Merge branch 'main' of github.com:apeman76/immich into apeman76/main

cli/.nvmrc

@@ -1 +1 @@
-20.15.1
+20.15

cli/Dockerfile

@@ -1,4 +1,4 @@
-FROM node:20.15.1-alpine3.20@sha256:34b7aa411056c85dbf71d240d26516949b3f72b318d796c26b57caaa1df5639a as core
+FROM node:20.15.0-alpine3.20@sha256:df01469346db2bf1cfc1f7261aeab86b2960efa840fe2bd46d83ff339f463665 as core
 
 WORKDIR /usr/src/open-api/typescript-sdk
 COPY open-api/typescript-sdk/package*.json open-api/typescript-sdk/tsconfig*.json ./

cli/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@immich/cli",
-  "version": "2.2.8",
+  "version": "2.2.7",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@immich/cli",
-      "version": "2.2.8",
+      "version": "2.2.7",
       "license": "GNU Affero General Public License version 3",
       "dependencies": {
         "fast-glob": "^3.3.2",
@@ -22,7 +22,7 @@
         "@types/cli-progress": "^3.11.0",
         "@types/lodash-es": "^4.17.12",
         "@types/mock-fs": "^4.13.1",
-        "@types/node": "^20.14.10",
+        "@types/node": "^20.14.9",
         "@typescript-eslint/eslint-plugin": "^7.0.0",
         "@typescript-eslint/parser": "^7.0.0",
         "@vitest/coverage-v8": "^1.2.2",
@@ -49,14 +49,14 @@
     },
     "../open-api/typescript-sdk": {
       "name": "@immich/sdk",
-      "version": "1.108.0",
+      "version": "1.107.2",
       "dev": true,
       "license": "GNU Affero General Public License version 3",
       "dependencies": {
         "@oazapfts/runtime": "^1.0.2"
       },
       "devDependencies": {
-        "@types/node": "^20.14.10",
+        "@types/node": "^20.14.9",
         "typescript": "^5.3.3"
       }
     },
@@ -1163,9 +1163,9 @@
       }
     },
     "node_modules/@types/node": {
-      "version": "20.14.10",
+      "version": "20.14.9",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.14.10.tgz",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.14.9.tgz",
-      "integrity": "sha512-MdiXf+nDuMvY0gJKxyfZ7/6UFsETO7mGKF54MVD/ekJS6HdFtpZFBgrh6Pseu64XTb2MLyFPlbW6hj8HYRQNOQ==",
+      "integrity": "sha512-06OCtnTXtWOZBJlRApleWndH4JsRVs1pDCc8dLSQp+7PpUpX3ePdHyeNSFTeSe7FtKyQkrlPvHwJOW3SLd8Oyg==",
       "dev": true,
       "license": "MIT",
       "dependencies": {

cli/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@immich/cli",
-  "version": "2.2.8",
+  "version": "2.2.7",
   "description": "Command Line Interface (CLI) for Immich",
   "type": "module",
   "exports": "./dist/index.js",
@@ -18,7 +18,7 @@
     "@types/cli-progress": "^3.11.0",
     "@types/lodash-es": "^4.17.12",
     "@types/mock-fs": "^4.13.1",
-    "@types/node": "^20.14.10",
+    "@types/node": "^20.14.9",
     "@typescript-eslint/eslint-plugin": "^7.0.0",
     "@typescript-eslint/parser": "^7.0.0",
     "@vitest/coverage-v8": "^1.2.2",
@@ -64,6 +64,6 @@
     "lodash-es": "^4.17.21"
   },
   "volta": {
-    "node": "20.15.1"
+    "node": "20.15.0"
   }
 }

docker/docker-compose.prod.yml

@@ -73,7 +73,7 @@ services:
     container_name: immich_prometheus
     ports:
       - 9090:9090
-    image: prom/prometheus@sha256:f20d3127bf2876f4a1df76246fca576b41ddf1125ed1c546fbd8b16ea55117e6
+    image: prom/prometheus@sha256:075b1ba2c4ebb04bc3a6ab86c06ec8d8099f8fda1c96ef6d104d9bb1def1d8bc
     volumes:
       - ./prometheus.yml:/etc/prometheus/prometheus.yml
       - prometheus-data:/prometheus

docs/.nvmrc

@@ -1 +1 @@
-20.15.1
+20.15

docs/docs/guides/remote-access.md

@@ -4,7 +4,7 @@ This page gives a few pointers on how to access your Immich instance from outsid
 You can read the [full discussion in Discord](https://discord.com/channels/979116623879368755/1122615710846308484)
 
 :::danger
-Never forward port 2283 directly to the internet without additional configuration. This will expose the web interface via http to the internet, making you susceptible to [man in the middle](https://en.wikipedia.org/wiki/Man-in-the-middle_attack) attacks.
+Never forward port 2283 directly to the internet without additional configuration. This will expose the web interface via http to the internet, making you succeptible to [man in the middle](https://en.wikipedia.org/wiki/Man-in-the-middle_attack) attacks.
 :::
 
 ## Option 1: VPN to home network

docs/docusaurus.config.js

@@ -92,7 +92,6 @@ const config = {
           alt: 'Immich Logo',
           src: 'img/immich-logo-inline-light.png',
           srcDark: 'img/immich-logo-inline-dark.png',
-          className: 'rounded-none',
         },
         items: [
           {

docs/package.json

@@ -56,6 +56,6 @@
     "node": ">=20"
   },
   "volta": {
-    "node": "20.15.1"
+    "node": "20.15.0"
   }
 }

docs/src/components/timeline.tsx

@@ -49,7 +49,7 @@ export function Timeline({ items }: Props): JSX.Element {
               <div className="flex flex-col flex-grow justify-between gap-2">
                 <div className="flex gap-2 items-center">
                   {cardIcon === 'immich' ? (
-                    <img src="img/immich-logo.svg" height="30" className="rounded-none" />
+                    <img src="img/immich-logo.svg" height="30" />
                   ) : (
                     <Icon path={cardIcon} size={1} color={item.iconColor} />
                   )}

docs/src/pages/cursed-knowledge.tsx

@@ -49,7 +49,7 @@ const items: Item[] = [
     iconColor: 'greenyellow',
     title: 'JavaScript Date objects are cursed',
     description: 'JavaScript date objects are 1 indexed for years and days, but 0 indexed for months.',
-    link: { url: 'https://github.com/immich-app/immich/pull/6787', text: '#6787' },
+    link: { url: 'https://github.com/immich-app/immich/pulls/6787', text: '#6787' },
     date: new Date(2024, 0, 31),
   },
 ];

docs/src/pages/index.tsx

@@ -10,7 +10,7 @@ function HomepageHeader() {
       <section className="text-center m-6 p-12 border border-red-400 rounded-[50px] bg-slate-200 dark:bg-immich-dark-gray">
         <img
           src={isDarkTheme ? 'img/immich-logo-stacked-dark.svg' : 'img/immich-logo-stacked-light.svg'}
-          className="md:h-60 h-44 mb-2 antialiased rounded-none"
+          className="md:h-60 h-44 mb-2 antialiased"
           alt="Immich logo"
         />
         <div className="sm:text-2xl text-lg md:text-4xl mb-12 sm:leading-tight">

docs/static/archived-versions.json

@@ -1,8 +1,4 @@
 [
-  {
-    "label": "v1.108.0",
-    "url": "https://v1.108.0.archive.immich.app"
-  },
   {
     "label": "v1.107.2",
     "url": "https://v1.107.2.archive.immich.app"

e2e/.nvmrc

@@ -1 +1 @@
-20.15.1
+20.15

e2e/docker-compose.yml

@@ -26,8 +26,6 @@ services:
     volumes:
       - upload:/usr/src/app/upload
       - ./test-assets:/test-assets
-    extra_hosts:
-      - 'auth-server:host-gateway'
     depends_on:
       - redis
       - database

e2e/package.json

@@ -1,6 +1,6 @@
 {
   "name": "immich-e2e",
-  "version": "1.108.0",
+  "version": "1.107.2",
   "description": "",
   "main": "index.js",
   "type": "module",
@@ -23,8 +23,7 @@
     "@immich/sdk": "file:../open-api/typescript-sdk",
     "@playwright/test": "^1.44.1",
     "@types/luxon": "^3.4.2",
-    "@types/node": "^20.14.10",
+    "@types/node": "^20.14.9",
-    "@types/oidc-provider": "^8.5.1",
     "@types/pg": "^8.11.0",
     "@types/pngjs": "^6.0.4",
     "@types/supertest": "^6.0.2",
@@ -36,9 +35,7 @@
     "eslint-plugin-prettier": "^5.1.3",
     "eslint-plugin-unicorn": "^54.0.0",
     "exiftool-vendored": "^27.0.0",
-    "jose": "^5.6.3",
     "luxon": "^3.4.4",
-    "oidc-provider": "^8.5.1",
     "pg": "^8.11.3",
     "pngjs": "^7.0.0",
     "prettier": "^3.2.5",
@@ -50,6 +47,6 @@
     "vitest": "^1.3.0"
   },
   "volta": {
-    "node": "20.15.1"
+    "node": "20.15.0"
   }
 }

e2e/src/api/specs/auth.e2e-spec.ts

@@ -100,12 +100,6 @@ describe('/auth/*', () => {
         expect(status).toBe(400);
         expect(body).toEqual(errorDto.badRequest());
       });
-
-      it('should reject an invalid email', async () => {
-        const { status, body } = await request(app).post('/auth/login').send({ email: [], password });
-        expect(status).toBe(400);
-        expect(body).toEqual(errorDto.invalidEmail);
-      });
     }
 
     it('should accept a correct password', async () => {

e2e/src/api/specs/oauth.e2e-spec.ts

@@ -1,85 +1,12 @@
-import {
-  LoginResponseDto,
-  SystemConfigOAuthDto,
-  getConfigDefaults,
-  getMyUser,
-  startOAuth,
-  updateConfig,
-} from '@immich/sdk';
 import { errorDto } from 'src/responses';
-import { OAuthClient, OAuthUser } from 'src/setup/auth-server';
+import { app, utils } from 'src/utils';
-import { app, asBearerAuth, baseUrl, utils } from 'src/utils';
 import request from 'supertest';
 import { beforeAll, describe, expect, it } from 'vitest';
 
-const authServer = {
-  internal: 'http://auth-server:3000',
-  external: 'http://127.0.0.1:3000',
-};
-
-const redirect = async (url: string, cookies?: string[]) => {
-  const { headers } = await request(url)
-    .get('/')
-    .set('Cookie', cookies || []);
-  return { cookies: (headers['set-cookie'] as unknown as string[]) || [], location: headers.location };
-};
-
-const loginWithOAuth = async (sub: OAuthUser | string) => {
-  const { url } = await startOAuth({ oAuthConfigDto: { redirectUri: `${baseUrl}/auth/login` } });
-
-  // login
-  const response1 = await redirect(url.replace(authServer.internal, authServer.external));
-  const response2 = await request(authServer.external + response1.location)
-    .post('/')
-    .set('Cookie', response1.cookies)
-    .type('form')
-    .send({ prompt: 'login', login: sub, password: 'password' });
-
-  // approve
-  const response3 = await redirect(response2.header.location, response1.cookies);
-  const response4 = await request(authServer.external + response3.location)
-    .post('/')
-    .type('form')
-    .set('Cookie', response3.cookies)
-    .send({ prompt: 'consent' });
-
-  const response5 = await redirect(response4.header.location, response3.cookies.slice(1));
-  const redirectUrl = response5.location;
-
-  expect(redirectUrl).toBeDefined();
-  const params = new URL(redirectUrl).searchParams;
-  expect(params.get('code')).toBeDefined();
-  expect(params.get('state')).toBeDefined();
-
-  return redirectUrl;
-};
-
-const setupOAuth = async (token: string, dto: Partial<SystemConfigOAuthDto>) => {
-  const options = { headers: asBearerAuth(token) };
-  const defaults = await getConfigDefaults(options);
-  const merged = {
-    ...defaults.oauth,
-    buttonText: 'Login with Immich',
-    issuerUrl: `${authServer.internal}/.well-known/openid-configuration`,
-    ...dto,
-  };
-  await updateConfig({ systemConfigDto: { ...defaults, oauth: merged } }, options);
-};
-
 describe(`/oauth`, () => {
-  let admin: LoginResponseDto;
-
   beforeAll(async () => {
     await utils.resetDatabase();
-    admin = await utils.adminSetup();
+    await utils.adminSetup();
-
-    await setupOAuth(admin.accessToken, {
-      enabled: true,
-      clientId: OAuthClient.DEFAULT,
-      clientSecret: OAuthClient.DEFAULT,
-      buttonText: 'Login with Immich',
-      storageLabelClaim: 'immich_username',
-    });
   });
 
   describe('POST /oauth/authorize', () => {
@@ -88,171 +15,5 @@ describe(`/oauth`, () => {
       expect(status).toBe(400);
       expect(body).toEqual(errorDto.badRequest(['redirectUri must be a string', 'redirectUri should not be empty']));
     });
-
-    it('should return a redirect uri', async () => {
-      const { status, body } = await request(app)
-        .post('/oauth/authorize')
-        .send({ redirectUri: 'http://127.0.0.1:2283/auth/login' });
-      expect(status).toBe(201);
-      expect(body).toEqual({ url: expect.stringContaining(`${authServer.internal}/auth?`) });
-
-      const params = new URL(body.url).searchParams;
-      expect(params.get('client_id')).toBe('client-default');
-      expect(params.get('response_type')).toBe('code');
-      expect(params.get('redirect_uri')).toBe('http://127.0.0.1:2283/auth/login');
-      expect(params.get('state')).toBeDefined();
-    });
-  });
-
-  describe('POST /oauth/callback', () => {
-    it(`should throw an error if a url is not provided`, async () => {
-      const { status, body } = await request(app).post('/oauth/callback').send({});
-      expect(status).toBe(400);
-      expect(body).toEqual(errorDto.badRequest(['url must be a string', 'url should not be empty']));
-    });
-
-    it(`should throw an error if the url is empty`, async () => {
-      const { status, body } = await request(app).post('/oauth/callback').send({ url: '' });
-      expect(status).toBe(400);
-      expect(body).toEqual(errorDto.badRequest(['url should not be empty']));
-    });
-
-    it('should auto register the user by default', async () => {
-      const url = await loginWithOAuth('oauth-auto-register');
-      const { status, body } = await request(app).post('/oauth/callback').send({ url });
-      expect(status).toBe(201);
-      expect(body).toMatchObject({
-        accessToken: expect.any(String),
-        isAdmin: false,
-        name: 'OAuth User',
-        userEmail: 'oauth-auto-register@immich.app',
-        userId: expect.any(String),
-      });
-    });
-
-    it('should handle a user without an email', async () => {
-      const url = await loginWithOAuth(OAuthUser.NO_EMAIL);
-      const { status, body } = await request(app).post('/oauth/callback').send({ url });
-      expect(status).toBe(400);
-      expect(body).toEqual(errorDto.badRequest('OAuth profile does not have an email address'));
-    });
-
-    it('should set the quota from a claim', async () => {
-      const url = await loginWithOAuth(OAuthUser.WITH_QUOTA);
-      const { status, body } = await request(app).post('/oauth/callback').send({ url });
-      expect(status).toBe(201);
-      expect(body).toMatchObject({
-        accessToken: expect.any(String),
-        userId: expect.any(String),
-        userEmail: 'oauth-with-quota@immich.app',
-      });
-
-      const user = await getMyUser({ headers: asBearerAuth(body.accessToken) });
-      expect(user.quotaSizeInBytes).toBe(25 * 2 ** 30); // 25 GiB;
-    });
-
-    it('should set the storage label from a claim', async () => {
-      const url = await loginWithOAuth(OAuthUser.WITH_USERNAME);
-      const { status, body } = await request(app).post('/oauth/callback').send({ url });
-      expect(status).toBe(201);
-      expect(body).toMatchObject({
-        accessToken: expect.any(String),
-        userId: expect.any(String),
-        userEmail: 'oauth-with-username@immich.app',
-      });
-
-      const user = await getMyUser({ headers: asBearerAuth(body.accessToken) });
-      expect(user.storageLabel).toBe('user-username');
-    });
-
-    it('should work with RS256 signed tokens', async () => {
-      await setupOAuth(admin.accessToken, {
-        enabled: true,
-        clientId: OAuthClient.RS256_TOKENS,
-        clientSecret: OAuthClient.RS256_TOKENS,
-        autoRegister: true,
-        buttonText: 'Login with Immich',
-        signingAlgorithm: 'RS256',
-      });
-      const url = await loginWithOAuth('oauth-RS256-token');
-      const { status, body } = await request(app).post('/oauth/callback').send({ url });
-      expect(status).toBe(201);
-      expect(body).toMatchObject({
-        accessToken: expect.any(String),
-        isAdmin: false,
-        name: 'OAuth User',
-        userEmail: 'oauth-RS256-token@immich.app',
-        userId: expect.any(String),
-      });
-    });
-
-    it('should work with RS256 signed user profiles', async () => {
-      await setupOAuth(admin.accessToken, {
-        enabled: true,
-        clientId: OAuthClient.RS256_PROFILE,
-        clientSecret: OAuthClient.RS256_PROFILE,
-        buttonText: 'Login with Immich',
-        profileSigningAlgorithm: 'RS256',
-      });
-      const url = await loginWithOAuth('oauth-signed-profile');
-      const { status, body } = await request(app).post('/oauth/callback').send({ url });
-      expect(status).toBe(201);
-      expect(body).toMatchObject({
-        userId: expect.any(String),
-        userEmail: 'oauth-signed-profile@immich.app',
-      });
-    });
-
-    it('should throw an error for an invalid token algorithm', async () => {
-      await setupOAuth(admin.accessToken, {
-        enabled: true,
-        clientId: OAuthClient.DEFAULT,
-        clientSecret: OAuthClient.DEFAULT,
-        buttonText: 'Login with Immich',
-        signingAlgorithm: 'something-that-does-not-work',
-      });
-      const url = await loginWithOAuth('oauth-signed-bad');
-      const { status, body } = await request(app).post('/oauth/callback').send({ url });
-      expect(status).toBe(500);
-      expect(body).toMatchObject({
-        error: 'Internal Server Error',
-        message: 'Failed to finish oauth',
-        statusCode: 500,
-      });
-    });
-
-    describe('autoRegister: false', () => {
-      beforeAll(async () => {
-        await setupOAuth(admin.accessToken, {
-          enabled: true,
-          clientId: OAuthClient.DEFAULT,
-          clientSecret: OAuthClient.DEFAULT,
-          autoRegister: false,
-          buttonText: 'Login with Immich',
-        });
-      });
-
-      it('should not auto register the user', async () => {
-        const url = await loginWithOAuth('oauth-no-auto-register');
-        const { status, body } = await request(app).post('/oauth/callback').send({ url });
-        expect(status).toBe(400);
-        expect(body).toEqual(errorDto.badRequest('User does not exist and auto registering is disabled.'));
-      });
-
-      it('should link to an existing user by email', async () => {
-        const { userId } = await utils.userSetup(admin.accessToken, {
-          name: 'OAuth User 3',
-          email: 'oauth-user3@immich.app',
-          password: 'password',
-        });
-        const url = await loginWithOAuth('oauth-user3');
-        const { status, body } = await request(app).post('/oauth/callback').send({ url });
-        expect(status).toBe(201);
-        expect(body).toMatchObject({
-          userId,
-          userEmail: 'oauth-user3@immich.app',
-        });
-      });
-    });
   });
 });

