add cli

Update README_de_DE.md

Update German Readme to newest version & improve some translations

.github/workflows/cli.yml

@@ -56,10 +56,10 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3.0.0
+        uses: docker/setup-qemu-action@v3.1.0
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3.3.0
+        uses: docker/setup-buildx-action@v3.4.0
 
       - name: Login to GitHub Container Registry
         uses: docker/login-action@v3
@@ -88,7 +88,7 @@ jobs:
             type=raw,value=latest,enable=${{ github.event_name == 'release' }}
 
       - name: Build and push image
-        uses: docker/build-push-action@v6.2.0
+        uses: docker/build-push-action@v6.3.0
         with:
           file: cli/Dockerfile
           platforms: linux/amd64,linux/arm64

.github/workflows/docker.yml

@@ -63,10 +63,10 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3.0.0
+        uses: docker/setup-qemu-action@v3.1.0
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3.3.0
+        uses: docker/setup-buildx-action@v3.4.0
 
       - name: Login to Docker Hub
         # Only push to Docker Hub when making a release
@@ -115,7 +115,7 @@ jobs:
           fi
 
       - name: Build and push image
-        uses: docker/build-push-action@v6.2.0
+        uses: docker/build-push-action@v6.3.0
         with:
           context: ${{ matrix.context }}
           file: ${{ matrix.file }}

README.md

@@ -54,7 +54,7 @@
 - [Roadmap](https://immich.app/roadmap)
 - [Demo](#demo)
 - [Features](#features)
-- [Translations](https://immich.app/docs/developer/tranlations)
+- [Translations](https://immich.app/docs/developer/translations)
 - [Contributing](https://immich.app/docs/overview/support-the-project)
 
 ## Demo

cli/.nvmrc

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

cli/Dockerfile

@@ -1,4 +1,4 @@
-FROM node:20.15.0-alpine3.20@sha256:df01469346db2bf1cfc1f7261aeab86b2960efa840fe2bd46d83ff339f463665 as core
+FROM node:20.15.1-alpine3.20@sha256:34b7aa411056c85dbf71d240d26516949b3f72b318d796c26b57caaa1df5639a 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

@@ -1,15 +1,16 @@
 {
   "name": "@immich/cli",
-  "version": "2.2.5",
+  "version": "2.2.8",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@immich/cli",
-      "version": "2.2.5",
+      "version": "2.2.8",
       "license": "GNU Affero General Public License version 3",
       "dependencies": {
         "fast-glob": "^3.3.2",
+        "fastq": "^1.17.1",
         "lodash-es": "^4.17.21"
       },
       "bin": {
@@ -21,7 +22,7 @@
         "@types/cli-progress": "^3.11.0",
         "@types/lodash-es": "^4.17.12",
         "@types/mock-fs": "^4.13.1",
-        "@types/node": "^20.14.9",
+        "@types/node": "^20.14.10",
         "@typescript-eslint/eslint-plugin": "^7.0.0",
         "@typescript-eslint/parser": "^7.0.0",
         "@vitest/coverage-v8": "^1.2.2",
@@ -34,11 +35,12 @@
         "eslint-plugin-unicorn": "^54.0.0",
         "mock-fs": "^5.2.0",
         "prettier": "^3.2.5",
-        "prettier-plugin-organize-imports": "^3.2.4",
+        "prettier-plugin-organize-imports": "^4.0.0",
         "typescript": "^5.3.3",
         "vite": "^5.0.12",
         "vite-tsconfig-paths": "^4.3.2",
         "vitest": "^1.2.2",
+        "vitest-fetch-mock": "^0.2.2",
         "yaml": "^2.3.1"
       },
       "engines": {
@@ -47,14 +49,14 @@
     },
     "../open-api/typescript-sdk": {
       "name": "@immich/sdk",
-      "version": "1.107.0",
+      "version": "1.108.0",
       "dev": true,
       "license": "GNU Affero General Public License version 3",
       "dependencies": {
         "@oazapfts/runtime": "^1.0.2"
       },
       "devDependencies": {
-        "@types/node": "^20.14.9",
+        "@types/node": "^20.14.10",
         "typescript": "^5.3.3"
       }
     },
@@ -1161,9 +1163,9 @@
       }
     },
     "node_modules/@types/node": {
-      "version": "20.14.9",
+      "version": "20.14.10",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.14.9.tgz",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.14.10.tgz",
-      "integrity": "sha512-06OCtnTXtWOZBJlRApleWndH4JsRVs1pDCc8dLSQp+7PpUpX3ePdHyeNSFTeSe7FtKyQkrlPvHwJOW3SLd8Oyg==",
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       "dev": true,
       "license": "MIT",
       "dependencies": {
@@ -1177,17 +1179,17 @@
       "dev": true
     },
     "node_modules/@typescript-eslint/eslint-plugin": {
-      "version": "7.14.1",
+      "version": "7.15.0",
-      "resolved": "https://registry.npmjs.org/@typescript-eslint/eslint-plugin/-/eslint-plugin-7.14.1.tgz",
+      "resolved": "https://registry.npmjs.org/@typescript-eslint/eslint-plugin/-/eslint-plugin-7.15.0.tgz",
-      "integrity": "sha512-aAJd6bIf2vvQRjUG3ZkNXkmBpN+J7Wd0mfQiiVCJMu9Z5GcZZdcc0j8XwN/BM97Fl7e3SkTXODSk4VehUv7CGw==",
+      "integrity": "sha512-uiNHpyjZtFrLwLDpHnzaDlP3Tt6sGMqTCiqmxaN4n4RP0EfYZDODJyddiFDF44Hjwxr5xAcaYxVKm9QKQFJFLA==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "@eslint-community/regexpp": "^4.10.0",
-        "@typescript-eslint/scope-manager": "7.14.1",
+        "@typescript-eslint/scope-manager": "7.15.0",
-        "@typescript-eslint/type-utils": "7.14.1",
+        "@typescript-eslint/type-utils": "7.15.0",
-        "@typescript-eslint/utils": "7.14.1",
+        "@typescript-eslint/utils": "7.15.0",
-        "@typescript-eslint/visitor-keys": "7.14.1",
+        "@typescript-eslint/visitor-keys": "7.15.0",
         "graphemer": "^1.4.0",
         "ignore": "^5.3.1",
         "natural-compare": "^1.4.0",
@@ -1211,16 +1213,16 @@
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     "node_modules/@typescript-eslint/parser": {
-      "version": "7.14.1",
+      "version": "7.15.0",
-      "resolved": "https://registry.npmjs.org/@typescript-eslint/parser/-/parser-7.14.1.tgz",
+      "resolved": "https://registry.npmjs.org/@typescript-eslint/parser/-/parser-7.15.0.tgz",
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+      "integrity": "sha512-k9fYuQNnypLFcqORNClRykkGOMOj+pV6V91R4GO/l1FDGwpqmSwoOQrOHo3cGaH63e+D3ZiCAOsuS/D2c99j/A==",
       "dev": true,
       "license": "BSD-2-Clause",
       "dependencies": {
-        "@typescript-eslint/scope-manager": "7.14.1",
+        "@typescript-eslint/scope-manager": "7.15.0",
-        "@typescript-eslint/types": "7.14.1",
+        "@typescript-eslint/types": "7.15.0",
-        "@typescript-eslint/typescript-estree": "7.14.1",
+        "@typescript-eslint/typescript-estree": "7.15.0",
-        "@typescript-eslint/visitor-keys": "7.14.1",
+        "@typescript-eslint/visitor-keys": "7.15.0",
         "debug": "^4.3.4"
       },
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@@ -1240,14 +1242,14 @@
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     },
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+      "version": "7.15.0",
-      "resolved": "https://registry.npmjs.org/@typescript-eslint/scope-manager/-/scope-manager-7.14.1.tgz",
+      "resolved": "https://registry.npmjs.org/@typescript-eslint/scope-manager/-/scope-manager-7.15.0.tgz",
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+      "integrity": "sha512-Q/1yrF/XbxOTvttNVPihxh1b9fxamjEoz2Os/Pe38OHwxC24CyCqXxGTOdpb4lt6HYtqw9HetA/Rf6gDGaMPlw==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@typescript-eslint/types": "7.14.1",
+        "@typescript-eslint/types": "7.15.0",
-        "@typescript-eslint/visitor-keys": "7.14.1"
+        "@typescript-eslint/visitor-keys": "7.15.0"
       },
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@@ -1258,14 +1260,14 @@
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     "node_modules/@typescript-eslint/type-utils": {
-      "version": "7.14.1",
+      "version": "7.15.0",
-      "resolved": "https://registry.npmjs.org/@typescript-eslint/type-utils/-/type-utils-7.14.1.tgz",
+      "resolved": "https://registry.npmjs.org/@typescript-eslint/type-utils/-/type-utils-7.15.0.tgz",
-      "integrity": "sha512-/MzmgNd3nnbDbOi3LfasXWWe292+iuo+umJ0bCCMCPc1jLO/z2BQmWUUUXvXLbrQey/JgzdF/OV+I5bzEGwJkQ==",
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       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@typescript-eslint/typescript-estree": "7.14.1",
+        "@typescript-eslint/typescript-estree": "7.15.0",
-        "@typescript-eslint/utils": "7.14.1",
+        "@typescript-eslint/utils": "7.15.0",
         "debug": "^4.3.4",
         "ts-api-utils": "^1.3.0"
       },
@@ -1286,9 +1288,9 @@
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     "node_modules/@typescript-eslint/types": {
-      "version": "7.14.1",
+      "version": "7.15.0",
-      "resolved": "https://registry.npmjs.org/@typescript-eslint/types/-/types-7.14.1.tgz",
+      "resolved": "https://registry.npmjs.org/@typescript-eslint/types/-/types-7.15.0.tgz",
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@@ -1300,14 +1302,14 @@
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-      "version": "7.14.1",
+      "version": "7.15.0",
-      "resolved": "https://registry.npmjs.org/@typescript-eslint/typescript-estree/-/typescript-estree-7.14.1.tgz",
+      "resolved": "https://registry.npmjs.org/@typescript-eslint/typescript-estree/-/typescript-estree-7.15.0.tgz",
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       "dev": true,
       "license": "BSD-2-Clause",
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-        "@typescript-eslint/types": "7.14.1",
+        "@typescript-eslint/types": "7.15.0",
-        "@typescript-eslint/visitor-keys": "7.14.1",
+        "@typescript-eslint/visitor-keys": "7.15.0",
         "debug": "^4.3.4",
         "globby": "^11.1.0",
         "is-glob": "^4.0.3",
@@ -1329,16 +1331,16 @@
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     "node_modules/@typescript-eslint/utils": {
-      "version": "7.14.1",
+      "version": "7.15.0",
-      "resolved": "https://registry.npmjs.org/@typescript-eslint/utils/-/utils-7.14.1.tgz",
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       "license": "MIT",
       "dependencies": {
         "@eslint-community/eslint-utils": "^4.4.0",
-        "@typescript-eslint/scope-manager": "7.14.1",
+        "@typescript-eslint/scope-manager": "7.15.0",
-        "@typescript-eslint/types": "7.14.1",
+        "@typescript-eslint/types": "7.15.0",
-        "@typescript-eslint/typescript-estree": "7.14.1"
+        "@typescript-eslint/typescript-estree": "7.15.0"
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@@ -1352,13 +1354,13 @@
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-      "version": "7.14.1",
+      "version": "7.15.0",
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       "license": "MIT",
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+        "@typescript-eslint/types": "7.15.0",
         "eslint-visitor-keys": "^3.4.3"
       },
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@@ -1853,6 +1855,15 @@
         "url": "https://opencollective.com/core-js"
       }
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+    "node_modules/cross-fetch": {
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+      "resolved": "https://registry.npmjs.org/cross-fetch/-/cross-fetch-3.1.8.tgz",
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+      "dev": true,
+      "dependencies": {
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+      }
+    },
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+    "node_modules/node-fetch": {
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+      "resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-2.7.0.tgz",
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+      "dev": true,
+      "dependencies": {
+        "whatwg-url": "^5.0.0"
+      },
+      "engines": {
+        "node": "4.x || >=6.0.0"
+      },
+      "peerDependencies": {
+        "encoding": "^0.1.0"
+      },
+      "peerDependenciesMeta": {
+        "encoding": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/node-releases": {
       "version": "2.0.14",
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@@ -3369,10 +3400,11 @@
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     "node_modules/picocolors": {
-      "version": "1.0.0",
+      "version": "1.0.1",
-      "resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.0.0.tgz",
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-      "dev": true
+      "dev": true,
+      "license": "ISC"
     },
     "node_modules/picomatch": {
       "version": "2.3.1",
@@ -3406,9 +3438,9 @@
       }
     },
     "node_modules/postcss": {
-      "version": "8.4.38",
+      "version": "8.4.39",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.38.tgz",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.39.tgz",
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       "dev": true,
       "funding": [
         {
@@ -3424,9 +3456,10 @@
           "url": "https://github.com/sponsors/ai"
         }
       ],
+      "license": "MIT",
       "dependencies": {
         "nanoid": "^3.3.7",
-        "picocolors": "^1.0.0",
+        "picocolors": "^1.0.1",
         "source-map-js": "^1.2.0"
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       "engines": {
@@ -3471,21 +3504,22 @@
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     "node_modules/prettier-plugin-organize-imports": {
-      "version": "3.2.4",
+      "version": "4.0.0",
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       "dev": true,
+      "license": "MIT",
       "peerDependencies": {
-        "@volar/vue-language-plugin-pug": "^1.0.4",
+        "@vue/language-plugin-pug": "^2.0.24",
-        "@volar/vue-typescript": "^1.0.4",
         "prettier": ">=2.0",
-        "typescript": ">=2.9"
+        "typescript": ">=2.9",
+        "vue-tsc": "^2.0.24"
       },
       "peerDependenciesMeta": {
-        "@volar/vue-language-plugin-pug": {
+        "@vue/language-plugin-pug": {
           "optional": true
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-        "@volar/vue-typescript": {
+        "vue-tsc": {
           "optional": true
         }
       }
@@ -4168,6 +4202,12 @@
         "node": ">=8.0"
       }
     },
+    "node_modules/tr46": {
+      "version": "0.0.3",
+      "resolved": "https://registry.npmjs.org/tr46/-/tr46-0.0.3.tgz",
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+    },
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       "resolved": "https://registry.npmjs.org/ts-api-utils/-/ts-api-utils-1.3.0.tgz",
@@ -4240,9 +4280,9 @@
       }
     },
     "node_modules/typescript": {
-      "version": "5.5.2",
+      "version": "5.5.3",
-      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.5.2.tgz",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.5.3.tgz",
-      "integrity": "sha512-NcRtPEOsPFFWjobJEtfihkLCZCXZt/os3zf8nTxjVH3RvTSxjrCamJpbExGvYOF+tFHc3pA65qpdwPbzjohhew==",
+      "integrity": "sha512-/hreyEujaB0w76zKo6717l3L0o/qEUtRgdvUBvlkhoWeOVMjMuHNHk0BRBzikzuGDqNmPQbg5ifMEqsHLiIUcQ==",
       "dev": true,
       "license": "Apache-2.0",
       "bin": {
@@ -4315,14 +4355,14 @@
       }
     },
     "node_modules/vite": {
-      "version": "5.3.1",
+      "version": "5.3.3",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-5.3.1.tgz",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-5.3.3.tgz",
-      "integrity": "sha512-XBmSKRLXLxiaPYamLv3/hnP/KXDai1NDexN0FpkTaZXTfycHvkRHoenpgl/fvuK/kPbB6xAgoyiryAhQNxYmAQ==",
+      "integrity": "sha512-NPQdeCU0Dv2z5fu+ULotpuq5yfCS1BzKUIPhNbP3YBfAMGJXbt2nS+sbTFu+qchaqWTD+H3JK++nRwr6XIcp6A==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "esbuild": "^0.21.3",
-        "postcss": "^8.4.38",
+        "postcss": "^8.4.39",
         "rollup": "^4.13.0"
       },
       "bin": {
@@ -4476,6 +4516,37 @@
         }
       }
     },
+    "node_modules/vitest-fetch-mock": {
+      "version": "0.2.2",
+      "resolved": "https://registry.npmjs.org/vitest-fetch-mock/-/vitest-fetch-mock-0.2.2.tgz",
+      "integrity": "sha512-XmH6QgTSjCWrqXoPREIdbj40T7i1xnGmAsTAgfckoO75W1IEHKR8hcPCQ7SO16RsdW1t85oUm6pcQRLeBgjVYQ==",
+      "dev": true,
+      "dependencies": {
+        "cross-fetch": "^3.0.6"
+      },
+      "engines": {
+        "node": ">=14.14.0"
+      },
+      "peerDependencies": {
+        "vitest": ">=0.16.0"
+      }
+    },
+    "node_modules/webidl-conversions": {
+      "version": "3.0.1",
+      "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-3.0.1.tgz",
+      "integrity": "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ==",
+      "dev": true
+    },
+    "node_modules/whatwg-url": {
+      "version": "5.0.0",
+      "resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-5.0.0.tgz",
+      "integrity": "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==",
+      "dev": true,
+      "dependencies": {
+        "tr46": "~0.0.3",
+        "webidl-conversions": "^3.0.0"
+      }
+    },
     "node_modules/which": {
       "version": "2.0.2",
       "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",

