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v1.130.2
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fix/server
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2
.github/workflows/fix-format.yml
vendored
2
.github/workflows/fix-format.yml
vendored
@@ -13,7 +13,7 @@ jobs:
|
||||
steps:
|
||||
- name: Generate a token
|
||||
id: generate-token
|
||||
uses: actions/create-github-app-token@af35edadc00be37caa72ed9f3e6d5f7801bfdf09 # v1
|
||||
uses: actions/create-github-app-token@d72941d797fd3113feb6b93fd0dec494b13a2547 # v1
|
||||
with:
|
||||
app-id: ${{ secrets.PUSH_O_MATIC_APP_ID }}
|
||||
private-key: ${{ secrets.PUSH_O_MATIC_APP_KEY }}
|
||||
|
||||
6
.github/workflows/prepare-release.yml
vendored
6
.github/workflows/prepare-release.yml
vendored
@@ -31,7 +31,7 @@ jobs:
|
||||
steps:
|
||||
- name: Generate a token
|
||||
id: generate-token
|
||||
uses: actions/create-github-app-token@af35edadc00be37caa72ed9f3e6d5f7801bfdf09 # v1
|
||||
uses: actions/create-github-app-token@d72941d797fd3113feb6b93fd0dec494b13a2547 # v1
|
||||
with:
|
||||
app-id: ${{ secrets.PUSH_O_MATIC_APP_ID }}
|
||||
private-key: ${{ secrets.PUSH_O_MATIC_APP_KEY }}
|
||||
@@ -42,7 +42,7 @@ jobs:
|
||||
token: ${{ steps.generate-token.outputs.token }}
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@22695119d769bdb6f7032ad67b9bca0ef8c4a174 # v5
|
||||
uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5
|
||||
|
||||
- name: Bump version
|
||||
run: misc/release/pump-version.sh -s "${{ inputs.serverBump }}" -m "${{ inputs.mobileBump }}"
|
||||
@@ -70,7 +70,7 @@ jobs:
|
||||
steps:
|
||||
- name: Generate a token
|
||||
id: generate-token
|
||||
uses: actions/create-github-app-token@af35edadc00be37caa72ed9f3e6d5f7801bfdf09 # v1
|
||||
uses: actions/create-github-app-token@d72941d797fd3113feb6b93fd0dec494b13a2547 # v1
|
||||
with:
|
||||
app-id: ${{ secrets.PUSH_O_MATIC_APP_ID }}
|
||||
private-key: ${{ secrets.PUSH_O_MATIC_APP_KEY }}
|
||||
|
||||
14
.github/workflows/test.yml
vendored
14
.github/workflows/test.yml
vendored
@@ -384,7 +384,7 @@ jobs:
|
||||
steps:
|
||||
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@22695119d769bdb6f7032ad67b9bca0ef8c4a174 # v5
|
||||
uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5
|
||||
- uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5
|
||||
# TODO: add caching when supported (https://github.com/actions/setup-python/pull/818)
|
||||
# with:
|
||||
@@ -395,16 +395,16 @@ jobs:
|
||||
uv sync --extra cpu
|
||||
- name: Lint with ruff
|
||||
run: |
|
||||
uv run ruff check --output-format=github app export
|
||||
uv run ruff check --output-format=github immich_ml
|
||||
- name: Check black formatting
|
||||
run: |
|
||||
uv run black --check app export
|
||||
uv run black --check immich_ml
|
||||
- name: Run mypy type checking
|
||||
run: |
|
||||
uv run mypy --strict app/
|
||||
uv run mypy --strict immich_ml/
|
||||
- name: Run tests and coverage
|
||||
run: |
|
||||
uv run pytest app --cov=app --cov-report term-missing
|
||||
uv run pytest --cov=immich_ml --cov-report term-missing
|
||||
|
||||
github-files-formatting:
|
||||
name: .github Files Formatting
|
||||
@@ -525,7 +525,7 @@ jobs:
|
||||
|
||||
- name: Generate new migrations
|
||||
continue-on-error: true
|
||||
run: npm run typeorm:migrations:generate ./src/migrations/TestMigration
|
||||
run: npm run migrations:generate TestMigration
|
||||
|
||||
- name: Find file changes
|
||||
uses: tj-actions/verify-changed-files@a1c6acee9df209257a246f2cc6ae8cb6581c1edf # v20
|
||||
@@ -538,7 +538,7 @@ jobs:
|
||||
run: |
|
||||
echo "ERROR: Generated migration files not up to date!"
|
||||
echo "Changed files: ${{ steps.verify-changed-files.outputs.changed_files }}"
|
||||
cat ./src/migrations/*-TestMigration.ts
|
||||
cat ./src/*-TestMigration.ts
|
||||
exit 1
|
||||
|
||||
- name: Run SQL generation
|
||||
|
||||
@@ -104,7 +104,7 @@ For the mobile app, you can use `https://demo.immich.app` for the `Server Endpoi
|
||||
| Read-only gallery | Yes | Yes |
|
||||
| Stacked Photos | Yes | Yes |
|
||||
| Tags | No | Yes |
|
||||
| Folder View | No | Yes |
|
||||
| Folder View | Yes | Yes |
|
||||
|
||||
## Translations
|
||||
|
||||
|
||||
@@ -1,39 +1,29 @@
|
||||
import { FlatCompat } from '@eslint/eslintrc';
|
||||
import js from '@eslint/js';
|
||||
import typescriptEslint from '@typescript-eslint/eslint-plugin';
|
||||
import tsParser from '@typescript-eslint/parser';
|
||||
import eslintPluginPrettierRecommended from 'eslint-plugin-prettier/recommended';
|
||||
import eslintPluginUnicorn from 'eslint-plugin-unicorn';
|
||||
import globals from 'globals';
|
||||
import path from 'node:path';
|
||||
import { fileURLToPath } from 'node:url';
|
||||
import typescriptEslint from 'typescript-eslint';
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = path.dirname(__filename);
|
||||
const compat = new FlatCompat({
|
||||
baseDirectory: __dirname,
|
||||
recommendedConfig: js.configs.recommended,
|
||||
allConfig: js.configs.all,
|
||||
});
|
||||
|
||||
export default [
|
||||
export default typescriptEslint.config([
|
||||
eslintPluginUnicorn.configs.recommended,
|
||||
eslintPluginPrettierRecommended,
|
||||
js.configs.recommended,
|
||||
typescriptEslint.configs.recommended,
|
||||
{
|
||||
ignores: ['eslint.config.mjs', 'dist'],
|
||||
},
|
||||
...compat.extends(
|
||||
'plugin:@typescript-eslint/recommended',
|
||||
'plugin:prettier/recommended',
|
||||
'plugin:unicorn/recommended',
|
||||
),
|
||||
{
|
||||
plugins: {
|
||||
'@typescript-eslint': typescriptEslint,
|
||||
},
|
||||
|
||||
languageOptions: {
|
||||
globals: {
|
||||
...globals.node,
|
||||
},
|
||||
|
||||
parser: tsParser,
|
||||
parser: typescriptEslint.parser,
|
||||
ecmaVersion: 5,
|
||||
sourceType: 'module',
|
||||
|
||||
@@ -58,4 +48,4 @@ export default [
|
||||
'object-shorthand': ['error', 'always'],
|
||||
},
|
||||
},
|
||||
];
|
||||
]);
|
||||
|
||||
1000
cli/package-lock.json
generated
1000
cli/package-lock.json
generated
File diff suppressed because it is too large
Load Diff
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "@immich/cli",
|
||||
"version": "2.2.56",
|
||||
"version": "2.2.60",
|
||||
"description": "Command Line Interface (CLI) for Immich",
|
||||
"type": "module",
|
||||
"exports": "./dist/index.js",
|
||||
@@ -21,9 +21,7 @@
|
||||
"@types/lodash-es": "^4.17.12",
|
||||
"@types/micromatch": "^4.0.9",
|
||||
"@types/mock-fs": "^4.13.1",
|
||||
"@types/node": "^22.13.10",
|
||||
"@typescript-eslint/eslint-plugin": "^8.15.0",
|
||||
"@typescript-eslint/parser": "^8.15.0",
|
||||
"@types/node": "^22.13.14",
|
||||
"@vitest/coverage-v8": "^3.0.0",
|
||||
"byte-size": "^9.0.0",
|
||||
"cli-progress": "^3.12.0",
|
||||
@@ -31,12 +29,13 @@
|
||||
"eslint": "^9.14.0",
|
||||
"eslint-config-prettier": "^10.0.0",
|
||||
"eslint-plugin-prettier": "^5.1.3",
|
||||
"eslint-plugin-unicorn": "^56.0.1",
|
||||
"eslint-plugin-unicorn": "^57.0.0",
|
||||
"globals": "^16.0.0",
|
||||
"mock-fs": "^5.2.0",
|
||||
"prettier": "^3.2.5",
|
||||
"prettier-plugin-organize-imports": "^4.0.0",
|
||||
"typescript": "^5.3.3",
|
||||
"typescript-eslint": "^8.28.0",
|
||||
"vite": "^6.0.0",
|
||||
"vite-tsconfig-paths": "^5.0.0",
|
||||
"vitest": "^3.0.0",
|
||||
|
||||
|
Before Width: | Height: | Size: 4.9 MiB After Width: | Height: | Size: 4.9 MiB |
@@ -71,7 +71,7 @@ You do not need to redo any machine learning jobs after enabling hardware accele
|
||||
|
||||
1. If you do not already have it, download the latest [`hwaccel.ml.yml`][hw-file] file and ensure it's in the same folder as the `docker-compose.yml`.
