SuperRes128 is an open-weight model from Sabre, released under MIT License. Its published files total 32.4 MB.
A lightweight neural network that upscales 128×128 RGB images to 256×256. No deep learning frameworks or image libraries required. Python 3.10+. Both comparisons use the best weights from 100 training epochs. Originals are displayed at 2× size.
Parameters—
Context—
Weights69.5 KB
Licensemit
AccessOpen weights
Monthly Downloads—
Model Card
By Sabre, published under mit, revision 9a9f598fbd07.
A lightweight neural network that upscales 128×128 RGB images to 256×256. No deep learning frameworks or image libraries required. Python 3.10+. Both comparisons use the best weights from 100 training epochs. Originals are displayed at 2× size. Held-out validation PSNR: 26.75 dB, compared with 25.05 dB for nearest-neighbor upscaling. Install and upscale using the included weights: Saves the 256×256 output and a labeled outputs/upscaled-comparison.png. Inputs must be non-interlaced, 8-bit PNGs; convert WebP images to PNG first. The included superressamples/ dataset contains 199 image pairs, with matching filenames in 128x128/ and 256x256/. Train a new model: Training uses random aligned…
Read Sabre's full model card
Super Resolution 128
A lightweight neural network that upscales 128×128 RGB images to 256×256. Built with pure NumPy: 2,460 parameters, a residual CNN, and 2× pixel shuffle. No deep learning frameworks or image libraries required. Python 3.10+.
Both comparisons use the best weights from 100 training epochs. Originals are displayed at 2× size. Held-out validation PSNR: 26.75 dB, compared with 25.05 dB for nearest-neighbor upscaling.
Install and upscale using the included weights:
python -m pip install -r requirements.txt
python upscale.py super_res_samples/128x128/4c5da1ff81ec7207.png outputs/upscaled.png
Saves the 256×256 output and a labeled outputs/upscaled-comparison.png.
Inputs must be non-interlaced, 8-bit PNGs; convert WebP images to PNG first.
The included super_res_samples/ dataset contains 199 image pairs, with
matching filenames in 128x128/ and 256x256/. Train a new model:
python train.py --data super_res_samples --out checkpoints/new-run --epochs 100
Training uses random aligned crops, Adam, and a fixed 90/10 train/validation
split. Each run saves best.npz, resumable last.npz, metrics, and previews.
To continue the new run to 200 total epochs:
python train.py --out checkpoints/new-run --resume checkpoints/new-run/last.npz --epochs 200
Run the numerical and checkpoint checks with python test_model.py.
This small model is an experiment; fine details may remain soft.
MIT License · Copyright (c) 2026 SabrePrometheus
Identity and Version
- Repository
- SabrePrometheus/SuperRes128
- Publisher
- Sabre
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- Not stated by the source
- Languages
- en
- Revision
- 9a9f598fbd07e846eb27e32b0791fe50cf6522b9
- First published
- 2026-09-19
- Last updated
- 2026-09-19
Files and Weights
419 files, 32.4 MB in total. The weights are 2 files totalling 69.5 KB in npz.
Weights2 files · 69.5 KB
Configuration8 files · 54.6 KB
Documentation2 files · 2.8 KB
Other405 files · 32.2 MB
Repository2 files · 17.3 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| checkpoints/best.npz | Weights | 34.8 KB | 095fa4621e74 |
| checkpoints/last.npz | Weights | 34.7 KB | 2330bf6e6d1d |
| checkpoints/split.json | Configuration | 5.8 KB | — |
| comparison.py | Configuration | 2.4 KB | — |
| model.py | Configuration | 4.6 KB | — |
| png_io.py | Configuration | 3.7 KB | — |
| super_res_samples/dataset.json | Configuration | 25.3 KB | — |
| test_model.py | Configuration | 3.9 KB | — |
| train.py | Configuration | 7.8 KB | — |
| upscale.py | Configuration | 1.1 KB | — |
| LICENSE | Documentation | 1.1 KB | — |
| README.md | Documentation | 1.7 KB | — |
| checkpoints/metrics.jsonl | Other | 20.3 KB | — |
| docs/comparison.png | Other | 228.1 KB | e938a966fef4 |
| docs/luna-comparison.png | Other | 216.9 KB | 42093232ae69 |
| outputs/luna_128x128.png | Other | 34.0 KB | — |
| outputs/luna_256x256.png | Other | 160.4 KB | 973bb8a689c9 |
| outputs/upscaled.png | Other | 169.8 KB | 87a5625ed702 |
| requirements.txt | Other | 12 B | — |
| super_res_samples/128x128/0118051a028f27ab.png | Other | 35.7 KB | — |
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| super_res_samples/256x256/f8190219af8bc6bf.png | Other | 84.8 KB | — |
| super_res_samples/256x256/fa22874af9d67a93.png | Other | 141.1 KB | 3af2212d8b92 |
| super_res_samples/256x256/fba8b4d006741e52.png | Other | 111.8 KB | d7cef15e291e |
| super_res_samples/256x256/fc36d08fa592a301.png | Other | 84.8 KB | — |
| super_res_samples/256x256/fff9ca809ba5f5ff.png | Other | 138.8 KB | ab2feb5986a6 |
| .gitattributes | Repository | 17.2 KB | — |
| .gitignore | Repository | 126 B | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 69.5 KB
Download from Sabre
Released by Sabre through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 69.5 KB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About SuperRes128
Can I use SuperRes128 commercially?
Yes. SuperRes128 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.