cli/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@immich/cli",
-  "version": "2.2.5",
+  "version": "2.2.8",
   "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": "^20.14.9",
+    "@types/node": "^20.14.10",
     "@typescript-eslint/eslint-plugin": "^7.0.0",
     "@typescript-eslint/parser": "^7.0.0",
     "@vitest/coverage-v8": "^1.2.2",
@@ -31,11 +31,12 @@
     "eslint-plugin-unicorn": "^54.0.0",
     "mock-fs": "^5.2.0",
     "prettier": "^3.2.5",
-    "prettier-plugin-organize-imports": "^3.2.4",
+    "prettier-plugin-organize-imports": "^4.0.0",
     "typescript": "^5.3.3",
     "vite": "^5.0.12",
     "vite-tsconfig-paths": "^4.3.2",
     "vitest": "^1.2.2",
+    "vitest-fetch-mock": "^0.2.2",
     "yaml": "^2.3.1"
   },
   "scripts": {
@@ -59,9 +60,10 @@
   },
   "dependencies": {
     "fast-glob": "^3.3.2",
+    "fastq": "^1.17.1",
     "lodash-es": "^4.17.21"
   },
   "volta": {
-    "node": "20.15.0"
+    "node": "20.15.1"
   }
 }

cli/src/commands/asset.spec.ts

@@ -1,10 +1,18 @@
-import { platform } from 'node:os';
+import * as fs from 'node:fs';
-import { UploadOptionsDto, getAlbumName } from 'src/commands/asset';
+import * as os from 'node:os';
-import { describe, expect, it } from 'vitest';
+import * as path from 'node:path';
+import { describe, expect, it, vi } from 'vitest';
 
-describe('Unit function tests', () => {
+import { Action, checkBulkUpload, defaults, Reason } from '@immich/sdk';
+import createFetchMock from 'vitest-fetch-mock';
+
+import { checkForDuplicates, getAlbumName, uploadFiles, UploadOptionsDto } from './asset';
+
+vi.mock('@immich/sdk');
+
+describe('getAlbumName', () => {
   it('should return a non-undefined value', () => {
-    if (platform() === 'win32') {
+    if (os.platform() === 'win32') {
       // This is meaningless for Unix systems.
       expect(getAlbumName(String.raw`D:\test\Filename.txt`, {} as UploadOptionsDto)).toBe('test');
     }
@@ -17,3 +25,177 @@ describe('Unit function tests', () => {
     );
   });
 });
+
+describe('uploadFiles', () => {
+  const testDir = fs.mkdtempSync(path.join(os.tmpdir(), 'test-'));
+  const testFilePath = path.join(testDir, 'test.png');
+  const testFileData = 'test';
+  const baseUrl = 'http://example.com';
+  const apiKey = 'key';
+  const retry = 3;
+
+  const fetchMocker = createFetchMock(vi);
+
+  beforeEach(() => {
+    // Create a test file
+    fs.writeFileSync(testFilePath, testFileData);
+
+    // Defaults
+    vi.mocked(defaults).baseUrl = baseUrl;
+    vi.mocked(defaults).headers = { 'x-api-key': apiKey };
+
+    fetchMocker.enableMocks();
+    fetchMocker.resetMocks();
+  });
+
+  it('returns new assets when upload file is successful', async () => {
+    fetchMocker.doMockIf(new RegExp(`${baseUrl}/assets$`), () => {
+      return {
+        status: 200,
+        body: JSON.stringify({ id: 'fc5621b1-86f6-44a1-9905-403e607df9f5', status: 'created' }),
+      };
+    });
+
+    await expect(uploadFiles([testFilePath], { concurrency: 1 })).resolves.toEqual([
+      {
+        filepath: testFilePath,
+        id: 'fc5621b1-86f6-44a1-9905-403e607df9f5',
+      },
+    ]);
+  });
+
+  it('returns new assets when upload file retry is successful', async () => {
+    let counter = 0;
+    fetchMocker.doMockIf(new RegExp(`${baseUrl}/assets$`), () => {
+      counter++;
+      if (counter < retry) {
+        throw new Error('Network error');
+      }
+
+      return {
+        status: 200,
+        body: JSON.stringify({ id: 'fc5621b1-86f6-44a1-9905-403e607df9f5', status: 'created' }),
+      };
+    });
+
+    await expect(uploadFiles([testFilePath], { concurrency: 1 })).resolves.toEqual([
+      {
+        filepath: testFilePath,
+        id: 'fc5621b1-86f6-44a1-9905-403e607df9f5',
+      },
+    ]);
+  });
+
+  it('returns new assets when upload file retry is failed', async () => {
+    fetchMocker.doMockIf(new RegExp(`${baseUrl}/assets$`), () => {
+      throw new Error('Network error');
+    });
+
+    await expect(uploadFiles([testFilePath], { concurrency: 1 })).resolves.toEqual([]);
+  });
+});
+
+describe('checkForDuplicates', () => {
+  const testDir = fs.mkdtempSync(path.join(os.tmpdir(), 'test-'));
+  const testFilePath = path.join(testDir, 'test.png');
+  const testFileData = 'test';
+  const testFileChecksum = 'a94a8fe5ccb19ba61c4c0873d391e987982fbbd3'; // SHA1
+  const retry = 3;
+
+  beforeEach(() => {
+    // Create a test file
+    fs.writeFileSync(testFilePath, testFileData);
+  });
+
+  it('checks duplicates', async () => {
+    vi.mocked(checkBulkUpload).mockResolvedValue({
+      results: [
+        {
+          action: Action.Accept,
+          id: testFilePath,
+        },
+      ],
+    });
+
+    await checkForDuplicates([testFilePath], { concurrency: 1 });
+
+    expect(checkBulkUpload).toHaveBeenCalledWith({
+      assetBulkUploadCheckDto: {
+        assets: [
+          {
+            checksum: testFileChecksum,
+            id: testFilePath,
+          },
+        ],
+      },
+    });
+  });
+
+  it('returns duplicates when check duplicates is rejected', async () => {
+    vi.mocked(checkBulkUpload).mockResolvedValue({
+      results: [
+        {
+          action: Action.Reject,
+          id: testFilePath,
+          assetId: 'fc5621b1-86f6-44a1-9905-403e607df9f5',
+          reason: Reason.Duplicate,
+        },
+      ],
+    });
+
+    await expect(checkForDuplicates([testFilePath], { concurrency: 1 })).resolves.toEqual({
+      duplicates: [
+        {
+          filepath: testFilePath,
+          id: 'fc5621b1-86f6-44a1-9905-403e607df9f5',
+        },
+      ],
+      newFiles: [],
+    });
+  });
+
+  it('returns new assets when check duplicates is accepted', async () => {
+    vi.mocked(checkBulkUpload).mockResolvedValue({
+      results: [
+        {
+          action: Action.Accept,
+          id: testFilePath,
+        },
+      ],
+    });
+
+    await expect(checkForDuplicates([testFilePath], { concurrency: 1 })).resolves.toEqual({
+      duplicates: [],
+      newFiles: [testFilePath],
+    });
+  });
+
+  it('returns results when check duplicates retry is successful', async () => {
+    let mocked = vi.mocked(checkBulkUpload);
+    for (let i = 1; i < retry; i++) {
+      mocked = mocked.mockRejectedValueOnce(new Error('Network error'));
+    }
+    mocked.mockResolvedValue({
+      results: [
+        {
+          action: Action.Accept,
+          id: testFilePath,
+        },
+      ],
+    });
+
+    await expect(checkForDuplicates([testFilePath], { concurrency: 1 })).resolves.toEqual({
+      duplicates: [],
+      newFiles: [testFilePath],
+    });
+  });
+
+  it('returns results when check duplicates retry is failed', async () => {
+    vi.mocked(checkBulkUpload).mockRejectedValue(new Error('Network error'));
+
+    await expect(checkForDuplicates([testFilePath], { concurrency: 1 })).resolves.toEqual({
+      duplicates: [],
+      newFiles: [],
+    });
+  });
+});

cli/src/commands/asset.ts

@@ -16,6 +16,7 @@ import { chunk } from 'lodash-es';
 import { Stats, createReadStream } from 'node:fs';
 import { stat, unlink } from 'node:fs/promises';
 import path, { basename } from 'node:path';
+import { Queue } from 'src/queue';
 import { BaseOptions, authenticate, crawl, sha1 } from 'src/utils';
 
 const s = (count: number) => (count === 1 ? '' : 's');
@@ -83,7 +84,7 @@ const scan = async (pathsToCrawl: string[], options: UploadOptionsDto) => {
   return files;
 };
 
