Jacid23/third-eye-models

Third Eye - Model Weights Bundle

Reliable mirror of AI model weights used by 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

SwinIR (Swin Transformer Image Restoration)

Initial set wired through the engine:

• classicalSR_DF2K_s64w8_SwinIR-M_x4.pth — classical 4x super-resolution
• classicalSR_DF2K_s64w8_SwinIR-M_x2.pth — classical 2x super-resolution
• lightweightSR_DIV2K_s64w8_SwinIR-S_x4.pth — lightweight 4x (smaller/faster)
• realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth — real-world 4x (BSRGAN-trained GAN)
• colorCAR_DFWB_s126w7_SwinIR-M_jpeg40.pth — JPEG artifact removal (qf≈40)
• colorDN_DFWB_s128w8_SwinIR-M_noise25.pth — color denoise (sigma=25)

Authors: Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, Radu Timofte Upstream: https://github.com/JingyunLiang/SwinIR License: Apache 2.0 Paper: "SwinIR: Image Restoration Using Swin Transformer" (ICCVW 2021)

Additional SwinIR checkpoints (JPEG qf=10/20/30, noise=15/50, grayscale variants, x3, x8) are available from the upstream releases and can be wired with one MODEL_CONFIGS entry each — the architecture supports all of them.

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:

install.bat

Or directly:

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.