|
||||
2. In the `docker-compose.yml` under `immich-machine-learning`, uncomment the `extends` section and change `cpu` to the appropriate backend.
|
||||
3. Still in `immich-machine-learning`, add one of -[armnn, cuda, rocm, openvino] to the `image` section's tag at the end of the line.
|
||||
3. Still in `immich-machine-learning`, add one of -[armnn, cuda, rocm, openvino, rknn] to the `image` section's tag at the end of the line.
|
||||
4. Redeploy the `immich-machine-learning` container with these updated settings.
|
||||
|
||||
### Confirming Device Usage
|
||||
|
||||
@@ -45,7 +45,7 @@ Some search examples:
|
||||
</TabItem>
|
||||
<TabItem value="Mobile" label="Mobile">
|
||||
|
||||
<img src={require('./img/moblie-smart-serach.webp').default} width="30%" title='Smart search on mobile' />
|
||||
<img src={require('./img/mobile-smart-search.webp').default} width="30%" title='Smart search on mobile' />
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
@@ -56,7 +56,20 @@ Navigating to `Administration > Settings > Machine Learning Settings > Smart Sea
|
||||
|
||||
### CLIP models
|
||||
|
||||
More powerful models can be used for more accurate search results, but are slower and can require more server resources. Check the dropdowns below to see how they compare in memory usage, speed and quality by language.
|
||||
The default search model is fast, but there are many other options that can provide better search results. The tradeoff of using these models is that they're slower and/or use more memory (both when indexing images with background Smart Search jobs and when searching).
|
||||
|
||||
The first step of choosing the right model for you is to know which languages your users will search in.
|
||||
|
||||
If your users will only search in English, then the [CLIP][huggingface-clip] section is the first place to look. This is a curated list of the models that generally perform the best for their size class. The models here are ordered from higher to lower quality. This means that the top models will generally rank the most relevant results higher and have a higher capacity to understand descriptive, detailed, and/or niche queries. The models are also generally ordered from larger to smaller, so consider the impact on memory usage, job processing and search speed when deciding on one. The smaller models in this list are not too different in quality and many times faster.
|
||||
|
||||
[Multilingual models][huggingface-multilingual-clip] are also available so users can search in their native language. Use these models if you expect non-English searches to be common. They can be separated into two search patterns:
|
||||
|
||||
- `nllb` models expect the search query to be in the language specified in the user settings
|
||||
- `xlm` and `siglip2` models understand search text regardless of the current language setting
|
||||
|
||||
`nllb` models tend to perform the best and are recommended when users primarily searches in their native, non-English language. `xlm` and `siglip2` models are more flexible and are recommended for mixed language search, where the same user might search in different languages at different times.
|
||||
|
||||
For more details, check the tables below to see how they compare in memory usage, speed and quality by language.
|
||||
|
||||
Once you've chosen a model, follow these steps:
|
||||
|
||||
@@ -81,7 +94,7 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
|
||||
**Memory (MiB)**: The peak RSS usage of the process afer performing the above timing benchmark. Does not include image decoding, concurrent processing, the web server, etc., which are relatively constant factors.
|
||||
|
||||
**Recall (%)**: Evaluated on Crossmodal-3600, the average of the recall@1, recall@5 and recall@10 results for zeroshot image retrieval.
|
||||
**Recall (%)**: Evaluated on Crossmodal-3600, the average of the recall@1, recall@5 and recall@10 results for zeroshot image retrieval. Chinese (Simplified), English, French, German, Italian, Japanese, Korean, Polish, Russian, Spanish and Turkish are additionally tested on XTD-10. Chinese (Simplified) and English are additionally tested on Flickr30k. The recall metrics are the average across all tested datasets.
|
||||
|
||||
**Pareto Optimal**: Whether the model is not completely outclassed by another model. Try to use models that are optimal for the languages relevant to you. Specifically, for a given model and language, if there's another model that's better for that language in at least one respect (memory usage, execution time, recall) while being at least as good for that language in every other way, then the model is not optimal for that language.