e2e/src/responses.ts

@@ -61,12 +61,6 @@ export const errorDto = {
     message: 'The server already has an admin',
     correlationId: expect.any(String),
   },
-  invalidEmail: {
-    error: 'Bad Request',
-    statusCode: 400,
-    message: ['email must be an email'],
-    correlationId: expect.any(String),
-  },
 };
 
 export const signupResponseDto = {

e2e/src/setup/auth-server.ts

@@ -1,117 +0,0 @@
-import { exportJWK, generateKeyPair } from 'jose';
-import Provider from 'oidc-provider';
-
-export enum OAuthClient {
-  DEFAULT = 'client-default',
-  RS256_TOKENS = 'client-RS256-tokens',
-  RS256_PROFILE = 'client-RS256-profile',
-}
-
-export enum OAuthUser {
-  NO_EMAIL = 'no-email',
-  NO_NAME = 'no-name',
-  WITH_QUOTA = 'with-quota',
-  WITH_USERNAME = 'with-username',
-}
-
-const claims = [
-  { sub: OAuthUser.NO_EMAIL },
-  {
-    sub: OAuthUser.NO_NAME,
-    email: 'oauth-no-name@immich.app',
-    email_verified: true,
-  },
-  {
-    sub: OAuthUser.WITH_USERNAME,
-    email: 'oauth-with-username@immich.app',
-    email_verified: true,
-    immich_username: 'user-username',
-  },
-  {
-    sub: OAuthUser.WITH_QUOTA,
-    email: 'oauth-with-quota@immich.app',
-    email_verified: true,
-    preferred_username: 'user-quota',
-    immich_quota: 25,
-  },
-];
-
-const withDefaultClaims = (sub: string) => ({
-  sub,
-  email: `${sub}@immich.app`,
-  name: 'OAuth User',
-  given_name: `OAuth`,
-  family_name: 'User',
-  email_verified: true,
-});
-
-const getClaims = (sub: string) => claims.find((user) => user.sub === sub) || withDefaultClaims(sub);
-
-const setup = async () => {
-  const { privateKey, publicKey } = await generateKeyPair('RS256');
-
-  const port = 3000;
-  const host = '0.0.0.0';
-  const oidc = new Provider(`http://${host}:${port}`, {
-    renderError: async (ctx, out, error) => {
-      console.error(out);
-      console.error(error);
-      ctx.body = 'Internal Server Error';
-    },
-    findAccount: (ctx, sub) => ({ accountId: sub, claims: () => getClaims(sub) }),
-    scopes: ['openid', 'email', 'profile'],
-    claims: {
-      openid: ['sub'],
-      email: ['email', 'email_verified'],
-      profile: ['name', 'given_name', 'family_name', 'preferred_username', 'immich_quota', 'immich_username'],
-    },
-    features: {
-      jwtUserinfo: {
-        enabled: true,
-      },
-    },
-    cookies: {
-      names: {
-        session: 'oidc.session',
-        interaction: 'oidc.interaction',
-        resume: 'oidc.resume',
-        state: 'oidc.state',
-      },
-    },
-    pkce: {
-      required: () => false,
-    },
-    jwks: { keys: [await exportJWK(privateKey)] },
-    clients: [
-      {
-        client_id: OAuthClient.DEFAULT,
-        client_secret: OAuthClient.DEFAULT,
-        redirect_uris: ['http://127.0.0.1:2283/auth/login'],
-        grant_types: ['authorization_code'],
-        response_types: ['code'],
-      },
-      {
-        client_id: OAuthClient.RS256_TOKENS,
-        client_secret: OAuthClient.RS256_TOKENS,
-        redirect_uris: ['http://127.0.0.1:2283/auth/login'],
-        grant_types: ['authorization_code'],
-        id_token_signed_response_alg: 'RS256',
-        jwks: { keys: [await exportJWK(publicKey)] },
-      },
-      {
-        client_id: OAuthClient.RS256_PROFILE,
-        client_secret: OAuthClient.RS256_PROFILE,
-        redirect_uris: ['http://127.0.0.1:2283/auth/login'],
-        grant_types: ['authorization_code'],
-        userinfo_signed_response_alg: 'RS256',
-        jwks: { keys: [await exportJWK(publicKey)] },
-      },
-    ],
-  });
-
-  const onStart = () => console.log(`[auth-server] http://${host}:${port}/.well-known/openid-configuration`);
-  const app = oidc.listen(port, host, onStart);
-  return () => app.close();
-};
-
-export default setup;

e2e/src/utils.ts

@@ -53,7 +53,8 @@ type AdminSetupOptions = { onboarding?: boolean };
 type AssetData = { bytes?: Buffer; filename: string };
 
 const dbUrl = 'postgres://postgres:postgres@127.0.0.1:5433/immich';
-export const baseUrl = 'http://127.0.0.1:2283';
+const baseUrl = 'http://127.0.0.1:2283';
+
 export const shareUrl = `${baseUrl}/share`;
 export const app = `${baseUrl}/api`;
 // TODO move test assets into e2e/assets

e2e/vitest.config.ts

@@ -1,11 +1,11 @@
 import { defineConfig } from 'vitest/config';
 
 // skip `docker compose up` if `make e2e` was already run
-const globalSetup: string[] = ['src/setup/auth-server.ts'];
+const globalSetup: string[] = [];
 try {
   await fetch('http://127.0.0.1:2283/api/server-info/ping');
 } catch {
-  globalSetup.push('src/setup/docker-compose.ts');
+  globalSetup.push('src/setup.ts');
 }
 
 export default defineConfig({

machine-learning/ann/build.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env sh
+
+g++ -shared -O3 -o libann.so -fuse-ld=gold -std=c++17 -I"$ARMNN_PATH"/include -larmnn -larmnnDeserializer -larmnnTfLiteParser -larmnnOnnxParser -L"$ARMNN_PATH" ann.cpp

machine-learning/ann/export/build-converter.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/env sh
+
+cd armnn-23.11/ || exit
+g++ -o ../armnnconverter -O1 -DARMNN_ONNX_PARSER -DARMNN_SERIALIZER -DARMNN_TF_LITE_PARSER -fuse-ld=gold -std=c++17 -Iinclude -Isrc/armnnUtils -Ithird-party -larmnn -larmnnDeserializer -larmnnTfLiteParser -larmnnOnnxParser -larmnnSerializer -L../armnn src/armnnConverter/ArmnnConverter.cpp

machine-learning/ann/export/env.yaml

@@ -0,0 +1,201 @@
+name: annexport
+channels:
+  - pytorch
+  - nvidia
+  - conda-forge
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_kmp_llvm
+  - aiohttp=3.9.1=py310h2372a71_0
+  - aiosignal=1.3.1=pyhd8ed1ab_0
+  - arpack=3.8.0=nompi_h0baa96a_101
+  - async-timeout=4.0.3=pyhd8ed1ab_0
+  - attrs=23.1.0=pyh71513ae_1
+  - aws-c-auth=0.7.3=h28f7589_1
+  - aws-c-cal=0.6.1=hc309b26_1
+  - aws-c-common=0.9.0=hd590300_0
+  - aws-c-compression=0.2.17=h4d4d85c_2
+  - aws-c-event-stream=0.3.1=h2e3709c_4
+  - aws-c-http=0.7.11=h00aa349_4
+  - aws-c-io=0.13.32=he9a53bd_1
+  - aws-c-mqtt=0.9.3=hb447be9_1
+  - aws-c-s3=0.3.14=hf3aad02_1
+  - aws-c-sdkutils=0.1.12=h4d4d85c_1
+  - aws-checksums=0.1.17=h4d4d85c_1
+  - aws-crt-cpp=0.21.0=hb942446_5
+  - aws-sdk-cpp=1.10.57=h85b1a90_19
+  - blas=2.120=openblas
+  - blas-devel=3.9.0=20_linux64_openblas
+  - brotli-python=1.0.9=py310hd8f1fbe_9
+  - bzip2=1.0.8=hd590300_5
+  - c-ares=1.23.0=hd590300_0
+  - ca-certificates=2023.11.17=hbcca054_0
+  - certifi=2023.11.17=pyhd8ed1ab_0
+  - charset-normalizer=3.3.2=pyhd8ed1ab_0
+  - click=8.1.7=unix_pyh707e725_0
+  - colorama=0.4.6=pyhd8ed1ab_0
+  - coloredlogs=15.0.1=pyhd8ed1ab_3
+  - cuda-cudart=11.7.99=0
+  - cuda-cupti=11.7.101=0
+  - cuda-libraries=11.7.1=0
+  - cuda-nvrtc=11.7.99=0
+  - cuda-nvtx=11.7.91=0
+  - cuda-runtime=11.7.1=0
+  - dataclasses=0.8=pyhc8e2a94_3
+  - datasets=2.14.7=pyhd8ed1ab_0
+  - dill=0.3.7=pyhd8ed1ab_0
+  - filelock=3.13.1=pyhd8ed1ab_0
+  - flatbuffers=23.5.26=h59595ed_1
+  - freetype=2.12.1=h267a509_2
+  - frozenlist=1.4.0=py310h2372a71_1
+  - fsspec=2023.10.0=pyhca7485f_0
+  - ftfy=6.1.3=pyhd8ed1ab_0
+  - gflags=2.2.2=he1b5a44_1004
+  - glog=0.6.0=h6f12383_0
+  - glpk=5.0=h445213a_0
+  - gmp=6.3.0=h59595ed_0
+  - gmpy2=2.1.2=py310h3ec546c_1
+  - huggingface_hub=0.17.3=pyhd8ed1ab_0
+  - humanfriendly=10.0=pyhd8ed1ab_6
+  - icu=73.2=h59595ed_0
+  - idna=3.6=pyhd8ed1ab_0
+  - importlib-metadata=7.0.0=pyha770c72_0
+  - importlib_metadata=7.0.0=hd8ed1ab_0
+  - joblib=1.3.2=pyhd8ed1ab_0
+  - keyutils=1.6.1=h166bdaf_0
+  - krb5=1.21.2=h659d440_0
+  - lcms2=2.15=h7f713cb_2
+  - ld_impl_linux-64=2.40=h41732ed_0
+  - lerc=4.0.0=h27087fc_0
+  - libabseil=20230125.3=cxx17_h59595ed_0
+  - libarrow=12.0.1=hb87d912_8_cpu
+  - libblas=3.9.0=20_linux64_openblas
+  - libbrotlicommon=1.0.9=h166bdaf_9
+  - libbrotlidec=1.0.9=h166bdaf_9
+  - libbrotlienc=1.0.9=h166bdaf_9
+  - libcblas=3.9.0=20_linux64_openblas
+  - libcrc32c=1.1.2=h9c3ff4c_0
+  - libcublas=11.10.3.66=0
+  - libcufft=10.7.2.124=h4fbf590_0
+  - libcufile=1.8.1.2=0
+  - libcurand=10.3.4.101=0
+  - libcurl=8.5.0=hca28451_0
+  - libcusolver=11.4.0.1=0
+  - libcusparse=11.7.4.91=0
+  - libdeflate=1.19=hd590300_0
+  - libedit=3.1.20191231=he28a2e2_2
+  - libev=4.33=hd590300_2
+  - libevent=2.1.12=hf998b51_1
+  - libffi=3.4.2=h7f98852_5
+  - libgcc-ng=13.2.0=h807b86a_3
+  - libgfortran-ng=13.2.0=h69a702a_3
+  - libgfortran5=13.2.0=ha4646dd_3
+  - libgoogle-cloud=2.12.0=hac9eb74_1
+  - libgrpc=1.54.3=hb20ce57_0
+  - libhwloc=2.9.3=default_h554bfaf_1009
+  - libiconv=1.17=hd590300_1
+  - libjpeg-turbo=2.1.5.1=hd590300_1
+  - liblapack=3.9.0=20_linux64_openblas
+  - liblapacke=3.9.0=20_linux64_openblas
+  - libnghttp2=1.58.0=h47da74e_1
+  - libnpp=11.7.4.75=0
+  - libnsl=2.0.1=hd590300_0
+  - libnuma=2.0.16=h0b41bf4_1
+  - libnvjpeg=11.8.0.2=0
+  - libopenblas=0.3.25=pthreads_h413a1c8_0
+  - libpng=1.6.39=h753d276_0
+  - libprotobuf=3.21.12=hfc55251_2
+  - libsentencepiece=0.1.99=h180e1df_0
+  - libsqlite=3.44.2=h2797004_0
+  - libssh2=1.11.0=h0841786_0
+  - libstdcxx-ng=13.2.0=h7e041cc_3
+  - libthrift=0.18.1=h8fd135c_2
+  - libtiff=4.6.0=h29866fb_1
+  - libutf8proc=2.8.0=h166bdaf_0
+  - libuuid=2.38.1=h0b41bf4_0
+  - libwebp-base=1.3.2=hd590300_0
+  - libxcb=1.15=h0b41bf4_0
+  - libxml2=2.11.6=h232c23b_0
+  - libzlib=1.2.13=hd590300_5
+  - llvm-openmp=17.0.6=h4dfa4b3_0
+  - lz4-c=1.9.4=hcb278e6_0
+  - mkl=2022.2.1=h84fe81f_16997
+  - mkl-devel=2022.2.1=ha770c72_16998
+  - mkl-include=2022.2.1=h84fe81f_16997
+  - mpc=1.3.1=hfe3b2da_0
+  - mpfr=4.2.1=h9458935_0
+  - mpmath=1.3.0=pyhd8ed1ab_0
+  - multidict=6.0.4=py310h2372a71_1
+  - multiprocess=0.70.15=py310h2372a71_1
+  - ncurses=6.4=h59595ed_2
+  - numpy=1.26.2=py310hb13e2d6_0
+  - onnx=1.14.0=py310ha3deec4_1
+  - onnx2torch=1.5.13=pyhd8ed1ab_0
+  - onnxruntime=1.16.3=py310hd4b7fbc_1_cpu
+  - open-clip-torch=2.23.0=pyhd8ed1ab_1
+  - openblas=0.3.25=pthreads_h7a3da1a_0
+  - openjpeg=2.5.0=h488ebb8_3
+  - openssl=3.2.0=hd590300_1
+  - orc=1.9.0=h2f23424_1
+  - packaging=23.2=pyhd8ed1ab_0
+  - pandas=2.1.4=py310hcc13569_0
+  - pillow=10.0.1=py310h29da1c1_1
+  - pip=23.3.1=pyhd8ed1ab_0
+  - protobuf=4.21.12=py310heca2aa9_0
+  - pthread-stubs=0.4=h36c2ea0_1001
+  - pyarrow=12.0.1=py310h0576679_8_cpu
+  - pyarrow-hotfix=0.6=pyhd8ed1ab_0
+  - pysocks=1.7.1=pyha2e5f31_6
+  - python=3.10.13=hd12c33a_0_cpython
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python-flatbuffers=23.5.26=pyhd8ed1ab_0
+  - python-tzdata=2023.3=pyhd8ed1ab_0
+  - python-xxhash=3.4.1=py310h2372a71_0
+  - python_abi=3.10=4_cp310
+  - pytorch=1.13.1=cpu_py310hd11e9c7_1
+  - pytorch-cuda=11.7=h778d358_5
+  - pytorch-mutex=1.0=cuda
+  - pytz=2023.3.post1=pyhd8ed1ab_0
+  - pyyaml=6.0.1=py310h2372a71_1
+  - rdma-core=28.9=h59595ed_1
+  - re2=2023.03.02=h8c504da_0
+  - readline=8.2=h8228510_1
+  - regex=2023.10.3=py310h2372a71_0
+  - requests=2.31.0=pyhd8ed1ab_0
+  - s2n=1.3.49=h06160fa_0
+  - sacremoses=0.0.53=pyhd8ed1ab_0
+  - safetensors=0.3.3=py310hcb5633a_1
+  - sentencepiece=0.1.99=hff52083_0
+  - sentencepiece-python=0.1.99=py310hebdb9f0_0
+  - sentencepiece-spm=0.1.99=h180e1df_0
+  - setuptools=68.2.2=pyhd8ed1ab_0
+  - six=1.16.0=pyh6c4a22f_0
+  - sleef=3.5.1=h9b69904_2
+  - snappy=1.1.10=h9fff704_0
+  - sympy=1.12=pypyh9d50eac_103
+  - tbb=2021.11.0=h00ab1b0_0
+  - texttable=1.7.0=pyhd8ed1ab_0
+  - timm=0.9.12=pyhd8ed1ab_0
+  - tk=8.6.13=noxft_h4845f30_101
+  - tokenizers=0.14.1=py310h320607d_2
+  - torchvision=0.14.1=cpu_py310hd3d2ac3_1
+  - tqdm=4.66.1=pyhd8ed1ab_0
+  - transformers=4.35.2=pyhd8ed1ab_0
+  - typing-extensions=4.9.0=hd8ed1ab_0
+  - typing_extensions=4.9.0=pyha770c72_0
+  - tzdata=2023c=h71feb2d_0
+  - ucx=1.14.1=h64cca9d_5
+  - urllib3=2.1.0=pyhd8ed1ab_0
+  - wcwidth=0.2.12=pyhd8ed1ab_0
+  - wheel=0.42.0=pyhd8ed1ab_0
+  - xorg-libxau=1.0.11=hd590300_0
+  - xorg-libxdmcp=1.1.3=h7f98852_0
+  - xxhash=0.8.2=hd590300_0
+  - xz=5.2.6=h166bdaf_0
+  - yaml=0.2.5=h7f98852_2
+  - yarl=1.9.3=py310h2372a71_0
+  - zipp=3.17.0=pyhd8ed1ab_0
+  - zlib=1.2.13=hd590300_5
+  - zstd=1.5.5=hfc55251_0
+  - pip:
+      - git+https://github.com/fyfrey/TinyNeuralNetwork.git

