Gpen-bfr-2048.pth 🚀

Table 1: Comparison on CelebA-Test (2048×2048). Ours consistently outperforms. Our model restores finer hair strands, eye textures, and skin pores. Identity preservation is visibly superior in challenging poses and occlusions. See supplementary material. 4.5 Ablation Study | Latent Dim | PSNR | LPIPS | FID | Training Time | |------------|------|-------|------|----------------| | 256 | 24.2 | 0.21 | 32.4 | 5.2 days | | 512 | 25.0 | 0.185 | 29.8 | 6.1 days | | 1024 | 25.5 | 0.172 | 27.9 | 7.3 days | | 2048 | 25.87 | 0.162 | 26.4 | 8.9 days |

It seems you are asking to create a proper academic paper based on the filename gpen-bfr-2048.pth . This filename is a checkpoint file ( .pth ) associated with , specifically a model variant likely trained for blind face restoration (BFR) with a 2048-dimensional latent or input resolution. gpen-bfr-2048.pth

: 256→512→1024 progressive growing, batch size 32, learning rate 2e-4. Stage 2 (high resolution) : 1024→2048 with gradient checkpointing, batch size 8, learning rate 5e-5. Table 1: Comparison on CelebA-Test (2048×2048)

[ \mathcalL = \lambda_1 \mathcalL perceptual + \lambda_2 \mathcalL adv + \lambda_3 \mathcalL identity + \lambda_4 \mathcalL freq ] This filename is a checkpoint file (

Below is a structured, hypothetical academic paper that would correspond to such a model. The paper is written in standard computer vision conference format (e.g., CVPR/ICCV style). Anonymous Author(s) Affiliation email Abstract Blind face restoration (BFR) aims to recover high-quality facial images from unknown degradations. Existing methods often struggle with preserving identity and generating fine-grained details at high resolutions. We propose GPEN-BFR-2048 , a novel framework that extends the generative facial prior (GPEN) paradigm to support 2048×2048 restoration. By incorporating a multi-scale encoder-decoder with a 2048-dimensional latent space and a progressive training strategy, our model reconstructs high-frequency textures while maintaining identity consistency. Experiments on synthetic and real-world datasets demonstrate that GPEN-BFR-2048 outperforms state-of-the-art methods in perceptual quality, fidelity, and inference speed. The model checkpoint is released as gpen-bfr-2048.pth . 1. Introduction Blind face restoration is a highly ill-posed problem due to unknown degradation kernels, noise, and compression artifacts. Recent advances leverage generative priors from GANs (e.g., StyleGAN2) to regularize the solution space. GPEN [1] introduced a compact architecture that embeds a pretrained GAN prior into a restoration network. However, the original GPEN operates at resolutions ≤1024×1024 and uses a 512-dimensional latent code, limiting detail recovery in high-resolution inputs.

We use a composite loss: