AutoShade

Describe the picture. Keep the RAW.

AI decides what to change — a deterministic Rust engine does it.
An editable, Lightroom-ready recipe instead of AI pixels.

Download v1.9.0  ·  Website  ·  Manual  ·  Showcase  ·  In depth

_{Straight conversion → generated target → recovered recipe on the 61 MP RAW → same recipe after AI denoise. Bottom row: 1:1 crops.}

Tip

The generated picture is a target, never the delivery. An image model paints the look you describe; AutoShade fits an editable develop recipe to it and renders that recipe on the full-resolution RAW. The recipe carries the look, the sensor carries the detail — and in the recipe path, the AI never touches a pixel.

Highlights

🎯 Reverse-fit any look reimagine generates the picture from your frame; match recovers a recipe from it — or from any finished rendition of the same shot. Look error 0.161 → 0.023.	🌌 A RAW denoiser trained here Cleans the sensor mosaic before demosaic. Keeps 98.70 % of 17,817 faint stars against Lightroom Denoise's 98.80 %.
📐 Lightroom parity, measured Texture 45 / 45 anchors within ±0.02, radial masks 41 / 41 within 1 px. Every residual is published.	🔁 Round-trips through Lightroom Sidecar XMP is merged byte for byte; the full develop survives a Lightroom rewrite and reopens exactly.
🖼️ Your library is the style guide Point it at finished photos you love; each develop sees the 3 closest as a reference. Embeddings run locally.	🧩 Local AI masks, no API key Subject, sky and point-prompted objects (BiRefNet, OneFormer, SAM 2.1), plus HDR, focus and noise stacking.

Showcase

_{Reverse-fit. Prompt: “a clearer afternoon, a little more contrast, a slightly deeper blue sky.” The recipe renders the look on the 9504 × 6336 RAW; the generated frame carries neither the detail nor the resolution.}

_{One RAW, four looks. Straight conversion, then moody, golden and vivid — only the direction text changes.}

_{ISO 2500 star field, 1:1. Top: Lightroom, Denoise off / 50. Bottom: AutoShade, off / its own denoiser at 71 %. Clean colour, natural grain, faint stars intact.}

_{Every number and prompt → Showcase}

What is new here

Six techniques you will not find in another RAW developer. Each is written up in full — equations, figures, refused training runs — in AutoShade in depth.

	Technique	In one line
1	Generate, then reverse-fit	An editable recipe from any finished look, solved at the distribution level
2	Evidence-gated local fit	A divergence score D picks full solve or bounded mode; every local move must earn its place
3	Style by retrieval	SigLIP 2 search over your own finished photos, not a preset
4	Measured Lightroom parity	Tone, Texture, Sharpening, masks and lens geometry fitted to Lightroom exports
5	A denoiser with pre-written pass marks	Trained on this project's data; two later runs failed their marks and were refused
6	The camera's own look	A per-photo base curve read from the embedded preview, like with like

How it works

• One engine, three front ends — desktop app, CLI and loopback web UI share one Rust library and one per-user develop store.
• Local first — six Python sidecars (masks, two denoisers, correspondence, descriptions, embeddings) run on your machine; pixels leave it only for an AI operation you ask for.
• Non-destructive — your photo library stays read-only; generated pixels always land on their own labelled card.

_{Diagram generated from autoshade.architecture.json · zoom it online · full design in ARCHITECTURE.md}

Measured numbers

What	Measured
Automated test battery	1643 library / 25 CLI / 243 GUI / 2+2 contract tests
RAW coverage	24 extensions · 725 camera bodies · nine-camera zoo 9/9
Reverse-fit, stone viaduct	look error 0.161 → 0.023 at confidence 0.63
Reverse-fit, desert canyon	look error 0.129 → 0.048 · sky ΔE 3.8
Faint stars kept, vs Lightroom Denoise 50	98.70 % vs 98.80 % of 17,817 stars
Lightroom Sharpening fit	per-pixel R² 0.93 (0.62 for plain unsharp mask)
Brush geometry vs Lightroom	error 874 px → 9.8 px