|
||||
|
||||
@@ -93,59 +106,59 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
<summary>English</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 75.73 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 75.44 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 75.19 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 75.09 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 75.07 | ❌ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 75.01 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 74.92 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 74.9 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 74.87 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 74.87 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 74.77 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 74.28 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 74.26 | ✅ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 73.15 | ✅ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 72.78 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP-384__webli | 4417 | 72.19 | 72.58 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 72.57 | ❌ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 72.47 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 72.45 | ✅ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 72.44 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 72.37 | ❌ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 71.64 | ✅ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 71.63 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 71.45 | ❌ |
|
||||
| ViT-H-14__laion2b-s32b-b79k | 4676 | 39.06 | 71.33 | ❌ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 71.19 | ❌ |
|
||||
| ViT-L-14__laion2b-s32b-b82k | 2233 | 20.56 | 69.86 | ❌ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 69.66 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 69.38 | ❌ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 68.78 | ✅ |
|
||||
| ViT-L-14__laion400m_e31 | 2183 | 19.87 | 68.53 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 68.53 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 68.53 | ❌ |
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 68.51 | ❌ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 68.41 | ❌ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 68.41 | ❌ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 68.33 | ❌ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 66.96 | ✅ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 66.95 | ✅ |
|
||||
| ViT-B-32__laion400m_e31 | 999 | 2.28 | 65.65 | ✅ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 65.49 | ❌ |
|
||||
| ViT-L-14__openai | 2212 | 19.91 | 60.12 | ❌ |
|
||||
| ViT-B-32__openai | 1004 | 2.26 | 59.37 | ✅ |
|
||||
| RN50x64__openai | 5079 | 48.79 | 59.36 | ❌ |
|
||||
| RN50x16__openai | 2221 | 15.87 | 59.17 | ❌ |
|
||||
| ViT-L-14-336__openai | 2616 | 43.45 | 59.09 | ❌ |
|
||||
| RN50__openai | 913 | 2.39 | 58.32 | ✅ |
|
||||
| ViT-B-16__openai | 985 | 5.03 | 58.27 | ❌ |
|
||||
| RN50x4__openai | 1416 | 5.85 | 57.88 | ❌ |
|
||||
| RN50__cc12m | 914 | 2.37 | 57.75 | ✅ |
|
||||
| RN101__openai | 1111 | 3.21 | 57.7 | ❌ |
|
||||
| RN101__yfcc15m | 1111 | 3.22 | 50.11 | ❌ |
|
||||
| RN50__yfcc15m | 908 | 2.34 | 48.28 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 85.99 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 85.96 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 85.96 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 85.93 | ❌ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 85.78 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 85.75 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 85.62 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 85.53 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 85.48 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 85.47 | ✅ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 85.09 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 85.03 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 84.86 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP-384__webli | 4417 | 72.19 | 84.61 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 84.17 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 83.51 | ❌ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 83.28 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 83.24 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 83.23 | ❌ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 83.19 | ✅ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 82.54 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 82.43 | ❌ |
|
||||
| ViT-H-14__laion2b-s32b-b79k | 4676 | 39.06 | 82.36 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 82.28 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 81.9 | ✅ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 81.9 | ❌ |
|
||||
| ViT-L-14__laion2b-s32b-b82k | 2233 | 20.56 | 80.82 | ❌ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 80.65 | ❌ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 80.16 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 79.78 | ❌ |
|
||||
| ViT-L-14__laion400m_e31 | 2183 | 19.87 | 78.64 | ❌ |
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 78.6 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 78.06 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 78.06 | ❌ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 77.62 | ✅ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 77.47 | ❌ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 76.91 | ❌ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 76.43 | ✅ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 76.35 | ❌ |
|
||||
| ViT-B-32__laion400m_e31 | 999 | 2.28 | 73.83 | ✅ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 73.62 | ❌ |
|
||||
| RN50x64__openai | 5079 | 48.79 | 73.34 | ❌ |
|
||||
| ViT-L-14__openai | 2212 | 19.91 | 72.99 | ❌ |
|
||||
| ViT-L-14-336__openai | 2616 | 43.45 | 72.76 | ❌ |
|
||||
| RN50x16__openai | 2221 | 15.87 | 72.59 | ❌ |
|
||||
| RN50x4__openai | 1416 | 5.85 | 70.8 | ❌ |
|
||||
| ViT-B-16__openai | 985 | 5.03 | 70.01 | ❌ |
|
||||
| ViT-B-32__openai | 1004 | 2.26 | 69.9 | ✅ |
|
||||
| RN101__openai | 1111 | 3.21 | 69.3 | ❌ |
|
||||
| RN50__openai | 913 | 2.39 | 69.02 | ✅ |
|
||||
| RN50__cc12m | 914 | 2.37 | 64.59 | ✅ |
|
||||
| RN101__yfcc15m | 1111 | 3.22 | 55.21 | ❌ |
|
||||
| RN50__yfcc15m | 908 | 2.34 | 53.63 | ✅ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>Arabic</summary>
|
||||
@@ -156,8 +169,8 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 74.03 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 73.19 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 69.31 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 69.29 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 69.29 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 69.29 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 68.64 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 68.35 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 68.25 | ✅ |
|
||||
@@ -195,25 +208,25 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
<summary>Chinese (Simplified)</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 77.49 | ✅ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 77.19 | ✅ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 76.98 | ❌ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 72.89 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 72.65 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 72.52 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 67.83 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 67.81 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 67.51 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 67.39 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 67.33 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 67.23 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 67.05 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 66.87 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 66.24 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 66.1 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 65.56 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 64.39 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 62.56 | ❌ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 79.7 | ✅ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 78.94 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 75.22 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 74.8 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 73.91 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 72.8 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 72.77 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 72.41 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 72.36 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 71.59 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 71.37 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 71.3 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 71.11 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 70.95 | ✅ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 70.51 | ✅ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 67.48 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 66.84 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 65.7 | ✅ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 63.38 | ❌ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>Croatian</summary>
|
||||
@@ -324,8 +337,8 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 80.05 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 79.81 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 79.72 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 79.72 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 79.72 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 79.64 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 79.49 | ✅ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 79.41 | ❌ |
|
||||
@@ -357,8 +370,8 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 29.56 | ❌ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 29.54 | ✅ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 29.36 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 27.76 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 27.76 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 27.76 | ❌ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 25.67 | ✅ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 25.59 | ❌ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 25.53 | ❌ |
|
||||
@@ -384,8 +397,8 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 34.27 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 34.14 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 33.98 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 30.57 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 30.57 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 30.57 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 30.05 | ✅ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 24.92 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 24.02 | ❌ |
|
||||
@@ -422,110 +435,111 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
<summary>French</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 88.01 | ✅ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 87.74 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 87.69 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 87.6 | ✅ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 87.58 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 87.51 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 87.23 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 86.9 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 86.9 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 86.44 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 86.44 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 86.28 | ❌ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 86.11 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 86.08 | ✅ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 84.49 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 84.3 | ✅ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 83.03 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 82.93 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 82.27 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 82.14 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 80.96 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 80.64 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 80.28 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 79.65 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 77.4 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 76.88 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 76.3 | ✅ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 75.68 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP-384__webli | 4417 | 72.19 | 69.59 | ❌ |
|
||||
| ViT-H-14__laion2b-s32b-b79k | 4676 | 39.06 | 68.36 | ❌ |
|
||||
| ViT-L-14__laion2b-s32b-b82k | 2233 | 20.56 | 61.78 | ❌ |
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 58.4 | ❌ |
|
||||
