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README.md

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-license: mit
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+---
+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. The project ships with a Gradio application for interactive experimentation and can be accelerated further with TensorRT.
+
+## 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
+
+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