Develop every raw through the AI denoise by default, with a strength, cached
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.
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@@ -180,8 +180,8 @@ pub struct EditGraph {
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/// as [`crate::learned_denoise`], captured, stored and undone with the
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/// rest (FR-DEV-3c).
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denoise_applied: bool,
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/// How much of the removed noise's brightness to put back, 0–100.
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denoise_grain: f32,
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/// How strongly to denoise, 0–100; what is not taken goes back as grain.
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denoise_strength: f32,
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}
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/// TRACES: FR-DEV-3f
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@@ -239,8 +239,8 @@ impl EditGraph {
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lens_profile: None,
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lens_profile_applied: true,
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denoise_available: false,
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denoise_applied: false,
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denoise_grain: 0.0,
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denoise_applied: true,
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denoise_strength: 100.0,
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}
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}
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@@ -429,9 +429,9 @@ impl EditGraph {
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}
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/// TRACES: FR-DEV-3g
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/// The grain to keep, 0–1.
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/// The grain to keep, 0–1: what the strength does not take.
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pub fn denoise_grain(&self) -> f32 {
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self.denoise_grain / 100.0
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(100.0 - self.denoise_strength) / 100.0
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}
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/// TRACES: FR-DEV-3
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@@ -643,10 +643,12 @@ impl EditGraph {
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}
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});
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switch
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// The learned denoise first: it decides what every control below
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// is applied to, so it heads the panel (docs/dev/denoise.md §7).
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denoise
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.into_iter()
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.chain(switch)
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.chain(warps)
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.chain(denoise)
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.chain(ops)
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.chain(std::iter::once(framing))
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.collect()
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@@ -742,7 +744,7 @@ impl EditGraph {
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denoise_available: _,
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// Edits, in the state through `capabilities` like the lens switch.
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denoise_applied: _,
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denoise_grain: _,
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denoise_strength: _,
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masks,
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film,
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spots,
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@@ -817,8 +819,12 @@ impl EditGraph {
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if op == crate::learned_denoise::ID {
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match param {
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p if p == crate::learned_denoise::APPLY => self.denoise_applied = value != 0.0,
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p if p == crate::learned_denoise::STRENGTH => {
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self.denoise_strength = value.clamp(0.0, 100.0)
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}
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// 0.21.0's grain, the strength's inverse (see `GRAIN`).
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p if p == crate::learned_denoise::GRAIN => {
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self.denoise_grain = value.clamp(0.0, 100.0)
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self.denoise_strength = 100.0 - value.clamp(0.0, 100.0)
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}
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_ => log::warn!("unknown parameter {param} on {op}; ignoring"),
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}
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@@ -886,7 +892,8 @@ impl EditGraph {
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p if p == crate::learned_denoise::APPLY => {
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Some(if self.denoise_applied { 1.0 } else { 0.0 })
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}
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p if p == crate::learned_denoise::GRAIN => Some(self.denoise_grain),
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p if p == crate::learned_denoise::STRENGTH => Some(self.denoise_strength),
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p if p == crate::learned_denoise::GRAIN => Some(100.0 - self.denoise_strength),
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_ => None,
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};
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}
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@@ -938,8 +945,8 @@ impl EditGraph {
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self.set_lens_profile_applied(true);
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// The learned denoise returns to off; whether it is available is the
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// file's and stays.
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self.denoise_applied = false;
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self.denoise_grain = 0.0;
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self.denoise_applied = true;
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self.denoise_strength = 100.0;
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}
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/// Set the crop rectangle. Clamped to keep it inside the frame.
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@@ -2098,23 +2105,38 @@ mod tests {
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.into_iter()
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.find(|c| c.id == learned_denoise::ID)
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.expect("offered");
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assert!(!cap.active, "off until asked for");
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g.set_param(learned_denoise::ID, learned_denoise::APPLY, 1.0);
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g.set_param(learned_denoise::ID, learned_denoise::GRAIN, 30.0);
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assert!(g.denoise_applied());
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assert!(cap.active, "on by default");
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assert_eq!(g.denoise_grain(), 0.0, "at full strength");
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assert_eq!(cap.id, g.capabilities()[0].id, "and first in the panel");
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g.set_param(learned_denoise::ID, learned_denoise::APPLY, 0.0);
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g.set_param(learned_denoise::ID, learned_denoise::STRENGTH, 70.0);
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assert!(!g.denoise_applied());
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assert!((g.denoise_grain() - 0.3).abs() < 1e-6);
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g.reset();
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assert!(!g.denoise_applied());
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assert!(g.denoise_applied(), "reset is back to on");
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assert_eq!(g.denoise_grain(), 0.0);
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}
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#[test]
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fn an_edit_saved_with_grain_keeps_its_look() {
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// TRACES: FR-DEV-3g
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// 0.21.0 stored the grain kept rather than the strength.
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use crate::learned_denoise;
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let mut g = EditGraph::default_chain();
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g.set_param(learned_denoise::ID, learned_denoise::GRAIN, 25.0);
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assert_eq!(
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g.param(learned_denoise::ID, learned_denoise::STRENGTH),
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Some(75.0)
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);
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assert!((g.denoise_grain() - 0.25).abs() < 1e-6);
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}
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#[test]
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fn the_learned_denoise_travels_in_the_state() {
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use crate::learned_denoise;
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let mut g = EditGraph::default_chain();
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g.set_denoise_available(true);
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g.set_param(learned_denoise::ID, learned_denoise::APPLY, 1.0);
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g.set_param(learned_denoise::ID, learned_denoise::GRAIN, 40.0);
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g.set_param(learned_denoise::ID, learned_denoise::STRENGTH, 60.0);
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let state = g.state();
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let mut h = EditGraph::default_chain();
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h.set_denoise_available(true);
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@@ -18,23 +18,32 @@ use crate::{OpId, ParamId};
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pub const ID: OpId = OpId("learned_denoise");
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pub const APPLY: ParamId = ParamId("apply");
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/// TRACES: FR-DEV-3g
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/// How strongly to denoise, 0–100: 100 is the network's result as it is, and
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/// lower puts the removed noise's brightness back as grain.
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pub const STRENGTH: ParamId = ParamId("strength");
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/// What 0.21.0 stored instead of [`STRENGTH`]: the grain kept, its inverse.
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/// Still read, so an edit saved by that release keeps its look; never
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/// written, and not offered as a control.
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pub const GRAIN: ParamId = ParamId("grain");
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/// Off by default: it costs seconds per photograph and replaces the
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/// demosaic, which is the photographer's call. Grain 0 is the network's
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/// result as it is.
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/// On by default, at full strength: every Bayer raw is developed from the
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/// learned demosaic, and the switch and the slider are there to take it back
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/// or ease it off. It costs seconds per photograph the first time, while
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/// the classical demosaic shows; the result is cached, so a photograph
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/// reopened or exported does not pay again (docs/dev/denoise.md §7).
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pub(crate) static DESCRIPTOR: LazyLock<Arc<OpDescriptor>> = LazyLock::new(|| {
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Arc::new(OpDescriptor {
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id: ID,
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label: LocalizedKey("op.learned_denoise"),
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params: vec![
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ParamDescriptor::switch("apply", "param.learned_denoise.apply"),
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ParamDescriptor::switch_on("apply", "param.learned_denoise.apply"),
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ParamDescriptor::scalar(
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"grain",
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"param.learned_denoise.grain",
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"strength",
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"param.learned_denoise.strength",
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0.0,
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100.0,
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0.0,
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100.0,
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Unit::Percent,
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Scale::Linear,
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0,
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