Nacre v1 (full-v1 @110k EMA): weights, VQ-f4 (MIT), config, card, attribution

.gitattributes

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ATTRIBUTION.csv

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LICENSE

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NOTICE

@@ -0,0 +1,10 @@
+Nacre v1
+Copyright 2026 Xocialize. Licensed under the Apache License, Version 2.0 (see LICENSE).
+
+This distribution includes the VQ-f4 autoencoder weights from CompVis latent-diffusion
+(https://github.com/CompVis/latent-diffusion), released under the MIT License:
+  Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+
+The denoiser was trained on images from Wikimedia Commons under CC0, public-domain and
+Creative Commons Attribution licences. Each training image, its licence and its author are
+listed in ATTRIBUTION.csv.

README.md

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+---
+license: apache-2.0
+tags:
+  - super-resolution
+  - image-restoration
+  - diffusion
+  - resshift
+pipeline_tag: image-to-image
+---
+
+# Nacre v1 — provenance-clean 4× real-world super-resolution
+
+A clean-room, ResShift-class generative upscaler: a 118.6M-parameter SwinUNet denoiser driving a
+**4-step residual-shift diffusion** in the latent space of the CompVis VQ-f4 autoencoder. It takes a
+degraded low-resolution photo (blur, noise, JPEG, resize — the Real-ESRGAN two-stage family) and returns
+a 4× restoration with plausible fine detail.
+
+**Why it exists:** every comparable generative SR model is blocked somewhere in its stack — non-commercial
+weights (ResShift, StableSR, SUPIR), a use-restricted base (SD2.1-based PiSA-SR / OSEDiff / SeeSR), or an
+undisclosed training corpus. Nacre is permissive **all the way down**:
+
+| Component | Licence | Provenance |
+|---|---|---|
+| Code (architecture + trainer) | Apache-2.0 | written from the papers; no reference code copied |
+| Denoiser weights (`nacre_v1.safetensors`) | Apache-2.0 | trained from scratch by Xocialize |
+| Autoencoder (`vq_f4.safetensors`) | MIT | CompVis latent-diffusion VQ-f4, unmodified (training head removed) |
+| Training images | CC0 / Public domain / CC BY family | 51,759 Wikimedia Commons images, per-file licences in `ATTRIBUTION.csv` |
+
+## Results — neutral benchmarks, vs the released ResShift v3
+
+Same inputs, same degradation draws and same chain noise for both models; each run through its own
+autoencoder. Real-image sets are centre-cropped to 256 px LQ (→ 1024 px output).
+
+| Set | PSNR-Y ↑ | SSIM-Y ↑ | LPIPS ↓ ¹ | CLIPIQA ↑ | MUSIQ ↑ |
+|---|---|---|---|---|---|
+| RealSR V3 ×4 (100 real camera pairs) | **23.98** vs 23.93 | **0.705** vs 0.687 | **0.318** vs 0.364 | 0.572 vs 0.574 | **63.5** vs 59.3 |
+| DIV2K-val (200 crops, synthetic damage) | 22.85 vs 22.86 | **0.579** vs 0.571 | **0.347** vs 0.356 | 0.517 vs **0.549** | **58.2** vs 56.8 |
+| RealSet80 (no ground truth) | — | — | — | 0.606 vs **0.648** | **66.3** vs 64.1 |
+
+At or above ResShift on fidelity everywhere and on MUSIQ everywhere; behind on CLIPIQA on DIV2K and
+RealSet by 0.03–0.04. ¹ LPIPS is part of Nacre's training loss, so its lead there is not independent evidence.
+
+## Usage
+
+Inputs are RGB in [0, 1]. The network is conditioned on the **LQ image in pixel space** (scaled to [-1, 1]);
+the LQ's *latent* — `AE.encode(bicubic_x4(lq))` — is only the endpoint of the residual-shift chain.
+Pad LQ sides to a multiple of 64 (four U-Net levels, then 8-wide shifted windows) and crop the output.
+`config.json` carries the full model, diffusion and preprocessing parameters. Reference implementation:
+`nacre/` (PyTorch, Apache-2.0); a Swift/MLX port is in progress.
+
+## Training
+
+- **Data:** 122,021 lossless 512² tiles from 51,759 Wikimedia Commons photos (CC0/PD/CC BY), filtered
+  with blockiness / HyperIQA / bytes-per-pixel floors (worst 10/5/5 % removed), ≤ 4 tiles per image.
+  Every source image was **downscaled by a per-image factor in [0.15, 0.45]** before tiling: native-resolution
+  camera frames are soft at the pixel level, and a model trained on them learns to produce soft output.
+- **Degradations:** Real-ESRGAN two-stage (blur, resize, noise, JPEG, sinc), synthesised on the fly.
+- **Schedule:** batch 96, AdamW 5e-5 → 2e-5 cosine, EMA 0.999, latent MSE + LPIPS (weight 2× for the
+  first 100k steps, 1× after). Selected checkpoint: step 110k (EMA), by a selection rule fixed before the
+  candidates were scored (fidelity floor first, then realism by CLIPIQA + MUSIQ; LPIPS excluded as in-loss).
+- **Compute:** ~300k steps on one H200 (Hugging Face Jobs), ≈ 0.43 it/s.
+
+## Limitations
+
+- **Generative detail is invented, not recovered.** On heavily degraded input the model synthesises
+  plausible texture; it can differ from the true scene. Do not use it where exact detail matters
+  (forensics, measurement, medical).
+- **Small text:** generative SR models are known to substitute letters in already-legible small text.
+  Nacre's text behaviour is being measured (SA-Text); until then, route text-heavy regions to a
+  fidelity-oriented upscaler.
+- Trained at 4× only; motion blur is not in the degradation set.
+
+## Attribution
+
+Training images are listed with title, URL, licence and author in `ATTRIBUTION.csv`. ⚠️ **300 CC BY
+rows still lack a resolved author name** — this repository stays private until they are resolved.