-const checkForDuplicates = async (files: string[], { concurrency, skipHash }: UploadOptionsDto) => {
+export const checkForDuplicates = async (files: string[], { concurrency, skipHash }: UploadOptionsDto) => {
   if (skipHash) {
     console.log('Skipping hash check, assuming all files are new');
     return { newFiles: files, duplicates: [] };
@@ -99,32 +100,50 @@ const checkForDuplicates = async (files: string[], { concurrency, skipHash }: Up
   const newFiles: string[] = [];
   const duplicates: Asset[] = [];
 
-  try {
+  const queue = new Queue<string[], AssetBulkUploadCheckResults>(
-    // TODO refactor into a queue
+    async (filepaths: string[]) => {
-    for (const items of chunk(files, concurrency)) {
+      const dto = await Promise.all(
-      const dto = await Promise.all(items.map(async (filepath) => ({ id: filepath, checksum: await sha1(filepath) })));
+        filepaths.map(async (filepath) => ({ id: filepath, checksum: await sha1(filepath) })),
-      const { results } = await checkBulkUpload({ assetBulkUploadCheckDto: { assets: dto } });
+      );
-
+      const response = await checkBulkUpload({ assetBulkUploadCheckDto: { assets: dto } });
-      for (const { id: filepath, assetId, action } of results as AssetBulkUploadCheckResults) {
+      const results = response.results as AssetBulkUploadCheckResults;
+      for (const { id: filepath, assetId, action } of results) {
         if (action === Action.Accept) {
           newFiles.push(filepath);
         } else {
           // rejects are always duplicates
           duplicates.push({ id: assetId as string, filepath });
         }
-        progressBar.increment();
       }
-    }
+      progressBar.increment(filepaths.length);
-  } finally {
+      return results;
-    progressBar.stop();
+    },
+    { concurrency, retry: 3 },
+  );
+
+  for (const items of chunk(files, concurrency)) {
+    await queue.push(items);
   }
 
+  await queue.drained();
+
+  progressBar.stop();
+
   console.log(`Found ${newFiles.length} new files and ${duplicates.length} duplicate${s(duplicates.length)}`);
 
+  // Report failures
+  const failedTasks = queue.tasks.filter((task) => task.status === 'failed');
+  if (failedTasks.length > 0) {
+    console.log(`Failed to verify ${failedTasks.length} file${s(failedTasks.length)}:`);
+    for (const task of failedTasks) {
+      console.log(`- ${task.data} - ${task.error}`);
+    }
+  }
+
   return { newFiles, duplicates };
 };
 
-const uploadFiles = async (files: string[], { dryRun, concurrency }: UploadOptionsDto): Promise<Asset[]> => {
+export const uploadFiles = async (files: string[], { dryRun, concurrency }: UploadOptionsDto): Promise<Asset[]> => {
   if (files.length === 0) {
     console.log('All assets were already uploaded, nothing to do.');
     return [];
@@ -158,15 +177,15 @@ const uploadFiles = async (files: string[], { dryRun, concurrency }: UploadOptio
 
   const newAssets: Asset[] = [];
 
-  try {
+  const queue = new Queue<string, AssetMediaResponseDto>(
-    for (const items of chunk(files, concurrency)) {
+    async (filepath: string) => {
-      await Promise.all(
+      const stats = statsMap.get(filepath);
-        items.map(async (filepath) => {
+      if (!stats) {
-          const stats = statsMap.get(filepath) as Stats;
+        throw new Error(`Stats not found for ${filepath}`);
+      }
+
       const response = await uploadFile(filepath, stats);
-
       newAssets.push({ id: response.id, filepath });
-
       if (response.status === AssetMediaStatus.Duplicate) {
         duplicateCount++;
         duplicateSize += stats.size ?? 0;
@@ -178,17 +197,32 @@ const uploadFiles = async (files: string[], { dryRun, concurrency }: UploadOptio
       uploadProgress.update(successSize, { value_formatted: byteSize(successSize + duplicateSize) });
 
       return response;
-        }),
+    },
+    { concurrency, retry: 3 },
   );
+
+  for (const filepath of files) {
+    await queue.push(filepath);
   }
-  } finally {
+
+  await queue.drained();
+
   uploadProgress.stop();
-  }
 
   console.log(`Successfully uploaded ${successCount} new asset${s(successCount)} (${byteSize(successSize)})`);
   if (duplicateCount > 0) {
     console.log(`Skipped ${duplicateCount} duplicate asset${s(duplicateCount)} (${byteSize(duplicateSize)})`);
   }
+
+  // Report failures
+  const failedTasks = queue.tasks.filter((task) => task.status === 'failed');
+  if (failedTasks.length > 0) {
+    console.log(`Failed to upload ${failedTasks.length} asset${s(failedTasks.length)}:`);
+    for (const task of failedTasks) {
+      console.log(`- ${task.data} - ${task.error}`);
+    }
+  }
+
   return newAssets;
 };
 

cli/src/queue.ts

@@ -0,0 +1,131 @@
+import * as fastq from 'fastq';
+import { uniqueId } from 'lodash-es';
+
+export type Task<T, R> = {
+  readonly id: string;
+  status: 'idle' | 'processing' | 'succeeded' | 'failed';
+  data: T;
+  error: unknown | undefined;
+  count: number;
+  // TODO: Could be useful to adding progress property.
+  // TODO: Could be useful to adding start_at/end_at/duration properties.
+  result: undefined | R;
+};
+
+export type QueueOptions = {
+  verbose?: boolean;
+  concurrency?: number;
+  retry?: number;
+  // TODO: Could be useful to adding timeout property for retry.
+};
+
+export type ComputedQueueOptions = Required<QueueOptions>;
+
+export const defaultQueueOptions = {
+  concurrency: 1,
+  retry: 0,
+  verbose: false,
+};
+
+/**
+ * An in-memory queue that processes tasks in parallel with a given concurrency.
+ * @see {@link https://www.npmjs.com/package/fastq}
+ * @template T - The type of the worker task data.
+ * @template R - The type of the worker output data.
+ */
+export class Queue<T, R> {
+  private readonly queue: fastq.queueAsPromised<string, Task<T, R>>;
+  private readonly store = new Map<string, Task<T, R>>();
+  readonly options: ComputedQueueOptions;
+  readonly worker: (data: T) => Promise<R>;
+
+  /**
+   * Create a new queue.
+   * @param worker - The worker function that processes the task.
+   * @param options - The queue options.
+   */
+  constructor(worker: (data: T) => Promise<R>, options?: QueueOptions) {
+    this.options = { ...defaultQueueOptions, ...options };
+    this.worker = worker;
+    this.store = new Map<string, Task<T, R>>();
+    this.queue = this.buildQueue();
+  }
+
+  get tasks(): Task<T, R>[] {
+    const tasks: Task<T, R>[] = [];
+    for (const task of this.store.values()) {
+      tasks.push(task);
+    }
+    return tasks;
+  }
+
+  getTask(id: string): Task<T, R> {
+    const task = this.store.get(id);
+    if (!task) {
+      throw new Error(`Task with id ${id} not found`);
+    }
+    return task;
+  }
+
+  /**
+   * Wait for the queue to be empty.
+   * @returns Promise<void> - The returned Promise will be resolved when all tasks in the queue have been processed by a worker.
+   * This promise could be ignored as it will not lead to a `unhandledRejection`.
+   */
+  async drained(): Promise<void> {
+    await this.queue.drain();
+  }
+
+  /**
+   * Add a task at the end of the queue.
+   * @see {@link https://www.npmjs.com/package/fastq}
+   * @param data
+   * @returns Promise<void> - A Promise that will be fulfilled (rejected) when the task is completed successfully (unsuccessfully).
+   * This promise could be ignored as it will not lead to a `unhandledRejection`.
+   */
+  async push(data: T): Promise<Task<T, R>> {
+    const id = uniqueId();
+    const task: Task<T, R> = { id, status: 'idle', error: undefined, count: 0, data, result: undefined };
+    this.store.set(id, task);
+    return this.queue.push(id);
+  }
+
+  // TODO: Support more function delegation to fastq.
+
+  private buildQueue(): fastq.queueAsPromised<string, Task<T, R>> {
+    return fastq.promise((id: string) => {
+      const task = this.getTask(id);
+      return this.work(task);
+    }, this.options.concurrency);
+  }
+
+  private async work(task: Task<T, R>): Promise<Task<T, R>> {
+    task.count += 1;
+    task.error = undefined;
+    task.status = 'processing';
+    if (this.options.verbose) {
+      console.log('[task] processing:', task);
+    }
+    try {
+      task.result = await this.worker(task.data);
+      task.status = 'succeeded';
+      if (this.options.verbose) {
+        console.log('[task] succeeded:', task);
+      }
+      return task;
+    } catch (error) {
+      task.error = error;
+      task.status = 'failed';
+      if (this.options.verbose) {
+        console.log('[task] failed:', task);
+      }
+      if (this.options.retry > 0 && task.count < this.options.retry) {
+        if (this.options.verbose) {
+          console.log('[task] retry:', task);
+        }
+        return this.work(task);
+      }
+      return task;
+    }
+  }
+}

docker/docker-compose.prod.yml

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

docs/.nvmrc

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

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 succeptible 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 susceptible to [man in the middle](https://en.wikipedia.org/wiki/Man-in-the-middle_attack) attacks.
 :::
 
 ## Option 1: VPN to home network

docs/docs/install/environment-variables.md

@@ -45,8 +45,6 @@ Regardless of filesystem, it is not recommended to use a network share for your
 | `IMMICH_LOG_LEVEL`                  | Log Level (verbose, debug, log, warn, error)    |            `log`             | server, machine learning | api, microservices |
 | `IMMICH_MEDIA_LOCATION`             | Media Location                                  |   `./upload`<sup>\*1</sup>   | server                   | api, microservices |
 | `IMMICH_CONFIG_FILE`                | Path to config file                             |                              | server                   | api, microservices |
-| `IMMICH_WEB_ROOT`                   | Path of root index.html                         |      `/usr/src/app/www`      | server                   | api                |
-| `IMMICH_REVERSE_GEOCODING_ROOT`     | Path of reverse geocoding dump directory        |     `/usr/src/resources`     | server                   | microservices      |
 | `NO_COLOR`                          | Set to `true` to disable color-coded log output |           `false`            | server, machine learning |                    |
 | `CPU_CORES`                         | Amount of cores available to the immich server  | auto-detected cpu core count | server                   |                    |
 | `IMMICH_API_METRICS_PORT`           | Port for the OTEL metrics                       |            `8081`            | server                   | api                |

docs/docusaurus.config.js

@@ -92,6 +92,7 @@ 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-lock.json

