LuSIR

LuSIR: Latent Upscaling via Self-trained Image Restoration is a vision-only x4 super-resolution research project trained without a pretrained text-to-image diffusion model.

GitHub: https://github.com/BitIntx/LuSIR

The repository stores selected research checkpoints, configs, metrics, and sample grids. It does not redistribute training datasets.

Current Selected Detail Artifact

The latest public detail-branch research checkpoint is:

checkpoints/detail_branch_v1d_deep3m_photo130k_lsdir_best99500.pt

It is a deterministic 3.02M-parameter image-space detail branch on top of the frozen dual-context LSDIR Stage 2 step 98000 condition encoder and frozen Stage 1 decoder. The run completed 100086 micro-steps, exactly three epochs, and selected step 99500 by eval/detail_score.

Selected ordinary photo_detail_mix val100 result:

aggregate PSNR delta vs frozen base: +0.1646 dB
mean PSNR delta vs frozen base:      +0.1888 dB
SSIM delta vs frozen base:           +0.00647
PSNR wins:                           99/100
detail wins:                         100/100

Exploratory strict-bicubic DIV2K five-center-crop result:

mean RGB PSNR: 31.9513 dB
vs frozen base: +0.2102 dB
vs detail v1c:  +0.1358 dB
wins:           5/5

The strict-bicubic result is not a formal SOTA benchmark. It uses five 512x512 center crops, PIL bicubic x4 degradation, full-image RGB PSNR, and no border shave.

Formal full-image clean-bicubic benchmark, reported as Y PSNR / Y SSIM:

Dataset	Dual-context base	Detail v1d
DIV2K validation	29.9575 / 0.82887	30.1602 / 0.83421
Set5	31.6621 / 0.88952	31.8892 / 0.89440
Set14	28.2441 / 0.77340	28.4123 / 0.77998
Urban100	25.4816 / 0.76473	25.8755 / 0.77875

This uses public x4 LR pairs, MATLAB-compatible BT.601 Y, a four-pixel border shave, and MATLAB-style SSIM. V1d improves its frozen base on all four datasets. These clean-bicubic fidelity results are not a claim of classical-SR SOTA or a substitute for real-degradation and perceptual evaluation.

For scale, the official SwinIR classical x4 checkpoint reaches 31.0838 / 0.85228 on the same DIV2K evaluator, +0.9235 dB Y PSNR ahead of detail v1d. The next clean-fidelity priority is therefore the Stage 2/base reconstruction path rather than a larger detail branch.

Download

From a LuSIR GitHub clone:

python scripts/download_hf_checkpoints.py --preset detail_branch_v1d

Other useful presets include:

residual_refiner_v2
stage2_photo130k_lsdir_dual
detail_branch_v1b
photo100k_xl_stage4_edge

The public Colab default remains the conservative deterministic residual refiner v2 path. Detail v1d is preserved as the latest research detail candidate and is available in the Colab WebUI with single-image and tiled inference.

Runtime Paths

public deterministic default:
  LR -> Stage 2 XL -> residual refiner v2 -> Stage 1 decoder -> SR

selected detail research path:
  LR -> dual-context LSDIR Stage 2 -> Stage 1 decoder
     -> detail branch v1d -> SR

generative comparison:
  LR -> Stage 2 condition encoder -> Stage 3 OR Stage 4 diffusion U-Net
     -> Stage 1 decoder -> SR

Stage numbers describe training order. Stage 3 and Stage 4 are alternative diffusion checkpoints, not modules executed sequentially.

License

• Checkpoints, generated samples, metrics, and other non-code artifacts: CC BY-NC 4.0.
• Source code: PolyForm Noncommercial License 1.0.0.

Commercial use is not permitted without separate written permission.