_{The full table — 20+ rows with conditions and sources — is in AutoShade in depth; scripts/check_docs.py re-derives the pinned claims.}

Install and quickstart

Platform	Get it
Windows	AutoShade-Setup-1.9.0.exe — per-user, no admin. Or the portable autoshade-1.9.0-windows-x64.zip
macOS	AutoShade-1.9.0-macos-universal.zip (Apple silicon + Intel), or -macos-cli.zip
Linux	AutoShade-1.9.0-linux-x64.zip — command line only

All on the v1.9.0 release page. Python 3 is needed only for the local AI sidecars; model weights download on first use.

Note

macOS: the app is ad-hoc signed, not notarised — on first launch use System Settings → Privacy & Security → Open Anyway (once per machine).

Desktop app — open a photo (Ctrl+O), move a slider, export with Ctrl+Shift+E. The original is never touched.

CLI

# manual develop
autoshade apply photo.ARW recipe.json -o developed.tif

# AI develop, end to end
autoshade auto photo.ARW --guidance "natural color; protect highlights" -o developed.tif

# describe a look, then recover it as an editable recipe on the RAW
autoshade reimagine photo.ARW --prompt "a clearer afternoon, deeper blue sky"
autoshade match photo.ARW out/photo.reimagine.png --render

Upgrades, silent installs and every command: User manual.

Release assets and checksums

Built by GitHub Actions from the tag; checksums.txt carries every SHA-256. The RAW denoiser weights are uploaded by hand to the release the sidecar pins — releases/download/v1.6.0/autoshade-raw-denoise-v2.pth, mirrored byte-exact on Hugging Face — and refused unless size and SHA-256 match the pin in python/denoise_raw.py.

File	Size	SHA-256
autoshade.exe (CLI)	22,929,408 bytes	d55ae959b9d032b2dbd68590315f999e2b61ca94cefe4d8e43b010b029a79dc3
autoshade-gui.exe (desktop app)	29,768,192 bytes	0beaf85924428bd5ed4a33cf9af022622708c1752933915fb346f0b73070f7ef
AutoShade-Setup-1.9.0.exe (installer)	15,278,432 bytes	5255475c32c199cfb2df517dd4021a8950b5d3a2fba8854cc3346bfcbb1a8183
autoshade-1.9.0-windows-x64.zip (portable archive)	20,636,181 bytes	ee946d7a88512ca13f130114a7b21f6382133f43ab75d7c16e90e778ed0f55d5
AutoShade-1.9.0-macos-universal.zip (macOS app bundle)	41,487,882 bytes	ce8210d86e18a6f5028f5680d419810835d577f0ff1c5f0297a92c990ad6ff17
AutoShade-1.9.0-linux-x64.zip (Linux command line only)	9,948,140 bytes	ef868ed17f31466748cabc63e8d5e137d7733aba9b10e19fc81b0629f18d6cdd
AutoShade-1.9.0-macos-cli.zip (macOS command line only)	17,993,468 bytes	f7fa28bb3eb78b2ea4585ff001e5d425cfa43a379cca7368062c2b601c2a7dc0
autoshade-raw-denoise-v2.pth (RAW denoiser weights, fetched on demand from the v1.6.0 release)	130,590,559 bytes	ffafa40a53f52092149db2fcf03636117ad6855e1068142d4f6b03b634e9f9c4

Build from source

Rust edition 2024, rustc/cargo 1.94.

cargo build --release                                   # CLI
cargo build --release --features gui --bin autoshade-gui  # desktop app

Local AI tools need Python packages (weights download on first use):

Tool	Packages
Subject mask (BiRefNet)	torchvision timm einops
Subject fallback (U²-Net)	rembg
Sky / objects (OneFormer, SAM 2.1)	transformers torch
AI denoise	torch opencv-python numpy einops requests

