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nafnet-realestate
SebRincon/nafnet-realestate
nafnet-realestate is a image-to-image model from SebRincon. Use it when you need one image transformed into another. It is set up for pytorch. The card lists the license as apache-2.0.
A fine-tuned NAFNet model for enhancing real estate photography. Trained on 577 before/after image pairs to improve lighting, color, and overall image quality.
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Public
Updated Dec 14, 2025
Repo size
1 GB
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Repo makeup
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.jpg500 MB · 49%
At a glance
- Task
- Image-to-Image
- Library
- pytorch
- License
- apache-2.0
- Access
- Public
- Created
- Dec 13, 2025
- Updated
- Dec 14, 2025
- SHA
- edbae50d
Datasets
Model card
From the Hugging Face model README
NAFNet Real Estate Enhancement
A fine-tuned NAFNet model for enhancing real estate photography. Trained on 577 before/after image pairs to improve lighting, color, and overall image quality.
Model Details
| Metric | Value |
|---|---|
| Architecture | NAFNet (width=32) |
| Parameters | 29.2 million |
| Model Size | 111 MB (FP32) / 56 MB (FP16) |
| Training Time | 5 hours |
| Training Images | 577 pairs |
| Final PSNR | 21.69 dB |
| Final SSIM | 0.8968 |
Available Formats
| Format | File | Size | Use Case |
|---|---|---|---|
| PyTorch | nafnet_realestate.pth | 117 MB | Training, fine-tuning |
| ONNX | nafnet_realestate.onnx | 117 MB | Cross-platform deployment |
| Core ML | Convert from ONNX | ~56 MB | iOS/macOS apps |
Performance Benchmarks
Tested on 100 high-resolution real estate images (avg 7.25 megapixels):
Timing
| Metric | Value |
|---|---|
| Average per image | 4.0 seconds |
| Throughput | 0.25 images/second |
| Megapixels/second | 1.81 MP/s |
Memory Usage
| Resource | Usage |
|---|---|
| RAM | 581 MB total |
| GPU VRAM | 8.3 GB peak |
Scaling by Resolution
| Resolution | RAM | GPU | Time |
|---|---|---|---|
| 1080p (2.1 MP) | 150-250 MB | ~2.5 GB | ~1.2s |
| 1440p (3.7 MP) | 250-400 MB | ~4.3 GB | ~2.0s |
| 3K (7.3 MP) | 500-800 MB | ~8.3 GB | ~4.0s |
| 4K (8.3 MP) | 600-900 MB | ~9.5 GB | ~4.6s |
Usage
PyTorch
import torch
from PIL import Image
import numpy as np
# Load model
model = NAFNet(img_channel=3, width=32, middle_blk_num=12,
enc_blk_nums=[2, 2, 4, 8], dec_blk_nums=[2, 2, 2, 2])
checkpoint = torch.load("nafnet_realestate.pth", map_location="cpu")
model.load_state_dict(checkpoint["params"])
model.eval()
# Process image
img = Image.open("input.jpg")
img_tensor = torch.from_numpy(np.array(img)).permute(2, 0, 1).unsqueeze(0).float() / 255.0
with torch.no_grad():
output = model(img_tensor)
output_img = (output.squeeze(0).permute(1, 2, 0).numpy() * 255).astype(np.uint8)
Image.fromarray(output_img).save("enhanced.jpg")
ONNX Runtime
import onnxruntime as ort
import numpy as np
from PIL import Image
sess = ort.InferenceSession("nafnet_realestate.onnx")
img = np.array(Image.open("input.jpg")).astype(np.float32) / 255.0
img = img.transpose(2, 0, 1)[np.newaxis, ...]
output = sess.run(None, {"input": img})[0]
output_img = (output[0].transpose(1, 2, 0) * 255).astype(np.uint8)
Image.fromarray(output_img).save("enhanced.jpg")
Mobile Deployment (iOS)
All resolutions fit within typical mobile RAM budgets (3-4 GB):
- Convert ONNX to Core ML on macOS:
pip install coremltools
python convert_on_mac.py
- Add
.mlpackageto Xcode project - Use Vision framework for inference
Training
- Framework: BasicSR + PyTorch
- Base Model: NAFNet-SIDD-width32 (pretrained on denoising)
- Loss: L1 + Perceptual (VGG19)
- Optimizer: AdamW (lr=1e-3)
- Iterations: 12,000
License
Apache 2.0
Citation
@article{chen2022simple,
title={Simple Baselines for Image Restoration},
author={Chen, Liangyu and Chu, Xiaojie and Zhang, Xiangyu and Sun, Jian},
journal={arXiv preprint arXiv:2204.04676},
year={2022}
}
Links
- GitHub: SebRincon/pixel-sorcery
- Original NAFNet: megvii-research/NAFNet
- Task
- Image-to-Image
- Library
- pytorch
- License
- apache-2.0
- Languages
- en
- Created
- Dec 13, 2025
- Updated
- Dec 14, 2025