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.
The learned demosaic was an option under Detail, off by default. It is
now how a Bayer raw is developed: on by default at full strength on
every device — which hardware runs it is the inference engine's choice
— and first in the Adjust panel, since it decides what every control
below is applied to.
Strength (0-100, default 100) replaces Keep grain: grain = 100 -
strength, the same luminance-only blend, so moving it is one GPU pass
and never a re-run. 0.21.0's sidecars stored grain; it is still read,
as the inverse, and never written.
With it on for every photograph, the result is now kept on disk
(denoise.md §7.1, §12): the network's output as half floats, keyed on
a SHA-256 of the file's bytes and the model, oldest first past a 5 GB
budget, beside the inference engine's cache. A reopened photograph and
an export of one already developed read it back instead of running the
network again; a damaged entry is a miss.
The learned-denoise branch merged while this one was open, and two of
its test fixtures build a RawImage without the baseline_exposure field
this branch added; zero is the no-op value.
A Bayer photograph keeps its mosaic in the session and is offered the AI
Denoise switch. Asked for, the network runs on the decode executor from a
hot-pixel-repaired copy — the app's own pass — with the frame's noise from
its best source, and its progress in the activity bar; the classical
demosaic shows until the result lands, and the finished job says where the
noise figures came from. Keep grain is a GrainBlend of the two, made once
per value; the render draws it as its source and the adjust pass never
knows. demosaiced stays the classical result, so the raw histogram, the
white balance picker, masks and segmentation still read the sensor.
The develop view reconciles on a 250 ms poll rather than on each way an
edit can change (slider, undo, preset, version, a sidecar from another
device): two comparisons when nothing changed, and no path that can forget.
A failure is not retried until the switch is toggled. An export of a
photograph that asks for it waits for a running job or computes it.
dtourolle