config.json

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+{
+ "architecture": "SwinUNet (ResShift-class), residual-shift diffusion, 4 steps",
+ "model": {
+  "image_size": 64,
+  "in_channels": 3,
+  "model_channels": 160,
+  "out_channels": 3,
+  "num_res_blocks": [
+   2,
+   2,
+   2,
+   2
+  ],
+  "attention_resolutions": [
+   64,
+   32,
+   16,
+   8
+  ],
+  "channel_mult": [
+   1,
+   2,
+   2,
+   4
+  ],
+  "num_head_channels": 32,
+  "swin_depth": 2,
+  "swin_embed_dim": 192,
+  "window_size": 8,
+  "mlp_ratio": 4.0,
+  "dropout": 0.0,
+  "cond_lq": true,
+  "lq_size": 64
+ },
+ "diffusion": {
+  "num_timesteps": 4,
+  "kappa": 2.0,
+  "power": 0.3,
+  "min_noise_level": 0.2,
+  "etas_end": 0.99,
+  "normalize_input": true
+ },
+ "autoencoder": {
+  "file": "vq_f4.safetensors",
+  "type": "VQ-f4 (embed_dim 3, n_embed 8192)",
+  "scale_factor": 1.0,
+  "decode": "force_not_quantize (continuous latents)"
+ },
+ "scale": 4,
+ "conventions": {
+  "pixel_range": "[-1,1] for the AE input and the network's lq condition",
+  "y0": "AE.encode(bicubic_x4(lq)) \u2014 the chain endpoint",
+  "cond": "the LQ image itself in pixel space (64x64 for a 256 output tile), NOT its latent",
+  "pad": "LQ sides to a multiple of 64 (4 levels then 8-wide shifted windows)"
+ },
+ "training": {
+  "step": 110000,
+  "weights": "EMA 0.999",
+  "loss_coef": [
+   1.0,
+   1.0
+  ],
+  "corpus": "commons-sr tiles_v3"
+ }
+}

nacre_v1.safetensors

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vq_f4.safetensors

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