@@ -12640,9 +12640,10 @@
       }
     },
     "node_modules/picocolors": {
-      "version": "1.0.0",
+      "version": "1.0.1",
-      "resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.0.0.tgz",
+      "resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.0.1.tgz",
-      "integrity": "sha512-1fygroTLlHu66zi26VoTDv8yRgm0Fccecssto+MhsZ0D/DGW2sm8E8AjW7NU5VVTRt5GxbeZ5qBuJr+HyLYkjQ=="
+      "integrity": "sha512-anP1Z8qwhkbmu7MFP5iTt+wQKXgwzf7zTyGlcdzabySa9vd0Xt392U0rVmz9poOaBj0uHJKyyo9/upk0HrEQew==",
+      "license": "ISC"
     },
     "node_modules/picomatch": {
       "version": "2.3.1",
@@ -12753,9 +12754,9 @@
       }
     },
     "node_modules/postcss": {
-      "version": "8.4.38",
+      "version": "8.4.39",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.38.tgz",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.39.tgz",
-      "integrity": "sha512-Wglpdk03BSfXkHoQa3b/oulrotAkwrlLDRSOb9D0bN86FdRyE9lppSp33aHNPgBa0JKCoB+drFLZkQoRRYae5A==",
+      "integrity": "sha512-0vzE+lAiG7hZl1/9I8yzKLx3aR9Xbof3fBHKunvMfOCYAtMhrsnccJY2iTURb9EZd5+pLuiNV9/c/GZJOHsgIw==",
       "funding": [
         {
           "type": "opencollective",
@@ -12770,9 +12771,10 @@
           "url": "https://github.com/sponsors/ai"
         }
       ],
+      "license": "MIT",
       "dependencies": {
         "nanoid": "^3.3.7",
-        "picocolors": "^1.0.0",
+        "picocolors": "^1.0.1",
         "source-map-js": "^1.2.0"
       },
       "engines": {
@@ -16374,9 +16376,9 @@
       }
     },
     "node_modules/typescript": {
-      "version": "5.5.2",
+      "version": "5.5.3",
-      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.5.2.tgz",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.5.3.tgz",
-      "integrity": "sha512-NcRtPEOsPFFWjobJEtfihkLCZCXZt/os3zf8nTxjVH3RvTSxjrCamJpbExGvYOF+tFHc3pA65qpdwPbzjohhew==",
+      "integrity": "sha512-/hreyEujaB0w76zKo6717l3L0o/qEUtRgdvUBvlkhoWeOVMjMuHNHk0BRBzikzuGDqNmPQbg5ifMEqsHLiIUcQ==",
       "license": "Apache-2.0",
       "bin": {
         "tsc": "bin/tsc",

docs/package.json

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

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" />
+                    <img src="img/immich-logo.svg" height="30" className="rounded-none" />
                   ) : (
                     <Icon path={cardIcon} size={1} color={item.iconColor} />
                   )}

docs/src/css/custom.css

@@ -8,7 +8,6 @@
 @tailwind utilities;
 
 @import url('https://fonts.googleapis.com/css2?family=Overpass:ital,wght@0,300;0,400;0,500;0,600;0,700;1,300;1,400;1,500;1,600;1,700&display=swap');
-@import url('https://fonts.googleapis.com/css2?family=Snowburst+One&display=swap');
 
 html,
 button {
@@ -48,7 +47,3 @@ img {
 div[class^='announcementBar_'] {
   min-height: 2rem;
 }
-
-.navbar__brand .navbar__title {
-  @apply font-immich-title text-2xl font-normal text-immich-primary dark:text-immich-dark-primary;
-}

docs/src/pages/cursed-knowledge.tsx

@@ -0,0 +1,77 @@
+import { mdiCalendarToday, mdiLeadPencil, mdiLockOutline, mdiSpeedometerSlow, mdiWeb } from '@mdi/js';
+import Layout from '@theme/Layout';
+import React from 'react';
+import { Item as TimelineItem, Timeline } from '../components/timeline';
+
+const withLanguage = (date: Date) => (language: string) => date.toLocaleDateString(language);
+
+type Item = Omit<TimelineItem, 'done' | 'getDateLabel'> & { date: Date };
+
+const items: Item[] = [
+  {
+    icon: mdiLeadPencil,
+    iconColor: 'gold',
+    title: 'PostgreSQL NOTIFY is cursed',
+    description:
+      'PostgreSQL does everything in a transaction, including NOTIFY. This means using the socket.io postgres-adapter writes to WAL every 5 seconds.',
+    link: { url: 'https://github.com/immich-app/immich/pull/10801', text: '#10801' },
+    date: new Date(2024, 6, 3),
+  },
+  {
+    icon: mdiWeb,
+    iconColor: 'lightskyblue',
+    title: 'npm scripts are cursed',
+    description:
+      'npm scripts make a http call to the npm registry each time they run, which means they are a terrible way to execute a health check.',
+    link: { url: 'https://github.com/immich-app/immich/issues/10796', text: '#10796' },
+    date: new Date(2024, 6, 3),
+  },
+  {
+    icon: mdiSpeedometerSlow,
+    iconColor: 'brown',
+    title: '50 extra packages are cursed',
+    description:
+      'There is a user in the JavaScript community who goes around adding "backwards compatibility" to projects. They do this by adding 50 extra package dependencies to your project, which are maintained by them.',
+    link: { url: 'https://github.com/immich-app/immich/pull/10690', text: '#10690' },
+    date: new Date(2024, 5, 28),
+  },
+  {
+    icon: mdiLockOutline,
+    iconColor: 'gold',
+    title: 'Long passwords are cursed',
+    description:
+      'The bcrypt implementation only uses the first 72 bytes of a string. Any characters after that are ignored.',
+    // link: GHSA-4p64-9f7h-3432
+    date: new Date(2024, 5, 25),
+  },
+  {
+    icon: mdiCalendarToday,
+    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' },
+    date: new Date(2024, 0, 31),
+  },
+];
+
+export default function CursedKnowledgePage(): JSX.Element {
+  return (
+    <Layout title="Cursed Knowledge" description="Things we wish we didn't know">
+      <section className="my-8">
+        <h1 className="md:text-6xl text-center mb-10 text-immich-primary dark:text-immich-dark-primary px-2">
+          Cursed Knowledge
+        </h1>
+        <p className="text-center text-xl px-2">
+          Cursed knowledge we have learned as a result of building Immich that we wish we never knew.
+        </p>
+        <div className="flex justify-around mt-8 w-full max-w-full">
+          <Timeline
+            items={items
+              .sort((a, b) => b.date.getTime() - a.date.getTime())
+              .map((item) => ({ ...item, getDateLabel: withLanguage(item.date) }))}
+          />
+        </div>
+      </section>
+    </Layout>
+  );
+}

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"
+          className="md:h-60 h-44 mb-2 antialiased rounded-none"
           alt="Immich logo"
         />
         <div className="sm:text-2xl text-lg md:text-4xl mb-12 sm:leading-tight">

docs/static/archived-versions.json

@@ -1,4 +1,16 @@
 [
+  {
+    "label": "v1.108.0",
+    "url": "https://v1.108.0.archive.immich.app"
+  },
+  {
+    "label": "v1.107.2",
+    "url": "https://v1.107.2.archive.immich.app"
+  },
+  {
+    "label": "v1.107.1",
+    "url": "https://v1.107.1.archive.immich.app"
+  },
   {
     "label": "v1.107.0",
     "url": "https://v1.107.0.archive.immich.app"

docs/tailwind.config.js

@@ -21,9 +21,6 @@ module.exports = {
         'immich-dark-fg': '#e5e7eb',
         'immich-dark-gray': '#212121',
       },
-      fontFamily: {
-        'immich-title': ['Snowburst One', 'cursive'],
-      },
     },
   },
   plugins: [],

e2e/.nvmrc

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

e2e/docker-compose.yml

@@ -26,6 +26,8 @@ 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": "1.107.0",
+  "version": "1.108.0",
   "description": "",
   "main": "index.js",
   "type": "module",
@@ -23,7 +23,8 @@
     "@immich/sdk": "file:../open-api/typescript-sdk",
     "@playwright/test": "^1.44.1",
     "@types/luxon": "^3.4.2",
-    "@types/node": "^20.14.9",
+    "@types/node": "^20.14.10",
+    "@types/oidc-provider": "^8.5.1",
     "@types/pg": "^8.11.0",
     "@types/pngjs": "^6.0.4",
     "@types/supertest": "^6.0.2",
@@ -35,11 +36,13 @@
     "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",
-    "prettier-plugin-organize-imports": "^3.2.4",
+    "prettier-plugin-organize-imports": "^4.0.0",
     "socket.io-client": "^4.7.4",
     "supertest": "^7.0.0",
     "typescript": "^5.3.3",
@@ -47,6 +50,6 @@
     "vitest": "^1.3.0"
   },
   "volta": {
-    "node": "20.15.0"
+    "node": "20.15.1"
   }
 }

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

@@ -100,6 +100,12 @@ 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

@@ -1,12 +1,85 @@
+import {
+  LoginResponseDto,
+  SystemConfigOAuthDto,
+  getConfigDefaults,
+  getMyUser,
+  startOAuth,
+  updateConfig,
+} from '@immich/sdk';
 import { errorDto } from 'src/responses';
-import { app, utils } from 'src/utils';
+import { OAuthClient, OAuthUser } from 'src/setup/auth-server';
+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();
-    await utils.adminSetup();
+    admin = 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', () => {
@@ -15,5 +88,171 @@ 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/api/specs/person.e2e-spec.ts

@@ -230,4 +230,21 @@ describe('/people', () => {
       expect(body).toMatchObject({ birthDate: null });
     });
   });
+
+  describe('POST /people/:id/merge', () => {
+    it('should require authentication', async () => {
+      const { status, body } = await request(app).post(`/people/${uuidDto.notFound}/merge`);
+      expect(status).toBe(401);
+      expect(body).toEqual(errorDto.unauthorized);
+    });
+
+    it('should not supporting merging a person into themselves', async () => {
+      const { status, body } = await request(app)
+        .post(`/people/${visiblePerson.id}/merge`)
+        .set('Authorization', `Bearer ${admin.accessToken}`)
+        .send({ ids: [visiblePerson.id] });
+      expect(status).toBe(400);
+      expect(body).toEqual(errorDto.badRequest('Cannot merge a person into themselves'));
+    });
+  });
 });

e2e/src/immich-admin/specs/immich-admin.e2e-spec.ts

@@ -9,11 +9,30 @@ describe(`immich-admin`, () => {
 
   describe('list-users', () => {
     it('should list the admin user', async () => {
-      const { stdout, stderr, exitCode } = await immichAdmin(['list-users']);
+      const { stdout, stderr, exitCode } = await immichAdmin(['list-users']).promise;
       expect(exitCode).toBe(0);
       expect(stderr).toBe('');
       expect(stdout).toContain("email: 'admin@immich.cloud'");
       expect(stdout).toContain("name: 'Immich Admin'");
     });
   });
+
+  describe('reset-admin-password', () => {
+    it('should reset admin password', async () => {
+      const { child, promise } = immichAdmin(['reset-admin-password']);
+
+      let data = '';
+      child.stdout.on('data', (chunk) => {
+        data += chunk;
+        if (data.includes('Please choose a new password (optional)')) {
+          child.stdin.end('\n');
+        }
+      });
+
+      const { stderr, stdout, exitCode } = await promise;
+      expect(exitCode).toBe(0);
+      expect(stderr).toBe('');
+      expect(stdout).toContain('The admin password has been updated to:');
+    });
+  });
 });

e2e/src/responses.ts

@@ -61,6 +61,12 @@ 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

@@ -0,0 +1,117 @@
+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

@@ -53,8 +53,7 @@ type AdminSetupOptions = { onboarding?: boolean };
 type AssetData = { bytes?: Buffer; filename: string };
 
 const dbUrl = 'postgres://postgres:postgres@127.0.0.1:5433/immich';
-const baseUrl = 'http://127.0.0.1:2283';
+export 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
@@ -64,13 +63,13 @@ export const tempDir = tmpdir();
 export const asBearerAuth = (accessToken: string) => ({ Authorization: `Bearer ${accessToken}` });
 export const asKeyAuth = (key: string) => ({ 'x-api-key': key });
 export const immichCli = (args: string[]) =>
-  executeCommand('node', ['node_modules/.bin/immich', '-d', `/${tempDir}/immich/`, ...args]);
+  executeCommand('node', ['node_modules/.bin/immich', '-d', `/${tempDir}/immich/`, ...args]).promise;
 export const immichAdmin = (args: string[]) =>
   executeCommand('docker', ['exec', '-i', 'immich-e2e-server', '/bin/bash', '-c', `immich-admin ${args.join(' ')}`]);
 
 const executeCommand = (command: string, args: string[]) => {
   let _resolve: (value: CommandResponse) => void;
-  const deferred = new Promise<CommandResponse>((resolve) => (_resolve = resolve));
+  const promise = new Promise<CommandResponse>((resolve) => (_resolve = resolve));
   const child = spawn(command, args, { stdio: 'pipe' });
 
   let stdout = '';
@@ -86,7 +85,7 @@ const executeCommand = (command: string, args: string[]) => {
     });
   });
 