| ViT-L-14__laion400m_e31 | 2183 | 19.87 | 58.35 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 57.17 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 57.17 | ❌ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 57.05 | ✅ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 56.08 | ✅ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 52.96 | ✅ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 52.83 | ✅ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 51.88 | ❌ |
|
||||
| ViT-B-32__laion400m_e31 | 999 | 2.28 | 51.82 | ✅ |
|
||||
| RN50x64__openai | 5079 | 48.79 | 42.86 | ❌ |
|
||||
| ViT-L-14-336__openai | 2616 | 43.45 | 42.81 | ❌ |
|
||||
| ViT-L-14__openai | 2212 | 19.91 | 42.54 | ❌ |
|
||||
| RN50x16__openai | 2221 | 15.87 | 41.72 | ❌ |
|
||||
| RN50x4__openai | 1416 | 5.85 | 38.85 | ❌ |
|
||||
| RN101__openai | 1111 | 3.21 | 36.79 | ❌ |
|
||||
| ViT-B-16__openai | 985 | 5.03 | 36.47 | ❌ |
|
||||
| ViT-B-32__openai | 1004 | 2.26 | 35.17 | ✅ |
|
||||
| RN50__openai | 913 | 2.39 | 34.44 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 86.5 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 86.5 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 86.39 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 86.15 | ❌ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 86.1 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 86.07 | ❌ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 86.06 | ❌ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 85.89 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 85.67 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 85.67 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 85.63 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 85.39 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 85.35 | ✅ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 84.97 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 83.8 | ✅ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 82.96 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 82.91 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 82.52 | ❌ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 81.21 | ✅ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 80.23 | ✅ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 79.85 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 79.47 | ✅ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 79.3 | ❌ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 77.49 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 76.82 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 75.94 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 75.3 | ✅ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 75.24 | ❌ |
|
||||
| ViT-H-14__laion2b-s32b-b79k | 4676 | 39.06 | 69.33 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP-384__webli | 4417 | 72.19 | 64.41 | ❌ |
|
||||
| ViT-L-14__laion2b-s32b-b82k | 2233 | 20.56 | 62.86 | ❌ |
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 59.27 | ❌ |
|
||||
| ViT-L-14__laion400m_e31 | 2183 | 19.87 | 59.09 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 58.25 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 58.25 | ❌ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 56.97 | ✅ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 56.21 | ✅ |
|
||||
| ViT-B-32__laion400m_e31 | 999 | 2.28 | 53.36 | ✅ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 53.33 | ✅ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 53.26 | ❌ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 53.22 | ❌ |
|
||||
| ViT-L-14__openai | 2212 | 19.91 | 46.34 | ❌ |
|
||||
| RN50x64__openai | 5079 | 48.79 | 46.3 | ❌ |
|
||||
| ViT-L-14-336__openai | 2616 | 43.45 | 45.95 | ❌ |
|
||||
| RN50x16__openai | 2221 | 15.87 | 45.69 | ❌ |
|
||||
| RN50x4__openai | 1416 | 5.85 | 42.48 | ❌ |
|
||||
| RN101__openai | 1111 | 3.21 | 40.16 | ❌ |
|
||||
| ViT-B-16__openai | 985 | 5.03 | 40.1 | ❌ |
|
||||
| ViT-B-32__openai | 1004 | 2.26 | 38.27 | ✅ |
|
||||
| RN50__openai | 913 | 2.39 | 37.8 | ✅ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>German</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 90.04 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 89.97 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 89.85 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 89.81 | ✅ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 89.77 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 89.69 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 89.45 | ✅ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 89.44 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 89.39 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 89.35 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 89.03 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 88.82 | ✅ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 88.55 | ❌ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 88.42 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 87.19 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 86.44 | ✅ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 84.81 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 84.81 | ❌ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 84.58 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 84.44 | ✅ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 83.33 | ✅ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 82.75 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 82.32 | ❌ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 81.63 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 76.76 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 76.33 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 75.19 | ✅ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 75.07 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP-384__webli | 4417 | 72.19 | 64.61 | ❌ |
|
||||
| ViT-H-14__laion2b-s32b-b79k | 4676 | 39.06 | 52.81 | ❌ |
|
||||
| ViT-L-14__laion2b-s32b-b82k | 2233 | 20.56 | 42.88 | ❌ |
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 38.65 | ❌ |
|
||||
| ViT-L-14__laion400m_e31 | 2183 | 19.87 | 38.37 | ❌ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 37.65 | ✅ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 36.6 | ✅ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 35.44 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 35.44 | ❌ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 32.46 | ✅ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 32.31 | ✅ |
|
||||
| ViT-B-32__laion400m_e31 | 999 | 2.28 | 31.85 | ✅ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 31.81 | ❌ |
|
||||
| RN50x64__openai | 5079 | 48.79 | 28.41 | ❌ |
|
||||
| ViT-L-14__openai | 2212 | 19.91 | 27.63 | ❌ |
|
||||
| ViT-L-14-336__openai | 2616 | 43.45 | 27.09 | ❌ |
|
||||
| RN50x16__openai | 2221 | 15.87 | 24.48 | ❌ |
|
||||
| RN50x4__openai | 1416 | 5.85 | 23.49 | ❌ |
|
||||
| RN50__openai | 913 | 2.39 | 20.91 | ✅ |
|
||||
| ViT-B-16__openai | 985 | 5.03 | 20.83 | ❌ |
|
||||
| RN101__openai | 1111 | 3.21 | 20.39 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 87.32 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 87.29 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 87.29 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 87.21 | ✅ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 87.18 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 87.14 | ❌ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 87.07 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 86.83 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 86.81 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 86.75 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 86.74 | ✅ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 86.68 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 86.56 | ✅ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 86.16 | ✅ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 84.54 | ❌ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 84.41 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 84.25 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 83.8 | ❌ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 82.59 | ✅ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 81.53 | ✅ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 81.34 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 81.15 | ✅ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 81.05 | ❌ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 78.35 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 76.56 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 76.0 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 75.21 | ✅ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 75.14 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP-384__webli | 4417 | 72.19 | 65.86 | ❌ |
|
||||
| ViT-H-14__laion2b-s32b-b79k | 4676 | 39.06 | 56.87 | ❌ |
|
||||
| ViT-L-14__laion2b-s32b-b82k | 2233 | 20.56 | 47.19 | ❌ |
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 43.36 | ❌ |
|
||||
| ViT-L-14__laion400m_e31 | 2183 | 19.87 | 43.0 | ❌ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 41.81 | ✅ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 40.43 | ✅ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 40.41 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 40.41 | ❌ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 37.71 | ✅ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 37.64 | ✅ |
|
||||
| ViT-B-32__laion400m_e31 | 999 | 2.28 | 36.04 | ✅ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 35.9 | ❌ |
|
||||
| RN50x64__openai | 5079 | 48.79 | 34.19 | ❌ |
|
||||
| ViT-L-14__openai | 2212 | 19.91 | 33.1 | ❌ |
|
||||
| ViT-L-14-336__openai | 2616 | 43.45 | 32.25 | ❌ |
|
||||
| RN50x16__openai | 2221 | 15.87 | 30.56 | ❌ |
|
||||
| RN50x4__openai | 1416 | 5.85 | 29.2 | ❌ |
|
||||
| ViT-B-16__openai | 985 | 5.03 | 25.77 | ❌ |
|
||||
| RN101__openai | 1111 | 3.21 | 25.46 | ❌ |
|
||||
| RN50__openai | 913 | 2.39 | 24.92 | ✅ |
|
||||
| ViT-B-32__openai | 1004 | 2.26 | 24.13 | ✅ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>Greek</summary>
|
||||
@@ -542,10 +556,10 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 60.63 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 60.41 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 60.1 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 60.06 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 60.06 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 59.44 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 60.06 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 59.44 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 59.44 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 59.43 | ✅ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 58.78 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 53.42 | ❌ |
|
||||
@@ -670,99 +684,104 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
<summary>Italian</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 88.6 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 88.25 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 88.12 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 88.04 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 87.97 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 87.69 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 87.29 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 87.06 | ❌ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 86.91 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 86.88 | ✅ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 86.68 | ✅ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 86.61 | ❌ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 85.55 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 85.37 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 83.78 | ✅ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 83.0 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 81.81 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 81.77 | ❌ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 81.32 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 80.97 | ❌ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 80.53 | ✅ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 80.1 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 79.71 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 77.31 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 75.19 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 74.49 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP-384__webli | 4417 | 72.19 | 74.04 | ❌ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 73.68 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 73.57 | ✅ |
|
||||
| ViT-H-14__laion2b-s32b-b79k | 4676 | 39.06 | 51.04 | ❌ |
|
||||
| ViT-L-14__laion2b-s32b-b82k | 2233 | 20.56 | 41.73 | ❌ |
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 36.87 | ❌ |
|
||||
| ViT-L-14__laion400m_e31 | 2183 | 19.87 | 36.84 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 34.68 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 34.68 | ❌ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 34.64 | ✅ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 33.8 | ✅ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 30.11 | ✅ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 30.04 | ❌ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 29.89 | ❌ |