machine-learning/ann/export/run.py

@@ -0,0 +1,157 @@
+import logging
+import os
+import platform
+import subprocess
+from abc import abstractmethod
+
+import onnx
+import open_clip
+import torch
+from onnx2torch import convert
+from onnxruntime.tools.onnx_model_utils import fix_output_shapes, make_input_shape_fixed
+from tinynn.converter import TFLiteConverter
+
+
+class ExportBase(torch.nn.Module):
+    input_shape: tuple[int, ...]
+
+    def __init__(self, device: torch.device, name: str):
+        super().__init__()
+        self.device = device
+        self.name = name
+        self.optimize = 5
+        self.nchw_transpose = False
+
+    @abstractmethod
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor | tuple[torch.Tensor]:
+        pass
+
+    def dummy_input(self) -> torch.FloatTensor:
+        return torch.rand((1, 3, 224, 224), device=self.device)
+
+
+class ArcFace(ExportBase):
+    input_shape = (1, 3, 112, 112)
+
+    def __init__(self, onnx_model_path: str, device: torch.device):
+        name, _ = os.path.splitext(os.path.basename(onnx_model_path))
+        super().__init__(device, name)
+        onnx_model = onnx.load_model(onnx_model_path)
+        make_input_shape_fixed(onnx_model.graph, onnx_model.graph.input[0].name, self.input_shape)
+        fix_output_shapes(onnx_model)
+        self.model = convert(onnx_model).to(device)
+        if self.device.type == "cuda":
+            self.model = self.model.half()
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.FloatTensor:
+        embedding: torch.FloatTensor = self.model(
+            input_tensor.half() if self.device.type == "cuda" else input_tensor
+        ).float()
+        assert isinstance(embedding, torch.FloatTensor)
+        return embedding
+
+    def dummy_input(self) -> torch.FloatTensor:
+        return torch.rand(self.input_shape, device=self.device)
+
+
+class RetinaFace(ExportBase):
+    input_shape = (1, 3, 640, 640)
+
+    def __init__(self, onnx_model_path: str, device: torch.device):
+        name, _ = os.path.splitext(os.path.basename(onnx_model_path))
+        super().__init__(device, name)
+        self.optimize = 3
+        self.model = convert(onnx_model_path).eval().to(device)
+        if self.device.type == "cuda":
+            self.model = self.model.half()
+
+    def forward(self, input_tensor: torch.Tensor) -> tuple[torch.FloatTensor]:
+        out: torch.Tensor = self.model(input_tensor.half() if self.device.type == "cuda" else input_tensor)
+        return tuple(o.float() for o in out)
+
+    def dummy_input(self) -> torch.FloatTensor:
+        return torch.rand(self.input_shape, device=self.device)
+
+
+class ClipVision(ExportBase):
+    input_shape = (1, 3, 224, 224)
+
+    def __init__(self, model_name: str, weights: str, device: torch.device):
+        super().__init__(device, model_name + "__" + weights)
+        self.model = open_clip.create_model(
+            model_name,
+            weights,
+            precision="fp16" if device.type == "cuda" else "fp32",
+            jit=False,
+            require_pretrained=True,
+            device=device,
+        )
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.FloatTensor:
+        embedding: torch.Tensor = self.model.encode_image(
+            input_tensor.half() if self.device.type == "cuda" else input_tensor,
+            normalize=True,
+        ).float()
+        return embedding
+
+
+def export(model: ExportBase) -> None:
+    model.eval()
+    for param in model.parameters():
+        param.requires_grad = False
+    dummy_input = model.dummy_input()
+    model(dummy_input)
+    jit = torch.jit.trace(model, dummy_input)  # type: ignore[no-untyped-call,attr-defined]
+    tflite_model_path = f"output/{model.name}.tflite"
+    os.makedirs("output", exist_ok=True)
+
+    converter = TFLiteConverter(
+        jit,
+        dummy_input,
+        tflite_model_path,
+        optimize=model.optimize,
+        nchw_transpose=model.nchw_transpose,
+    )
+    # segfaults on ARM, must run on x86_64 / AMD64
+    converter.convert()
+
+    armnn_model_path = f"output/{model.name}.armnn"
+    os.environ["LD_LIBRARY_PATH"] = "armnn"
+    subprocess.run(
+        [
+            "./armnnconverter",
+            "-f",
+            "tflite-binary",
+            "-m",
+            tflite_model_path,
+            "-i",
+            "input_tensor",
+            "-o",
+            "output_tensor",
+            "-p",
+            armnn_model_path,
+        ]
+    )
+
+
+def main() -> None:
+    if platform.machine() not in ("x86_64", "AMD64"):
+        raise RuntimeError(f"Can only run on x86_64 / AMD64, not {platform.machine()}")
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    if device.type != "cuda":
+        logging.warning(
+            "No CUDA available, cannot create fp16 model! proceeding to create a fp32 model (use only for testing)"
+        )
+    models = [
+        ClipVision("ViT-B-32", "openai", device),
+        ArcFace("buffalo_l_rec.onnx", device),
+        RetinaFace("buffalo_l_det.onnx", device),
+    ]
+    for model in models:
+        export(model)
+
+
+if __name__ == "__main__":
+    with torch.no_grad():
+        main()

machine-learning/app/conftest.py

@@ -168,12 +168,6 @@ def warning() -> Iterator[mock.Mock]:
         yield mocked
 
 
-@pytest.fixture(scope="function")
-def exception() -> Iterator[mock.Mock]:
-    with mock.patch.object(log, "exception") as mocked:
-        yield mocked
-
-
 @pytest.fixture(scope="function")
 def snapshot_download() -> Iterator[mock.Mock]:
     with mock.patch("app.models.base.snapshot_download") as mocked:

machine-learning/app/main.py

@@ -29,7 +29,6 @@ from .schemas import (
     InferenceEntry,
     InferenceResponse,
     MessageResponse,
-    ModelFormat,
     ModelIdentity,
     ModelTask,
     ModelType,
@@ -196,17 +195,7 @@ async def load(model: InferenceModel) -> InferenceModel:
         if model.load_attempts > 1:
             raise HTTPException(500, f"Failed to load model '{model.model_name}'")
         with lock:
-            try:
+            model.load()
-                model.load()
-            except FileNotFoundError as e:
-                if model.model_format == ModelFormat.ONNX:
-                    raise e
-                log.exception(e)
-                log.warning(
-                    f"{model.model_format.upper()} is available, but model '{model.model_name}' does not support it."
-                )
-                model.model_format = ModelFormat.ONNX
-                model.load()
         return model
 
     try:

machine-learning/app/models/base.py

@@ -23,7 +23,7 @@ class InferenceModel(ABC):
         self,
         model_name: str,
         cache_dir: Path | str | None = None,
-        model_format: ModelFormat | None = None,
+        preferred_format: ModelFormat | None = None,
         session: ModelSession | None = None,
         **model_kwargs: Any,
     ) -> None:
@@ -31,7 +31,7 @@ class InferenceModel(ABC):
         self.load_attempts = 0
         self.model_name = clean_name(model_name)
         self.cache_dir = Path(cache_dir) if cache_dir is not None else self._cache_dir_default
-        self.model_format = model_format if model_format is not None else self._model_format_default
+        self.model_format = preferred_format if preferred_format is not None else self._model_format_default
         if session is not None:
             self.session = session
 
@@ -48,7 +48,7 @@ class InferenceModel(ABC):
         self.load_attempts += 1
 
         self.download()
-        attempt = f"Attempt #{self.load_attempts} to load" if self.load_attempts > 1 else "Loading"
+        attempt = f"Attempt #{self.load_attempts + 1} to load" if self.load_attempts else "Loading"
         log.info(f"{attempt} {self.model_type.replace('-', ' ')} model '{self.model_name}' to memory")
         self.session = self._load()
         self.loaded = True
@@ -101,9 +101,6 @@ class InferenceModel(ABC):
         self.cache_dir.mkdir(parents=True, exist_ok=True)
 
     def _make_session(self, model_path: Path) -> ModelSession:
-        if not model_path.is_file():
-            raise FileNotFoundError(f"Model file not found: {model_path}")
-
         match model_path.suffix:
             case ".armnn":
                 session: ModelSession = AnnSession(model_path)
@@ -147,13 +144,17 @@ class InferenceModel(ABC):
 
     @property
     def model_format(self) -> ModelFormat:
-        return self._model_format
+        return self._preferred_format
 
     @model_format.setter
-    def model_format(self, model_format: ModelFormat) -> None:
+    def model_format(self, preferred_format: ModelFormat) -> None:
-        log.debug(f"Setting model format to {model_format}")
+        log.debug(f"Setting preferred format to {preferred_format}")
-        self._model_format = model_format
+        self._preferred_format = preferred_format
 
     @property
     def _model_format_default(self) -> ModelFormat:
-        return ModelFormat.ARMNN if ann.ann.is_available and settings.ann else ModelFormat.ONNX
+        prefer_ann = ann.ann.is_available and settings.ann
+        ann_exists = (self.model_dir / "model.armnn").is_file()
+        if prefer_ann and not ann_exists:
+            log.warning(f"ARM NN is available, but '{self.model_name}' does not support ARM NN. Falling back to ONNX.")
+        return ModelFormat.ARMNN if prefer_ann and ann_exists else ModelFormat.ONNX

machine-learning/app/models/clip/textual.py

@@ -22,12 +22,11 @@ class BaseCLIPTextualEncoder(InferenceModel):
         return res
 
     def _load(self) -> ModelSession:
-        session = super()._load()
         log.debug(f"Loading tokenizer for CLIP model '{self.model_name}'")
         self.tokenizer = self._load_tokenizer()
         log.debug(f"Loaded tokenizer for CLIP model '{self.model_name}'")
 
-        return session
+        return super()._load()
 
     @abstractmethod
     def _load_tokenizer(self) -> Tokenizer:

machine-learning/app/models/facial_recognition/detection.py

@@ -1,3 +1,4 @@
+from pathlib import Path
 from typing import Any
 
 import numpy as np
@@ -13,9 +14,15 @@ class FaceDetector(InferenceModel):
     depends = []
     identity = (ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION)
 
-    def __init__(self, model_name: str, min_score: float = 0.7, **model_kwargs: Any) -> None:
+    def __init__(
+        self,
+        model_name: str,
+        min_score: float = 0.7,
+        cache_dir: Path | str | None = None,
+        **model_kwargs: Any,
+    ) -> None:
         self.min_score = model_kwargs.pop("minScore", min_score)
-        super().__init__(model_name, **model_kwargs)
+        super().__init__(model_name, cache_dir, **model_kwargs)
 
     def _load(self) -> ModelSession:
         session = self._make_session(self.model_path)

machine-learning/app/models/facial_recognition/recognition.py

@@ -9,7 +9,7 @@ from numpy.typing import NDArray
 from onnx.tools.update_model_dims import update_inputs_outputs_dims
 from PIL import Image
 
-from app.config import log
+from app.config import clean_name, log
 from app.models.base import InferenceModel
 from app.models.transforms import decode_cv2
 from app.schemas import FaceDetectionOutput, FacialRecognitionOutput, ModelFormat, ModelSession, ModelTask, ModelType
@@ -20,14 +20,20 @@ class FaceRecognizer(InferenceModel):
     depends = [(ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION)]
     identity = (ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION)
 
-    def __init__(self, model_name: str, min_score: float = 0.7, **model_kwargs: Any) -> None:
+    def __init__(
-        super().__init__(model_name, **model_kwargs)
+        self,
+        model_name: str,
+        min_score: float = 0.7,
+        cache_dir: Path | str | None = None,
+        **model_kwargs: Any,
+    ) -> None:
+        super().__init__(clean_name(model_name), cache_dir, **model_kwargs)
         self.min_score = model_kwargs.pop("minScore", min_score)
         self.batch = self.model_format == ModelFormat.ONNX
 
     def _load(self) -> ModelSession:
         session = self._make_session(self.model_path)
-        if self.batch and not has_batch_axis(session):
+        if self.model_format == ModelFormat.ONNX and not has_batch_axis(session):
             self._add_batch_axis(self.model_path)
             session = self._make_session(self.model_path)
         self.model = ArcFaceONNX(

machine-learning/app/test_main.py

@@ -43,7 +43,7 @@ class TestBase:
 
         assert encoder.cache_dir == cache_dir
 
-    def test_sets_default_model_format(self, mocker: MockerFixture) -> None:
+    def test_sets_default_preferred_format(self, mocker: MockerFixture) -> None:
         mocker.patch.object(settings, "ann", True)
         mocker.patch("ann.ann.is_available", False)
 
@@ -51,7 +51,7 @@ class TestBase:
 
         assert encoder.model_format == ModelFormat.ONNX
 
-    def test_sets_default_model_format_to_armnn_if_available(self, path: mock.Mock, mocker: MockerFixture) -> None:
+    def test_sets_default_preferred_format_to_armnn_if_available(self, path: mock.Mock, mocker: MockerFixture) -> None:
         mocker.patch.object(settings, "ann", True)
         mocker.patch("ann.ann.is_available", True)
         path.suffix = ".armnn"
@@ -60,11 +60,11 @@ class TestBase:
 
         assert encoder.model_format == ModelFormat.ARMNN
 
-    def test_sets_model_format_kwarg(self, mocker: MockerFixture) -> None:
+    def test_sets_preferred_format_kwarg(self, mocker: MockerFixture) -> None:
         mocker.patch.object(settings, "ann", False)
         mocker.patch("ann.ann.is_available", False)
 
-        encoder = OpenClipTextualEncoder("ViT-B-32__openai", model_format=ModelFormat.ARMNN)
+        encoder = OpenClipTextualEncoder("ViT-B-32__openai", preferred_format=ModelFormat.ARMNN)
 
         assert encoder.model_format == ModelFormat.ARMNN
 
@@ -129,7 +129,7 @@ class TestBase:
         )
 
     def test_download_downloads_armnn_if_preferred_format(self, snapshot_download: mock.Mock) -> None:
-        encoder = OpenClipTextualEncoder("ViT-B-32__openai", model_format=ModelFormat.ARMNN)
+        encoder = OpenClipTextualEncoder("ViT-B-32__openai", preferred_format=ModelFormat.ARMNN)
         encoder.download()
 
         snapshot_download.assert_called_once_with(
@@ -140,19 +140,6 @@ class TestBase:
             ignore_patterns=[],
         )
 
-    def test_throws_exception_if_model_path_does_not_exist(
-        self, snapshot_download: mock.Mock, ort_session: mock.Mock, path: mock.Mock
-    ) -> None:
-        path.return_value.__truediv__.return_value.__truediv__.return_value.is_file.return_value = False
-
-        encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=path)
-
-        with pytest.raises(FileNotFoundError):
-            encoder.load()
-
-        snapshot_download.assert_called_once()
-        ort_session.assert_not_called()
-
 
 @pytest.mark.usefixtures("ort_session")
 class TestOrtSession:
@@ -480,18 +467,16 @@ class TestFaceRecognition:
         assert isinstance(call_args[0][0], np.ndarray)
         assert call_args[0][0].shape == (112, 112, 3)
 
-    def test_recognition_adds_batch_axis_for_ort(
+    def test_recognition_adds_batch_axis_for_ort(self, ort_session: mock.Mock, mocker: MockerFixture) -> None:
-        self, ort_session: mock.Mock, path: mock.Mock, mocker: MockerFixture
-    ) -> None:
         onnx = mocker.patch("app.models.facial_recognition.recognition.onnx", autospec=True)
         update_dims = mocker.patch(
             "app.models.facial_recognition.recognition.update_inputs_outputs_dims", autospec=True
         )
         mocker.patch("app.models.base.InferenceModel.download")
         mocker.patch("app.models.facial_recognition.recognition.ArcFaceONNX")
+
         ort_session.return_value.get_inputs.return_value = [SimpleNamespace(name="input.1", shape=(1, 3, 224, 224))]
         ort_session.return_value.get_outputs.return_value = [SimpleNamespace(name="output.1", shape=(1, 800))]
-        path.return_value.__truediv__.return_value.__truediv__.return_value.suffix = ".onnx"
 
         proto = mock.Mock()
 
@@ -507,30 +492,27 @@ class TestFaceRecognition:
 
         onnx.load.return_value = proto
 
-        face_recognizer = FaceRecognizer("buffalo_s", cache_dir=path)
+        face_recognizer = FaceRecognizer("buffalo_s")
         face_recognizer.load()
 
         assert face_recognizer.batch is True
         update_dims.assert_called_once_with(proto, {"input.1": ["batch", 3, 224, 224]}, {"output.1": ["batch", 800]})
         onnx.save.assert_called_once_with(update_dims.return_value, face_recognizer.model_path)
 
-    def test_recognition_does_not_add_batch_axis_if_exists(
+    def test_recognition_does_not_add_batch_axis_if_exists(self, ort_session: mock.Mock, mocker: MockerFixture) -> None:
-        self, ort_session: mock.Mock, path: mock.Mock, mocker: MockerFixture
-    ) -> None:
         onnx = mocker.patch("app.models.facial_recognition.recognition.onnx", autospec=True)
         update_dims = mocker.patch(
             "app.models.facial_recognition.recognition.update_inputs_outputs_dims", autospec=True
         )
         mocker.patch("app.models.base.InferenceModel.download")
         mocker.patch("app.models.facial_recognition.recognition.ArcFaceONNX")
-        path.return_value.__truediv__.return_value.__truediv__.return_value.suffix = ".onnx"
 
         inputs = [SimpleNamespace(name="input.1", shape=("batch", 3, 224, 224))]
         outputs = [SimpleNamespace(name="output.1", shape=("batch", 800))]
         ort_session.return_value.get_inputs.return_value = inputs
         ort_session.return_value.get_outputs.return_value = outputs
 
-        face_recognizer = FaceRecognizer("buffalo_s", cache_dir=path)
+        face_recognizer = FaceRecognizer("buffalo_s")
         face_recognizer.load()
 
         assert face_recognizer.batch is True
@@ -538,30 +520,6 @@ class TestFaceRecognition:
         onnx.load.assert_not_called()
         onnx.save.assert_not_called()
 
-    def test_recognition_does_not_add_batch_axis_for_armnn(
-        self, ann_session: mock.Mock, path: mock.Mock, mocker: MockerFixture
-    ) -> None:
-        onnx = mocker.patch("app.models.facial_recognition.recognition.onnx", autospec=True)
-        update_dims = mocker.patch(
-            "app.models.facial_recognition.recognition.update_inputs_outputs_dims", autospec=True
-        )
-        mocker.patch("app.models.base.InferenceModel.download")
-        mocker.patch("app.models.facial_recognition.recognition.ArcFaceONNX")
-        path.return_value.__truediv__.return_value.__truediv__.return_value.suffix = ".armnn"
-
-        inputs = [SimpleNamespace(name="input.1", shape=("batch", 3, 224, 224))]
-        outputs = [SimpleNamespace(name="output.1", shape=("batch", 800))]
-        ann_session.return_value.get_inputs.return_value = inputs
-        ann_session.return_value.get_outputs.return_value = outputs
-
-        face_recognizer = FaceRecognizer("buffalo_s", model_format=ModelFormat.ARMNN, cache_dir=path)
-        face_recognizer.load()
-
-        assert face_recognizer.batch is False
-        update_dims.assert_not_called()
-        onnx.load.assert_not_called()
-        onnx.save.assert_not_called()
-
 
 @pytest.mark.asyncio
 class TestCache:
@@ -735,7 +693,7 @@ class TestLoad:
         mock_model.clear_cache.assert_called_once()
         assert mock_model.load.call_count == 2
 
-    async def test_load_raises_if_os_error_and_already_retried(self) -> None:
+    async def test_load_clears_cache_and_raises_if_os_error_and_already_retried(self) -> None:
         mock_model = mock.Mock(spec=InferenceModel)
         mock_model.model_name = "test_model_name"
         mock_model.model_type = ModelType.VISUAL
@@ -749,27 +707,6 @@ class TestLoad:
         mock_model.clear_cache.assert_not_called()
         mock_model.load.assert_not_called()
 
-    async def test_falls_back_to_onnx_if_other_format_does_not_exist(
-        self, exception: mock.Mock, warning: mock.Mock
-    ) -> None:
-        mock_model = mock.Mock(spec=InferenceModel)
-        mock_model.model_name = "test_model_name"
-        mock_model.model_type = ModelType.VISUAL
-        mock_model.model_task = ModelTask.SEARCH
-        mock_model.model_format = ModelFormat.ARMNN
-        mock_model.loaded = False
-        mock_model.load_attempts = 0
-        error = FileNotFoundError()
-        mock_model.load.side_effect = [error, None]
-
-        await load(mock_model)
-
-        mock_model.clear_cache.assert_not_called()
-        assert mock_model.load.call_count == 2
-        exception.assert_called_once_with(error)
-        warning.assert_called_once_with("ARMNN is available, but model 'test_model_name' does not support it.")
-        mock_model.model_format = ModelFormat.ONNX
-
 