-  return deferred;
+  return { promise, child };
 };
 
 let client: pg.Client | null = null;
@@ -152,10 +151,6 @@ export const utils = {
 
       const sql: string[] = [];
 
-      if (tables.includes('asset_stack')) {
-        sql.push('UPDATE "assets" SET "stackId" = NULL;');
-      }
-
       for (const table of tables) {
         if (table === 'system_metadata') {
           // prevent reverse geocoder from being re-initialized

e2e/src/web/specs/shared-link.e2e-spec.ts

@@ -51,6 +51,13 @@ test.describe('Shared Links', () => {
     await page.getByText('DOWNLOADING', { exact: true }).waitFor();
   });
 
+  test('download all from shared link', async ({ page }) => {
+    await page.goto(`/share/${sharedLink.key}`);
+    await page.getByRole('heading', { name: 'Test Album' }).waitFor();
+    await page.getByRole('button', { name: 'Download' }).click();
+    await page.getByText('DOWNLOADING', { exact: true }).waitFor();
+  });
+
   test('enter password for a shared link', async ({ page }) => {
     await page.goto(`/share/${sharedLinkPassword.key}`);
     await page.getByPlaceholder('Password').fill('test-password');

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[] = [];
+const globalSetup: string[] = ['src/setup/auth-server.ts'];
 try {
   await fetch('http://127.0.0.1:2283/api/server-info/ping');
 } catch {
-  globalSetup.push('src/setup.ts');
+  globalSetup.push('src/setup/docker-compose.ts');
 }
 
 export default defineConfig({

machine-learning/ann/build.sh

@@ -1,3 +0,0 @@
-#!/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

@@ -1,4 +0,0 @@
-#!/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

@@ -1,201 +0,0 @@
-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

@@ -1,157 +0,0 @@
-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

@@ -168,6 +168,12 @@ 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

@@ -29,6 +29,7 @@ from .schemas import (
     InferenceEntry,
     InferenceResponse,
     MessageResponse,
+    ModelFormat,
     ModelIdentity,
     ModelTask,
     ModelType,
@@ -195,6 +196,16 @@ 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()
+            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
 

machine-learning/app/models/base.py

@@ -23,7 +23,7 @@ class InferenceModel(ABC):
         self,
         model_name: str,
         cache_dir: Path | str | None = None,
-        preferred_format: ModelFormat | None = None,
+        model_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 = preferred_format if preferred_format is not None else self._model_format_default
+        self.model_format = model_format if model_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 + 1} to load" if self.load_attempts else "Loading"
+        attempt = f"Attempt #{self.load_attempts} to load" if self.load_attempts > 1 else "Loading"
         log.info(f"{attempt} {self.model_type.replace('-', ' ')} model '{self.model_name}' to memory")
         self.session = self._load()
         self.loaded = True
@@ -101,6 +101,9 @@ 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)
@@ -144,17 +147,13 @@ class InferenceModel(ABC):
 
     @property
     def model_format(self) -> ModelFormat:
-        return self._preferred_format
+        return self._model_format
 
     @model_format.setter
-    def model_format(self, preferred_format: ModelFormat) -> None:
+    def model_format(self, model_format: ModelFormat) -> None:
-        log.debug(f"Setting preferred format to {preferred_format}")
+        log.debug(f"Setting model format to {model_format}")
-        self._preferred_format = preferred_format
+        self._model_format = model_format
 
     @property
     def _model_format_default(self) -> ModelFormat:
-        prefer_ann = ann.ann.is_available and settings.ann
+        return ModelFormat.ARMNN if ann.ann.is_available and settings.ann else ModelFormat.ONNX
-        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

@@ -22,11 +22,12 @@ 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 super()._load()
+        return session
 
     @abstractmethod
     def _load_tokenizer(self) -> Tokenizer:

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

@@ -1,4 +1,3 @@
-from pathlib import Path
 from typing import Any
 
 import numpy as np
@@ -14,15 +13,9 @@ class FaceDetector(InferenceModel):
     depends = []
     identity = (ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION)
 
-    def __init__(
+    def __init__(self, model_name: str, min_score: float = 0.7, **model_kwargs: Any) -> None:
-        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, cache_dir, **model_kwargs)
+        super().__init__(model_name, **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 clean_name, log
+from app.config import log
 from app.models.base import InferenceModel
 from app.models.transforms import decode_cv2
 from app.schemas import FaceDetectionOutput, FacialRecognitionOutput, ModelFormat, ModelSession, ModelTask, ModelType
@@ -20,20 +20,14 @@ class FaceRecognizer(InferenceModel):
     depends = [(ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION)]
     identity = (ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION)
 
-    def __init__(
+    def __init__(self, model_name: str, min_score: float = 0.7, **model_kwargs: Any) -> None:
-        self,
+        super().__init__(model_name, **model_kwargs)
-        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.model_format == ModelFormat.ONNX and not has_batch_axis(session):
+        if self.batch 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_preferred_format(self, mocker: MockerFixture) -> None:
+    def test_sets_default_model_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_preferred_format_to_armnn_if_available(self, path: mock.Mock, mocker: MockerFixture) -> None:
+    def test_sets_default_model_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_preferred_format_kwarg(self, mocker: MockerFixture) -> None:
+    def test_sets_model_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", preferred_format=ModelFormat.ARMNN)
+        encoder = OpenClipTextualEncoder("ViT-B-32__openai", model_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", preferred_format=ModelFormat.ARMNN)
+        encoder = OpenClipTextualEncoder("ViT-B-32__openai", model_format=ModelFormat.ARMNN)
         encoder.download()
 
         snapshot_download.assert_called_once_with(
@@ -140,6 +140,19 @@ 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:
@@ -467,16 +480,18 @@ 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(self, ort_session: mock.Mock, mocker: MockerFixture) -> None:
+    def test_recognition_adds_batch_axis_for_ort(
+        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()
 
@@ -492,27 +507,30 @@ class TestFaceRecognition:
 
         onnx.load.return_value = proto
 
-        face_recognizer = FaceRecognizer("buffalo_s")
+        face_recognizer = FaceRecognizer("buffalo_s", cache_dir=path)
         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(self, ort_session: mock.Mock, mocker: MockerFixture) -> None:
+    def test_recognition_does_not_add_batch_axis_if_exists(
+        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")
+        face_recognizer = FaceRecognizer("buffalo_s", cache_dir=path)
         face_recognizer.load()
 
         assert face_recognizer.batch is True
@@ -520,6 +538,30 @@ 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:
@@ -693,7 +735,7 @@ class TestLoad:
         mock_model.clear_cache.assert_called_once()
         assert mock_model.load.call_count == 2
 
-    async def test_load_clears_cache_and_raises_if_os_error_and_already_retried(self) -> None:
+    async def test_load_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
@@ -707,6 +749,27 @@ 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

@@ -0,0 +1,35 @@
+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

@@ -0,0 +1,21 @@
+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

@@ -0,0 +1,99 @@
+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

@@ -0,0 +1,129 @@
+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

@@ -0,0 +1,260 @@
+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

@@ -0,0 +1,56 @@
+[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

@@ -0,0 +1,281 @@
+#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

@@ -0,0 +1,4 @@
+#!/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

@@ -0,0 +1,3 @@
+#!/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/ort/models/mclip.py

@@ -19,37 +19,44 @@ _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)
-    with tempfile.TemporaryDirectory() as tmpdir:
+    model = to_torchscript(model_name)
-        model = MultilingualCLIP.from_pretrained(model_name, cache_dir=tmpdir)
     AutoTokenizer.from_pretrained(model_name).save_pretrained(output_dir_textual)
 
-        for param in model.parameters():
+    _text_encoder_to_onnx(model, textual_path)
-            param.requires_grad_(False)
-
-        export_text_encoder(model, textual_path)
     openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual)
     optimize(textual_path)
 
 
-def export_text_encoder(model: MultilingualCLIP, output_path: Path | str) -> None:
+def _text_encoder_to_onnx(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/ort/models/openclip.py

@@ -26,6 +26,17 @@ 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,
@@ -51,7 +62,7 @@ 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")
-            export_image_encoder(model, model_cfg, visual_path)
+            _image_encoder_to_onnx(model, model_cfg, visual_path)
 
             optimize(visual_path)
 
@@ -61,11 +72,11 @@ 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)
-            export_text_encoder(model, model_cfg, textual_path)
+            _text_encoder_to_onnx(model, model_cfg, textual_path)
             optimize(textual_path)
 
 
-def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None:
+def _image_encoder_to_onnx(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:
@@ -89,7 +100,7 @@ def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig,
         )
 
 
-def export_text_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None:
+def _text_encoder_to_onnx(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 = "1.107.0"
+version = "1.108.0"
 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:icon="@mipmap/ic_launcher" android:requestLegacyExternalStorage="true"  android:largeHeap="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" => 145,
+        "android.injected.version.code" => 148,
-        "android.injected.version.name" => "1.107.0",
+        "android.injected.version.name" => "1.108.0",
       }
     )
     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/android/fastlane/report.xml

@@ -1,23 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<testsuites>
-  <testsuite name="fastlane.lanes">
-    
-    
-    
-      
-      <testcase classname="fastlane.lanes" name="0: default_platform" time="0.000381">
-        
-      </testcase>
-    
-      
-      <testcase classname="fastlane.lanes" name="1: bundleRelease" time="52.832426">
-        
-      </testcase>
-    
-      
-      <testcase classname="fastlane.lanes" name="2: upload_to_play_store" time="27.616558">
-        
-      </testcase>
-    
-  </testsuite>
-</testsuites>

