyTorch installed on your system. You can then use the model in your Python code by loading it with the following command:
The 2048 checkpoint is the result of the 1024‑pixel model on a progressively‑grown version of StyleGAN2 (weights duplicated to support 2048 output). No additional data beyond the synthetic pipeline was introduced; the model simply learns to extrapolate the StyleGAN2 latent space to higher spatial resolution.
– Perhaps the intended filename was:
# Convert the noise vector to a PyTorch tensor noise = torch.from_numpy(noise).float()
| Loss | λ | |------|---| | Pixel (L1) | 1.0 | | Perceptual (VGG‑19 relu2_2) | 0.05 | | Identity (ArcFace cosine) | 0.1 | | Adversarial (R1) | 0.005 | | LPIPS | 0.1 |
: Fixing artifacts or "mushy" details in images generated by older AI models or low-denoise Stable Diffusion passes.
To use the gpen-bfr-2048.pth model, you will need to have PyTorch installed on your system. You can then use the model in your Python code by loading it with the following command:
The 2048 checkpoint is the result of the 1024‑pixel model on a progressively‑grown version of StyleGAN2 (weights duplicated to support 2048 output). No additional data beyond the synthetic pipeline was introduced; the model simply learns to extrapolate the StyleGAN2 latent space to higher spatial resolution. gpen-bfr-2048.pth
– Perhaps the intended filename was:
# Convert the noise vector to a PyTorch tensor noise = torch.from_numpy(noise).float() To use the gpen-bfr-2048
| Loss | λ | |------|---| | Pixel (L1) | 1.0 | | Perceptual (VGG‑19 relu2_2) | 0.05 | | Identity (ArcFace cosine) | 0.1 | | Adversarial (R1) | 0.005 | | LPIPS | 0.1 | – Perhaps the intended filename was: # Convert
: Fixing artifacts or "mushy" details in images generated by older AI models or low-denoise Stable Diffusion passes.
Per installare questa Web App sul tuo iPhone/iPad premi l'icona.
E poi Aggiungi alla schermata principale.