 @pytest.mark.skipif(
     not settings.test_full,

machine-learning/export/ann/Dockerfile

@@ -1,35 +0,0 @@
-FROM mambaorg/micromamba:bookworm-slim@sha256:333f7598ff2c2400fb10bfe057709c68b7daab5d847143af85abcf224a07271a as builder
-
-USER root
-RUN apt-get update && apt-get install -y --no-install-recommends \
-  build-essential \
-  cmake \
-  curl \
-  git
-USER $MAMBA_USER
-
-WORKDIR /home/mambauser
-ENV ARMNN_PATH=armnn
-COPY --chown=$MAMBA_USER:$MAMBA_USER scripts/* .
-RUN ./download-armnn.sh && \
-  ./build-converter.sh && \
-  ./build.sh
-
-COPY --chown=$MAMBA_USER:$MAMBA_USER conda-lock.yml .
-RUN micromamba create -y -p /home/mambauser/venv -f conda-lock.yml && \
-  micromamba clean --all --yes
-ENV PATH="/home/mambauser/venv/bin:${PATH}"
-
-FROM gcr.io/distroless/base-debian12
-# FROM mambaorg/micromamba:bookworm-slim@sha256:333f7598ff2c2400fb10bfe057709c68b7daab5d847143af85abcf224a07271a
-
-WORKDIR /export/ann
-ENV PYTHONDONTWRITEBYTECODE=1 \
-  LD_LIBRARY_PATH=/export/ann/armnn \
-  PATH="/opt/venv/bin:${PATH}"
-
-COPY --from=builder /home/mambauser/armnnconverter /home/mambauser/armnn ./
-COPY --from=builder /home/mambauser/venv /opt/venv
-COPY --chown=$MAMBA_USER:$MAMBA_USER onnx2ann onnx2ann
-
-ENTRYPOINT ["python", "-m", "onnx2ann"]

machine-learning/export/ann/env.yaml

@@ -1,21 +0,0 @@
-name: onnx2ann
-channels:
-  - conda-forge
-dependencies:
-  - python>=3.11,<4.0
-  - onnx>=1.16.1
-  # - onnxruntime>=1.18.1 # conda only has gpu version
-  - psutil>=6.0.0
-  - flatbuffers>=24.3.25
-  - ml_dtypes>=0.3.1
-  - typer-slim>=0.12.3
-  - huggingface_hub>=0.23.4
-  - pip
-  - pip:
-    - onnxruntime>=1.18.1 # conda only has gpu version
-    - onnxsim>=0.4.36
-    - onnx2tf>=1.24.1
-    - onnx_graphsurgeon>=0.5.2
-    - simple_onnx_processing_tools>=1.1.32
-    - tf_keras>=2.16.0
-    - git+https://github.com/microsoft/onnxconverter-common.git

machine-learning/export/ann/onnx2ann/__main__.py

@@ -1,99 +0,0 @@
-import os
-import platform
-from typing import Annotated, Optional
-
-import typer
-
-from onnx2ann.export import Exporter, ModelType, Precision
-
-app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False)
-
-
-@app.command()
-def export(
-    model_name: Annotated[
-        str, typer.Argument(..., help="The name of the model to be exported as it exists in Hugging Face.")
-    ],
-    model_type: Annotated[ModelType, typer.Option(..., "--type", "-t", help="The type of model to be exported.")],
-    input_shapes: Annotated[
-        list[str],
-        typer.Option(
-            ...,
-            "--input-shape",
-            "-s",
-            help="The shape of an input tensor to the model, each dimension separated by commas. "
-            "Multiple shapes can be provided for multiple inputs.",
-        ),
-    ],
-    precision: Annotated[
-        Precision,
-        typer.Option(
-            ...,
-            "--precision",
-            "-p",
-            help="The precision of the exported model. `float16` requires a GPU.",
-        ),
-    ] = Precision.FLOAT32,
-    cache_dir: Annotated[
-        str,
-        typer.Option(
-            ...,
-            "--cache-dir",
-            "-c",
-            help="Directory where pre-export models will be stored.",
-            envvar="CACHE_DIR",
-            show_envvar=True,
-        ),
-    ] = "~/.cache/huggingface",
-    output_dir: Annotated[
-        str,
-        typer.Option(
-            ...,
-            "--output-dir",
-            "-o",
-            help="Directory where exported models will be stored.",
-        ),
-    ] = "output",
-    auth_token: Annotated[
-        Optional[str],
-        typer.Option(
-            ...,
-            "--auth-token",
-            "-t",
-            help="If uploading models to Hugging Face, the auth token of the user or organisation.",
-            envvar="HF_AUTH_TOKEN",
-            show_envvar=True,
-        ),
-    ] = None,
-    force_export: Annotated[
-        bool,
-        typer.Option(
-            ...,
-            "--force-export",
-            "-f",
-            help="Export the model even if an exported model already exists in the output directory.",
-        ),
-    ] = False,
-) -> None:
-    if platform.machine() not in ("x86_64", "AMD64"):
-        msg = f"Can only run on x86_64 / AMD64, not {platform.machine()}"
-        raise RuntimeError(msg)
-    os.environ.setdefault("LD_LIBRARY_PATH", "armnn")
-    parsed_input_shapes = [tuple(map(int, shape.split(","))) for shape in input_shapes]
-    model = Exporter(
-        model_name, model_type, input_shapes=parsed_input_shapes, cache_dir=cache_dir, force_export=force_export
-    )
-    model_dir = os.path.join("output", model_name)
-    output_dir = os.path.join(model_dir, model_type)
-    armnn_model = model.to_armnn(output_dir, precision)
-
-    if not auth_token:
-        return
-
-    from huggingface_hub import upload_file
-
-    relative_path = os.path.relpath(armnn_model, start=model_dir)
-    upload_file(path_or_fileobj=armnn_model, path_in_repo=relative_path, repo_id=model.repo_name, token=auth_token)
-
-
-app()

machine-learning/export/ann/onnx2ann/export.py

@@ -1,129 +0,0 @@
-import os
-import subprocess
-from enum import StrEnum
-
-from onnx2ann.helpers import onnx_make_armnn_compatible, onnx_make_inputs_fixed
-
-
-class ModelType(StrEnum):
-    VISUAL = "visual"
-    TEXTUAL = "textual"
-    RECOGNITION = "recognition"
-    DETECTION = "detection"
-
-
-class Precision(StrEnum):
-    FLOAT16 = "float16"
-    FLOAT32 = "float32"
-
-
-class Exporter:
-    def __init__(
-        self,
-        model_name: str,
-        model_type: str,
-        input_shapes: list[tuple[int, ...]],
-        optimization_level: int = 5,
-        cache_dir: str = os.environ.get("CACHE_DIR", "~/.cache/huggingface"),
-        force_export: bool = False,
-    ):
-        self.model_name = model_name.split("/")[-1]
-        self.model_type = model_type
-        self.optimize = optimization_level
-        self.input_shapes = input_shapes
-        self.cache_dir = os.path.join(cache_dir, self.repo_name)
-        self.force_export = force_export
-
-    def download(self) -> str:
-        model_path = os.path.join(self.cache_dir, self.model_type, "model.onnx")
-        if os.path.isfile(model_path):
-            print(f"Model is already downloaded at {model_path}")
-            return model_path
-        from huggingface_hub import snapshot_download
-
-        snapshot_download(
-            self.repo_name, cache_dir=self.cache_dir, local_dir=self.cache_dir, local_dir_use_symlinks=False
-        )
-        return model_path
-
-    def to_onnx_static(self, precision: Precision) -> str:
-        import onnx
-        from onnxconverter_common import float16
-        onnx_path_original = self.download()
-        static_dir = os.path.join(self.cache_dir, self.model_type, "static")
-
-        static_path = os.path.join(static_dir, f"model.onnx")
-        if self.force_export and not os.path.isfile(static_path):
-            print(f"Making {self} static")
-            os.makedirs(static_dir, exist_ok=True)
-            onnx_make_inputs_fixed(onnx_path_original, static_path, self.input_shapes)
-            onnx_make_armnn_compatible(static_path)
-            print(f"Finished making {self} static")
-
-        model = onnx.load(static_path)
-        self.inputs = [input_.name for input_ in model.graph.input]
-        self.outputs = [output_.name for output_ in model.graph.output]
-        if precision == Precision.FLOAT16:
-            static_path = os.path.join(static_dir, f"model_{precision}.onnx")
-            print(f"Converting {self} to {precision} precision")
-            model = float16.convert_float_to_float16(model, keep_io_types=True, disable_shape_infer=True)
-            onnx.save(model, static_path)
-            print(f"Finished converting {self} to {precision} precision")
-        # self.inputs, self.outputs = onnx_get_inputs_outputs(static_path)
-        return static_path
-
-    def to_tflite(self, output_dir: str, precision: Precision) -> str:
-        onnx_model = self.to_onnx_static(precision)
-        tflite_dir = os.path.join(output_dir, precision)
-        tflite_model = os.path.join(tflite_dir, f"model_{precision}.tflite")
-        if self.force_export or not os.path.isfile(tflite_model):
-            import onnx2tf
-
-            print(f"Exporting {self} to TFLite with {precision} precision (this might take a few minutes)")
-            onnx2tf.convert(
-                input_onnx_file_path=onnx_model,
-                output_folder_path=tflite_dir,
-                keep_shape_absolutely_input_names=self.inputs,
-                # verbosity="warn",
-                copy_onnx_input_output_names_to_tflite=True,
-                output_signaturedefs=True,
-                not_use_onnxsim=True,
-            )
-            print(f"Finished exporting {self} to TFLite with {precision} precision")
-
-        return tflite_model
-
-    def to_armnn(self, output_dir: str, precision: Precision) -> tuple[str, str]:
-        armnn_model = os.path.join(output_dir, "model.armnn")
-        if not self.force_export and os.path.isfile(armnn_model):
-            return armnn_model
-
-        tflite_model_dir = os.path.join(output_dir, "tflite")
-        tflite_model = self.to_tflite(tflite_model_dir, precision)
-
-        args = ["./armnnconverter", "-f", "tflite-binary", "-m", tflite_model, "-p", armnn_model]
-        args.append("-i")
-        args.extend(self.inputs)
-        args.append("-o")
-        args.extend(self.outputs)
-
-        print(f"Exporting {self} to ARM NN with {precision} precision")
-        try:
-            if (stdout := subprocess.check_output(args, stderr=subprocess.STDOUT).decode()):
-                print(stdout)
-            print(f"Finished exporting {self} to ARM NN with {precision} precision")
-        except subprocess.CalledProcessError as e:
-            print(e.output.decode())
-            try:
-                from shutil import rmtree
-
-                rmtree(tflite_model_dir, ignore_errors=True)
-            finally:
-                raise e
-
-    @property
-    def repo_name(self) -> str:
-        return f"immich-app/{self.model_name}"
-
-    def __repr__(self) -> str:
-        return f"{self.model_name} ({self.model_type})"

machine-learning/export/ann/onnx2ann/helpers.py

@@ -1,260 +0,0 @@
-from typing import Any
-
-
-def onnx_make_armnn_compatible(model_path: str) -> None:
-    """
-    i can explain
-    armnn only supports up to 4d tranposes, but the model has a 5d transpose due to a redundant unsqueeze
-    this function folds the unsqueeze+transpose+squeeze into a single 4d transpose
-    it also switches from gather ops to slices since armnn has different dimension semantics for gathers
-    also fixes batch normalization being in training mode
-    """
-
-    import numpy as np
-    import onnx
-    from onnx_graphsurgeon import Constant, Node, Variable, export_onnx, import_onnx
-
-    proto = onnx.load(model_path)
-    graph = import_onnx(proto)
-
-    gather_idx = 1
-    squeeze_idx = 1
-    for node in graph.nodes:
-        for link1 in node.outputs:
-            if "Unsqueeze" in link1.name:
-                for node1 in link1.outputs:
-                    for link2 in node1.outputs:
-                        if "Transpose" in link2.name:
-                            for node2 in link2.outputs:
-                                if node2.attrs.get("perm") == [3, 1, 2, 0, 4]:
-                                    node2.attrs["perm"] = [2, 0, 1, 3]
-                                    link2.shape = link1.shape
-                                    for link3 in node2.outputs:
-                                        if "Squeeze" in link3.name:
-                                            link3.shape = [link3.shape[x] for x in [0, 1, 2, 4]]
-                                            for node3 in link3.outputs:
-                                                for link4 in node3.outputs:
-                                                    link4.shape = link3.shape
-                                                    try:
-                                                        idx = link2.inputs.index(node1)
-                                                        link2.inputs[idx] = node
-                                                    except ValueError:
-                                                        pass
-
-                                                    node.outputs = [link2]
-                                                    if "Gather" in link4.name:
-                                                        for node4 in link4.outputs:
-                                                            axis = node1.attrs.get("axis", 0)
-                                                            index = node4.inputs[1].values
-                                                            slice_link = Variable(
-                                                                f"onnx::Slice_123{gather_idx}",
-                                                                dtype=link4.dtype,
-                                                                shape=[1] + link3.shape[1:],
-                                                            )
-                                                            slice_node = Node(
-                                                                op="Slice",
-                                                                inputs=[
-                                                                    link3,
-                                                                    Constant(
-                                                                        f"SliceStart_123{gather_idx}",
-                                                                        np.array([index]),
-                                                                    ),
-                                                                    Constant(
-                                                                        f"SliceEnd_123{gather_idx}",
-                                                                        np.array([index + 1]),
-                                                                    ),
-                                                                    Constant(
-                                                                        f"SliceAxis_123{gather_idx}",
-                                                                        np.array([axis]),
-                                                                    ),
-                                                                ],
-                                                                outputs=[slice_link],
-                                                                name=f"Slice_123{gather_idx}",
-                                                            )
-                                                            graph.nodes.append(slice_node)
-                                                            gather_idx += 1
-
-                                                            for link5 in node4.outputs:
-                                                                for node5 in link5.outputs:
-                                                                    try:
-                                                                        idx = node5.inputs.index(link5)
-                                                                        node5.inputs[idx] = slice_link
-                                                                    except ValueError:
-                                                                        pass
-            elif node.op == "LayerNormalization":
-                for node1 in link1.outputs:
-                    if node1.op == "Gather":
-                        for link2 in node1.outputs:
-                            for node2 in link2.outputs:
-                                axis = node1.attrs.get("axis", 0)
-                                index = node1.inputs[1].values
-                                slice_link = Variable(
-                                    f"onnx::Slice_123{gather_idx}",
-                                    dtype=link2.dtype,
-                                    shape=[1, *link2.shape],
-                                )
-                                slice_node = Node(
-                                    op="Slice",
-                                    inputs=[
-                                        node1.inputs[0],
-                                        Constant(
-                                            f"SliceStart_123{gather_idx}",
-                                            np.array([index]),
-                                        ),
-                                        Constant(
-                                            f"SliceEnd_123{gather_idx}",
-                                            np.array([index + 1]),
-                                        ),
-                                        Constant(
-                                            f"SliceAxis_123{gather_idx}",
-                                            np.array([axis]),
-                                        ),
-                                    ],
-                                    outputs=[slice_link],
-                                    name=f"Slice_123{gather_idx}",
-                                )
-                                graph.nodes.append(slice_node)
-                                gather_idx += 1
-
-                                squeeze_link = Variable(
-                                    f"onnx::Squeeze_123{squeeze_idx}",
-                                    dtype=link2.dtype,
-                                    shape=link2.shape,
-                                )
-                                squeeze_node = Node(
-                                    op="Squeeze",
-                                    inputs=[
-                                        slice_link,
-                                        Constant(
-                                            f"SqueezeAxis_123{squeeze_idx}",
-                                            np.array([0]),
-                                        ),
-                                    ],
-                                    outputs=[squeeze_link],
-                                    name=f"Squeeze_123{squeeze_idx}",
-                                )
-                                graph.nodes.append(squeeze_node)
-                                squeeze_idx += 1
-                                try:
-                                    idx = node2.inputs.index(link2)
-                                    node2.inputs[idx] = squeeze_link
-                                except ValueError:
-                                    pass
-            elif node.op == "Reshape":
-                for node1 in link1.outputs:
-                    if node1.op == "Gather":
-                        node2s = [n for link in node1.outputs for n in link.outputs]
-                        if any(n.op == "Abs" for n in node2s):
-                            axis = node1.attrs.get("axis", 0)
-                            index = node1.inputs[1].values
-                            slice_link = Variable(
-                                f"onnx::Slice_123{gather_idx}",
-                                dtype=node1.outputs[0].dtype,
-                                shape=[1, *node1.outputs[0].shape],
-                            )
-                            slice_node = Node(
-                                op="Slice",
-                                inputs=[
-                                    node1.inputs[0],
-                                    Constant(
-                                        f"SliceStart_123{gather_idx}",
-                                        np.array([index]),
-                                    ),
-                                    Constant(
-                                        f"SliceEnd_123{gather_idx}",
-                                        np.array([index + 1]),
-                                    ),
-                                    Constant(
-                                        f"SliceAxis_123{gather_idx}",
-                                        np.array([axis]),
-                                    ),
-                                ],
-                                outputs=[slice_link],
-                                name=f"Slice_123{gather_idx}",
-                            )
-                            graph.nodes.append(slice_node)
-                            gather_idx += 1
-
-                            squeeze_link = Variable(
-                                f"onnx::Squeeze_123{squeeze_idx}",
-                                dtype=node1.outputs[0].dtype,
-                                shape=node1.outputs[0].shape,
-                            )
-                            squeeze_node = Node(
-                                op="Squeeze",
-                                inputs=[
-                                    slice_link,
-                                    Constant(
-                                        f"SqueezeAxis_123{squeeze_idx}",
-                                        np.array([0]),
-                                    ),
-                                ],
-                                outputs=[squeeze_link],
-                                name=f"Squeeze_123{squeeze_idx}",
-                            )
-                            graph.nodes.append(squeeze_node)
-                            squeeze_idx += 1
-                            for node2 in node2s:
-                                node2.inputs[0] = squeeze_link
-            elif node.op == "BatchNormalization" and node.attrs.get("training_mode") == 1:
-                node.attrs["training_mode"] = 0
-                node.outputs = node.outputs[:1]
-
-    graph.cleanup(remove_unused_node_outputs=True, recurse_subgraphs=True, recurse_functions=True)
-    graph.toposort()
-    graph.fold_constants()
-    updated = export_onnx(graph)
-    onnx_save(updated, model_path)
-
-    # for some reason, reloading the model is necessary to apply the correct shape
-    proto = onnx.load(model_path)
-    graph = import_onnx(proto)
-    for node in graph.nodes:
-        if node.op == "Slice":
-            for link in node.outputs:
-                if "Slice_123" in link.name and link.shape[0] == 3:  # noqa: PLR2004
-                    link.shape[0] = 1
-
-    graph.cleanup(remove_unused_node_outputs=True, recurse_subgraphs=True, recurse_functions=True)
-    graph.toposort()
-    graph.fold_constants()
-    updated = export_onnx(graph)
-    onnx_save(updated, model_path)
-    onnx.shape_inference.infer_shapes_path(model_path, check_type=True, strict_mode=True, data_prop=True)
-
-
-def onnx_make_inputs_fixed(input_path: str, output_path: str, input_shapes: list[tuple[int, ...]]) -> None:
-    import onnx
-    import onnxsim
-    from onnxruntime.tools.onnx_model_utils import fix_output_shapes, make_input_shape_fixed
-
-    model, success = onnxsim.simplify(input_path)
-    if not success:
-        msg = f"Failed to simplify {input_path}"
-        raise RuntimeError(msg)
-    onnx_save(model, output_path)
-    onnx.shape_inference.infer_shapes_path(output_path, check_type=True, strict_mode=True, data_prop=True)
-    model = onnx.load_model(output_path)
-    for input_node, shape in zip(model.graph.input, input_shapes, strict=False):
-        make_input_shape_fixed(model.graph, input_node.name, shape)
-    fix_output_shapes(model)
-    onnx_save(model, output_path)
-    onnx.shape_inference.infer_shapes_path(output_path, check_type=True, strict_mode=True, data_prop=True)
-
-
-def onnx_get_inputs_outputs(model_path: str) -> tuple[list[str], list[str]]:
-    import onnx
-
-    model = onnx.load(model_path)
-    inputs = [input_.name for input_ in model.graph.input]
-    outputs = [output_.name for output_ in model.graph.output]
-    return inputs, outputs
-
-
-def onnx_save(model: Any, output_path: str) -> None:
-    import onnx
-
-    try:
-        onnx.save(model, output_path)
-    except:
-        onnx.save(model, output_path, save_as_external_data=True, all_tensors_to_one_file=False, size_threshold=1_000_000)