|
||||
| ViT-B-32__laion400m_e31 | 999 | 2.28 | 29.88 | ✅ |
|
||||
| RN50x64__openai | 5079 | 48.79 | 26.67 | ❌ |
|
||||
| ViT-L-14__openai | 2212 | 19.91 | 25.51 | ❌ |
|
||||
| ViT-L-14-336__openai | 2616 | 43.45 | 25.3 | ❌ |
|
||||
| RN50x16__openai | 2221 | 15.87 | 21.37 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 87.17 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 86.91 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 86.83 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 86.77 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 86.67 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 86.42 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 86.35 | ✅ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 86.34 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 86.18 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 86.17 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 85.84 | ✅ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 85.8 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 85.7 | ✅ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 85.67 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 83.32 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 82.95 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 82.73 | ❌ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 82.72 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 81.07 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 80.8 | ✅ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 80.6 | ✅ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 80.35 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 78.79 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 76.62 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 76.51 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 76.08 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 75.29 | ✅ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 75.29 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP-384__webli | 4417 | 72.19 | 74.84 | ❌ |
|
||||
| ViT-H-14__laion2b-s32b-b79k | 4676 | 39.06 | 56.32 | ❌ |
|
||||
| ViT-L-14__laion2b-s32b-b82k | 2233 | 20.56 | 47.25 | ❌ |
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 43.09 | ❌ |
|
||||
| ViT-L-14__laion400m_e31 | 2183 | 19.87 | 42.99 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 40.29 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 40.29 | ❌ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 39.67 | ✅ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 39.03 | ✅ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 36.14 | ✅ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 35.89 | ❌ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 35.59 | ❌ |
|
||||
| ViT-B-32__laion400m_e31 | 999 | 2.28 | 35.56 | ✅ |
|
||||
| RN50x64__openai | 5079 | 48.79 | 33.53 | ❌ |
|
||||
| ViT-L-14__openai | 2212 | 19.91 | 32.19 | ❌ |
|
||||
| ViT-L-14-336__openai | 2616 | 43.45 | 30.95 | ❌ |
|
||||
| RN50x16__openai | 2221 | 15.87 | 28.85 | ❌ |
|
||||
| RN50x4__openai | 1416 | 5.85 | 25.75 | ❌ |
|
||||
| ViT-B-16__openai | 985 | 5.03 | 25.18 | ❌ |
|
||||
| RN101__openai | 1111 | 3.21 | 24.48 | ❌ |
|
||||
| RN50__openai | 913 | 2.39 | 23.89 | ✅ |
|
||||
| ViT-B-32__openai | 1004 | 2.26 | 23.39 | ✅ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>Japanese</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 86.97 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 85.15 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 84.69 | ❌ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 81.77 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 81.26 | ❌ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 81.19 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 69.99 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 68.58 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 68.35 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 68.29 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 67.99 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 67.68 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 67.67 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 66.85 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 66.54 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 65.77 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 61.48 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 58.1 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 55.31 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 83.95 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 82.21 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 81.55 | ❌ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 78.72 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 78.53 | ❌ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 75.93 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 66.86 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 65.59 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 65.48 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 65.36 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 64.47 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 64.17 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 64.08 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 63.69 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 63.33 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 63.02 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 58.39 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 56.38 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 53.16 | ❌ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>Korean</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 77.21 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 76.89 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 75.72 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 75.06 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 74.94 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 74.36 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 74.09 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 73.61 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 73.55 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 73.41 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 73.18 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 72.79 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 72.27 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 71.73 | ✅ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 71.12 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 70.25 | ✅ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 67.54 | ✅ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 67.37 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 65.44 | ✅ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 80.56 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 80.53 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 77.09 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 77.08 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 76.97 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 76.92 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 76.58 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 76.2 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 75.95 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 75.86 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 75.67 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 75.49 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 74.6 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 74.52 | ✅ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 73.88 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 71.09 | ✅ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 68.87 | ✅ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 67.94 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 66.39 | ✅ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>Maori</summary>
|
||||
@@ -834,34 +853,34 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
<summary>Polish</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 80.6 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 80.17 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 80.06 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 80.04 | ✅ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 79.98 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 79.8 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 79.72 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 79.66 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 79.45 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 79.26 | ❌ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 79.21 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 79.14 | ✅ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 78.23 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 75.33 | ✅ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 74.7 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 74.63 | ❌ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 73.69 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 73.44 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 70.34 | ❌ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 59.4 | ❌ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 59.14 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 48.74 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 48.35 | ❌ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 40.76 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 39.13 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 39.09 | ❌ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 38.55 | ❌ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 38.46 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 83.49 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 83.45 | ❌ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 83.11 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 82.99 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 82.96 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 82.93 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 82.61 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 82.26 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 82.24 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 82.03 | ✅ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 82.03 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 81.92 | ✅ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 81.27 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 80.0 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 79.65 | ✅ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 76.75 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 76.52 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 75.1 | ✅ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 73.9 | ❌ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 65.03 | ❌ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 64.89 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 51.6 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 51.29 | ❌ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 46.15 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 41.55 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 41.17 | ✅ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 40.9 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 40.76 | ✅ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>Portuguese</summary>
|
||||
@@ -955,84 +974,87 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