Supported formats

.cr2 · Canon EOS 40D	.cr3 · Canon EOS R6	.nef · Nikon D700
.arw · Sony α7 III	.orf · Olympus E-M5	.rw2 · Panasonic DMC-GX85
.pef · Pentax K-5	.dng · Ricoh GR II	.raf · Fujifilm X-S10 — X-Trans, approximate

_{Nine real CC0 files, fully decoded and neutral-rendered — not embedded previews.}

Camera RAW — 24 extensions

arw, dng, raw, raf, nef, cr2, cr3, orf, rw2, pef, srw, 3fr,
fff, iiq, mef, mos, erf, kdc, dcr, dcs, crw, nrw, mrw, ari

Decoded by rawler 0.7.2, which carries 725 camera models. No embedded preview: 12 of the 24 formats store none. They are orf, srw, nrw, mef, mos, kdc, dcr, dcs, erf, iiq, crw, and ari; AutoShade renders its own neutral version instead.

Baked rasters — 8 extensions: jpg jpeg png tif tiff bmp webp gif, with ICC conversion.

Tech stack

Rust (rustc/cargo 1.94, edition 2024) · rawler · image · qcms · rayon · egui/eframe · tiny_http — Python sidecars on PyTorch.

RAW decode, 24 formats, 725 bodies · deterministic f32 develop engine · hand-rolled XMP layer (no XML crate) for byte-exact Lightroom merges. The current battery is 1643 library (1628 pass + 15 #[ignore]d forensic probes) / 25 CLI / 243 GUI / 2+2 contract tests. Per-subsystem notes: In depth → Implementation · equations and provenance: TECH_STACK.md.

Status and limitations

• Release gates at v1.9.0 cover the CLI, desktop GUI, sidecar contracts, format fixtures and the deterministic renderer.
• Not in scope: bit-exact Adobe rendering (parity is measured, not identical), an exact X-Trans demosaic, a notarised macOS build, a Linux desktop app.
• Honest markers: AI masks are local re-creations, not Adobe's pixels; linear masks are not pixel-closed (radial closes 41/41 to ≤ 1 px); the macOS app is CI-verified but has no reported interactive users yet.
• Release ledger and rulings: ROADMAP.md.

License

MIT — see LICENSE. Showcase photographs © 2026 skymanbp, all rights reserved (not MIT).

Models, fonts and samples

Model weights download separately and remain their authors' property; every pinned model also has a byte-exact mirror on Hugging Face (Azng0/autoshade-mirror-*), checksum-verified either way — table in python/_mirror.py.

Model	Purpose	License
SCUNet	AI denoise (baked sources)	Apache-2.0
DRUNet-colour (DPIR), fine-tuned here as autoshade-raw-denoise-v2.pth	AI denoise (RAW mosaic)	MIT; fine-tuning pairs from RawNIND, CC BY-SA 4.0
BiRefNet	Subject segmentation	MIT
U²-Net	Subject fallback	Apache-2.0
OneFormer ADE20K	Sky segmentation	MIT
SAM 2.1	Point-prompted object masks	Apache-2.0
MoGe-2 ViT-L (code in python/_moge/)	AI lens blur depth map	MIT (DINOv2 backbone code Apache-2.0)
SigLIP 2	Style embeddings	Apache-2.0
Qwen3-VL-2B-Instruct	Local look descriptions	Apache-2.0
Stable Diffusion 2.1	DIFT correspondence field	CreativeML Open RAIL++-M

Fonts: subset Noto faces (SIL OFL) and a Lucide icon face (ISC) — texts in assets/fonts/. RAW samples: the nine format-grid files are from raw.pixls.us under CC0 1.0, SHA-256-verified.

Thanks to the rawler, image, qcms, rayon, clap, serde, ureq, egui/eframe, tiny_http and local-model communities.

_{Made for photographers who want to know what the AI changed — in numbers.}