dtourolle
06422a07db
AI Denoise's Apply switch becomes Method: Bilinear, Fast, Medium, Best, default Best, so an untouched raw writes nothing and develops through the mixture. `apply` is still read and never written: 0 is Bilinear, 1 keeps a network already chosen. - Best is the mixture of a flat and an edge expert with a learned gate; Medium and Fast are students distilled from it. 2.48 s, 0.79 s and 0.57 s for a 20 MP frame on TensorRT fp16. - Each network carries its own tile border (256 for the mixture, 192 for the students) through `dr_denoise::Shipped` and `TileNet::halo`. - The file is hashed once at open and each network keys its own cached result; Bilinear keeps the result in memory for the way back. - Each has an .a16w16 sibling for the Hexagon: 0.00 dB on the 6D gate, at most 0.11 dB with the noise scaled x0.5 to x4. - APK BUNDLED 19 -> 23; the PKGBUILD installs all three.
150 lines
8.7 KiB
Markdown
150 lines
8.7 KiB
Markdown
# Model weights — licensing
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Two Ultralytics checkpoints ship here, both exported by
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`tools/export-seg-model.sh`, each with its class vocabulary written out by the
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same script:
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| File | Checkpoint | Trained on | Used by |
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| `segment/yolo26n-seg.onnx` | `yolo26n-seg.pt` | COCO, 80 *thing* classes | local adjustments, subject selection |
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| `scene/yolo26s-sem-ade20k.onnx` | `yolo26s-sem-ade20k.pt` | ADE20K, 150 classes | the scene tab's per-category grades |
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Both come from `https://huggingface.co/Ultralytics/YOLO26`. The face weights in
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`face/` are a separate matter with a separate grant — see `face/README.md`.
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The keypoint weights in `keypoints/` and the border filler in `inpaint/` are
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the other two, and the easiest — see the last two sections.
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Every quantised sibling — `*.int8.onnx`, `*.a16w8.onnx`, `*.a16w16.onnx`, the
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forms the tablet's Hexagon runs (`tools/quantise-models.sh`) — is the same
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weights rounded, and carries exactly the grant of the file it was made from.
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What calibration adds is one minimum and maximum per tensor: from public COCO
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val2017 photographs (CC-BY 4.0) for the image models, and for the denoiser from
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the same training tiles its weights were learned from. No image is in the files.
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## The grant
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**Ultralytics releases YOLO under AGPL-3.0**, and the weights carry the same
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grant as the framework — the HuggingFace repository declares `agpl-3.0` for the
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checkpoints themselves, not merely for the training code. A commercial licence
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is offered separately; DarkRoom does not use it and does not need it.
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## What that means for DarkRoom
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DarkRoom is GPL-3.0-or-later. **GPLv3 §13 explicitly permits combination with
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AGPL-3.0 code**, so redistributing these weights inside this repository is
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allowed — this is *not* the situation the InsightFace "buffalo" weights would
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have created, where a non-commercial research grant is simply incompatible with
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the project's licence and with F-Droid, Flatpak and Play distribution
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(NFR-COMPAT-2, D13).
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The consequence, and it is a real one: **the combined work is effectively
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AGPL-3.0.** §13's permission runs one way — the AGPL's §13 network-use condition
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attaches to the portion under that licence. For a local-first desktop and
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Android photo editor that condition has no practical bite, because there is no
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network service offering the combined work to remote users. It would acquire
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bite the moment any hosted or server-side rendering appeared, and that is the
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thing to remember rather than rediscover.
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This was decided deliberately (D14), not arrived at by accident, and
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`docs/dev/segmentation.md` §7 records the reasoning.
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## Class vocabulary — a caveat worth reading
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`docs/dev/segmentation.md` §4 specified YOLO **pretrained on ADE20K**, whose 150
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classes include the *stuff* categories that matter most in photography — sky,
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vegetation, water, wall, mountain.
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**This was true when written and is not any more.** Checked 2026-08-21, no
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YOLO/ADE20K combination existed: Ultralytics shipped YOLO26-seg on **COCO**
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only, and the one HuggingFace repository claiming otherwise
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(`laxmacl/yolov8-ade20k`) was empty. Re-checked 2026-08-30: Ultralytics now
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ships a `semantic` task with ADE20K checkpoints
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(`https://docs.ultralytics.com/tasks/semantic`), and `yolo26s-sem-ade20k` is
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what `scene/` holds.
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So the two vocabularies divide the work rather than compete:
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- **`segment/`, COCO, 80 things.** Separates *instances* — clicking one of
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three people selects that person. This is what local adjustments need, and a
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semantic model cannot do it: it would return one "person" region covering all
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three.
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- **`scene/`, ADE20K, 150 classes.** Labels every pixel, including the *stuff*
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COCO has no word for — sky, vegetation, water, mountain, wall. This is what
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the scene tab's per-category grades need, and it does not care that instances
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are merged, because a per-category grade applies to the whole category.