<summary>Russian</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 87.65 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 87.62 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 87.4 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 87.39 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 86.88 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 86.87 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 86.74 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 86.26 | ✅ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 85.98 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 85.66 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 85.54 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 84.69 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 84.29 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 84.24 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 82.86 | ✅ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 81.59 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 80.56 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 80.44 | ❌ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 79.99 | ❌ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 39.51 | ❌ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 39.16 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 23.33 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 22.4 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 84.54 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 84.41 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 84.36 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 84.31 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 84.22 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 83.9 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 83.69 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 83.5 | ✅ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 83.31 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 83.21 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 83.11 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 82.7 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 82.69 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 80.91 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 79.75 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 79.35 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 78.91 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 78.06 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 76.44 | ✅ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 42.81 | ❌ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 42.1 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 24.95 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 24.25 | ❌ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 20.85 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 20.44 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 20.41 | ❌ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>Spanish</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 84.24 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 83.94 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 83.91 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 83.78 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 83.71 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 83.59 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 83.2 | ✅ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 83.0 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 82.91 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 82.58 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 82.5 | ✅ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 82.48 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 82.22 | ✅ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 81.34 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 80.18 | ❌ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 80.14 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 78.99 | ✅ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 78.19 | ✅ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 78.15 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 77.93 | ✅ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 77.64 | ❌ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 77.21 | ❌ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 76.36 | ❌ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 75.73 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 75.56 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 75.01 | ✅ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 74.62 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 74.6 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP-384__webli | 4417 | 72.19 | 70.31 | ❌ |
|
||||
| ViT-H-14__laion2b-s32b-b79k | 4676 | 39.06 | 58.31 | ❌ |
|
||||
| ViT-L-14__laion2b-s32b-b82k | 2233 | 20.56 | 49.56 | ❌ |
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 46.69 | ❌ |
|
||||
| ViT-L-14__laion400m_e31 | 2183 | 19.87 | 46.53 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 44.05 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 44.05 | ❌ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 43.67 | ✅ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 42.5 | ✅ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 41.03 | ✅ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 40.91 | ❌ |
|
||||
| ViT-B-32__laion400m_e31 | 999 | 2.28 | 40.3 | ✅ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 40.3 | ❌ |
|
||||
| RN50x64__openai | 5079 | 48.79 | 37.92 | ❌ |
|
||||
| ViT-L-14-336__openai | 2616 | 43.45 | 37.7 | ❌ |
|
||||
| ViT-L-14__openai | 2212 | 19.91 | 37.59 | ❌ |
|
||||
| RN50x16__openai | 2221 | 15.87 | 34.75 | ❌ |
|
||||
| ViT-B-16__openai | 985 | 5.03 | 32.1 | ❌ |
|
||||
| RN50x4__openai | 1416 | 5.85 | 32.08 | ❌ |
|
||||
| RN101__openai | 1111 | 3.21 | 30.77 | ❌ |
|
||||
| RN50__openai | 913 | 2.39 | 30.2 | ✅ |
|
||||
| ViT-B-32__openai | 1004 | 2.26 | 29.84 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 85.47 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 85.44 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 85.32 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 85.22 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 85.15 | ❌ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 84.81 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 84.68 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 84.6 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 84.55 | ✅ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 84.27 | ❌ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 84.15 | ✅ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 83.87 | ❌ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 83.74 | ❌ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 83.61 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 83.15 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 81.7 | ❌ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 80.91 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 80.73 | ✅ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 80.69 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 80.3 | ❌ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 79.8 | ❌ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 79.71 | ✅ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 79.64 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 78.0 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 77.83 | ❌ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 76.87 | ✅ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 76.66 | ❌ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 75.99 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP-384__webli | 4417 | 72.19 | 71.96 | ❌ |
|
||||
| ViT-H-14__laion2b-s32b-b79k | 4676 | 39.06 | 62.06 | ❌ |
|
||||
| ViT-L-14__laion2b-s32b-b82k | 2233 | 20.56 | 53.78 | ❌ |
|
||||
| ViT-L-14__laion400m_e32 | 2218 | 19.73 | 50.13 | ❌ |
|
||||
| ViT-L-14__laion400m_e31 | 2183 | 19.87 | 50.0 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e32 | 1246 | 6.95 | 47.39 | ❌ |
|
||||
| ViT-B-16-plus-240__laion400m_e31 | 1263 | 6.94 | 47.39 | ❌ |
|
||||
| ViT-B-32__laion2b_e16 | 1004 | 2.38 | 46.47 | ✅ |
|
||||
| ViT-B-32__laion2b-s34b-b79k | 1001 | 2.29 | 45.68 | ✅ |
|
||||
| ViT-B-16__laion400m_e31 | 991 | 5.04 | 44.0 | ✅ |
|
||||
| ViT-B-16__laion400m_e32 | 975 | 4.98 | 43.98 | ✅ |
|
||||
| ViT-B-32__laion400m_e32 | 1003 | 2.35 | 43.8 | ❌ |
|
||||
| ViT-B-32__laion400m_e31 | 999 | 2.28 | 43.73 | ✅ |
|
||||
| RN50x64__openai | 5079 | 48.79 | 43.01 | ❌ |
|
||||
| ViT-L-14__openai | 2212 | 19.91 | 42.96 | ❌ |
|
||||
| ViT-L-14-336__openai | 2616 | 43.45 | 41.67 | ❌ |
|
||||
| RN50x16__openai | 2221 | 15.87 | 40.21 | ❌ |
|
||||
| RN50x4__openai | 1416 | 5.85 | 36.06 | ❌ |
|
||||
| ViT-B-16__openai | 985 | 5.03 | 35.67 | ❌ |
|
||||
| RN101__openai | 1111 | 3.21 | 34.62 | ❌ |
|
||||
| ViT-B-32__openai | 1004 | 2.26 | 32.6 | ✅ |
|
||||
| RN50__openai | 913 | 2.39 | 31.79 | ✅ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>Swahili</summary>
|
||||
@@ -1057,8 +1079,8 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 72.1 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 72.06 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 71.84 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 71.7 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 71.7 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 71.7 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 71.61 | ❌ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 71.51 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 71.45 | ✅ |
|
||||
@@ -1115,31 +1137,34 @@ Memory and execution time estimates were obtained without acceleration on a 7800
|
||||
<summary>Turkish</summary>
|
||||
| Model | Memory (MiB) | Execution Time (ms) | Recall (%) | Pareto Optimal |
|
||||
|------------------------------------------------------|--------------|---------------------|------------|----------------|
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 81.15 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 80.89 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 78.11 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 77.51 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 77.36 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 77.28 | ❌ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 77.24 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 77.01 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 76.37 | ❌ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 75.92 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 75.69 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 75.68 | ❌ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 75.54 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 75.16 | ✅ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 73.83 | ✅ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 70.15 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 69.19 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 66.72 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 64.76 | ❌ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 38.8 | ❌ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 38.48 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 30.83 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 30.28 | ❌ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 21.31 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 20.08 | ✅ |
|
||||
| nllb-clip-large-siglip__mrl | 4248 | 75.44 | 83.91 | ✅ |
|
||||
| nllb-clip-large-siglip__v1 | 4226 | 75.05 | 83.74 | ✅ |
|
||||
| nllb-clip-base-siglip__mrl | 4696 | 16.95 | 81.26 | ✅ |
|
||||
| nllb-clip-base-siglip__v1 | 4675 | 15.17 | 80.21 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-512__webli | 4050 | 107.67 | 79.34 | ✅ |
|
||||
| ViT-SO400M-14-SigLIP2-378__webli | 3940 | 72.25 | 79.22 | ✅ |
|
||||
| XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k | 4014 | 39.14 | 78.9 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-384__webli | 3854 | 56.57 | 78.85 | ✅ |
|
||||
| ViT-SO400M-16-SigLIP2-256__webli | 3611 | 27.84 | 78.29 | ✅ |
|
||||
| ViT-gopt-16-SigLIP2-384__webli | 6585 | 146.84 | 78.27 | ❌ |
|
||||
| ViT-gopt-16-SigLIP2-256__webli | 6475 | 64.51 | 78.0 | ❌ |
|
||||
| ViT-SO400M-14-SigLIP2__webli | 3622 | 27.63 | 77.81 | ✅ |
|
||||
| ViT-L-16-SigLIP2-512__webli | 3358 | 92.59 | 77.67 | ✅ |
|
||||
| ViT-L-16-SigLIP2-384__webli | 3057 | 51.7 | 77.33 | ✅ |
|
||||
| ViT-L-16-SigLIP2-256__webli | 2830 | 23.77 | 76.42 | ✅ |
|
||||
| ViT-B-16-SigLIP-i18n-256__webli | 3029 | 6.87 | 72.44 | ✅ |
|
||||
| XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k | 3030 | 3.2 | 69.84 | ✅ |
|
||||
| ViT-B-16-SigLIP2__webli | 3038 | 5.81 | 69.83 | ❌ |
|
||||
| ViT-B-32-SigLIP2-256__webli | 3061 | 3.31 | 67.13 | ❌ |
|
||||
| ViT-H-14-378-quickgelu__dfn5b | 5049 | 108.4 | 44.43 | ❌ |
|
||||
| ViT-H-14-quickgelu__dfn5b | 4701 | 38.74 | 43.87 | ❌ |
|
||||
| ViT-L-16-SigLIP-384__webli | 3396 | 47.6 | 35.1 | ❌ |
|
||||
| ViT-L-16-SigLIP-256__webli | 3160 | 23.84 | 34.92 | ❌ |
|
||||
| ViT-L-14-quickgelu__dfn2b | 2212 | 20.49 | 25.2 | ✅ |
|
||||
| ViT-B-16-SigLIP-512__webli | 1828 | 26.17 | 24.55 | ✅ |
|
||||
| ViT-B-16-SigLIP__webli | 1081 | 5.77 | 24.13 | ✅ |
|
||||
| ViT-B-16-SigLIP-384__webli | 1128 | 13.53 | 24.08 | ❌ |
|
||||
| ViT-B-16-SigLIP-256__webli | 1102 | 7.11 | 23.95 | ❌ |
|
||||
</details>
|
||||
<details>
|
||||
<summary>Ukrainian</summary>
|
||||
|
||||
@@ -23,12 +23,12 @@ name: immich_remote_ml
|
||||
services:
|
||||
immich-machine-learning:
|
||||
container_name: immich_machine_learning
|
||||
# For hardware acceleration, add one of -[armnn, cuda, rocm, openvino] to the image tag.
|
||||
# For hardware acceleration, add one of -[armnn, cuda, rocm, openvino, rknn] to the image tag.
|
||||
# Example tag: ${IMMICH_VERSION:-release}-cuda
|
||||
image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
|
||||
# extends:
|
||||
# file: hwaccel.ml.yml
|
||||
# service: # set to one of [armnn, cuda, rocm, openvino, openvino-wsl] for accelerated inference - use the `-wsl` version for WSL2 where applicable
|
||||
# service: # set to one of [armnn, cuda, rocm, openvino, openvino-wsl, rknn] for accelerated inference - use the `-wsl` version for WSL2 where applicable
|
||||
volumes:
|
||||
- model-cache:/cache
|
||||
restart: always
|
||||
|
||||
@@ -1,3 +1,7 @@
|
||||
---
|
||||
sidebar_position: 100
|
||||
---
|
||||
|
||||
# Config File
|
||||
|
||||
A config file can be provided as an alternative to the UI configuration.
