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Given its high-resolution focus, it is recommended to use the --tile_size argument to handle large images without crashing your GPU.
The variant is the highest‑resolution checkpoint released by the GPEN authors. It is intended for professional pipelines (e.g., film restoration, forensic analysis, high‑end portrait editing) where the final output must be printable or suitable for close‑up inspection.
Improves the clarity of faces in images where the subject is far away or the lighting is poor.
. When used locally, it is often placed in specific cache folders (e.g., ~/.cache/modelscope/hub/damo ) or within the folder of a specific AI tool. GPEN/README.md at main - GitHub
: Capable of filling in missing parts of a face image.
While you'll need a capable computer to run it, the results are often stunning. By integrating it into your workflow with simple Python code or through user-friendly applications like ComfyUI, you can breathe new life into your most precious memories or take your digital art to the next level.
This signifies the output resolution. The model upscales and restores faces up to a crisp 2048x2048 pixel resolution.
The .pth extension identifies it as a PyTorch model file, containing the learned weights and parameters required to run the restoration algorithm. KenjieDec - Hugging Face
When using this model, there are a few important things to keep in mind.
is a high-resolution pre-trained model weight file for the GAN Prior Embedded Network (GPEN) , specifically designed for "Blind Face Restoration" (BFR). What is it?
def get_encoder(): backbone = models.resnet50(pretrained=False) # Remove classification head and the final BN (keep conv layers) modules = list(backbone.children())[:-2] # up to conv5_x (feature map) encoder = nn.Sequential(*modules) # output shape: (B, 2048, H/32, W/32) return encoder
: The "2048" suffix indicates it supports high-resolution output up to
GPEN is one of several leading face restoration models. Here is how the 2048 model generally compares to other popular options like GFPGAN and CodeFormer: