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fix(tagger): separate JoyTag confidence threshold

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Store confidence thresholds per tagger model so JoyTag no longer inherits WD tuning. Refresh the active threshold when switching models, guard stale threshold saves, and keep UI Lab mocks in sync.

Also tightens the onboarding model selector so the segmented control no longer stretches across the row.
This commit is contained in:
LyAhn
2026-07-03 09:21:39 +01:00
parent f1116c6c26
commit 68932b55c5
7 changed files with 82 additions and 35 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src-tauri/src/commands.rs
+6 -1
View File
@@ -2159,6 +2159,7 @@ pub struct SetTaggerModelParams {
#[derive(Deserialize)]
pub struct SetTaggerThresholdParams {
pub threshold: f32,
pub model: Option<TaggerModel>,
}
#[derive(Deserialize)]
@@ -2258,7 +2259,11 @@ pub async fn set_tagger_threshold(
params: SetTaggerThresholdParams,
) -> Result<f32, String> {
let app_dir = app.path().app_data_dir().map_err(|e| e.to_string())?;
tagger::set_tagger_threshold(&app_dir, params.threshold).map_err(|e| e.to_string())
match params.model {
Some(model) => tagger::set_tagger_threshold_for_model(&app_dir, model, params.threshold),
None => tagger::set_tagger_threshold(&app_dir, params.threshold),
}
.map_err(|e| e.to_string())
}
#[tauri::command]
src-tauri/src/tagger.rs
+38 -19
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@@ -16,6 +16,7 @@ pub const WD_TAGGER_MODEL_NAME: &str = "wd-swinv2-tagger-v3";
const TAGGER_ACCELERATION_FILE: &str = "settings/tagger_acceleration.txt";
const TAGGER_THRESHOLD_FILE: &str = "settings/tagger_threshold.txt";
const JOYTAG_THRESHOLD_FILE: &str = "settings/joytag_threshold.txt";
const TAGGER_BATCH_SIZE_FILE: &str = "settings/tagger_batch_size.txt";
const TAGGER_MODEL_FILE: &str = "settings/tagger_model.txt";
@@ -27,8 +28,7 @@ pub const JOYTAG_MODEL_NAME: &str = "joytag";
// NCHW layout (not NHWC). These are the OpenAI CLIP normalization constants.
const JOYTAG_MEAN: [f32; 3] = [0.481_454_66, 0.457_827_5, 0.408_210_73];
const JOYTAG_STD: [f32; 3] = [0.268_629_54, 0.261_302_6, 0.275_777_1];
// JoyTag's recommended detection threshold (used as the default; the user's
// tagger_threshold setting still overrides it).
// JoyTag's recommended detection threshold.
const JOYTAG_DEFAULT_THRESHOLD: f32 = 0.4;
// Shared ONNX Runtime DLLs (live in the caption model's `onnxruntime/` dir and
@@ -64,8 +64,8 @@ pub const TAGGER_INFER_CHUNK: usize = 4;
/// claim the GPU/CPU between dispatches. Negligible against per-chunk inference.
pub const TAGGER_INFER_YIELD_MS: u64 = 40;
/// Set to `true` by `set_tagger_acceleration` so the tagging worker loop
/// knows to drop its cached `WdTagger` and reload with the new EP.
/// Set to `true` by tagger setting changes so the tagging worker loop knows to
/// drop its cached model session and reload with the current settings.
pub static TAGGER_SESSION_DIRTY: AtomicBool = AtomicBool::new(false);
// ---------------------------------------------------------------------------
@@ -112,6 +112,20 @@ impl TaggerModel {
}
}
fn threshold_file(self) -> &'static str {
match self {
Self::Wd => TAGGER_THRESHOLD_FILE,
Self::JoyTag => JOYTAG_THRESHOLD_FILE,
}
}
fn default_threshold(self) -> f32 {
match self {
Self::Wd => DEFAULT_THRESHOLD,
Self::JoyTag => JOYTAG_DEFAULT_THRESHOLD,
}
}
/// Hugging Face repo the model files are fetched from.