|
||||
|
||||
@@ -69,39 +69,4 @@ If you get an error `can't set healthcheck.start_interval as feature require Doc
|
||||
|
||||
## Next Steps
|
||||
|
||||
Read the [Post Installation](/docs/install/post-install.mdx) steps or setup optional features below.
|
||||
|
||||
### Setting up optional features
|
||||
|
||||
- [External Libraries](/docs/features/libraries.md): Adding your existing photo library to Immich
|
||||
- [Hardware Transcoding](/docs/features/hardware-transcoding.md): Speeding up video transcoding
|
||||
- [Hardware-Accelerated Machine Learning](/docs/features/ml-hardware-acceleration.md): Speeding up various machine learning tasks in Immich
|
||||
|
||||
### Upgrading
|
||||
|
||||
:::danger Read the release notes
|
||||
Immich is currently under heavy development, which means you can expect [breaking changes][breaking] and bugs. Therefore, we recommend reading the release notes prior to updating and to take special care when using automated tools like [Watchtower][watchtower].
|
||||
|
||||
You can see versions that had breaking changes [here][breaking].
|
||||
:::
|
||||
|
||||
If `IMMICH_VERSION` is set, it will need to be updated to the latest or desired version.
|
||||
|
||||
When a new version of Immich is [released][releases], the application can be upgraded and restarted with the following commands, run in the directory with the `docker-compose.yml` file:
|
||||
|
||||
```bash title="Upgrade and restart Immich"
|
||||
docker compose pull && docker compose up -d
|
||||
```
|
||||
|
||||
To clean up disk space, the old version's obsolete container images can be deleted with the following command:
|
||||
|
||||
```bash title="Clean up unused Docker images"
|
||||
docker image prune
|
||||
```
|
||||
|
||||
[compose-file]: https://github.com/immich-app/immich/releases/latest/download/docker-compose.yml
|
||||
[env-file]: https://github.com/immich-app/immich/releases/latest/download/example.env
|
||||
[watchtower]: https://containrrr.dev/watchtower/
|
||||
[breaking]: https://github.com/immich-app/immich/discussions?discussions_q=label%3Achangelog%3Abreaking-change+sort%3Adate_created
|
||||
[container-auth]: https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry#authenticating-to-the-container-registry
|
||||
[releases]: https://github.com/immich-app/immich/releases
|
||||
Read the [Post Installation](/docs/install/post-install.mdx) steps and [upgrade instructions](/docs/install/upgrading.md).
|
||||
|
||||
@@ -41,3 +41,9 @@ A list of common steps to take after installing Immich include:
|
||||
## Step 7 - Setup Server Backups
|
||||
|
||||
<ServerBackup />
|
||||
|
||||
## Setting up optional features
|
||||
|
||||
- [External Libraries](/docs/features/libraries.md): Adding your existing photo library to Immich
|
||||
- [Hardware Transcoding](/docs/features/hardware-transcoding.md): Speeding up video transcoding
|
||||
- [Hardware-Accelerated Machine Learning](/docs/features/ml-hardware-acceleration.md): Speeding up various machine learning tasks in Immich
|
||||
|
||||
@@ -67,10 +67,4 @@ Click "**Edit Rules**" and add the following firewall rules:
|
||||
|
||||
## Next Steps
|
||||
|
||||
Read the [Post Installation](/docs/install/post-install.mdx) steps or setup optional features below.
|
||||
|
||||
### Setting up optional features
|
||||
|
||||
- [External Libraries](/docs/features/libraries.md): Adding your existing photo library to Immich
|
||||
- [Hardware Transcoding](/docs/features/hardware-transcoding.md): Speeding up video transcoding
|
||||
- [Hardware-Accelerated Machine Learning](/docs/features/ml-hardware-acceleration.md): Speeding up various machine learning tasks in Immich
|
||||
Read the [Post Installation](/docs/install/post-install.mdx) steps and [upgrade instructions](/docs/install/upgrading.md).
|
||||
|
||||
@@ -247,6 +247,10 @@ Some examples are: `IMMICH_VERSION`, `UPLOAD_LOCATION`, `DB_DATA_LOCATION`, `TZ`
|
||||
|
||||
## Updating the App
|
||||
|
||||
:::danger
|
||||
Make sure to read the general [upgrade instructions](/docs/install/upgrading.md).
|
||||
:::
|
||||
|
||||
When updates become available, SCALE alerts and provides easy updates.
|
||||
To update the app to the latest version:
|
||||
|
||||
|
||||
@@ -131,6 +131,10 @@ For more information on how to use the application once installed, please refer
|
||||
|
||||
## Updating Steps
|
||||
|
||||
:::danger
|
||||
Make sure to read the general [upgrade instructions](/docs/install/upgrading.md).
|
||||
:::
|
||||
|
||||
Updating is extremely easy however it's important to be aware that containers managed via the Docker Compose Manager plugin do not integrate with Unraid's native dockerman UI, the label "_update ready_" will always be present on containers installed via the Docker Compose Manager.
|
||||
|
||||
<img
|
||||
|
||||
32
docs/docs/install/upgrading.md
Normal file
32
docs/docs/install/upgrading.md
Normal file
@@ -0,0 +1,32 @@
|
||||
---
|
||||
sidebar_position: 95
|
||||
---
|
||||
|
||||
# Upgrading
|
||||
|
||||
:::danger Read the release notes
|
||||
Immich is currently under heavy development, which means you can expect [breaking changes][breaking] and bugs. You should read the release notes prior to updating and take special care when using automated tools like [Watchtower][watchtower].
|
||||
|
||||
You can see versions that had breaking changes [here][breaking].
|
||||
:::
|
||||
|
||||
When a new version of Immich is [released][releases], you should read the release notes and account for any breaking changes noted (as mentioned above).
|
||||
If you use `IMMICH_VERSION` in your `.env` file, it will need to be updated to the latest or desired version.
|
||||
After that, the application can be upgraded and restarted with the following commands, run in the directory with the `docker-compose.yml` file:
|
||||
|
||||
```bash title="Upgrade and restart Immich"
|
||||
docker compose pull && docker compose up -d
|
||||
```
|
||||
|
||||
To clean up disk space, the old version's obsolete container images can be deleted with the following command:
|
||||
|
||||
```bash title="Clean up unused Docker images"
|
||||
docker image prune
|
||||
```
|
||||
|
||||
[compose-file]: https://github.com/immich-app/immich/releases/latest/download/docker-compose.yml
|
||||
[env-file]: https://github.com/immich-app/immich/releases/latest/download/example.env
|
||||
[watchtower]: https://containrrr.dev/watchtower/
|
||||
[breaking]: https://github.com/immich-app/immich/discussions?discussions_q=label%3Achangelog%3Abreaking-change+sort%3Adate_created
|
||||
[container-auth]: https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry#authenticating-to-the-container-registry
|
||||
[releases]: https://github.com/immich-app/immich/releases
|
||||
@@ -1,2 +1,7 @@
|
||||
Now that you have imported some pictures, you should setup server backups to preserve your memories.
|
||||
You can do so by following our [backup guide](/docs/administration/backup-and-restore.md).
|
||||
|
||||
:::danger
|
||||
Immich is still under heavy development _and_ handles very important data.
|
||||
It is essential that you set up good backups, and test them.