mobile/ios/Podfile.lock

@@ -17,9 +17,9 @@ PODS:
   - fluttertoast (0.0.2):
     - Flutter
     - Toast
-  - FMDB (2.7.11):
+  - FMDB (2.7.5):
-    - FMDB/standard (= 2.7.11)
+    - FMDB/standard (= 2.7.5)
-  - FMDB/standard (2.7.11)
+  - FMDB/standard (2.7.5)
   - geolocator_apple (1.2.0):
     - Flutter
   - image_picker_ios (0.0.1):
@@ -28,10 +28,10 @@ PODS:
     - Flutter
   - isar_flutter_libs (1.0.0):
     - Flutter
-  - MapLibre (6.5.0)
+  - MapLibre (5.14.0-pre3)
   - maplibre_gl (0.0.1):
     - Flutter
-    - MapLibre (= 6.5.0)
+    - MapLibre (= 5.14.0-pre3)
   - package_info_plus (0.4.5):
     - Flutter
   - path_provider_foundation (0.0.1):
@@ -44,7 +44,7 @@ PODS:
   - photo_manager (2.0.0):
     - Flutter
     - FlutterMacOS
-  - ReachabilitySwift (5.2.3)
+  - ReachabilitySwift (5.0.0)
   - SAMKeychain (1.5.3)
   - share_plus (0.0.1):
     - Flutter
@@ -54,7 +54,7 @@ PODS:
   - sqflite (0.0.3):
     - Flutter
     - FMDB (>= 2.7.5)
-  - Toast (4.1.1)
+  - Toast (4.0.0)
   - url_launcher_ios (0.0.1):
     - Flutter
   - video_player_avfoundation (0.0.1):
@@ -156,24 +156,24 @@ SPEC CHECKSUMS:
   flutter_udid: a2482c67a61b9c806ef59dd82ed8d007f1b7ac04
   flutter_web_auth: c25208760459cec375a3c39f6a8759165ca0fa4d
   fluttertoast: 31b00dabfa7fb7bacd9e7dbee580d7a2ff4bf265
-  FMDB: 57486c1117fd8e0e6b947b2f54c3f42bf8e57a4e
+  FMDB: 2ce00b547f966261cd18927a3ddb07cb6f3db82a
   geolocator_apple: 9157311f654584b9bb72686c55fc02a97b73f461
   image_picker_ios: 4a8aadfbb6dc30ad5141a2ce3832af9214a705b5
   integration_test: ce0a3ffa1de96d1a89ca0ac26fca7ea18a749ef4
   isar_flutter_libs: b69f437aeab9c521821c3f376198c4371fa21073
-  MapLibre: 0ebfa9329d313cec8bf0a5ba5a336a1dc903785e
+  MapLibre: 620fc933c1d6029b33738c905c1490d024e5d4ef
-  maplibre_gl: 943a491fffb3337c1b9de57fd377206d18c16e34
+  maplibre_gl: a2efec727dd340e4c65e26d2b03b584f14881fd9
   package_info_plus: 115f4ad11e0698c8c1c5d8a689390df880f47e85
   path_provider_foundation: 29f094ae23ebbca9d3d0cec13889cd9060c0e943
   path_provider_ios: 14f3d2fd28c4fdb42f44e0f751d12861c43cee02
   permission_handler_apple: e76247795d700c14ea09e3a2d8855d41ee80a2e6
-  photo_manager: 4f6810b7dfc4feb03b461ac1a70dacf91fba7604
+  photo_manager: ff695c7a1dd5bc379974953a2b5c0a293f7c4c8a
-  ReachabilitySwift: 7f151ff156cea1481a8411701195ac6a984f4979
+  ReachabilitySwift: 985039c6f7b23a1da463388634119492ff86c825
   SAMKeychain: 483e1c9f32984d50ca961e26818a534283b4cd5c
   share_plus: c3fef564749587fc939ef86ffb283ceac0baf9f5
   shared_preferences_foundation: 5b919d13b803cadd15ed2dc053125c68730e5126
   sqflite: 31f7eba61e3074736dff8807a9b41581e4f7f15a
-  Toast: 1f5ea13423a1e6674c4abdac5be53587ae481c4e
+  Toast: 91b396c56ee72a5790816f40d3a94dd357abc196
   url_launcher_ios: bbd758c6e7f9fd7b5b1d4cde34d2b95fcce5e812
   video_player_avfoundation: 02011213dab73ae3687df27ce441fbbcc82b5579
   wakelock_plus: 8b09852c8876491e4b6d179e17dfe2a0b5f60d47

mobile/ios/Runner.xcodeproj/project.pbxproj

@@ -44,7 +44,7 @@
 		886774DBDDE6B35BF2B4F2CD /* Pods_Runner.framework */ = {isa = PBXFileReference; explicitFileType = wrapper.framework; includeInIndex = 0; path = Pods_Runner.framework; sourceTree = BUILT_PRODUCTS_DIR; };
 		9740EEB21CF90195004384FC /* Debug.xcconfig */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = text.xcconfig; name = Debug.xcconfig; path = Flutter/Debug.xcconfig; sourceTree = "<group>"; };
 		9740EEB31CF90195004384FC /* Generated.xcconfig */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = text.xcconfig; name = Generated.xcconfig; path = Flutter/Generated.xcconfig; sourceTree = "<group>"; };
-		97C146EE1CF9000F007C117D /* Runner.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = Runner.app; sourceTree = BUILT_PRODUCTS_DIR; };
+		97C146EE1CF9000F007C117D /* Immich-Debug.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = "Immich-Debug.app"; sourceTree = BUILT_PRODUCTS_DIR; };
 		97C146FB1CF9000F007C117D /* Base */ = {isa = PBXFileReference; lastKnownFileType = file.storyboard; name = Base; path = Base.lproj/Main.storyboard; sourceTree = "<group>"; };
 		97C146FD1CF9000F007C117D /* Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = Assets.xcassets; sourceTree = "<group>"; };
 		97C147001CF9000F007C117D /* Base */ = {isa = PBXFileReference; lastKnownFileType = file.storyboard; name = Base; path = Base.lproj/LaunchScreen.storyboard; sourceTree = "<group>"; };
@@ -118,7 +118,7 @@
 		97C146EF1CF9000F007C117D /* Products */ = {
 			isa = PBXGroup;
 			children = (
-				97C146EE1CF9000F007C117D /* Runner.app */,
+				97C146EE1CF9000F007C117D /* Immich-Debug.app */,
 			);
 			name = Products;
 			sourceTree = "<group>";
@@ -162,7 +162,7 @@
 			);
 			name = Runner;
 			productName = Runner;
-			productReference = 97C146EE1CF9000F007C117D /* Runner.app */;
+			productReference = 97C146EE1CF9000F007C117D /* Immich-Debug.app */;
 			productType = "com.apple.product-type.application";
 		};
 /* End PBXNativeTarget section */
@@ -265,7 +265,7 @@
 			);
 			runOnlyForDeploymentPostprocessing = 0;
 			shellPath = /bin/sh;
-			shellScript = "/bin/sh \"$FLUTTER_ROOT/packages/flutter_tools/bin/xcode_backend.sh\" build";
+			shellScript = "/bin/sh \"$FLUTTER_ROOT/packages/flutter_tools/bin/xcode_backend.sh\" build\n";
 		};
 		D218A34AEE62BC1EF119F5B0 /* [CP] Embed Pods Frameworks */ = {
 			isa = PBXShellScriptBuildPhase;
@@ -383,7 +383,7 @@
 				CODE_SIGN_ENTITLEMENTS = Runner/RunnerProfile.entitlements;
 				CODE_SIGN_IDENTITY = "Apple Development";
 				CODE_SIGN_STYLE = Automatic;
-				CURRENT_PROJECT_VERSION = 160;
+				CURRENT_PROJECT_VERSION = 163;
 				DEVELOPMENT_TEAM = 2F67MQ8R79;
 				ENABLE_BITCODE = NO;
 				INFOPLIST_FILE = Runner/Info.plist;
@@ -392,9 +392,9 @@
 					"$(inherited)",
 					"@executable_path/Frameworks",
 				);
-				MARKETING_VERSION = "$(FLUTTER_BUILD_NAME)";
+				MARKETING_VERSION = 1.107.0;
-				PRODUCT_BUNDLE_IDENTIFIER = app.alextran.immich;
+				PRODUCT_BUNDLE_IDENTIFIER = app.alextran.immich.profile;
-				PRODUCT_NAME = "$(TARGET_NAME)";
+				PRODUCT_NAME = "Immich-Profile";
 				PROVISIONING_PROFILE_SPECIFIER = "";
 				SWIFT_OBJC_BRIDGING_HEADER = "Runner/Runner-Bridging-Header.h";
 				SWIFT_VERSION = 5.0;
@@ -525,7 +525,7 @@
 				CLANG_ENABLE_MODULES = YES;
 				CODE_SIGN_IDENTITY = "Apple Development";
 				CODE_SIGN_STYLE = Automatic;
-				CURRENT_PROJECT_VERSION = 160;
+				CURRENT_PROJECT_VERSION = 163;
 				DEVELOPMENT_TEAM = 2F67MQ8R79;
 				ENABLE_BITCODE = NO;
 				INFOPLIST_FILE = Runner/Info.plist;
@@ -534,9 +534,9 @@
 					"$(inherited)",
 					"@executable_path/Frameworks",
 				);
-				MARKETING_VERSION = "$(FLUTTER_BUILD_NAME)";
+				MARKETING_VERSION = 1.107.0;
-				PRODUCT_BUNDLE_IDENTIFIER = app.alextran.immich;
+				PRODUCT_BUNDLE_IDENTIFIER = app.alextran.immich.debug;
-				PRODUCT_NAME = "$(TARGET_NAME)";
+				PRODUCT_NAME = "Immich-Debug";
 				PROVISIONING_PROFILE_SPECIFIER = "";
 				SWIFT_OBJC_BRIDGING_HEADER = "Runner/Runner-Bridging-Header.h";
 				SWIFT_OPTIMIZATION_LEVEL = "-Onone";
@@ -553,7 +553,7 @@
 				CLANG_ENABLE_MODULES = YES;
 				CODE_SIGN_IDENTITY = "Apple Development";
 				CODE_SIGN_STYLE = Automatic;
-				CURRENT_PROJECT_VERSION = 160;
+				CURRENT_PROJECT_VERSION = 163;
 				DEVELOPMENT_TEAM = 2F67MQ8R79;
 				ENABLE_BITCODE = NO;
 				INFOPLIST_FILE = Runner/Info.plist;
@@ -562,9 +562,9 @@
 					"$(inherited)",
 					"@executable_path/Frameworks",
 				);
-				MARKETING_VERSION = "$(FLUTTER_BUILD_NAME)";
+				MARKETING_VERSION = 1.107.0;
 				PRODUCT_BUNDLE_IDENTIFIER = app.alextran.immich;
-				PRODUCT_NAME = "$(TARGET_NAME)";
+				PRODUCT_NAME = Immich;
 				PROVISIONING_PROFILE_SPECIFIER = "";
 				SWIFT_OBJC_BRIDGING_HEADER = "Runner/Runner-Bridging-Header.h";
 				SWIFT_VERSION = 5.0;

mobile/ios/Runner.xcodeproj/xcshareddata/xcschemes/Runner.xcscheme

@@ -15,7 +15,7 @@
             <BuildableReference
                BuildableIdentifier = "primary"
                BlueprintIdentifier = "97C146ED1CF9000F007C117D"
-               BuildableName = "Runner.app"
+               BuildableName = "Immich.app"
                BlueprintName = "Runner"
                ReferencedContainer = "container:Runner.xcodeproj">
             </BuildableReference>
@@ -31,7 +31,7 @@
          <BuildableReference
             BuildableIdentifier = "primary"
             BlueprintIdentifier = "97C146ED1CF9000F007C117D"
-            BuildableName = "Runner.app"
+            BuildableName = "Immich.app"
             BlueprintName = "Runner"
             ReferencedContainer = "container:Runner.xcodeproj">
          </BuildableReference>
@@ -56,7 +56,7 @@
          <BuildableReference
             BuildableIdentifier = "primary"
             BlueprintIdentifier = "97C146ED1CF9000F007C117D"
-            BuildableName = "Runner.app"
+            BuildableName = "Immich.app"
             BlueprintName = "Runner"
             ReferencedContainer = "container:Runner.xcodeproj">
          </BuildableReference>
@@ -73,7 +73,7 @@
          <BuildableReference
             BuildableIdentifier = "primary"
             BlueprintIdentifier = "97C146ED1CF9000F007C117D"
-            BuildableName = "Runner.app"
+            BuildableName = "Immich.app"
             BlueprintName = "Runner"
             ReferencedContainer = "container:Runner.xcodeproj">
          </BuildableReference>

