| --- |
| title: GRDFNet |
| colorFrom: red |
| colorTo: yellow |
| license: mit |
| short_description: A lightweight image restoration architecture |
| pinned: true |
| --- |
| |
| # GRDFNet |
|
|
| GRDFNet is a lightweight image restoration network that combines gated and dilated residual blocks to deliver strong perceptual quality with modest compute requirements. |
|
|
| ## Recommended Configurations |
|
|
| - `num_sets = 3`, `feature_channels = 32`: strong quality while staying fast for most desktop workloads. |
| - `num_sets = 6`, `feature_channels = 48`: highest quality configuration; expect roughly a 4x slowdown versus the 32-channel model. |
| - `num_sets = 3`, `feature_channels = 24`: suggested for lightly compressed video inference; typically 50~75% faster than the 32-channel variant when deployed with TensorRT. |
|
|
| ## Performance Snapshot |
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|
| Example TensorRT run on an NVIDIA RTX 4080 Super (16 GB): |
| ``` |
| DEBUG: TensorRT initialized. Setting shape. |
| DEBUG: Shape set. Getting output shape. |
| [INFO] Input: 1280x720 -> 1280x720 -> ModelOut: 1280x720 @ 30000/1001 fps |
| DEBUG: Before NVENC initialization. |
| [prof] frames=209 avg=208.2 fps |
| [prof] frames=431 avg=215.1 fps |
| [prof] frames=654 avg=217.3 fps |
| [INFO] Processed 709 frames in 3.280s -> 216.2 FPS |
| ``` |
|
|
| ## Resources |
|
|
| - Model weights: https://huggingface.co/nicholasLane/GRDFNet |
| - Hosted demo: https://huggingface.co/spaces/nicholasLane/GRDFNet |
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|
