xinntao/gfpgan 🖼️🔢❓ → 🖼️

▶️ 47.2M runs 📅 Aug 2022 ⚙️ Cog 0.4.2 🔗 GitHub 📄 Paper ⚖️ License
face-restoration image-restoration image-upscaling

About

Practical face restoration algorithm for *old photos* or *AI-generated faces*

Example Output

Output

Example output

Performance Metrics

2.15s Prediction Time
2.39s Total Time
All Input Parameters 
{
  "img": "https://replicate.delivery/mgxm/e43791ea-dd72-4f4f-a954-7a8cf3dde8a0/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg",
  "scale": 2,
  "version": "v1.4"
}
Input Parameters
img (required) Type: string
Input
scale Type: numberDefault: 2
Rescaling factor
version Default: v1.4
GFPGAN version. v1.3: better quality. v1.4: more details and better identity.
Output Schema

Output

Type: string • Format: uri

Example Execution Logs 
/tmp/tmptckph25n187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg v1.4 2.0
/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torch/nn/functional.py:3103: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
  warnings.warn("The default behavior for interpolate/upsample with float scale_factor changed "
Version Details
Version ID
6129309904ce4debfde78de5c209bce0022af40e197e132f08be8ccce3050393
Version Created
September 18, 2022
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