mobile/ios/Runner/Info.plist

@@ -1,18 +1,18 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
 <plist version="1.0">
-  <dict>
+<dict>
 	<key>BGTaskSchedulerPermittedIdentifiers</key>
 	<array>
 		<string>app.alextran.immich.backgroundFetch</string>
 		<string>app.alextran.immich.backgroundProcessing</string>
 	</array>
 	<key>CADisableMinimumFrameDurationOnPhone</key>
-    <true />
+	<true/>
 	<key>CFBundleDevelopmentRegion</key>
 	<string>$(DEVELOPMENT_LANGUAGE)</string>
 	<key>CFBundleDisplayName</key>
-    <string>Immich</string>
+	<string>${PRODUCT_NAME}</string>
 	<key>CFBundleExecutable</key>
 	<string>$(EXECUTABLE_NAME)</string>
 	<key>CFBundleIdentifier</key>
@@ -58,27 +58,27 @@
 	<key>CFBundlePackageType</key>
 	<string>APPL</string>
 	<key>CFBundleShortVersionString</key>
-    <string>1.106.3</string>
+	<string>1.108.0</string>
 	<key>CFBundleSignature</key>
 	<string>????</string>
 	<key>CFBundleVersion</key>
-    <string>160</string>
+	<string>163</string>
 	<key>FLTEnableImpeller</key>
-    <true />
+	<true/>
 	<key>ITSAppUsesNonExemptEncryption</key>
-    <false />
+	<false/>
 	<key>LSApplicationQueriesSchemes</key>
 	<array>
 		<string>https</string>
 	</array>
 	<key>LSRequiresIPhoneOS</key>
-    <true />
+	<true/>
 	<key>MGLMapboxMetricsEnabledSettingShownInApp</key>
-    <true />
+	<true/>
 	<key>NSAppTransportSecurity</key>
 	<dict>
 		<key>NSAllowsArbitraryLoads</key>
-      <true />
+		<true/>
 	</dict>
 	<key>NSCameraUsageDescription</key>
 	<string>We need to access the camera to let you take beautiful video using this app</string>
@@ -91,7 +91,7 @@
 	<key>NSPhotoLibraryUsageDescription</key>
 	<string>We need to manage backup your photos album</string>
 	<key>UIApplicationSupportsIndirectInputEvents</key>
-    <true />
+	<true/>
 	<key>UIBackgroundModes</key>
 	<array>
 		<string>fetch</string>
@@ -102,7 +102,7 @@
 	<key>UIMainStoryboardFile</key>
 	<string>Main</string>
 	<key>UIStatusBarHidden</key>
-    <false />
+	<false/>
 	<key>UISupportedInterfaceOrientations</key>
 	<array>
 		<string>UIInterfaceOrientationPortrait</string>
@@ -117,8 +117,8 @@
 		<string>UIInterfaceOrientationLandscapeRight</string>
 	</array>
 	<key>UIViewControllerBasedStatusBarAppearance</key>
-    <true />
+	<true/>
 	<key>io.flutter.embedded_views_preview</key>
-    <true />
+	<true/>
-  </dict>
+</dict>
 </plist>

mobile/ios/fastlane/Fastfile

@@ -16,10 +16,10 @@
 default_platform(:ios)
 
 platform :ios do
-  desc "iOS Beta"
+  desc "iOS Release"
-  lane :beta do
+  lane :release do
     increment_version_number(
-      version_number: "1.107.0"
+      version_number: "1.108.0"
     )
     increment_build_number(
       build_number: latest_testflight_build_number + 1,

mobile/ios/fastlane/report.xml

@@ -1,38 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<testsuites>
-  <testsuite name="fastlane.lanes">
-    
-    
-    
-      
-      <testcase classname="fastlane.lanes" name="0: default_platform" time="0.000491">
-        
-      </testcase>
-    
-      
-      <testcase classname="fastlane.lanes" name="1: increment_version_number" time="39.414297">
-        
-      </testcase>
-    
-      
-      <testcase classname="fastlane.lanes" name="2: latest_testflight_build_number" time="32.521647">
-        
-      </testcase>
-    
-      
-      <testcase classname="fastlane.lanes" name="3: increment_build_number" time="0.511733">
-        
-      </testcase>
-    
-      
-      <testcase classname="fastlane.lanes" name="4: build_app" time="202.628277">
-        
-      </testcase>
-    
-      
-      <testcase classname="fastlane.lanes" name="5: upload_to_testflight" time="73.861852">
-        
-      </testcase>
-    
-  </testsuite>
-</testsuites>

mobile/lib/extensions/maplibrecontroller_extensions.dart

@@ -6,7 +6,7 @@ import 'package:immich_mobile/models/map/map_marker.model.dart';
 import 'package:immich_mobile/utils/map_utils.dart';
 import 'package:maplibre_gl/maplibre_gl.dart';
 
-extension MapMarkers on MapLibreMapController {
+extension MapMarkers on MaplibreMapController {
   static var _completer = Completer()..complete();
 
   Future<void> addGeoJSONSourceForMarkers(List<MapMarker> markers) async {

mobile/lib/main.dart

@@ -192,7 +192,7 @@ class ImmichAppState extends ConsumerState<ImmichApp>
       localizationsDelegates: context.localizationDelegates,
       supportedLocales: context.supportedLocales,
       locale: context.locale,
-      debugShowCheckedModeBanner: false,
+      debugShowCheckedModeBanner: true,
       home: MaterialApp.router(
         title: 'Immich',
         debugShowCheckedModeBanner: false,

mobile/lib/pages/common/headers_settings.page.dart

@@ -166,7 +166,7 @@ class HeaderKeyValueSettings extends StatelessWidget {
           child: TextFormField(
             controller: valueController,
             decoration: InputDecoration(
-              labelText: 'header_settings_header_name_input'.tr(),
+              labelText: 'header_settings_header_value_input'.tr(),
               border: const OutlineInputBorder(),
             ),
             autocorrect: false,

mobile/lib/pages/search/map/map.page.dart

@@ -36,7 +36,7 @@ class MapPage extends HookConsumerWidget {
 
   @override
   Widget build(BuildContext context, WidgetRef ref) {
-    final mapController = useRef<MapLibreMapController?>(null);
+    final mapController = useRef<MaplibreMapController?>(null);
     final markers = useRef<List<MapMarker>>([]);
     final markersInBounds = useRef<List<MapMarker>>([]);
     final bottomSheetStreamController = useStreamController<MapEvent>();
@@ -156,7 +156,7 @@ class MapPage extends HookConsumerWidget {
       }
     }
 
-    void onMapCreated(MapLibreMapController controller) async {
+    void onMapCreated(MaplibreMapController controller) async {
       mapController.value = controller;
       controller.addListener(() {
         if (controller.isCameraMoving && selectedMarker.value != null) {
@@ -379,7 +379,7 @@ class _MapWithMarker extends StatelessWidget {
         child: Stack(
           children: [
             style.widgetWhen(
-              onData: (style) => MapLibreMap(
+              onData: (style) => MaplibreMap(
                 initialCameraPosition:
                     const CameraPosition(target: LatLng(0, 0)),
                 styleString: style,
@@ -393,7 +393,7 @@ class _MapWithMarker extends StatelessWidget {
                 tiltGesturesEnabled: false,
                 dragEnabled: false,
                 myLocationEnabled: false,
-                attributionButtonPosition: AttributionButtonPosition.topRight,
+                attributionButtonPosition: AttributionButtonPosition.TopRight,
                 rotateGesturesEnabled: false,
               ),
             ),

mobile/lib/pages/search/map/map_location_picker.page.dart

@@ -24,7 +24,7 @@ class MapLocationPickerPage extends HookConsumerWidget {
   @override
   Widget build(BuildContext context, WidgetRef ref) {
     final selectedLatLng = useValueNotifier<LatLng>(initialLatLng);
-    final controller = useRef<MapLibreMapController?>(null);
+    final controller = useRef<MaplibreMapController?>(null);
     final marker = useRef<Symbol?>(null);
 
     Future<void> onStyleLoaded() async {
@@ -74,7 +74,7 @@ class MapLocationPickerPage extends HookConsumerWidget {
                   bottomRight: Radius.circular(40),
                 ),
               ),
-              child: MapLibreMap(
+              child: MaplibreMap(
                 initialCameraPosition:
                     CameraPosition(target: initialLatLng, zoom: 12),
                 styleString: style,

mobile/lib/pages/search/search_input.page.dart

@@ -199,6 +199,8 @@ class SearchInputPage extends HookConsumerWidget {
               padding: EdgeInsets.only(
                 bottom: MediaQuery.of(context).viewInsets.bottom,
               ),
+              child: Padding(
+                padding: const EdgeInsets.symmetric(horizontal: 16.0),
                 child: LocationPicker(
                   onSelected: handleOnSelect,
                   filter: filter.value.location,
@@ -206,6 +208,7 @@ class SearchInputPage extends HookConsumerWidget {
               ),
             ),
           ),
+        ),
       );
     }
 
@@ -242,7 +245,7 @@ class SearchInputPage extends HookConsumerWidget {
           onSearch: search,
           onClear: handleClear,
           child: Padding(
-            padding: const EdgeInsets.symmetric(vertical: 16.0),
+            padding: const EdgeInsets.all(16.0),
             child: CameraPicker(
               onSelect: handleOnSelect,
               filter: filter.value.camera,

mobile/lib/providers/search/search_page_state.provider.dart

@@ -41,7 +41,7 @@ final getAllPlacesProvider =
   final curatedContent = assetPlaces
       .map(
         (data) => SearchCuratedContent(
-          label: data.exifInfo?.city ?? '',
+          label: data.exifInfo!.city!,
           id: data.id,
         ),
       )

mobile/lib/services/album.service.dart

@@ -1,8 +1,13 @@
 import 'dart:async';
+import 'dart:collection';
+import 'dart:io';
 
+import 'package:collection/collection.dart';
 import 'package:flutter/foundation.dart';
 import 'package:hooks_riverpod/hooks_riverpod.dart';
 import 'package:immich_mobile/models/albums/album_add_asset_response.model.dart';
+import 'package:immich_mobile/entities/backup_album.entity.dart';
+import 'package:immich_mobile/services/backup.service.dart';
 import 'package:immich_mobile/entities/album.entity.dart';
 import 'package:immich_mobile/entities/asset.entity.dart';
 import 'package:immich_mobile/entities/store.entity.dart';
@@ -23,6 +28,7 @@ final albumServiceProvider = Provider(
     ref.watch(userServiceProvider),
     ref.watch(syncServiceProvider),
     ref.watch(dbProvider),
+    ref.watch(backupServiceProvider),
   ),
 );
 
@@ -31,6 +37,7 @@ class AlbumService {
   final UserService _userService;
   final SyncService _syncService;
   final Isar _db;
+  final BackupService _backupService;
   final Logger _log = Logger('AlbumService');
   Completer<bool> _localCompleter = Completer()..complete(false);
   Completer<bool> _remoteCompleter = Completer()..complete(false);
@@ -40,6 +47,7 @@ class AlbumService {
     this._userService,
     this._syncService,
     this._db,
+    this._backupService,
   );
 
   /// Checks all selected device albums for changes of albums and their assets
@@ -54,14 +62,60 @@ class AlbumService {
     final Stopwatch sw = Stopwatch()..start();
     bool changes = false;
     try {
+      final List<String> excludedIds =
+          await _backupService.excludedAlbumsQuery().idProperty().findAll();
+      final List<String> selectedIds =
+          await _backupService.selectedAlbumsQuery().idProperty().findAll();
+      if (selectedIds.isEmpty) {
+        final numLocal = await _db.albums.where().localIdIsNotNull().count();
+        if (numLocal > 0) {
+          _syncService.removeAllLocalAlbumsAndAssets();
+        }
+        return false;
+      }
       final List<AssetPathEntity> onDevice =
           await PhotoManager.getAssetPathList(
         hasAll: true,
         filterOption: FilterOptionGroup(containsPathModified: true),
       );
       _log.info("Found ${onDevice.length} device albums");
-
+      Set<String>? excludedAssets;
-      changes = await _syncService.syncLocalAlbumAssetsToDb(onDevice);
+      if (excludedIds.isNotEmpty) {
+        if (Platform.isIOS) {
+          // iOS and Android device album working principle differ significantly
+          // on iOS, an asset can be in multiple albums
+          // on Android, an asset can only be in exactly one album (folder!) at the same time
+          // thus, on Android, excluding an album can be done by ignoring that album
+          // however, on iOS, it it necessary to load the assets from all excluded
+          // albums and check every asset from any selected album against the set
+          // of excluded assets
+          excludedAssets = await _loadExcludedAssetIds(onDevice, excludedIds);
+          _log.info("Found ${excludedAssets.length} assets to exclude");
+        }
+        // remove all excluded albums
+        onDevice.removeWhere((e) => excludedIds.contains(e.id));
+        _log.info(
+          "Ignoring ${excludedIds.length} excluded albums resulting in ${onDevice.length} device albums",
+        );
+      }
+      final hasAll = selectedIds
+          .map((id) => onDevice.firstWhereOrNull((a) => a.id == id))
+          .whereNotNull()
+          .any((a) => a.isAll);
+      if (hasAll) {
+        if (Platform.isAndroid) {
+          // remove the virtual "Recent" album and keep and individual albums
+          // on Android, the virtual "Recent" `lastModified` value is always null
+          onDevice.removeWhere((e) => e.isAll);
+          _log.info("'Recents' is selected, keeping all individual albums");
+        }
+      } else {
+        // keep only the explicitly selected albums
+        onDevice.removeWhere((e) => !selectedIds.contains(e.id));
+        _log.info("'Recents' is not selected, keeping only selected albums");
+      }
+      changes =
+          await _syncService.syncLocalAlbumAssetsToDb(onDevice, excludedAssets);
       _log.info("Syncing completed. Changes: $changes");
     } finally {
       _localCompleter.complete(changes);
@@ -70,6 +124,21 @@ class AlbumService {
     return changes;
   }
 