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Neither replaces the other. Keeping both is the deliberate choice.
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The loader treats each vocabulary as model metadata rather than compiled-in
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knowledge, which is what made adding the second model a file plus a descriptor
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rather than a code change — as this document predicted it would be.
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## `keypoints/` — XFeat, Apache-2.0
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| File | Source | Trained on | Used by |
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| `keypoints/xfeat-1024.onnx` | `weights/xfeat.pt` from `https://github.com/verlab/accelerated_features` | MegaDepth + synthetic warps, by the authors | panorama alignment (FR-MRG-8), landscape frames |
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| `keypoints/xfeat-768.onnx` | the same weights | — | the same, portrait frames |
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Exported by `tools/export-xfeat.sh` at fixed grayscale inputs of 1024×768
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and 768×1024 — the same weights twice, because tract needs a static shape
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and a portrait frame in a landscape input wastes half of it. Only the
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convolutional network is in each file; the keypoint decoding is Rust.
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**The repository and its weights are Apache-2.0**, read on 2026-09-19 from the
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`LICENSE` at its root, with no separate grant on the checkpoint and no
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non-commercial clause anywhere in the tree. Apache-2.0 is GPLv3-compatible
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one way — code and weights under it may be combined into a GPLv3 work — so
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this is neither the InsightFace situation (D13, a use restriction that binds
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every user) nor the Ultralytics one (D14, where the combined work becomes
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AGPL). It is the licence position this document would have wanted for every
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model in it, and it was chosen over stronger detectors partly for that reason:
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SuperPoint and SuperGlue are non-commercial, R2D2 and SiLK are CC BY-NC.
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The training data is the authors' concern, not a licence on the weights:
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XFeat trains on MegaDepth, which is itself a research dataset, but the weights
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are released under the repository's licence without a data-derived
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restriction — unlike the gaze models §7 of the requirements declined, where
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the dataset licence restricts models trained on it by name.
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## `inpaint/` — MI-GAN, MIT
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| File | Source | Trained on | Used by |
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| `inpaint/migan-512.onnx` | `migan_512_places2.pt` from `https://github.com/Picsart-AI-Research/MI-GAN` (Sargsyan et al., ICCV 2023), **fine-tuned** in the `darkroom-infill` repository (2026-09-20, second model that evening: trained against MI-GAN's own discriminator) | Places2 by the authors, then ~7 400 of the maintainer's own photographs with border-shaped voids | the panorama border fill (FR-MRG-4) |
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The bare 512 generator at a fixed `1×4×512×512`, six operator types; the
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tiling, the context and the blend are Rust (`dr_pano::fill`). Since
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2026-09-20 the shipped file is the fine-tune (`docs/dev/panorama.md` §14),
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exported by `python -m infill.export` in `darkroom-infill`;
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`tools/export-migan.sh` still produces the stock generator from the upstream
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checkpoint, which the fine-tune starts from. The fine-tuned weights are a
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derivative of the MIT weights trained on photographs the maintainer owns,
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and carry the same MIT grant.
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**MIT, code and weights alike** — `LICENSE` and `LICENSE-WEIGHTS` in the
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repository, both read on 2026-09-19, both the plain MIT text with no further
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grant. GPL-compatible, store-compatible, nothing to read around: the cleanest
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position of any model here. The training set is Places2, a research dataset,
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but the weights are released under the repository's licence without a
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data-derived restriction (contrast the gaze models §7 of the requirements
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declined, and the InsightFace grant of D13).
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## `denoise/` — the mosaic denoiser, the project's own
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| File | Source | Trained on | Used by |
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| `denoise/mosaic-best-1408.onnx` | trained in the `darkroom-denoise` repository (2026-10-04, run `final`, 30 000 steps, from the experts of runs `m2` and `edges-100`) | 1,701 of the maintainer's own base-ISO raws and 6,000 synthetic scenes the repository draws itself, with the Canon EOS 6D's measured noise added | the learned demosaic and denoise, Best (FR-DEV-3g) |
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| `denoise/mosaic-medium-1408.onnx` | distilled from `final` in the same repository (2026-10-04, run `student-m`, 20 000 steps, from `m2`) | the same | Medium |
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| `denoise/mosaic-fast-1408.onnx` | distilled from `final` (2026-10-04, run `student-s`, 30 000 steps, from scratch) | the same | Fast |
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U-Nets of plain 3×3 convolutions, ReLU, strided and transposed
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convolutions and additive skips — no third-party architecture code or
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weights — and, for Best, two of them blended per photosite by a small gate
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network of the same parts. Each at a fixed `1×1×1408×1408` for `mosaic`
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and `sigma`, exported by `python -m denoise.export` in `darkroom-denoise`;
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the `.a16w16.onnx` siblings are the same networks quantised for the
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Hexagon by `tools/quantise-models.sh`. Trained only on
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photographs the maintainer owns, so the weights carry no grant but the
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project's own: GPL-3.0-or-later, like the code (denoise.md §10).
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