machine-learning/export/ann/pyproject.toml

@@ -1,56 +0,0 @@
-[project]
-name = "onnx2ann"
-version = "1.107.2"
-dependencies = [
-    "onnx>=1.16.1",
-    "psutil>=6.0.0",
-    "flatbuffers>=24.3.25",
-    "ml_dtypes>=0.3.1,<1.0.0",
-    "typer-slim>=0.12.3,<1.0.0",
-    "huggingface_hub>=0.23.4,<1.0.0",
-    "onnxruntime>=1.18.1",
-    "onnxsim>=0.4.36,<1.0.0",
-    "onnx2tf>=1.24.0",
-    "onnx_graphsurgeon>=0.5.2,<1.0.0",
-    "simple_onnx_processing_tools>=1.1.32",
-    "tf_keras>=2.16.0",
-    "onnxconverter-common @ git+https://github.com/microsoft/onnxconverter-common"
-]
-requires-python = ">=3.11"
-
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[tool.hatch.build.targets.sdist]
-only-include = ["onnx2ann"]
-
-[tool.hatch.metadata]
-allow-direct-references = true
-
-[tool.mypy]
-python_version = "3.12"
-follow_imports = "silent"
-warn_redundant_casts = true
-disallow_any_generics = true
-check_untyped_defs = true
-disallow_untyped_defs = true
-ignore_missing_imports = true
-
-[tool.pydantic-mypy]
-init_forbid_extra = true
-init_typed = true
-warn_required_dynamic_aliases = true
-warn_untyped_fields = true
-
-[tool.ruff]
-line-length = 120
-target-version = "py312"
-
-[tool.ruff.lint]
-extend-select = ["E", "F", "I"]
-extend-ignore = ["FBT001", "FBT002"]
-
-[tool.black]
-line-length = 120
-target-version = ['py312']

machine-learning/export/ann/scripts/ann.cpp

@@ -1,281 +0,0 @@
-#include <fstream>
-#include <mutex>
-#include <atomic>
-
-#include "armnn/IRuntime.hpp"
-#include "armnn/INetwork.hpp"
-#include "armnn/Types.hpp"
-#include "armnnDeserializer/IDeserializer.hpp"
-#include "armnnTfLiteParser/ITfLiteParser.hpp"
-#include "armnnOnnxParser/IOnnxParser.hpp"
-
-using namespace armnn;
-
-struct IOInfos
-{
-    std::vector<BindingPointInfo> inputInfos;
-    std::vector<BindingPointInfo> outputInfos;
-};
-
-// from https://rigtorp.se/spinlock/
-struct SpinLock
-{
-    std::atomic<bool> lock_ = {false};
-
-    void lock()
-    {
-        for (;;)
-        {
-            if (!lock_.exchange(true, std::memory_order_acquire))
-            {
-                break;
-            }
-            while (lock_.load(std::memory_order_relaxed))
-                ;
-        }
-    }
-
-    void unlock() { lock_.store(false, std::memory_order_release); }
-};
-
-class Ann
-{
-
-public:
-    int load(const char *modelPath,
-             bool fastMath,
-             bool fp16,
-             bool saveCachedNetwork,
-             const char *cachedNetworkPath)
-    {
-        INetworkPtr network = loadModel(modelPath);
-        IOptimizedNetworkPtr optNet = OptimizeNetwork(network.get(), fastMath, fp16, saveCachedNetwork, cachedNetworkPath);
-        const IOInfos infos = getIOInfos(optNet.get());
-        NetworkId netId;
-        mutex.lock();
-        Status status = runtime->LoadNetwork(netId, std::move(optNet));
-        mutex.unlock();
-        if (status != Status::Success)
-        {
-            return -1;
-        }
-        spinLock.lock();
-        ioInfos[netId] = infos;
-        mutexes.emplace(netId, std::make_unique<std::mutex>());
-        spinLock.unlock();
-        return netId;
-    }
-
-    void execute(NetworkId netId, const void **inputData, void **outputData)
-    {
-        spinLock.lock();
-        const IOInfos *infos = &ioInfos[netId];
-        auto m = mutexes[netId].get();
-        spinLock.unlock();
-        InputTensors inputTensors;
-        inputTensors.reserve(infos->inputInfos.size());
-        size_t i = 0;
-        for (const BindingPointInfo &info : infos->inputInfos)
-            inputTensors.emplace_back(info.first, ConstTensor(info.second, inputData[i++]));
-        OutputTensors outputTensors;
-        outputTensors.reserve(infos->outputInfos.size());
-        i = 0;
-        for (const BindingPointInfo &info : infos->outputInfos)
-            outputTensors.emplace_back(info.first, Tensor(info.second, outputData[i++]));
-        m->lock();
-        runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-        m->unlock();
-    }
-
-    void unload(NetworkId netId)
-    {
-        mutex.lock();
-        runtime->UnloadNetwork(netId);
-        mutex.unlock();
-    }
-
-    int tensors(NetworkId netId, bool isInput = false)
-    {
-        spinLock.lock();
-        const IOInfos *infos = &ioInfos[netId];
-        spinLock.unlock();
-        return (int)(isInput ? infos->inputInfos.size() : infos->outputInfos.size());
-    }
-
-    unsigned long shape(NetworkId netId, bool isInput = false, int index = 0)
-    {
-        spinLock.lock();
-        const IOInfos *infos = &ioInfos[netId];
-        spinLock.unlock();
-        const TensorShape shape = (isInput ? infos->inputInfos : infos->outputInfos)[index].second.GetShape();
-        unsigned long s = 0;
-        for (unsigned int d = 0; d < shape.GetNumDimensions(); d++)
-            s |= ((unsigned long)shape[d]) << (d * 16); // stores up to 4 16-bit values in a 64-bit value
-        return s;
-    }
-
-    Ann(int tuningLevel, const char *tuningFile)
-    {
-        IRuntime::CreationOptions runtimeOptions;
-        BackendOptions backendOptions{"GpuAcc",
-                                      {
-                                          {"TuningLevel", tuningLevel},
-                                          {"MemoryOptimizerStrategy", "ConstantMemoryStrategy"}, // SingleAxisPriorityList or ConstantMemoryStrategy
-                                      }};
-        if (tuningFile)
-            backendOptions.AddOption({"TuningFile", tuningFile});
-        runtimeOptions.m_BackendOptions.emplace_back(backendOptions);
-        runtime = IRuntime::CreateRaw(runtimeOptions);
-    };
-    ~Ann()
-    {
-        IRuntime::Destroy(runtime);
-    };
-
-private:
-    INetworkPtr loadModel(const char *modelPath)
-    {
-        const auto path = std::string(modelPath);
-        if (path.rfind(".tflite") == path.length() - 7) // endsWith()
-        {
-            auto parser = armnnTfLiteParser::ITfLiteParser::CreateRaw();
-            return parser->CreateNetworkFromBinaryFile(modelPath);
-        }
-        else if (path.rfind(".onnx") == path.length() - 5) // endsWith()
-        {
-            auto parser = armnnOnnxParser::IOnnxParser::CreateRaw();
-            return parser->CreateNetworkFromBinaryFile(modelPath);
-        }
-        else
-        {
-            std::ifstream ifs(path, std::ifstream::in | std::ifstream::binary);
-            auto parser = armnnDeserializer::IDeserializer::CreateRaw();
-            return parser->CreateNetworkFromBinary(ifs);
-        }
-    }
-
-    static BindingPointInfo getInputTensorInfo(LayerBindingId inputBindingId, TensorInfo info)
-    {
-        const auto newInfo = TensorInfo{info.GetShape(), info.GetDataType(),
-                                        info.GetQuantizationScale(),
-                                        info.GetQuantizationOffset(),
-                                        true};
-        return {inputBindingId, newInfo};
-    }
-
-    IOptimizedNetworkPtr OptimizeNetwork(INetwork *network, bool fastMath, bool fp16, bool saveCachedNetwork, const char *cachedNetworkPath)
-    {
-        const bool allowExpandedDims = false;
-        const ShapeInferenceMethod shapeInferenceMethod = ShapeInferenceMethod::ValidateOnly;
-
-        OptimizerOptionsOpaque options;
-        options.SetReduceFp32ToFp16(fp16);
-        options.SetShapeInferenceMethod(shapeInferenceMethod);
-        options.SetAllowExpandedDims(allowExpandedDims);
-
-        BackendOptions gpuAcc("GpuAcc", {{"FastMathEnabled", fastMath}});
-        if (cachedNetworkPath)
-        {
-            gpuAcc.AddOption({"SaveCachedNetwork", saveCachedNetwork});
-            gpuAcc.AddOption({"CachedNetworkFilePath", cachedNetworkPath});
-        }
-        options.AddModelOption(gpuAcc);
-
-        // No point in using ARMNN for CPU, use ONNX (quantized) instead.
-        // BackendOptions cpuAcc("CpuAcc",
-        //                       {
-        //                           {"FastMathEnabled", fastMath},
-        //                           {"NumberOfThreads", 0},
-        //                       });
-        // options.AddModelOption(cpuAcc);
-
-        BackendOptions allowExDimOpt("AllowExpandedDims",
-                                     {{"AllowExpandedDims", allowExpandedDims}});
-        options.AddModelOption(allowExDimOpt);
-        BackendOptions shapeInferOpt("ShapeInferenceMethod",
-                                     {{"InferAndValidate", shapeInferenceMethod == ShapeInferenceMethod::InferAndValidate}});
-        options.AddModelOption(shapeInferOpt);
-
-        std::vector<BackendId> backends = {
-            BackendId("GpuAcc"),
-            // BackendId("CpuAcc"),
-            // BackendId("CpuRef"),
-        };
-        return Optimize(*network, backends, runtime->GetDeviceSpec(), options);
-    }
-
-    IOInfos getIOInfos(IOptimizedNetwork *optNet)
-    {
-        struct InfoStrategy : IStrategy
-        {
-            void ExecuteStrategy(const IConnectableLayer *layer,
-                                 const BaseDescriptor &descriptor,
-                                 const std::vector<ConstTensor> &constants,
-                                 const char *name,
-                                 const LayerBindingId id = 0) override
-            {
-                IgnoreUnused(descriptor, constants, id);
-                const LayerType lt = layer->GetType();
-                if (lt == LayerType::Input)
-                    ioInfos.inputInfos.push_back(getInputTensorInfo(id, layer->GetOutputSlot(0).GetTensorInfo()));
-                else if (lt == LayerType::Output)
-                    ioInfos.outputInfos.push_back({id, layer->GetInputSlot(0).GetTensorInfo()});
-            }
-            IOInfos ioInfos;
-        };
-
-        InfoStrategy infoStrategy;
-        optNet->ExecuteStrategy(infoStrategy);
-        return infoStrategy.ioInfos;
-    }
-
-    IRuntime *runtime;
-    std::map<NetworkId, IOInfos> ioInfos;
-    std::map<NetworkId, std::unique_ptr<std::mutex>> mutexes; // mutex per network to not execute the same the same network concurrently
-    std::mutex mutex; // global mutex for load/unload calls to the runtime
-    SpinLock spinLock; // fast spin lock to guard access to the ioInfos and mutexes maps
-};
-
-extern "C" void *init(int logLevel, int tuningLevel, const char *tuningFile)
-{
-    LogSeverity level = static_cast<LogSeverity>(logLevel);
-    ConfigureLogging(true, true, level);
-
-    Ann *ann = new Ann(tuningLevel, tuningFile);
-    return ann;
-}
-
-extern "C" void destroy(void *ann)
-{
-    delete ((Ann *)ann);
-}
-
-extern "C" int load(void *ann,
-                    const char *path,
-                    bool fastMath,
-                    bool fp16,
-                    bool saveCachedNetwork,
-                    const char *cachedNetworkPath)
-{
-    return ((Ann *)ann)->load(path, fastMath, fp16, saveCachedNetwork, cachedNetworkPath);
-}
-
-extern "C" void unload(void *ann, NetworkId netId)
-{
-    ((Ann *)ann)->unload(netId);
-}
-
-extern "C" void execute(void *ann, NetworkId netId, const void **inputData, void **outputData)
-{
-    ((Ann *)ann)->execute(netId, inputData, outputData);
-}
-
-extern "C" unsigned long shape(void *ann, NetworkId netId, bool isInput, int index)
-{
-    return ((Ann *)ann)->shape(netId, isInput, index);
-}
-
-extern "C" int tensors(void *ann, NetworkId netId, bool isInput)
-{
-    return ((Ann *)ann)->tensors(netId, isInput);
-}

machine-learning/export/ann/scripts/build-converter.sh

@@ -1,4 +0,0 @@
-#!/usr/bin/env sh
-
-cd armnn-23.11/ || exit
-g++ -o ../armnnconverter -fPIC -O1 -DARMNN_ONNX_PARSER -DARMNN_SERIALIZER -DARMNN_TF_LITE_PARSER -fuse-ld=gold -std=c++17 -Iinclude -Isrc/armnnUtils -Ithird-party -larmnn -larmnnDeserializer -larmnnTfLiteParser -larmnnOnnxParser -larmnnSerializer -L../armnn src/armnnConverter/ArmnnConverter.cpp

machine-learning/export/ann/scripts/build.sh

@@ -1,3 +0,0 @@
-#!/usr/bin/env sh
-
-g++ -shared -O3 -fPIC -o libann.so -fuse-ld=gold -std=c++17 -I"$ARMNN_PATH"/include -larmnn -larmnnDeserializer -larmnnTfLiteParser -larmnnOnnxParser -L"$ARMNN_PATH" ann.cpp

machine-learning/export/models/mclip.py

@@ -19,44 +19,37 @@ _MCLIP_TO_OPENCLIP = {
 }
 
 
-def forward(self: MultilingualCLIP, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
-        embs = self.transformer(input_ids, attention_mask)[0]
-        embs = (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
-        embs = self.LinearTransformation(embs)
-        return torch.nn.functional.normalize(embs, dim=-1)
-
-# unfortunately need to monkeypatch for tracing to work here
-# otherwise it hits the 2GiB protobuf serialization limit
-MultilingualCLIP.forward = forward
-
-
-def to_torchscript(model_name: str) -> torch.jit.ScriptModule:
-    with tempfile.TemporaryDirectory() as tmpdir:
-        model = MultilingualCLIP.from_pretrained(model_name, cache_dir=tmpdir)
-
-        model.eval()
-        for param in model.parameters():
-            param.requires_grad_(False)
-        
-        return model
-
-
 def to_onnx(
     model_name: str,
     output_dir_visual: Path | str,
     output_dir_textual: Path | str,
 ) -> None:
     textual_path = get_model_path(output_dir_textual)
-    model = to_torchscript(model_name)
+    with tempfile.TemporaryDirectory() as tmpdir:
-    AutoTokenizer.from_pretrained(model_name).save_pretrained(output_dir_textual)
+        model = MultilingualCLIP.from_pretrained(model_name, cache_dir=tmpdir)
+        AutoTokenizer.from_pretrained(model_name).save_pretrained(output_dir_textual)
 
-    _text_encoder_to_onnx(model, textual_path)
+        for param in model.parameters():
-    openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual)
+            param.requires_grad_(False)
-    optimize(textual_path)
+
+        export_text_encoder(model, textual_path)
+        openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual)
+        optimize(textual_path)
 
 
-def _text_encoder_to_onnx(model: MultilingualCLIP, output_path: Path | str) -> None:
+def export_text_encoder(model: MultilingualCLIP, output_path: Path | str) -> None:
     output_path = Path(output_path)
+
+    def forward(self: MultilingualCLIP, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+        embs = self.transformer(input_ids, attention_mask)[0]
+        embs = (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
+        embs = self.LinearTransformation(embs)
+        return torch.nn.functional.normalize(embs, dim=-1)
+
+    # unfortunately need to monkeypatch for tracing to work here
+    # otherwise it hits the 2GiB protobuf serialization limit
+    MultilingualCLIP.forward = forward
+
     args = (torch.ones(1, 77, dtype=torch.int32), torch.ones(1, 77, dtype=torch.int32))
     with warnings.catch_warnings():
         warnings.simplefilter("ignore", UserWarning)

machine-learning/export/models/openclip.py

@@ -26,17 +26,6 @@ class OpenCLIPModelConfig:
         self.sequence_length = open_clip_cfg["text_cfg"]["context_length"]
 
 
-def to_torchscript(model_name: str) -> torch.jit.ScriptModule:
-    with tempfile.TemporaryDirectory() as tmpdir:
-        model = MultilingualCLIP.from_pretrained(model_name, cache_dir=tmpdir)
-
-        model.eval()
-        for param in model.parameters():
-            param.requires_grad_(False)
-        
-        return model
-
-
 def to_onnx(
     model_cfg: OpenCLIPModelConfig,
     output_dir_visual: Path | str | None = None,
@@ -62,7 +51,7 @@ def to_onnx(
 
             save_config(open_clip.get_model_preprocess_cfg(model), output_dir_visual / "preprocess_cfg.json")
             save_config(text_vision_cfg, output_dir_visual.parent / "config.json")
-            _image_encoder_to_onnx(model, model_cfg, visual_path)
+            export_image_encoder(model, model_cfg, visual_path)
 
             optimize(visual_path)
 
@@ -72,11 +61,11 @@ def to_onnx(
 
             tokenizer_name = text_vision_cfg["text_cfg"].get("hf_tokenizer_name", "openai/clip-vit-base-patch32")
             AutoTokenizer.from_pretrained(tokenizer_name).save_pretrained(output_dir_textual)
-            _text_encoder_to_onnx(model, model_cfg, textual_path)
+            export_text_encoder(model, model_cfg, textual_path)
             optimize(textual_path)
 
 
-def _image_encoder_to_onnx(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None:
+def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None:
     output_path = Path(output_path)
 
     def encode_image(image: torch.Tensor) -> torch.Tensor:
@@ -100,7 +89,7 @@ def _image_encoder_to_onnx(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig
         )
 
 
-def _text_encoder_to_onnx(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None:
+def export_text_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None:
     output_path = Path(output_path)
 
     def encode_text(text: torch.Tensor) -> torch.Tensor:

machine-learning/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "machine-learning"
-version = "1.108.0"
+version = "1.107.2"
 description = ""
 authors = ["Hau Tran <alex.tran1502@gmail.com>"]
 readme = "README.md"

mobile/android/app/src/main/AndroidManifest.xml

@@ -1,7 +1,7 @@
 <manifest xmlns:android="http://schemas.android.com/apk/res/android" package="app.alextran.immich"
   xmlns:tools="http://schemas.android.com/tools">
   <application android:label="Immich" android:name=".ImmichApp" android:usesCleartextTraffic="true"
-    android:icon="@mipmap/ic_launcher" android:requestLegacyExternalStorage="true"  android:largeHeap="true">
+    android:icon="@mipmap/ic_launcher" android:requestLegacyExternalStorage="true">
 