fn repo_id(self) -> &'static str {
match self {
@@ -280,21 +294,33 @@ pub fn set_tagger_acceleration(
Ok(acceleration)
}
pub fn tagger_threshold(app_data_dir: &Path) -> f32 {
let path = app_data_dir.join(TAGGER_THRESHOLD_FILE);
fn tagger_threshold_for_model(app_data_dir: &Path, model: TaggerModel) -> f32 {
let path = app_data_dir.join(model.threshold_file());
let Ok(value) = std::fs::read_to_string(path) else {
return DEFAULT_THRESHOLD;
return model.default_threshold();
};
value
.trim()
.parse::<f32>()
.unwrap_or(DEFAULT_THRESHOLD)
.unwrap_or_else(|_| model.default_threshold())
.clamp(0.01, 1.0)
}
pub fn tagger_threshold(app_data_dir: &Path) -> f32 {
tagger_threshold_for_model(app_data_dir, tagger_model(app_data_dir))
}
pub fn set_tagger_threshold(app_data_dir: &Path, threshold: f32) -> Result<f32> {
set_tagger_threshold_for_model(app_data_dir, tagger_model(app_data_dir), threshold)
}
pub fn set_tagger_threshold_for_model(
app_data_dir: &Path,
model: TaggerModel,
threshold: f32,
) -> Result<f32> {
let clamped = threshold.clamp(0.01, 1.0);
let path = app_data_dir.join(TAGGER_THRESHOLD_FILE);
let path = app_data_dir.join(model.threshold_file());
if let Some(parent) = path.parent() {
std::fs::create_dir_all(parent)?;
}
@@ -1057,17 +1083,10 @@ fn load_joytag_labels(path: &Path) -> Result<Vec<String>> {
Ok(labels)
}
/// JoyTag detection threshold. Honours the shared `tagger_threshold` setting if
/// the user has set it, otherwise uses JoyTag's recommended default (0.4).
/// JoyTag detection threshold. Uses JoyTag's own setting so WD tuning does not
/// leak into the general/photo-friendly model.
fn joytag_threshold(app_data_dir: &Path) -> f32 {
match std::fs::read_to_string(app_data_dir.join(TAGGER_THRESHOLD_FILE)) {
Ok(value) => value
.trim()
.parse::<f32>()
.unwrap_or(JOYTAG_DEFAULT_THRESHOLD)
.clamp(0.01, 1.0),
Err(_) => JOYTAG_DEFAULT_THRESHOLD,
}
tagger_threshold_for_model(app_data_dir, TaggerModel::JoyTag)
}
fn sigmoid(x: f32) -> f32 {
src/components/SettingsModal.tsx
+7 -4
View File
@@ -461,6 +461,9 @@ export function SettingsModal() {
current={taggerModel}
onSelect={(nextModel) => {
if (nextModel === taggerModel) return;
setTaggerThresholdDraft(null);
setTaggerThresholdError(null);
if (thresholdErrorTimerRef.current) clearTimeout(thresholdErrorTimerRef.current);
setTaggerModelSwitching(true);
setTaggerModelSwitchError(null);
void setTaggerModel(nextModel)
@@ -562,12 +565,12 @@ export function SettingsModal() {
className="w-20 rounded-md border border-white/10 bg-white/5 px-2 py-1.5 text-xs text-white focus:border-white/20 focus:outline-none"
value={thresholdDisplay}
onChange={(event) => setTaggerThresholdDraft(event.target.value)}
onBlur={() => {
const value = parseFloat(thresholdDisplay);
onBlur={(event) => {
const value = parseFloat(event.currentTarget.value);
if (!isNaN(value) && value >= 0.05 && value <= 0.99) {
setTaggerThresholdError(null);
setTaggerThresholdSaving(true);
void setTaggerThreshold(value)
void setTaggerThreshold(value, taggerModel)
.catch((error: unknown) => setTaggerThresholdError(String(error)))
.finally(() => {
setTaggerThresholdDraft(null);
@@ -584,7 +587,7 @@ export function SettingsModal() {
{taggerThresholdError ? (
<p className="text-[11px] text-amber-300">{taggerThresholdError}</p>
) : (
<p className="text-[11px] text-gray-600">{taggerThresholdSaving ? "Saving..." : `Default: ${taggerModel === "joytag" ? "0.4" : "0.35"}`}</p>
<p className="text-[11px] text-gray-600">{taggerThresholdSaving ? "Saving..." : `Default: ${TAGGER_MODELS[taggerModel].defaultThreshold}`}</p>
)}
</div>
</SettingsItem>
src/components/onboarding/StepAiFeatures.tsx
+1 -1
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@@ -75,7 +75,7 @@ export function StepAiFeatures() {
The AI tagger model labels images so you can search with{" "}
<code className="rounded bg-gray-900 px-1 py-0.5 text-[11px] text-gray-200 light-theme:bg-gray-800 light-theme:text-gray-100">/t</code> — tags look like:
</p>
<div className="mt-3 flex flex-wrap items-center gap-1 rounded-lg border border-white/[0.07] p-0.5 light-theme:border-gray-300/80">
<div className="mt-3 inline-flex max-w-full flex-wrap items-center gap-1 rounded-lg border border-white/[0.07] p-0.5 light-theme:border-gray-300/80">
{(["wd", "joytag"] as const).map((model) => (
