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 v1.12.0  ·  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	1656 library / 25 CLI / 246 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-1.12.0.exe — per-user, no admin. Or the portable autoshade-1.12.0-windows-x64.zip
macOS	AutoShade-1.12.0-macos-universal.zip (Apple silicon + Intel), or -macos-cli.zip
Linux	AutoShade-1.12.0-linux-x64.zip — command line only

All on the v1.12.0 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)	22,846,464 bytes	aa3b39044a83d61ee7ae4afc24b9a6fa4fcb3b8c085ba8237c1a226319a1a0be
autoshade-gui.exe (desktop app)	29,779,456 bytes	174f9b0a22a7ad19f994d1c385d803b73e281f7dcd9fcc22eedc9f85f7ad8b41
AutoShade-Setup-1.12.0.exe (installer)	15,329,987 bytes	f07644fe66444890ac7958a8757d74d9e872637378493ac0d7f8f88421fcb0d0
autoshade-1.12.0-windows-x64.zip (portable archive)	20,685,534 bytes	d70be6f95b8ccf897c8fb41a8fe310acce0467dec0406242f815ba713214837e
AutoShade-1.12.0-macos-universal.zip (macOS app bundle)	41,573,461 bytes	0f615ea53aa1d5c6532547326e6fa0d4f2dc99f2a9a3e5884d6555bd89c3a20d
AutoShade-1.12.0-linux-x64.zip (Linux command line only)	9,971,030 bytes	3bd290c9fed8701c78b8ab7cf14a062f6d9035e8445f10a764171370dfedc6da
AutoShade-1.12.0-macos-cli.zip (macOS command line only)	18,017,593 bytes	1eb700bdb82a3c8f474344f95dbbfc559d29fcbb06c728cafb98e4473bec4580
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 1656 library (1641 pass + 15 #[ignore]d forensic probes) / 25 CLI / 246 GUI / 2+2 contract tests. Per-subsystem notes: In depth → Implementation · equations and provenance: TECH_STACK.md.

Status and limitations

• Release gates at v1.12.0 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.}