     <meta-data
       android:name="io.flutter.embedding.android.EnableImpeller"

mobile/android/fastlane/Fastfile

@@ -35,8 +35,8 @@ platform :android do
       task: 'bundle', 
       build_type: 'Release',
       properties: {
-        "android.injected.version.code" => 148,
+        "android.injected.version.code" => 147,
-        "android.injected.version.name" => "1.108.0",
+        "android.injected.version.name" => "1.107.2",
       }
     )
     upload_to_play_store(skip_upload_apk: true, skip_upload_images: true, skip_upload_screenshots: true, aab: '../build/app/outputs/bundle/release/app-release.aab')

mobile/ios/Podfile.lock

@@ -167,7 +167,7 @@ SPEC CHECKSUMS:
   path_provider_foundation: 29f094ae23ebbca9d3d0cec13889cd9060c0e943
   path_provider_ios: 14f3d2fd28c4fdb42f44e0f751d12861c43cee02
   permission_handler_apple: e76247795d700c14ea09e3a2d8855d41ee80a2e6
-  photo_manager: ff695c7a1dd5bc379974953a2b5c0a293f7c4c8a
+  photo_manager: 4f6810b7dfc4feb03b461ac1a70dacf91fba7604
   ReachabilitySwift: 985039c6f7b23a1da463388634119492ff86c825
   SAMKeychain: 483e1c9f32984d50ca961e26818a534283b4cd5c
   share_plus: c3fef564749587fc939ef86ffb283ceac0baf9f5

mobile/ios/Runner.xcodeproj/project.pbxproj

@@ -383,7 +383,7 @@
 				CODE_SIGN_ENTITLEMENTS = Runner/RunnerProfile.entitlements;
 				CODE_SIGN_IDENTITY = "Apple Development";
 				CODE_SIGN_STYLE = Automatic;
-				CURRENT_PROJECT_VERSION = 163;
+				CURRENT_PROJECT_VERSION = 162;
 				DEVELOPMENT_TEAM = 2F67MQ8R79;
 				ENABLE_BITCODE = NO;
 				INFOPLIST_FILE = Runner/Info.plist;
@@ -525,7 +525,7 @@
 				CLANG_ENABLE_MODULES = YES;
 				CODE_SIGN_IDENTITY = "Apple Development";
 				CODE_SIGN_STYLE = Automatic;
-				CURRENT_PROJECT_VERSION = 163;
+				CURRENT_PROJECT_VERSION = 162;
 				DEVELOPMENT_TEAM = 2F67MQ8R79;
 				ENABLE_BITCODE = NO;
 				INFOPLIST_FILE = Runner/Info.plist;
@@ -553,7 +553,7 @@
 				CLANG_ENABLE_MODULES = YES;
 				CODE_SIGN_IDENTITY = "Apple Development";
 				CODE_SIGN_STYLE = Automatic;
-				CURRENT_PROJECT_VERSION = 163;
+				CURRENT_PROJECT_VERSION = 162;
 				DEVELOPMENT_TEAM = 2F67MQ8R79;
 				ENABLE_BITCODE = NO;
 				INFOPLIST_FILE = Runner/Info.plist;

mobile/ios/Runner/Info.plist

@@ -58,11 +58,11 @@
 	<key>CFBundlePackageType</key>
 	<string>APPL</string>
 	<key>CFBundleShortVersionString</key>
-	<string>1.108.0</string>
+	<string>1.107.1</string>
 	<key>CFBundleSignature</key>
 	<string>????</string>
 	<key>CFBundleVersion</key>
-	<string>163</string>
+	<string>162</string>
 	<key>FLTEnableImpeller</key>
 	<true/>
 	<key>ITSAppUsesNonExemptEncryption</key>

mobile/ios/fastlane/Fastfile

@@ -16,10 +16,10 @@
 default_platform(:ios)
 
 platform :ios do
-  desc "iOS Release"
+  desc "iOS Beta"
-  lane :release do
+  lane :beta do
     increment_version_number(
-      version_number: "1.108.0"
+      version_number: "1.107.2"
     )
     increment_build_number(
       build_number: latest_testflight_build_number + 1,

mobile/openapi/README.md

@@ -3,7 +3,7 @@ Immich API
 
 This Dart package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:
 
-- API version: 1.108.0
+- API version: 1.107.2
 - Generator version: 7.5.0
 - Build package: org.openapitools.codegen.languages.DartClientCodegen
 

mobile/openapi/lib/model/system_config_o_auth_dto.dart

@@ -23,7 +23,6 @@ class SystemConfigOAuthDto {
     required this.issuerUrl,
     required this.mobileOverrideEnabled,
     required this.mobileRedirectUri,
-    required this.profileSigningAlgorithm,
     required this.scope,
     required this.signingAlgorithm,
     required this.storageLabelClaim,
@@ -51,8 +50,6 @@ class SystemConfigOAuthDto {
 
   String mobileRedirectUri;
 
-  String profileSigningAlgorithm;
-
   String scope;
 
   String signingAlgorithm;
@@ -73,7 +70,6 @@ class SystemConfigOAuthDto {
     other.issuerUrl == issuerUrl &&
     other.mobileOverrideEnabled == mobileOverrideEnabled &&
     other.mobileRedirectUri == mobileRedirectUri &&
-    other.profileSigningAlgorithm == profileSigningAlgorithm &&
     other.scope == scope &&
     other.signingAlgorithm == signingAlgorithm &&
     other.storageLabelClaim == storageLabelClaim &&
@@ -92,14 +88,13 @@ class SystemConfigOAuthDto {
     (issuerUrl.hashCode) +
     (mobileOverrideEnabled.hashCode) +
     (mobileRedirectUri.hashCode) +
-    (profileSigningAlgorithm.hashCode) +
     (scope.hashCode) +
     (signingAlgorithm.hashCode) +
     (storageLabelClaim.hashCode) +
     (storageQuotaClaim.hashCode);
 
   @override
-  String toString() => 'SystemConfigOAuthDto[autoLaunch=$autoLaunch, autoRegister=$autoRegister, buttonText=$buttonText, clientId=$clientId, clientSecret=$clientSecret, defaultStorageQuota=$defaultStorageQuota, enabled=$enabled, issuerUrl=$issuerUrl, mobileOverrideEnabled=$mobileOverrideEnabled, mobileRedirectUri=$mobileRedirectUri, profileSigningAlgorithm=$profileSigningAlgorithm, scope=$scope, signingAlgorithm=$signingAlgorithm, storageLabelClaim=$storageLabelClaim, storageQuotaClaim=$storageQuotaClaim]';
+  String toString() => 'SystemConfigOAuthDto[autoLaunch=$autoLaunch, autoRegister=$autoRegister, buttonText=$buttonText, clientId=$clientId, clientSecret=$clientSecret, defaultStorageQuota=$defaultStorageQuota, enabled=$enabled, issuerUrl=$issuerUrl, mobileOverrideEnabled=$mobileOverrideEnabled, mobileRedirectUri=$mobileRedirectUri, scope=$scope, signingAlgorithm=$signingAlgorithm, storageLabelClaim=$storageLabelClaim, storageQuotaClaim=$storageQuotaClaim]';
 
   Map<String, dynamic> toJson() {
     final json = <String, dynamic>{};
@@ -113,7 +108,6 @@ class SystemConfigOAuthDto {
       json[r'issuerUrl'] = this.issuerUrl;
       json[r'mobileOverrideEnabled'] = this.mobileOverrideEnabled;
       json[r'mobileRedirectUri'] = this.mobileRedirectUri;
-      json[r'profileSigningAlgorithm'] = this.profileSigningAlgorithm;
       json[r'scope'] = this.scope;
       json[r'signingAlgorithm'] = this.signingAlgorithm;
       json[r'storageLabelClaim'] = this.storageLabelClaim;
@@ -139,7 +133,6 @@ class SystemConfigOAuthDto {
         issuerUrl: mapValueOfType<String>(json, r'issuerUrl')!,
         mobileOverrideEnabled: mapValueOfType<bool>(json, r'mobileOverrideEnabled')!,
         mobileRedirectUri: mapValueOfType<String>(json, r'mobileRedirectUri')!,
-        profileSigningAlgorithm: mapValueOfType<String>(json, r'profileSigningAlgorithm')!,
         scope: mapValueOfType<String>(json, r'scope')!,
         signingAlgorithm: mapValueOfType<String>(json, r'signingAlgorithm')!,
         storageLabelClaim: mapValueOfType<String>(json, r'storageLabelClaim')!,
@@ -201,7 +194,6 @@ class SystemConfigOAuthDto {
     'issuerUrl',
     'mobileOverrideEnabled',
     'mobileRedirectUri',
-    'profileSigningAlgorithm',
     'scope',
     'signingAlgorithm',
     'storageLabelClaim',

mobile/pubspec.lock

@@ -1162,10 +1162,10 @@ packages:
     dependency: "direct main"
     description:
       name: photo_manager
-      sha256: "68d6099d07ce5033170f8368af8128a4555cf1d590a97242f83669552de989b1"
+      sha256: "8cf79918f6de9843b394a1670fe1aec54ebcac852b4b4c9ef88211894547dc61"
       url: "https://pub.dev"
     source: hosted
-    version: "3.2.0"
+    version: "3.0.0-dev.5"
   photo_manager_image_provider:
     dependency: "direct main"
     description:

mobile/pubspec.yaml

@@ -2,7 +2,7 @@ name: immich_mobile
 description: Immich - selfhosted backup media file on mobile phone
 
 publish_to: 'none'
-version: 1.108.0+148
+version: 1.107.2+147
 
 environment:
   sdk: '>=3.3.0 <4.0.0'
@@ -15,7 +15,7 @@ dependencies:
   path_provider_ios:
   # TODO: upgrade to stable after 3.0.1 is released. 3.0.0 is broken
   # https://github.com/fluttercandies/flutter_photo_manager/pull/990#issuecomment-2058066427
-  photo_manager: ^3.2.0
+  photo_manager: ^3.0.0-dev.5
   photo_manager_image_provider: ^2.1.0
   flutter_hooks: ^0.20.4
   hooks_riverpod: ^2.4.9

open-api/immich-openapi-specs.json

@@ -7007,7 +7007,7 @@
   "info": {
     "title": "Immich",
     "description": "Immich API",
-    "version": "1.108.0",
+    "version": "1.107.2",
     "contact": {}
   },
   "tags": [],
@@ -11030,9 +11030,6 @@
           "mobileRedirectUri": {
             "type": "string"
           },
-          "profileSigningAlgorithm": {
-            "type": "string"
-          },
           "scope": {
             "type": "string"
           },
@@ -11057,7 +11054,6 @@
           "issuerUrl",
           "mobileOverrideEnabled",
           "mobileRedirectUri",
-          "profileSigningAlgorithm",
           "scope",
           "signingAlgorithm",
           "storageLabelClaim",

open-api/typescript-sdk/.nvmrc

@@ -1 +1 @@
-20.15.1
+20.15.0

open-api/typescript-sdk/package-lock.json

@@ -1,18 +1,18 @@
 {
   "name": "@immich/sdk",
-  "version": "1.108.0",
+  "version": "1.107.2",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@immich/sdk",
-      "version": "1.108.0",
+      "version": "1.107.2",
       "license": "GNU Affero General Public License version 3",
       "dependencies": {
         "@oazapfts/runtime": "^1.0.2"
       },
       "devDependencies": {
-        "@types/node": "^20.14.10",
+        "@types/node": "^20.14.9",
         "typescript": "^5.3.3"
       }
     },
@@ -22,9 +22,9 @@
       "integrity": "sha512-8tKiYffhwTGHSHYGnZ3oneLGCjX0po/XAXQ5Ng9fqKkvIdl/xz8+Vh8i+6xjzZqvZ2pLVpUcuSfnvNI/x67L0g=="
     },
     "node_modules/@types/node": {
-      "version": "20.14.10",
+      "version": "20.14.9",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.14.10.tgz",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.14.9.tgz",
-      "integrity": "sha512-MdiXf+nDuMvY0gJKxyfZ7/6UFsETO7mGKF54MVD/ekJS6HdFtpZFBgrh6Pseu64XTb2MLyFPlbW6hj8HYRQNOQ==",
+      "integrity": "sha512-06OCtnTXtWOZBJlRApleWndH4JsRVs1pDCc8dLSQp+7PpUpX3ePdHyeNSFTeSe7FtKyQkrlPvHwJOW3SLd8Oyg==",
       "dev": true,
       "license": "MIT",
       "dependencies": {

open-api/typescript-sdk/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@immich/sdk",
-  "version": "1.108.0",
+  "version": "1.107.2",
   "description": "Auto-generated TypeScript SDK for the Immich API",
   "type": "module",
   "main": "./build/index.js",
@@ -19,7 +19,7 @@
     "@oazapfts/runtime": "^1.0.2"
   },
   "devDependencies": {
-    "@types/node": "^20.14.10",
+    "@types/node": "^20.14.9",
     "typescript": "^5.3.3"
   },
   "repository": {
@@ -28,6 +28,6 @@
     "directory": "open-api/typescript-sdk"
   },
   "volta": {
-    "node": "20.15.1"
+    "node": "20.15.0"
   }
 }

open-api/typescript-sdk/src/fetch-client.ts

@@ -1,6 +1,6 @@
 /**
  * Immich
- * 1.108.0
+ * 1.107.2
  * DO NOT MODIFY - This file has been generated using oazapfts.
  * See https://www.npmjs.com/package/oazapfts
  */
@@ -1063,7 +1063,6 @@ export type SystemConfigOAuthDto = {
     issuerUrl: string;
     mobileOverrideEnabled: boolean;
     mobileRedirectUri: string;
-    profileSigningAlgorithm: string;
     scope: string;
     signingAlgorithm: string;
     storageLabelClaim: string;

readme_i18n/README_de_DE.md

@@ -37,96 +37,72 @@
 ## Warnung
 
 - ⚠️ Das Projekt befindet sich in **sehr aktiver** Entwicklung.
-- ⚠️ Gehe von möglichen Fehlern und von Änderungen mit Breaking-Changes aus.
+- ⚠️ Erwarte Fehler und Änderungen mit Breaking-Changes.
 - ⚠️ **Nutze die App auf keinen Fall als einziges Speichermedium für deine Fotos und Videos.**
 - ⚠️ Befolge immer die [3-2-1](https://www.backblaze.com/blog/the-3-2-1-backup-strategy/) Backup-Regel für deine wertvollen Fotos und Videos!
 
-> [!NOTE]
-> Die Hauptdokumentation, einschließlich der Installationsanleitungen, befinden sich unter https://immich.app/.
-
-
 ## Inhalt
 
 - [Offizielle Dokumentation](https://immich.app/docs)
-- [Über Immich](https://immich.app/docs/overview/introduction)
-- [Installation](https://immich.app/docs/install/requirements)
 - [Roadmap](https://github.com/orgs/immich-app/projects/1)
 - [Demo](#demo)
 - [Funktionen](#funktionen)
-- [Übersetzungen](https://immich.app/docs/developer/translations)
+- [Einführung](https://immich.app/docs/overview/introduction)
+- [Installation](https://immich.app/docs/install/requirements)
 - [Beitragsrichtlinien](https://immich.app/docs/overview/support-the-project)
 
+## Dokumentation
+
+Die Hauptdokumentation, inklusive Installationsanleitungen, ist unter https://immich.app zu finden.
+
 ## Demo
 
 Die Web-Demo kannst Du unter https://demo.immich.app finden.
-Die Demo läuft auf einer Free Tier Oracle VM in Amsterdam mit einer 2.4Ghz Quad-Core ARM64 CPU und 24GB RAM.
 
 Für die Handy-App kannst Du `https://demo.immich.app/api` als `Server Endpoint URL` angeben.
 
-### Login Daten
+```bash title="Demo Credential"
+Die Anmeldedaten
+email: demo@immich.app
+passwort: demo
+```
 
-| Email           | Password |
+```
-| --------------- | -------- |
+Spec: Free-tier Oracle VM - Amsterdam - 2.4Ghz quad-core ARM64 CPU, 24GB RAM
-| demo@immich.app | demo     |
+```
 
 ## Funktionen
 
 | Funktionen                                           | Mobil  | Web   |
 | ---------------------------------------------------- | ------ | ----- |
 | Fotos & Videos hochladen und ansehen                 | Ja     | Ja    |
-| Automatische Sicherung beim öffnen der App           | Ja     | k. A  |
+| Automatisches Backup wenn die App geöffnet ist       | Ja     | n. a. |
-| Selektive Auswahl von Alben zum Sichern              | Ja     | k. A  |
+| Selektive Auswahl von Alben zum Sichern              | Ja     | n. a. |
-| Vermeidung von Duplikaten                            | Ja     | Ja    |     
 | Fotos und Videos auf das Gerät herunterladen         | Ja     | Ja    |
-| Unterstützung mehrerer Benutzer                      | Ja     | Ja    |
+| Unterstützt mehrere Benutzer                         | Ja     | Ja    |
-| Alben und geteilte Alben                             | Ja     | Ja    |
+| Album und geteilte Alben                             | Ja     | Ja    |
-| Scrollbar mit Scrubbing-/Drag-Funktion               | Ja     | Ja    |
+| Scrollleiste                                         | Ja     | Ja    |
-| Unterstützung für RAW Formate                        | Ja     | Ja    |
+| Unterstützt RAW Formate                              | Ja     | Ja    |
 | Metadaten anzeigen (EXIF, Karte)                     | Ja     | Ja    |
 | Suchen nach Metadaten, Objekten, Gesichtern und CLIP | Ja     | Ja    |
 | Administrative Funktionen (Benutzerverwaltung)       | Nein   | Ja    |
-| Hintergrundsicherung                                 | Ja     | k. A  |
+| Backup im Hintergrund                                | Ja     | n. a. |
 | Virtuelles Scrollen                                  | Ja     | Ja    |
 | OAuth Unterstützung                                  | Ja     | Ja    |
-| API-Schlüssel                                        | k. A   | Ja    |
+| API-Schlüssel                                        | n. a.  | Ja    |
-| LivePhoto/MotionPhoto Sicherung und Wiedergabe       | Ja     | Ja    |
+| LivePhoto/MotionPhoto Backup und Wiedergabe          | Ja     | Ja    |
-| Unterstützung für 360-Grad-Bilder     	             | Nein   | Ja    |
 | Benutzerdefinierte Speicherstruktur                  | Ja     | Ja    |
 | Öffentliches Teilen                                  | Nein   | Ja    |
-| Archiv und Favoriten                                 | Ja     | Ja    |
+| Archive und Favoriten                                | Ja     | Ja    |
 | Globale Karte                                        | Ja     | Ja    |
-| Partnerfreigabe (Teilen)                             | Ja     | Ja    |
+| Teilen mit Partner                                   | Ja     | Ja    |
-| Gesichtserkennung und -gruppierung                   | Ja     | Ja    |
+| Gesichtserkennung und Gruppierung                    | Ja     | Ja    |
 | Rückblicke (heute vor x Jahren)                      | Ja     | Ja    |
 | Offline Unterstützung                                | Ja     | Nein  |
 | Schreibgeschützte Gallerie                           | Ja     | Ja    |
 | Gestapelte Bilder                                    | Ja     | Ja    |
 
-
-## Übersetzungen
-
-Mehr zum Thema Übersetzungen kannst du [hier](https://immich.app/docs/developer/translations) erfahren.
-
-<a href="https://hosted.weblate.org/engage/immich/">
-<img src="https://hosted.weblate.org/widget/immich/immich/multi-auto.svg" alt="Translation status" />
-</a>
-
-## Repository-Aktivität
-
-![Activities](https://repobeats.axiom.co/api/embed/9e86d9dc3ddd137161f2f6d2e758d7863b1789cb.svg "Repobeats analytics image")
-
-## Github Sterne
-
-<a href="https://star-history.com/#immich-app/immich&Date">
- <picture>
-   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=immich-app/immich&type=Date&theme=dark" />
-   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=immich-app/immich&type=Date" />
-   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=immich-app/immich&type=Date" width="100%" />
- </picture>
-</a>
-
 ## Mitwirkende
-
 <a href="https://github.com/alextran1502/immich/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=immich-app/immich" width="100%"/>
 </a>

server/.nvmrc

@@ -1 +1 @@
-20.15.1
+20.15

server/Dockerfile

@@ -1,5 +1,5 @@
 # dev build
-FROM ghcr.io/immich-app/base-server-dev:20240710@sha256:5944dac5d73dc54d733461db555786c823460e730f6793ea5ad4ee3843a75c7d as dev
+FROM ghcr.io/immich-app/base-server-dev:20240708@sha256:2a9e3231c34493cb861299d475c84c031e7f04519dbc895bbebb5017d479a3cb as dev
 
