File size: 4,751 Bytes
23c6e00 956df8e 23c6e00 956df8e 23c6e00 956df8e 23c6e00 956df8e 23c6e00 956df8e 23c6e00 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | ---
license: cc-by-nc-sa-4.0
tags:
- third-eye
- image-restoration
- video-restoration
- super-resolution
- denoise
- deblur
- colorization
- frame-interpolation
library_name: pytorch
---
# Third Eye - Model Weights Bundle
Reliable mirror of AI model weights used by [Third Eye](https://github.com/Jacid23/Third_Eye), a media organizer with hidden editing dimensions.
These weights are downloaded automatically by `scripts/fetch_models.py` during installation. Self-hosting them here removes dependency on upstream Google Drive links and unreliable community mirrors.
## License
The bundle is tagged **CC BY-NC-SA 4.0** — the most restrictive license among the included models. By using these weights you agree to:
- **Non-commercial use only**
- Provide **attribution** to the original authors (listed below)
- Distribute any derivatives under the **same license**
## Files and Attribution
Every weight in this repo is a verbatim copy of the file released by its original author. Original sources and licenses below.
### Denoise / Deblur (NAFNet)
- `NAFNet-SIDD-width64.pth` — denoise model (SIDD dataset)
- `NAFNet-REDS-width64.pth` — deblur model (REDS dataset)
- `NAFNet-GoPro-width64.pth` — deblur model (GoPro dataset, alternative to REDS)
**Authors:** Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, Jian Sun (Megvii Research)
**Upstream:** https://github.com/megvii-research/NAFNet
**License:** MIT
**Paper:** "Simple Baselines for Image Restoration" (ECCV 2022)
### Frame Interpolation (RIFE)
- `flownet.pkl` — RIFE 4.6 weights
**Authors:** Zhewei Huang et al. (Practical-RIFE team)
**Upstream:** https://github.com/hzwer/Practical-RIFE
**License:** MIT (code) / non-commercial (weights, per author note)
**Paper:** "Real-Time Intermediate Flow Estimation for Video Frame Interpolation"
### Community RRDBNet Upscale Models
**4x variants:**
- `4x-UltraSharp.pth` — community upscale model by Kim2091
- `foolhardy_Remacri.pth` — community model by foolhardy
- `RealisticRescaler_100000_G.pth` — community upscale model
- `4x-UniScale-Balanced-72000g.pth` — UniScale community variant
- `4x-UniScale-Strong-42400g.pth` — UniScale community variant
- `4xJaypeg90.pth` — JPEG-focused 4x cleanup upscaler
- `4xLSDIRplus.pth` — LSDIR dataset upscaler
- `4xLSDIRplusR.pth` — LSDIR refined variant
- `CountryRoads_377000_G.pth` — general-purpose community upscaler
- `NMKD-Superscale-SP_178000_G.pth` — NMKD standard print
- `NMKDSuperscale_Artisoft_120000_G.pth` — NMKD artistic-soft
- `A_ESRGAN_Single.pth` — A-ESRGAN single-pass
- `Filmify4K_v2_325000_G.pth` — film-look upscaler
**8x variants:**
- `8x_NMKD-Superscale_150000_G.pth` — NMKD general 8x
- `8x_NMKD-Typescale_175k.pth` — NMKD optimised for text/UI
- `TGHQFace8x_500k.pth` — face-specific 8x
**1x detail enhancers:**
- `x1_ITF_SkinDiffDetail_Lite_v1.pth` — skin texture enhancement
**Upstream catalog:** https://openmodeldb.info/
**License:** CC BY-NC-SA 4.0 (community convention for ESRGAN-derived models)
Architecture is RRDBNet from Real-ESRGAN. Original Real-ESRGAN architecture:
- **Authors:** Xintao Wang et al. (Tencent ARC Lab)
- **Upstream:** https://github.com/xinntao/Real-ESRGAN
- **License:** BSD-3-Clause
### Transformer Upscale Models (DAT / HAT-L / DRCT-L)
- `4xFFHQDAT.pth` — DAT architecture, trained on FFHQ
- `4xFaceUpSharpDAT.pth` — DAT, face sharpener
- `4xLSDIRDAT.pth` — DAT, LSDIR dataset
- `4xNomos8kHAT-L_otf.pth` — HAT-L architecture
- `4xNomos2_hq_drct-l.pth` — DRCT-L architecture
**Upstream catalog:** https://openmodeldb.info/
**License:** CC BY-NC-SA 4.0 (community convention)
These are mirrored here for download convenience, but Third Eye's engine does not yet implement the DAT, HAT-L, or DRCT-L architectures. They will be wired up in a future engine update.
Original transformer architecture papers:
- **DAT:** "Dual Aggregation Transformer for Image Super-Resolution" (ICCV 2023)
- **HAT:** "Activating More Pixels in Image Super-Resolution Transformer" (CVPR 2023)
- **DRCT:** "DRCT: Saving Image Super-Resolution away from Information Bottleneck"
## Usage
Download programmatically via the Third Eye installer:
```bat
install.bat
```
Or directly:
```bash
wget https://huggingface.co/Jacid23/third-eye-models/resolve/main/NAFNet-SIDD-width64.pth
```
## Source Code
Third Eye source: https://github.com/Jacid23/Third_Eye
Model download script: `scripts/fetch_models.py`
## Acknowledgements
All credit for the models goes to their original authors and research teams. This repository exists only to provide reliable download mirrors for an open-source application that integrates these models. No modifications have been made to any weight file.
|