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.
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.
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.
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.
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:jpgjpegpngtiftiffbmpwebpgif, with ICC conversion.
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.
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.
autoshade (skymanbp/autoshade)
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这是什么东西？AutoShade 是一款 AI 辅助的 RAW 照片修图工具：你描述想要的效果，GPT 风格的视觉分析步骤会提出一套调整方案，然后由确定性的 Rust 引擎把这套方案渲染到全分辨率的 RAW 文件上——生成与 Lightroom 兼容的 XMP sidecar 文件，而不是用 AI 生成像素。
AI 是真的生成我最终的照片，还是只是辅助修图？AI 只负责生成一张参考目标图或读取你的提示词，绝不会碰最终输出的像素。AutoShade 会根据这张目标图拟合出一份可编辑的修图方案，并渲染到你原始的 RAW 文件上，因此细节都来自传感器数据。
AutoShade 是一款 AI 辅助的 RAW 照片修图工具：你描述想要的效果，GPT 风格的视觉分析步骤会提出一套调整方案，然后由确定性的 Rust 引擎把这套方案渲染到全分辨率的 RAW 文件上——生成与 Lightroom 兼容的 XMP sidecar 文件，而不是用 AI 生成像素。