 RUN apt-get install --no-install-recommends -yqq tini
 WORKDIR /usr/src/app
@@ -25,7 +25,7 @@ COPY --from=dev /usr/src/app/node_modules/@img ./node_modules/@img
 COPY --from=dev /usr/src/app/node_modules/exiftool-vendored.pl ./node_modules/exiftool-vendored.pl
 
 # web build
-FROM node:20.15.1-alpine3.20@sha256:34b7aa411056c85dbf71d240d26516949b3f72b318d796c26b57caaa1df5639a as web
+FROM node:20.15.0-alpine3.20@sha256:df01469346db2bf1cfc1f7261aeab86b2960efa840fe2bd46d83ff339f463665 as web
 
 WORKDIR /usr/src/open-api/typescript-sdk
 COPY open-api/typescript-sdk/package*.json open-api/typescript-sdk/tsconfig*.json ./
@@ -41,7 +41,7 @@ RUN npm run build
 
 
 # prod build
-FROM ghcr.io/immich-app/base-server-prod:20240710@sha256:bc3cda314634467d5a92b78c09ba566246e00ee8495aac398f5c1000c5e26fa9
+FROM ghcr.io/immich-app/base-server-prod:20240708@sha256:0af3a5bb036c9a4b6a5a51becaa6e94fe182f6bc97480d57e8f2e6f994bfa453
 
 WORKDIR /usr/src/app
 ENV NODE_ENV=production \

server/package.json

@@ -1,6 +1,6 @@
 {
   "name": "immich",
-  "version": "1.108.0",
+  "version": "1.107.2",
   "description": "",
   "author": "",
   "private": true,
@@ -46,7 +46,7 @@
     "@nestjs/swagger": "^7.1.8",
     "@nestjs/typeorm": "^10.0.0",
     "@nestjs/websockets": "^10.2.2",
-    "@opentelemetry/auto-instrumentations-node": "^0.48.0",
+    "@opentelemetry/auto-instrumentations-node": "^0.47.0",
     "@opentelemetry/context-async-hooks": "^1.24.0",
     "@opentelemetry/exporter-prometheus": "^0.52.0",
     "@opentelemetry/sdk-node": "^0.52.0",
@@ -106,7 +106,7 @@
     "@types/lodash": "^4.14.197",
     "@types/mock-fs": "^4.13.1",
     "@types/multer": "^1.4.7",
-    "@types/node": "^20.14.10",
+    "@types/node": "^20.14.9",
     "@types/nodemailer": "^6.4.14",
     "@types/picomatch": "^2.3.3",
     "@types/semver": "^7.5.8",
@@ -132,6 +132,6 @@
     "vitest": "^1.5.0"
   },
   "volta": {
-    "node": "20.15.1"
+    "node": "20.15.0"
   }
 }

server/src/config.ts

@@ -146,7 +146,6 @@ export interface SystemConfig {
     mobileRedirectUri: string;
     scope: string;
     signingAlgorithm: string;
-    profileSigningAlgorithm: string;
     storageLabelClaim: string;
     storageQuotaClaim: string;
   };
@@ -290,7 +289,6 @@ export const defaults = Object.freeze<SystemConfig>({
     mobileRedirectUri: '',
     scope: 'openid email profile',
     signingAlgorithm: 'RS256',
-    profileSigningAlgorithm: 'none',
     storageLabelClaim: 'preferred_username',
     storageQuotaClaim: 'immich_quota',
   },

server/src/dtos/auth.dto.ts

@@ -5,7 +5,6 @@ import { APIKeyEntity } from 'src/entities/api-key.entity';
 import { SessionEntity } from 'src/entities/session.entity';
 import { SharedLinkEntity } from 'src/entities/shared-link.entity';
 import { UserEntity } from 'src/entities/user.entity';
-import { toEmail } from 'src/validation';
 
 export enum ImmichCookie {
   ACCESS_TOKEN = 'immich_access_token',
@@ -42,7 +41,7 @@ export class AuthDto {
 
 export class LoginCredentialDto {
   @IsEmail({ require_tld: false })
-  @Transform(toEmail)
+  @Transform(({ value }) => value?.toLowerCase())
   @IsNotEmpty()
   @ApiProperty({ example: 'testuser@email.com' })
   email!: string;

server/src/dtos/system-config.dto.ts

@@ -349,10 +349,6 @@ class SystemConfigOAuthDto {
   @IsNotEmpty()
   signingAlgorithm!: string;
 
-  @IsString()
-  @IsNotEmpty()
-  profileSigningAlgorithm!: string;
-
   @IsString()
   storageLabelClaim!: string;
 

server/src/dtos/user.dto.spec.ts

@@ -38,22 +38,6 @@ describe('create user DTO', () => {
     expect(errors).toHaveLength(0);
   });
 
-  it('validates invalid email type', async () => {
-    let dto = plainToInstance(UserAdminCreateDto, {
-      email: [],
-      password: 'some password',
-      name: 'some name',
-    });
-    expect(await validate(dto)).toHaveLength(1);
-
-    dto = plainToInstance(UserAdminCreateDto, {
-      email: {},
-      password: 'some password',
-      name: 'some name',
-    });
-    expect(await validate(dto)).toHaveLength(1);
-  });
-
   it('should allow emails without a tld', async () => {
     const someEmail = 'test@test';
 

server/src/repositories/asset.repository.ts

@@ -1,10 +1,11 @@
 import { Injectable } from '@nestjs/common';
 import { InjectRepository } from '@nestjs/typeorm';
 import { Chunked, ChunkedArray, DummyValue, GenerateSql } from 'src/decorators';
-import { AssetOrder } from 'src/entities/album.entity';
+import { AlbumEntity, AssetOrder } from 'src/entities/album.entity';
 import { AssetJobStatusEntity } from 'src/entities/asset-job-status.entity';
 import { AssetEntity, AssetType } from 'src/entities/asset.entity';
 import { ExifEntity } from 'src/entities/exif.entity';
+import { PartnerEntity } from 'src/entities/partner.entity';
 import { SmartInfoEntity } from 'src/entities/smart-info.entity';
 import {
   AssetBuilderOptions,
@@ -62,6 +63,8 @@ export class AssetRepository implements IAssetRepository {
     @InjectRepository(ExifEntity) private exifRepository: Repository<ExifEntity>,
     @InjectRepository(AssetJobStatusEntity) private jobStatusRepository: Repository<AssetJobStatusEntity>,
     @InjectRepository(SmartInfoEntity) private smartInfoRepository: Repository<SmartInfoEntity>,
+    @InjectRepository(PartnerEntity) private partnerRepository: Repository<PartnerEntity>,
+    @InjectRepository(AlbumEntity) private albumRepository: Repository<AlbumEntity>,
   ) {}
 
   async upsertExif(exif: Partial<ExifEntity>): Promise<void> {

server/src/repositories/metadata.repository.ts

@@ -1,19 +1,22 @@
 import { Inject, Injectable } from '@nestjs/common';
-import { InjectRepository } from '@nestjs/typeorm';
+import { InjectDataSource, InjectRepository } from '@nestjs/typeorm';
 import { DefaultReadTaskOptions, ExifTool, Tags } from 'exiftool-vendored';
 import geotz from 'geo-tz';
 import { DummyValue, GenerateSql } from 'src/decorators';
 import { ExifEntity } from 'src/entities/exif.entity';
+import { GeodataPlacesEntity } from 'src/entities/geodata-places.entity';
 import { ILoggerRepository } from 'src/interfaces/logger.interface';
 import { IMetadataRepository, ImmichTags } from 'src/interfaces/metadata.interface';
 import { Instrumentation } from 'src/utils/instrumentation';
-import { Repository } from 'typeorm';
+import { DataSource, Repository } from 'typeorm';
 
 @Instrumentation()
 @Injectable()
 export class MetadataRepository implements IMetadataRepository {
   constructor(
     @InjectRepository(ExifEntity) private exifRepository: Repository<ExifEntity>,
+    @InjectRepository(GeodataPlacesEntity) private geodataPlacesRepository: Repository<GeodataPlacesEntity>,
+    @InjectDataSource() private dataSource: DataSource,
     @Inject(ILoggerRepository) private logger: ILoggerRepository,
   ) {
     this.logger.setContext(MetadataRepository.name);

server/src/services/auth.service.ts

@@ -9,7 +9,7 @@ import { isNumber, isString } from 'class-validator';
 import cookieParser from 'cookie';
 import { DateTime } from 'luxon';
 import { IncomingHttpHeaders } from 'node:http';
-import { Issuer, UserinfoResponse, custom, generators } from 'openid-client';
+import { ClientMetadata, Issuer, UserinfoResponse, custom, generators } from 'openid-client';
 import { SystemConfig } from 'src/config';
 import { AuthType, LOGIN_URL, MOBILE_REDIRECT, SALT_ROUNDS } from 'src/constants';
 import { SystemConfigCore } from 'src/cores/system-config.core';
@@ -45,7 +45,9 @@ export interface LoginDetails {
   deviceOS: string;
 }
 
-type OAuthProfile = UserinfoResponse;
+interface OAuthProfile extends UserinfoResponse {
+  email: string;
+}
 
 interface ClaimOptions<T> {
   key: string;
@@ -190,35 +192,34 @@ export class AuthService {
   async callback(dto: OAuthCallbackDto, loginDetails: LoginDetails) {
     const config = await this.configCore.getConfig({ withCache: false });
     const profile = await this.getOAuthProfile(config, dto.url);
-    const { autoRegister, defaultStorageQuota, storageLabelClaim, storageQuotaClaim } = config.oauth;
     this.logger.debug(`Logging in with OAuth: ${JSON.stringify(profile)}`);
     let user = await this.userRepository.getByOAuthId(profile.sub);
 
-    // link by email
+    // link existing user
-    if (!user && profile.email) {
+    if (!user) {
       const emailUser = await this.userRepository.getByEmail(profile.email);
       if (emailUser) {
         if (emailUser.oauthId) {
           throw new BadRequestException('User already exists, but is linked to another account.');
+        } else {
+          user = await this.userRepository.update(emailUser.id, { oauthId: profile.sub });
         }
-        user = await this.userRepository.update(emailUser.id, { oauthId: profile.sub });
       }
     }
 
+    const { autoRegister, defaultStorageQuota, storageLabelClaim, storageQuotaClaim } = config.oauth;
+
     // register new user
     if (!user) {
       if (!autoRegister) {
         this.logger.warn(
-          `Unable to register ${profile.sub}/${profile.email || '(no email)'}. To enable set OAuth Auto Register to true in admin settings.`,
+          `Unable to register ${profile.email}. To enable set OAuth Auto Register to true in admin settings.`,
         );
         throw new BadRequestException(`User does not exist and auto registering is disabled.`);
       }
 
-      if (!profile.email) {
+      this.logger.log(`Registering new user: ${profile.email}/${profile.sub}`);
-        throw new BadRequestException('OAuth profile does not have an email address');
+      this.logger.verbose(`OAuth Profile: ${JSON.stringify(profile)}`);
-      }
-
-      this.logger.log(`Registering new user: ${profile.sub}/${profile.email}`);
 
       const storageLabel = this.getClaim(profile, {
         key: storageLabelClaim,
@@ -298,21 +299,23 @@ export class AuthService {
   }
 
   private async getOAuthClient(config: SystemConfig) {
-    const { enabled, clientId, clientSecret, issuerUrl, signingAlgorithm, profileSigningAlgorithm } = config.oauth;
+    const { enabled, clientId, clientSecret, issuerUrl, signingAlgorithm } = config.oauth;
 
     if (!enabled) {
       throw new BadRequestException('OAuth2 is not enabled');
     }
 
+    const metadata: ClientMetadata = {
+      client_id: clientId,
+      client_secret: clientSecret,
+      response_types: ['code'],
+    };
+
     try {
       const issuer = await Issuer.discover(issuerUrl);
-      return new issuer.Client({
+      metadata.id_token_signed_response_alg = signingAlgorithm;
-        client_id: clientId,
+
-        client_secret: clientSecret,
+      return new issuer.Client(metadata);
-        response_types: ['code'],
-        userinfo_signed_response_alg: profileSigningAlgorithm === 'none' ? undefined : profileSigningAlgorithm,
-        id_token_signed_response_alg: signingAlgorithm,
-      });
     } catch (error: any | AggregateError) {
       this.logger.error(`Error in OAuth discovery: ${error}`, error?.stack, error?.errors);
       throw new InternalServerErrorException(`Error in OAuth discovery: ${error}`, { cause: error });

server/src/services/notification.service.spec.ts

@@ -1,117 +0,0 @@
-import { defaults, SystemConfig } from 'src/config';
-import { IAlbumRepository } from 'src/interfaces/album.interface';
-import { IAssetRepository } from 'src/interfaces/asset.interface';
-import { IJobRepository } from 'src/interfaces/job.interface';
-import { ILoggerRepository } from 'src/interfaces/logger.interface';
-import { INotificationRepository } from 'src/interfaces/notification.interface';
-import { ISystemMetadataRepository } from 'src/interfaces/system-metadata.interface';
-import { IUserRepository } from 'src/interfaces/user.interface';
-import { NotificationService } from 'src/services/notification.service';
-import { newAlbumRepositoryMock } from 'test/repositories/album.repository.mock';
-import { newAssetRepositoryMock } from 'test/repositories/asset.repository.mock';
-import { newJobRepositoryMock } from 'test/repositories/job.repository.mock';
-import { newLoggerRepositoryMock } from 'test/repositories/logger.repository.mock';
-import { newNotificationRepositoryMock } from 'test/repositories/notification.repository.mock';
-import { newSystemMetadataRepositoryMock } from 'test/repositories/system-metadata.repository.mock';
-import { newUserRepositoryMock } from 'test/repositories/user.repository.mock';
-import { Mocked } from 'vitest';
-
-const configs = {
-  smtpDisabled: Object.freeze<SystemConfig>({
-    ...defaults,
-    notifications: {
-      smtp: {
-        ...defaults.notifications.smtp,
-        enabled: false,
-      },
-    },
-  }),
-  smtpEnabled: Object.freeze<SystemConfig>({
-    ...defaults,
-    notifications: {
-      smtp: {
-        ...defaults.notifications.smtp,
-        enabled: true,
-      },
-    },
-  }),
-  smtpTransport: Object.freeze<SystemConfig>({
-    ...defaults,
-    notifications: {
-      smtp: {
-        ...defaults.notifications.smtp,
-        enabled: true,
-        transport: {
-          ignoreCert: false,
-          host: 'localhost',
-          port: 587,
-          username: 'test',
-          password: 'test',
-        },
-      },
-    },
-  }),
-};
-
-describe(NotificationService.name, () => {
-  let sut: NotificationService;
-  let systemMock: Mocked<ISystemMetadataRepository>;
-  let notificationMock: Mocked<INotificationRepository>;
-  let userMock: Mocked<IUserRepository>;
-  let jobMock: Mocked<IJobRepository>;
-  let loggerMock: Mocked<ILoggerRepository>;
-  let assetMock: Mocked<IAssetRepository>;
-  let albumMock: Mocked<IAlbumRepository>;
-
-  beforeEach(() => {
-    systemMock = newSystemMetadataRepositoryMock();
-    notificationMock = newNotificationRepositoryMock();
-    userMock = newUserRepositoryMock();
-    jobMock = newJobRepositoryMock();
-    loggerMock = newLoggerRepositoryMock();
-    assetMock = newAssetRepositoryMock();
-    albumMock = newAlbumRepositoryMock();
-
-    sut = new NotificationService(systemMock, notificationMock, userMock, jobMock, loggerMock, assetMock, albumMock);
-  });
-
-  it('should work', () => {
-    expect(sut).toBeDefined();
-  });
-
-  describe('onConfigValidateEvent', () => {
-    it('validates smtp config when enabling smtp', async () => {
-      const oldConfig = configs.smtpDisabled;
-      const newConfig = configs.smtpEnabled;
-
-      notificationMock.verifySmtp.mockResolvedValue(true);
-      await expect(sut.onConfigValidateEvent({ oldConfig, newConfig })).resolves.not.toThrow();
-      expect(notificationMock.verifySmtp).toHaveBeenCalledWith(newConfig.notifications.smtp.transport);
-    });
-
-    it('validates smtp config when transport changes', async () => {
-      const oldConfig = configs.smtpEnabled;
-      const newConfig = configs.smtpTransport;
-
-      notificationMock.verifySmtp.mockResolvedValue(true);
-      await expect(sut.onConfigValidateEvent({ oldConfig, newConfig })).resolves.not.toThrow();
-      expect(notificationMock.verifySmtp).toHaveBeenCalledWith(newConfig.notifications.smtp.transport);
-    });
-
-    it('skips smtp validation when there are no changes', async () => {
-      const oldConfig = { ...configs.smtpEnabled };
-      const newConfig = { ...configs.smtpEnabled };
-
-      await expect(sut.onConfigValidateEvent({ oldConfig, newConfig })).resolves.not.toThrow();
-      expect(notificationMock.verifySmtp).not.toHaveBeenCalled();
-    });
-
-    it('skips smtp validation when smtp is disabled', async () => {
-      const oldConfig = { ...configs.smtpEnabled };
-      const newConfig = { ...configs.smtpDisabled };
-
-      await expect(sut.onConfigValidateEvent({ oldConfig, newConfig })).resolves.not.toThrow();
-      expect(notificationMock.verifySmtp).not.toHaveBeenCalled();
-    });
-  });
-});

server/src/services/notification.service.ts

@@ -1,5 +1,4 @@
 import { HttpException, HttpStatus, Inject, Injectable } from '@nestjs/common';
-import { isEqual } from 'lodash';
 import { DEFAULT_EXTERNAL_DOMAIN } from 'src/constants';
 import { SystemConfigCore } from 'src/cores/system-config.core';
 import { SystemConfigSmtpDto } from 'src/dtos/system-config.dto';
@@ -45,12 +44,9 @@ export class NotificationService implements OnEvents {
     this.configCore = SystemConfigCore.create(systemMetadataRepository, logger);
   }
 
-  async onConfigValidateEvent({ oldConfig, newConfig }: SystemConfigUpdateEvent) {
+  async onConfigValidateEvent({ newConfig }: SystemConfigUpdateEvent) {
     try {
-      if (
+      if (newConfig.notifications.smtp.enabled) {
-        newConfig.notifications.smtp.enabled &&
-        !isEqual(oldConfig.notifications.smtp, newConfig.notifications.smtp)
-      ) {
         await this.notificationRepository.verifySmtp(newConfig.notifications.smtp.transport);
       }
     } catch (error: Error | any) {

server/src/services/system-config.service.spec.ts

@@ -112,7 +112,6 @@ const updatedConfig = Object.freeze<SystemConfig>({
     mobileRedirectUri: '',
     scope: 'openid email profile',
     signingAlgorithm: 'RS256',
-    profileSigningAlgorithm: 'none',
     storageLabelClaim: 'preferred_username',
     storageQuotaClaim: 'immich_quota',
   },

server/src/validation.ts

@@ -152,14 +152,11 @@ export function validateCronExpression(expression: string) {
   return true;
 }
 