<TaggerModelChoice
key={model}
src/dev/mockBackend.ts
+14 -1
View File
@@ -19,6 +19,10 @@ let nextAlbumId = 100;
let nextTagId = 10_000;
let mockTaggerModel: TaggerModel = db.scenario === "joytag-unready" ? "joytag" : "wd";
let mockTaggerReady = db.scenario !== "unready" && db.scenario !== "joytag-unready";
const mockTaggerThresholds: Record<TaggerModel, number> = {
wd: 0.35,
joytag: 0.4,
};
type AnyPayload = Record<string, any> | undefined;
type Rgb = [number, number, number];
@@ -42,6 +46,10 @@ function isRgb(value: unknown): value is Rgb {
return Array.isArray(value) && value.length === 3 && value.every((entry) => typeof entry === "number");
}
function isTaggerModel(value: unknown): value is TaggerModel {
return value === "wd" || value === "joytag";
}
function colorDistanceSq(left: Rgb, right: Rgb): number {
return (left[0] - right[0]) ** 2 + (left[1] - right[1]) ** 2 + (left[2] - right[2]) ** 2;
}
@@ -528,8 +536,13 @@ export async function handleMockCommand(cmd: string, payload?: unknown): Promise
case "set_tagger_acceleration":
return p.acceleration ?? "auto";
case "get_tagger_threshold":
return mockTaggerThresholds[mockTaggerModel];
case "set_tagger_threshold":
return p.threshold ?? 0.35;
{
const model = isTaggerModel(p.model) ? p.model : mockTaggerModel;
mockTaggerThresholds[model] = p.threshold ?? mockTaggerThresholds[model];
return mockTaggerThresholds[model];
}
case "get_tagger_batch_size":
case "set_tagger_batch_size":
return p.batch_size ?? 8;
src/store.ts
+12 -7
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@@ -589,7 +589,7 @@ interface GalleryState {
loadTaggerModel: () => Promise<void>;
setTaggerModel: (model: TaggerModel) => Promise<void>;
loadTaggerThreshold: () => Promise<void>;
setTaggerThreshold: (threshold: number) => Promise<void>;
setTaggerThreshold: (threshold: number, model?: TaggerModel) => Promise<void>;
loadTaggerBatchSize: () => Promise<void>;
setTaggerBatchSize: (batchSize: number) => Promise<void>;
probeTaggerRuntime: () => Promise<void>;
@@ -2326,11 +2326,14 @@ export const useGalleryStore = create<GalleryState>((set, get) => ({
const taggerModel = await invoke<TaggerModel>("set_tagger_model", {
params: { model },
});
// Switching models changes which files are required on disk, so refresh the
// status too — the download/ready UI must reflect the newly-selected model.
// Switching models changes both readiness and the active threshold setting,
// so refresh them together for the selected model.
try {
const taggerModelStatus = await invoke<TaggerModelStatus>("get_tagger_model_status");
set({ taggerModel, taggerModelStatus, taggerModelError: null, taggerRuntimeProbe: null });
const [taggerModelStatus, taggerThreshold] = await Promise.all([
invoke<TaggerModelStatus>("get_tagger_model_status"),
invoke<number>("get_tagger_threshold"),
]);
set({ taggerModel, taggerModelStatus, taggerThreshold, taggerModelError: null, taggerRuntimeProbe: null });
} catch (error) {
set({ taggerModel, taggerRuntimeProbe: null, taggerModelError: String(error) });
}
@@ -2345,11 +2348,13 @@ export const useGalleryStore = create<GalleryState>((set, get) => ({
}
},
setTaggerThreshold: async (threshold) => {
setTaggerThreshold: async (threshold, model) => {
const taggerThreshold = await invoke<number>("set_tagger_threshold", {
params: { threshold },
params: { threshold, model },
});
if (!model || get().taggerModel === model) {
set({ taggerThreshold });
}
},
loadTaggerBatchSize: async () => {
src/taggerModels.ts
+3 -1
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@@ -1,15 +1,17 @@
import type { TaggerModel } from "./store";
export const TAGGER_MODELS: Record<TaggerModel, { name: string; tab: string; description: string }> = {
export const TAGGER_MODELS: Record<TaggerModel, { name: string; tab: string; description: string; defaultThreshold: number }> = {
wd: {
name: "WD SwinV2 Tagger v3",
tab: "WD (anime)",
defaultThreshold: 0.35,
description:
"Anime-focused vision model by SmilingWolf. Generates booru-style tags with configurable confidence thresholds.",
},
joytag: {
name: "JoyTag",
tab: "JoyTag (general)",
defaultThreshold: 0.4,
description:
"Booru-schema tagger that also handles photographic content and is strong on NSFW concepts. The explicitness rating is derived from its tags.",
},