| Problem | Solution | |--------|----------| | | Reduce --size to 1024, then upscale externally. | | Faces not detected | Install MTCNN or use --detector retinaface . | | Blurry output | Input too small (<256px). Pre-upscale to 512px. | | Artifacts on eyes/teeth | Lower --use_weight 0.5 (balance between prior & input). | | Slow inference | Use --fp16 for half-precision. | | Identity change | Increase --identity_weight 0.2 . |
refers to a variant trained specifically to:
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, then internal documentation or communications from the organization that produced it might be the best source of information.
Without more context, here are a few speculative answers: | Problem | Solution | |--------|----------| | |
from gpen_model import FullModel model = FullModel(model_path="GPEN-BFR-2048.pth") model.align_faces = True # Auto-align before restore
Key advantage over ESRGAN, GFPGAN, or CodeFormer: GPEN keeps sharper texture and better identity consistency for very high-resolution outputs. Pre-upscale to 512px
: Sometimes, acronyms and large numbers appear in discussions about cryptographic protocols or security standards. For instance, key sizes (like 2048-bit keys) are crucial in cryptographic systems for ensuring security.