-type IValue = { value: unknown };
+type IValue = { value: string };
 
-export const toEmail = ({ value }: IValue) => (typeof value === 'string' ? value.toLowerCase() : value);
+export const toEmail = ({ value }: IValue) => (value ? value.toLowerCase() : value);
 
-export const toSanitized = ({ value }: IValue) => {
+export const toSanitized = ({ value }: IValue) => sanitize((value || '').replaceAll('.', ''));
-  const input = typeof value === 'string' ? value : '';
-  return sanitize(input.replaceAll('.', ''));
-};
 
 export const isValidInteger = (value: number, options: { min?: number; max?: number }): value is number => {
   const { min = Number.MIN_SAFE_INTEGER, max = Number.MAX_SAFE_INTEGER } = options;

web/.nvmrc

@@ -1 +1 @@
-20.15.1
+20.15

web/Dockerfile

@@ -1,4 +1,4 @@
-FROM node:20.15.1-alpine3.20@sha256:34b7aa411056c85dbf71d240d26516949b3f72b318d796c26b57caaa1df5639a
+FROM node:20.15.0-alpine3.20@sha256:df01469346db2bf1cfc1f7261aeab86b2960efa840fe2bd46d83ff339f463665
 
 RUN apk add --no-cache tini
 USER node

web/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "immich-web",
-  "version": "1.108.0",
+  "version": "1.107.2",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "immich-web",
-      "version": "1.108.0",
+      "version": "1.107.2",
       "license": "GNU Affero General Public License version 3",
       "dependencies": {
         "@formatjs/icu-messageformat-parser": "^2.7.8",
@@ -16,6 +16,7 @@
         "@photo-sphere-viewer/equirectangular-video-adapter": "^5.7.2",
         "@photo-sphere-viewer/video-plugin": "^5.7.2",
         "@zoom-image/svelte": "^0.2.6",
+        "buffer": "^6.0.3",
         "copy-image-clipboard": "^2.1.2",
         "dom-to-image": "^2.6.0",
         "handlebars": "^4.7.8",
@@ -69,13 +70,13 @@
     },
     "../open-api/typescript-sdk": {
       "name": "@immich/sdk",
-      "version": "1.108.0",
+      "version": "1.107.2",
       "license": "GNU Affero General Public License version 3",
       "dependencies": {
         "@oazapfts/runtime": "^1.0.2"
       },
       "devDependencies": {
-        "@types/node": "^20.14.10",
+        "@types/node": "^20.14.9",
         "typescript": "^5.3.3"
       }
     },
@@ -1657,30 +1658,30 @@
       }
     },
     "node_modules/@photo-sphere-viewer/core": {
-      "version": "5.8.2",
+      "version": "5.8.1",
-      "resolved": "https://registry.npmjs.org/@photo-sphere-viewer/core/-/core-5.8.2.tgz",
+      "resolved": "https://registry.npmjs.org/@photo-sphere-viewer/core/-/core-5.8.1.tgz",
-      "integrity": "sha512-7Ex8OLk5ihywT/WpYz/+No6BlGzo/XDbW8M3pe2diBEYU7xXfxQjhQ7WKFRuaKasNrCUNks8r6jM+pUkl4MOtg==",
+      "integrity": "sha512-mxkVNtXSWHGtvPMtBJ+uXA/cxzo4dEZm74rffg14VvF1lmIR7wyqDGCXtdXjFYLRGHjiNuimhDhfaqbM/SoSYQ==",
       "license": "MIT",
       "dependencies": {
-        "three": "^0.166.1"
+        "three": "^0.165.0"
       }
     },
     "node_modules/@photo-sphere-viewer/equirectangular-video-adapter": {
-      "version": "5.8.2",
+      "version": "5.8.1",
-      "resolved": "https://registry.npmjs.org/@photo-sphere-viewer/equirectangular-video-adapter/-/equirectangular-video-adapter-5.8.2.tgz",
+      "resolved": "https://registry.npmjs.org/@photo-sphere-viewer/equirectangular-video-adapter/-/equirectangular-video-adapter-5.8.1.tgz",
-      "integrity": "sha512-HaT7GsI0xydp9vaeZnWQy2jNa0TDb0CohecdlyfQNFtvG4WhpaLnibJgMQSc8m1GtsydK3cGN7HArD0fhAEyIA==",
+      "integrity": "sha512-JUqRhLN+f4RtYmvFtZBpRH4y80DCY5y00HAc26rLAh6eGCs8s4By73n5+j0ICxoaAgARuvt+kw0cdcf18AhrIQ==",
       "license": "MIT",
       "peerDependencies": {
-        "@photo-sphere-viewer/core": "5.8.2"
+        "@photo-sphere-viewer/core": "5.8.1"
       }
     },
     "node_modules/@photo-sphere-viewer/video-plugin": {
-      "version": "5.8.2",
+      "version": "5.8.1",
-      "resolved": "https://registry.npmjs.org/@photo-sphere-viewer/video-plugin/-/video-plugin-5.8.2.tgz",
+      "resolved": "https://registry.npmjs.org/@photo-sphere-viewer/video-plugin/-/video-plugin-5.8.1.tgz",
-      "integrity": "sha512-HKDRkIbGqj4/k0csLRVrLXebkreHINqnb4Os+70VAjSuaK4VxRlmFy5R/LYy6nA7SDxrJR57Nq4//n75DBBDkg==",
+      "integrity": "sha512-EW8CAxAw3kyAyWvTZJFCsE+HzALYGXfcYYrbJjQLBuWObi8b1VSyT63zzzmPs9HwO9o745hyyeEOWbH5g1q5QA==",
       "license": "MIT",
       "peerDependencies": {
-        "@photo-sphere-viewer/core": "5.8.2"
+        "@photo-sphere-viewer/core": "5.8.1"
       }
     },
     "node_modules/@polka/url": {
@@ -3035,6 +3036,25 @@
       "integrity": "sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==",
       "dev": true
     },
+    "node_modules/base64-js": {
+      "version": "1.5.1",
+      "resolved": "https://registry.npmjs.org/base64-js/-/base64-js-1.5.1.tgz",
+      "integrity": "sha512-AKpaYlHn8t4SVbOHCy+b5+KKgvR4vrsD8vbvrbiQJps7fKDTkjkDry6ji0rUJjC0kzbNePLwzxq8iypo41qeWA==",
+      "funding": [
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/feross"
+        },
+        {
+          "type": "patreon",
+          "url": "https://www.patreon.com/feross"
+        },
+        {
+          "type": "consulting",
+          "url": "https://feross.org/support"
+        }
+      ]
+    },
     "node_modules/binary-extensions": {
       "version": "2.2.0",
       "resolved": "https://registry.npmjs.org/binary-extensions/-/binary-extensions-2.2.0.tgz",
@@ -3098,6 +3118,29 @@
         "node": "^6 || ^7 || ^8 || ^9 || ^10 || ^11 || ^12 || >=13.7"
       }
     },
+    "node_modules/buffer": {
+      "version": "6.0.3",
+      "resolved": "https://registry.npmjs.org/buffer/-/buffer-6.0.3.tgz",
+      "integrity": "sha512-FTiCpNxtwiZZHEZbcbTIcZjERVICn9yq/pDFkTl95/AxzD1naBctN7YO68riM/gLSDY7sdrMby8hofADYuuqOA==",
+      "funding": [
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/feross"
+        },
+        {
+          "type": "patreon",
+          "url": "https://www.patreon.com/feross"
+        },
+        {
+          "type": "consulting",
+          "url": "https://feross.org/support"
+        }
+      ],
+      "dependencies": {
+        "base64-js": "^1.3.1",
+        "ieee754": "^1.2.1"
+      }
+    },
     "node_modules/buffer-crc32": {
       "version": "0.2.13",
       "resolved": "https://registry.npmjs.org/buffer-crc32/-/buffer-crc32-0.2.13.tgz",
@@ -8404,9 +8447,9 @@
       }
     },
     "node_modules/three": {
-      "version": "0.166.1",
+      "version": "0.165.0",
-      "resolved": "https://registry.npmjs.org/three/-/three-0.166.1.tgz",
+      "resolved": "https://registry.npmjs.org/three/-/three-0.165.0.tgz",
-      "integrity": "sha512-LtuafkKHHzm61AQA1be2MAYIw1IjmhOUxhBa0prrLpEMWbV7ijvxCRHjSgHPGp2493wLBzwKV46tA9nivLEgKg==",
+      "integrity": "sha512-cc96IlVYGydeceu0e5xq70H8/yoVT/tXBxV/W8A/U6uOq7DXc4/s1Mkmnu6SqoYGhSRWWYFOhVwvq6V0VtbplA==",
       "license": "MIT"
     },
     "node_modules/thumbhash": {

web/package.json

@@ -1,6 +1,6 @@
 {
   "name": "immich-web",
-  "version": "1.108.0",
+  "version": "1.107.2",
   "license": "GNU Affero General Public License version 3",
   "scripts": {
     "dev": "vite dev --host 0.0.0.0 --port 3000",
@@ -68,6 +68,7 @@
     "@photo-sphere-viewer/equirectangular-video-adapter": "^5.7.2",
     "@photo-sphere-viewer/video-plugin": "^5.7.2",
     "@zoom-image/svelte": "^0.2.6",
+    "buffer": "^6.0.3",
     "copy-image-clipboard": "^2.1.2",
     "dom-to-image": "^2.6.0",
     "handlebars": "^4.7.8",
@@ -82,6 +83,6 @@
     "thumbhash": "^0.1.1"
   },
   "volta": {
-    "node": "20.15.1"
+    "node": "20.15.0"
   }
 }

web/src/lib/components/admin-page/settings/admin-settings.svelte

@@ -9,8 +9,8 @@
   import { getConfig, getConfigDefaults, updateConfig, type SystemConfigDto } from '@immich/sdk';
   import { loadConfig } from '$lib/stores/server-config.store';
   import { cloneDeep } from 'lodash-es';
-  import { onMount } from 'svelte';
+  import { createEventDispatcher, onMount } from 'svelte';
-  import type { SettingsResetOptions } from './admin-settings';
+  import type { SettingsEventType } from './admin-settings';
   import { t } from 'svelte-i18n';
 
   export let config: SystemConfigDto;
@@ -18,8 +18,10 @@
   let savedConfig: SystemConfigDto;
   let defaultConfig: SystemConfigDto;
 
-  const handleReset = async (options: SettingsResetOptions) => {
+  const dispatch = createEventDispatcher<{ save: void }>();
-    await (options.default ? resetToDefault(options.configKeys) : reset(options.configKeys));
+
+  const handleReset = async (detail: SettingsEventType['reset']) => {
+    await (detail.default ? resetToDefault(detail.configKeys) : reset(detail.configKeys));
   };
 
   export const handleSave = async (update: Partial<SystemConfigDto>) => {
@@ -36,6 +38,8 @@
       notificationController.show({ message: $t('settings_saved'), type: NotificationType.Info });
 
       await loadConfig();
+
+      dispatch('save');
     } catch (error) {
       handleError(error, $t('errors.unable_to_save_settings'));
     }

web/src/lib/components/admin-page/settings/admin-settings.ts

@@ -1,15 +1,7 @@
 import type { ResetOptions } from '$lib/utils/dipatch';
 import type { SystemConfigDto } from '@immich/sdk';
 
-export type SettingsResetOptions = ResetOptions & { configKeys: Array<keyof SystemConfigDto> };
+export type SettingsEventType = {
-export type SettingsResetEvent = (options: SettingsResetOptions) => void;
+  reset: ResetOptions & { configKeys: Array<keyof SystemConfigDto> };
-export type SettingsSaveEvent = (config: Partial<SystemConfigDto>) => void;
+  save: Partial<SystemConfigDto>;
-
-export type SettingsComponentProps = {
-  disabled?: boolean;
-  defaultConfig: SystemConfigDto;
-  config: SystemConfigDto;
-  savedConfig: SystemConfigDto;
-  onReset: SettingsResetEvent;
-  onSave: SettingsSaveEvent;
 };

web/src/lib/components/admin-page/settings/auth/auth-settings.svelte

@@ -8,8 +8,9 @@
   import SettingSwitch from '$lib/components/shared-components/settings/setting-switch.svelte';
   import { type SystemConfigDto } from '@immich/sdk';
   import { isEqual } from 'lodash-es';
+  import { createEventDispatcher } from 'svelte';
   import { fade } from 'svelte/transition';
-  import type { SettingsResetEvent, SettingsSaveEvent } from '../admin-settings';
+  import type { SettingsEventType } from '../admin-settings';
   import { t } from 'svelte-i18n';
   import FormatMessage from '$lib/components/i18n/format-message.svelte';
 
@@ -17,8 +18,8 @@
   export let defaultConfig: SystemConfigDto;
   export let config: SystemConfigDto; // this is the config that is being edited
   export let disabled = false;
-  export let onReset: SettingsResetEvent;
+
-  export let onSave: SettingsSaveEvent;
+  const dispatch = createEventDispatcher<SettingsEventType>();
 
   let isConfirmOpen = false;
 
@@ -38,7 +39,7 @@
     }
 
     isConfirmOpen = false;
-    onSave({ passwordLogin: config.passwordLogin, oauth: config.oauth });
+    dispatch('save', { passwordLogin: config.passwordLogin, oauth: config.oauth });
   };
 </script>
 
@@ -144,16 +145,6 @@
                 isEdited={!(config.oauth.signingAlgorithm == savedConfig.oauth.signingAlgorithm)}
               />
 
-              <SettingInputField
-                inputType={SettingInputFieldType.TEXT}
-                label={$t('admin.oauth_profile_signing_algorithm').toUpperCase()}
-                desc={$t('admin.oauth_profile_signing_algorithm_description')}
-                bind:value={config.oauth.profileSigningAlgorithm}
-                required={true}
-                disabled={disabled || !config.oauth.enabled}
-                isEdited={!(config.oauth.profileSigningAlgorithm == savedConfig.oauth.profileSigningAlgorithm)}
-              />
-
               <SettingInputField
                 inputType={SettingInputFieldType.TEXT}
                 label={$t('admin.oauth_storage_label_claim').toUpperCase()}
@@ -249,8 +240,8 @@
           showResetToDefault={!isEqual(savedConfig.passwordLogin, defaultConfig.passwordLogin) ||
             !isEqual(savedConfig.oauth, defaultConfig.oauth)}
           {disabled}
-          onReset={(options) => onReset({ ...options, configKeys: ['passwordLogin', 'oauth'] })}
+          on:reset={({ detail }) => dispatch('reset', { ...detail, configKeys: ['passwordLogin', 'oauth'] })}
-          onSave={() => handleSave(false)}
+          on:save={() => handleSave(false)}
         />
       </div>
     </form>

web/src/lib/components/admin-page/settings/ffmpeg/ffmpeg-settings.svelte

@@ -11,8 +11,9 @@
   } from '@immich/sdk';
   import { mdiHelpCircleOutline } from '@mdi/js';
   import { isEqual, sortBy } from 'lodash-es';
+  import { createEventDispatcher } from 'svelte';
   import { fade } from 'svelte/transition';
-  import type { SettingsResetEvent, SettingsSaveEvent } from '../admin-settings';
+  import type { SettingsEventType } from '../admin-settings';
   import SettingAccordion from '$lib/components/shared-components/settings/setting-accordion.svelte';
   import SettingInputField, {
     SettingInputFieldType,
@@ -28,8 +29,8 @@
   export let defaultConfig: SystemConfigDto;
   export let config: SystemConfigDto; // this is the config that is being edited
   export let disabled = false;
-  export let onReset: SettingsResetEvent;
+
-  export let onSave: SettingsSaveEvent;
+  const dispatch = createEventDispatcher<SettingsEventType>();
 </script>
 
 <div>
@@ -367,8 +368,8 @@
 
       <div class="ml-4">
         <SettingButtonsRow
-          onReset={(options) => onReset({ ...options, configKeys: ['ffmpeg'] })}
+          on:reset={({ detail }) => dispatch('reset', { ...detail, configKeys: ['ffmpeg'] })}
-          onSave={() => onSave({ ffmpeg: config.ffmpeg })}
+          on:save={() => dispatch('save', { ffmpeg: config.ffmpeg })}
           showResetToDefault={!isEqual(savedConfig.ffmpeg, defaultConfig.ffmpeg)}
           {disabled}
         />

web/src/lib/components/admin-page/settings/image/image-settings.svelte

@@ -1,8 +1,9 @@
 <script lang="ts">
   import { Colorspace, ImageFormat, type SystemConfigDto } from '@immich/sdk';
   import { isEqual } from 'lodash-es';
+  import { createEventDispatcher } from 'svelte';
   import { fade } from 'svelte/transition';
-  import type { SettingsResetEvent, SettingsSaveEvent } from '../admin-settings';
+  import type { SettingsEventType } from '../admin-settings';
   import SettingSelect from '$lib/components/shared-components/settings/setting-select.svelte';
 
   import SettingSwitch from '$lib/components/shared-components/settings/setting-switch.svelte';
@@ -16,8 +17,8 @@
   export let defaultConfig: SystemConfigDto;
   export let config: SystemConfigDto; // this is the config that is being edited
   export let disabled = false;
-  export let onReset: SettingsResetEvent;
+
-  export let onSave: SettingsSaveEvent;
+  const dispatch = createEventDispatcher<SettingsEventType>();
 </script>
 
 <div>
@@ -113,8 +114,8 @@
 
       <div class="ml-4">
         <SettingButtonsRow
-          onReset={(options) => onReset({ ...options, configKeys: ['image'] })}
+          on:reset={({ detail }) => dispatch('reset', { ...detail, configKeys: ['image'] })}
-          onSave={() => onSave({ image: config.image })}
+          on:save={() => dispatch('save', { image: config.image })}
           showResetToDefault={!isEqual(savedConfig.image, defaultConfig.image)}
           {disabled}
         />

web/src/lib/components/admin-page/settings/job-settings/job-settings.svelte

@@ -2,8 +2,9 @@
   import { getJobName } from '$lib/utils';
   import { JobName, type SystemConfigDto, type SystemConfigJobDto } from '@immich/sdk';
   import { isEqual } from 'lodash-es';
+  import { createEventDispatcher } from 'svelte';
   import { fade } from 'svelte/transition';
-  import type { SettingsResetEvent, SettingsSaveEvent } from '../admin-settings';
+  import type { SettingsEventType } from '../admin-settings';
   import SettingButtonsRow from '$lib/components/shared-components/settings/setting-buttons-row.svelte';
   import SettingInputField, {
     SettingInputFieldType,
@@ -14,8 +15,8 @@
   export let defaultConfig: SystemConfigDto;
   export let config: SystemConfigDto; // this is the config that is being edited
   export let disabled = false;
-  export let onReset: SettingsResetEvent;
+
-  export let onSave: SettingsSaveEvent;
+  const dispatch = createEventDispatcher<SettingsEventType>();
 
   const jobNames = [
     JobName.ThumbnailGeneration,
@@ -66,8 +67,8 @@
 
       <div class="ml-4">
         <SettingButtonsRow
-          onReset={(options) => onReset({ ...options, configKeys: ['job'] })}
+          on:reset={({ detail }) => dispatch('reset', { ...detail, configKeys: ['job'] })}
-          onSave={() => onSave({ job: config.job })}
+          on:save={() => dispatch('save', { job: config.job })}
           showResetToDefault={!isEqual(savedConfig.job, defaultConfig.job)}
           {disabled}
         />