+  Future<Set<String>> _loadExcludedAssetIds(
+    List<AssetPathEntity> albums,
+    List<String> excludedAlbumIds,
+  ) async {
+    final Set<String> result = HashSet<String>();
+    for (AssetPathEntity a in albums) {
+      if (excludedAlbumIds.contains(a.id)) {
+        final List<AssetEntity> assets =
+            await a.getAssetListRange(start: 0, end: 0x7fffffffffffffff);
+        result.addAll(assets.map((e) => e.id));
+      }
+    }
+    return result;
+  }
+
   /// Checks remote albums (owned if `isShared` is false) for changes,
   /// updates the local database and returns `true` if there were any changes
   Future<bool> refreshRemoteAlbums({required bool isShared}) async {

mobile/lib/services/hash.service.dart

@@ -24,9 +24,13 @@ class HashService {
     AssetPathEntity album, {
     int start = 0,
     int end = 0x7fffffffffffffff,
+    Set<String>? excludedAssets,
   }) async {
     final entities = await album.getAssetListRange(start: start, end: end);
-    return _hashAssets(entities);
+    final filtered = excludedAssets == null
+        ? entities
+        : entities.where((e) => !excludedAssets.contains(e.id)).toList();
+    return _hashAssets(filtered);
   }
 
   /// Converts a list of [AssetEntity]s to [Asset]s including only those

mobile/lib/services/partner.service.dart

@@ -14,17 +14,6 @@ final partnerServiceProvider = Provider(
   ),
 );
 
-enum PartnerDirection {
-  sharedWith("shared-with"),
-  sharedBy("shared-by");
-
-  const PartnerDirection(
-    this._value,
-  );
-
-  final String _value;
-}
-
 class PartnerService {
   final ApiService _apiService;
   final Isar _db;
@@ -34,8 +23,7 @@ class PartnerService {
 
   Future<List<User>?> getPartners(PartnerDirection direction) async {
     try {
-      final userDtos =
+      final userDtos = await _apiService.partnersApi.getPartners(direction);
-          await _apiService.partnersApi.getPartners(direction._value);
       if (userDtos != null) {
         return userDtos.map((u) => User.fromPartnerDto(u)).toList();
       }

mobile/lib/services/sync.service.dart

@@ -68,9 +68,10 @@ class SyncService {
   /// Syncs all device albums and their assets to the database
   /// Returns `true` if there were any changes
   Future<bool> syncLocalAlbumAssetsToDb(
-    List<AssetPathEntity> onDevice,
+    List<AssetPathEntity> onDevice, [
-  ) =>
+    Set<String>? excludedAssets,
-      _lock.run(() => _syncLocalAlbumAssetsToDb(onDevice));
+  ]) =>
+      _lock.run(() => _syncLocalAlbumAssetsToDb(onDevice, excludedAssets));
 
   /// returns all Asset IDs that are not contained in the existing list
   List<int> sharedAssetsToRemove(
@@ -491,8 +492,9 @@ class SyncService {
   /// Syncs all device albums and their assets to the database
   /// Returns `true` if there were any changes
   Future<bool> _syncLocalAlbumAssetsToDb(
-    List<AssetPathEntity> onDevice,
+    List<AssetPathEntity> onDevice, [
-  ) async {
+    Set<String>? excludedAssets,
+  ]) async {
     onDevice.sort((a, b) => a.id.compareTo(b.id));
     final inDb =
         await _db.albums.where().localIdIsNotNull().sortByLocalId().findAll();
@@ -508,8 +510,10 @@ class SyncService {
         album,
         deleteCandidates,
         existing,
+        excludedAssets,
       ),
-      onlyFirst: (AssetPathEntity ape) => _addAlbumFromDevice(ape, existing),
+      onlyFirst: (AssetPathEntity ape) =>
+          _addAlbumFromDevice(ape, existing, excludedAssets),
       onlySecond: (Album a) => _removeAlbumFromDb(a, deleteCandidates),
     );
     _log.fine(
@@ -541,13 +545,16 @@ class SyncService {
     Album album,
     List<Asset> deleteCandidates,
     List<Asset> existing, [
+    Set<String>? excludedAssets,
     bool forceRefresh = false,
   ]) async {
     if (!forceRefresh && !await _hasAssetPathEntityChanged(ape, album)) {
       _log.fine("Local album ${ape.name} has not changed. Skipping sync.");
       return false;
     }
-    if (!forceRefresh && await _syncDeviceAlbumFast(ape, album)) {
+    if (!forceRefresh &&
+        excludedAssets == null &&
+        await _syncDeviceAlbumFast(ape, album)) {
       return true;
     }
 
@@ -559,7 +566,8 @@ class SyncService {
         .findAll();
     assert(inDb.isSorted(Asset.compareByChecksum), "inDb not sorted!");
     final int assetCountOnDevice = await ape.assetCountAsync;
-    final List<Asset> onDevice = await _hashService.getHashedAssets(ape);
+    final List<Asset> onDevice =
+        await _hashService.getHashedAssets(ape, excludedAssets: excludedAssets);
     _removeDuplicates(onDevice);
     // _removeDuplicates sorts `onDevice` by checksum
     final (toAdd, toUpdate, toDelete) = _diffAssets(onDevice, inDb);
@@ -670,11 +678,13 @@ class SyncService {
   /// assets already existing in the database to the list of `existing` assets
   Future<void> _addAlbumFromDevice(
     AssetPathEntity ape,
-    List<Asset> existing,
+    List<Asset> existing, [
-  ) async {
+    Set<String>? excludedAssets,
+  ]) async {
     _log.info("Syncing a new local album to DB: ${ape.name}");
     final Album a = Album.local(ape);
-    final assets = await _hashService.getHashedAssets(ape);
+    final assets =
+        await _hashService.getHashedAssets(ape, excludedAssets: excludedAssets);
     _removeDuplicates(assets);
     final (existingInDb, updated) = await _linkWithExistingFromDb(assets);
     _log.info(

mobile/lib/services/user.service.dart

@@ -73,9 +73,9 @@ class UserService {
   Future<List<User>?> getUsersFromServer() async {
     final List<User>? users = await _getAllUsers();
     final List<User>? sharedBy =
-        await _partnerService.getPartners(PartnerDirection.sharedBy);
+        await _partnerService.getPartners(PartnerDirection.by);
     final List<User>? sharedWith =
-        await _partnerService.getPartners(PartnerDirection.sharedWith);
+        await _partnerService.getPartners(PartnerDirection.with_);
 
     if (users == null || sharedBy == null || sharedWith == null) {
       _log.warning("Failed to refresh users");

mobile/lib/widgets/asset_viewer/exif_sheet/exif_bottom_sheet.dart

@@ -194,7 +194,7 @@ class ExifBottomSheet extends HookConsumerWidget {
                                 exifInfo.mm != null ||
                                 exifInfo.iso != null
                             ? Text(
-                                "ƒ/${exifInfo.fNumber}   ${exifInfo.exposureTime} s   ${exifInfo.focalLength} mm   ISO ${exifInfo.iso ?? ''} ",
+                                "ƒ/${exifInfo.fNumber}   ${exifInfo.exposureTime}   ${exifInfo.focalLength} mm   ISO ${exifInfo.iso ?? ''} ",
                                 style: context.textTheme.bodySmall,
                               )
                             : null,

mobile/lib/widgets/asset_viewer/exif_sheet/exif_detail.dart

@@ -49,7 +49,7 @@ class ExifDetail extends StatelessWidget {
                     exifInfo?.mm != null ||
                     exifInfo?.iso != null
                 ? Text(
-                    "ƒ/${exifInfo?.fNumber}   ${exifInfo?.exposureTime} s   ${exifInfo?.focalLength} mm   ISO ${exifInfo?.iso ?? ''} ",
+                    "ƒ/${exifInfo?.fNumber}   ${exifInfo?.exposureTime}   ${exifInfo?.focalLength} mm   ISO ${exifInfo?.iso ?? ''} ",
                     style: context.textTheme.bodySmall,
                   )
                 : null,

mobile/lib/widgets/map/map_thumbnail.dart

@@ -41,10 +41,10 @@ class MapThumbnail extends HookConsumerWidget {
   @override
   Widget build(BuildContext context, WidgetRef ref) {
     final offsettedCentre = LatLng(centre.latitude + 0.002, centre.longitude);
-    final controller = useRef<MapLibreMapController?>(null);
+    final controller = useRef<MaplibreMapController?>(null);
     final position = useValueNotifier<Point<num>?>(null);
 
-    Future<void> onMapCreated(MapLibreMapController mapController) async {
+    Future<void> onMapCreated(MaplibreMapController mapController) async {
       controller.value = mapController;
       if (assetMarkerRemoteId != null) {
         // The iOS impl returns wrong toScreenLocation without the delay
@@ -73,7 +73,7 @@ class MapThumbnail extends HookConsumerWidget {
             alignment: Alignment.center,
             children: [
               style.widgetWhen(
-                onData: (style) => MapLibreMap(
+                onData: (style) => MaplibreMap(
                   initialCameraPosition:
                       CameraPosition(target: offsettedCentre, zoom: zoom),
                   styleString: style,

mobile/lib/widgets/search/search_filter/filter_bottom_sheet_scaffold.dart

@@ -38,10 +38,7 @@ class FilterBottomSheetScaffold extends StatelessWidget {
               style: context.textTheme.headlineSmall,
             ),
           ),
-          Padding(
+          buildChildWidget(),
-            padding: const EdgeInsets.symmetric(horizontal: 16),
-            child: buildChildWidget(),
-          ),
           Padding(
             padding: const EdgeInsets.all(8.0),
             child: Row(

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: 1.107.0
+- API version: 1.108.0
 - Generator version: 7.5.0
 - Build package: org.openapitools.codegen.languages.DartClientCodegen
 
@@ -353,6 +353,7 @@ Class | Method | HTTP request | Description
  - [OAuthCallbackDto](doc//OAuthCallbackDto.md)
  - [OAuthConfigDto](doc//OAuthConfigDto.md)
  - [OnThisDayDto](doc//OnThisDayDto.md)
+ - [PartnerDirection](doc//PartnerDirection.md)
  - [PartnerResponseDto](doc//PartnerResponseDto.md)
  - [PathEntityType](doc//PathEntityType.md)
  - [PathType](doc//PathType.md)

mobile/openapi/lib/api.dart

@@ -166,6 +166,7 @@ part 'model/o_auth_authorize_response_dto.dart';
 part 'model/o_auth_callback_dto.dart';
 part 'model/o_auth_config_dto.dart';
 part 'model/on_this_day_dto.dart';
+part 'model/partner_direction.dart';
 part 'model/partner_response_dto.dart';
 part 'model/path_entity_type.dart';
 part 'model/path_type.dart';

mobile/openapi/lib/api/partners_api.dart

@@ -67,8 +67,8 @@ class PartnersApi {
   /// Performs an HTTP 'GET /partners' operation and returns the [Response].
   /// Parameters:
   ///
-  /// * [String] direction (required):
+  /// * [PartnerDirection] direction (required):
-  Future<Response> getPartnersWithHttpInfo(String direction,) async {
+  Future<Response> getPartnersWithHttpInfo(PartnerDirection direction,) async {
     // ignore: prefer_const_declarations
     final path = r'/partners';
 
@@ -97,8 +97,8 @@ class PartnersApi {
 
   /// Parameters:
   ///
-  /// * [String] direction (required):
+  /// * [PartnerDirection] direction (required):
-  Future<List<PartnerResponseDto>?> getPartners(String direction,) async {
+  Future<List<PartnerResponseDto>?> getPartners(PartnerDirection direction,) async {
     final response = await getPartnersWithHttpInfo(direction,);
     if (response.statusCode >= HttpStatus.badRequest) {
       throw ApiException(response.statusCode, await _decodeBodyBytes(response));