|
||||
:::
|
||||
|
||||
16
docs/static/archived-versions.json
vendored
16
docs/static/archived-versions.json
vendored
@@ -1,4 +1,20 @@
|
||||
[
|
||||
{
|
||||
"label": "v1.131.2",
|
||||
"url": "https://v1.131.2.archive.immich.app"
|
||||
},
|
||||
{
|
||||
"label": "v1.131.1",
|
||||
"url": "https://v1.131.1.archive.immich.app"
|
||||
},
|
||||
{
|
||||
"label": "v1.131.0",
|
||||
"url": "https://v1.131.0.archive.immich.app"
|
||||
},
|
||||
{
|
||||
"label": "v1.130.3",
|
||||
"url": "https://v1.130.3.archive.immich.app"
|
||||
},
|
||||
{
|
||||
"label": "v1.130.2",
|
||||
"url": "https://v1.130.2.archive.immich.app"
|
||||
|
||||
@@ -1,39 +1,29 @@
|
||||
import { FlatCompat } from '@eslint/eslintrc';
|
||||
import js from '@eslint/js';
|
||||
import typescriptEslint from '@typescript-eslint/eslint-plugin';
|
||||
import tsParser from '@typescript-eslint/parser';
|
||||
import eslintPluginPrettierRecommended from 'eslint-plugin-prettier/recommended';
|
||||
import eslintPluginUnicorn from 'eslint-plugin-unicorn';
|
||||
import globals from 'globals';
|
||||
import path from 'node:path';
|
||||
import { fileURLToPath } from 'node:url';
|
||||
import typescriptEslint from 'typescript-eslint';
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = path.dirname(__filename);
|
||||
const compat = new FlatCompat({
|
||||
baseDirectory: __dirname,
|
||||
recommendedConfig: js.configs.recommended,
|
||||
allConfig: js.configs.all,
|
||||
});
|
||||
|
||||
export default [
|
||||
export default typescriptEslint.config([
|
||||
eslintPluginUnicorn.configs.recommended,
|
||||
eslintPluginPrettierRecommended,
|
||||
js.configs.recommended,
|
||||
typescriptEslint.configs.recommended,
|
||||
{
|
||||
ignores: ['eslint.config.mjs'],
|
||||
},
|
||||
...compat.extends(
|
||||
'plugin:@typescript-eslint/recommended',
|
||||
'plugin:prettier/recommended',
|
||||
'plugin:unicorn/recommended',
|
||||
),
|
||||
{
|
||||
plugins: {
|
||||
'@typescript-eslint': typescriptEslint,
|
||||
},
|
||||
|
||||
languageOptions: {
|
||||
globals: {
|
||||
...globals.node,
|
||||
},
|
||||
|
||||
parser: tsParser,
|
||||
parser: typescriptEslint.parser,
|
||||
ecmaVersion: 5,
|
||||
sourceType: 'module',
|
||||
|
||||
@@ -62,4 +52,4 @@ export default [
|
||||
'object-shorthand': ['error', 'always'],
|
||||
},
|
||||
},
|
||||
];
|
||||
]);
|
||||
|
||||
1439
e2e/package-lock.json
generated
1439
e2e/package-lock.json
generated
File diff suppressed because it is too large
Load Diff
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "immich-e2e",
|
||||
"version": "1.130.2",
|
||||
"version": "1.131.2",
|
||||
"description": "",
|
||||
"main": "index.js",
|
||||
"type": "module",
|
||||
@@ -25,18 +25,16 @@
|
||||
"@immich/sdk": "file:../open-api/typescript-sdk",
|
||||
"@playwright/test": "^1.44.1",
|
||||
"@types/luxon": "^3.4.2",
|
||||
"@types/node": "^22.13.10",
|
||||
"@types/node": "^22.13.14",
|
||||
"@types/oidc-provider": "^8.5.1",
|
||||
"@types/pg": "^8.11.0",
|
||||
"@types/pngjs": "^6.0.4",
|
||||
"@types/supertest": "^6.0.2",
|
||||
"@typescript-eslint/eslint-plugin": "^8.15.0",
|
||||
"@typescript-eslint/parser": "^8.15.0",
|
||||
"@vitest/coverage-v8": "^3.0.0",
|
||||
"eslint": "^9.14.0",
|
||||
"eslint-config-prettier": "^10.0.0",
|
||||
"eslint-plugin-prettier": "^5.1.3",
|
||||
"eslint-plugin-unicorn": "^56.0.1",
|
||||
"eslint-plugin-unicorn": "^57.0.0",
|
||||
"exiftool-vendored": "^28.3.1",
|
||||
"globals": "^16.0.0",
|
||||
"jose": "^5.6.3",
|
||||
@@ -49,6 +47,7 @@
|
||||
"socket.io-client": "^4.7.4",
|
||||
"supertest": "^7.0.0",
|
||||
"typescript": "^5.3.3",
|
||||
"typescript-eslint": "^8.28.0",
|
||||
"utimes": "^5.2.1",
|
||||
"vitest": "^3.0.0"
|
||||
},
|
||||
|
||||
@@ -1257,6 +1257,7 @@ describe('/asset', () => {
|
||||
|
||||
for (const { id, status } of assets) {
|
||||
expect(status).toBe(AssetMediaStatus.Created);
|
||||
// longer timeout as the thumbnail generation from full-size raw files can take a while
|
||||
await utils.waitForWebsocketEvent({ event: 'assetUpload', id });
|
||||
}
|
||||
|
||||
|
||||
@@ -329,7 +329,7 @@ describe('/libraries', () => {
|
||||
const library = await utils.createLibrary(admin.accessToken, {
|
||||
ownerId: admin.userId,
|
||||
importPaths: [`${testAssetDirInternal}/temp`],
|
||||
exclusionPatterns: ['**/directoryA'],
|
||||
exclusionPatterns: ['**/directoryA/**'],
|
||||
});
|
||||
|
||||
await utils.scan(admin.accessToken, library.id);
|
||||
@@ -337,7 +337,82 @@ describe('/libraries', () => {
|
||||
const { assets } = await utils.searchAssets(admin.accessToken, { libraryId: library.id });
|
||||
|
||||
expect(assets.count).toBe(1);
|
||||
expect(assets.items[0].originalPath.includes('directoryB'));
|
||||
|
||||
expect(assets.items).toEqual(
|
||||
expect.arrayContaining([
|
||||
expect.objectContaining({ originalPath: expect.stringContaining('directoryB/assetB.png') }),
|
||||
]),
|
||||
);
|
||||
});
|
||||
|
||||
it('should scan external library with multiple exclusion patterns', async () => {
|
||||
const library = await utils.createLibrary(admin.accessToken, {
|
||||
ownerId: admin.userId,
|
||||
importPaths: [`${testAssetDirInternal}/temp`],
|
||||
exclusionPatterns: ['**/directoryA/**', '**/directoryB/**'],
|
||||
});
|
||||
|
||||
await utils.scan(admin.accessToken, library.id);
|
||||
|
||||
const { assets } = await utils.searchAssets(admin.accessToken, { libraryId: library.id });
|
||||
|
||||
expect(assets.count).toBe(0);
|
||||
|
||||
expect(assets.items).toEqual([]);
|
||||
});
|
||||
|
||||
it('should remove assets covered by a new exclusion pattern', async () => {
|
||||
const library = await utils.createLibrary(admin.accessToken, {
|
||||
ownerId: admin.userId,
|
||||
importPaths: [`${testAssetDirInternal}/temp`],
|
||||
});
|
||||
|
||||
await utils.scan(admin.accessToken, library.id);
|
||||
|
||||
{
|
||||
const { assets } = await utils.searchAssets(admin.accessToken, { libraryId: library.id });
|
||||
|
||||
expect(assets.count).toBe(2);
|
||||
|
||||
expect(assets.items).toEqual(
|
||||
expect.arrayContaining([
|
||||
expect.objectContaining({ originalPath: expect.stringContaining('directoryA/assetA.png') }),
|
||||
expect.objectContaining({ originalPath: expect.stringContaining('directoryB/assetB.png') }),
|
||||
]),
|
||||
);
|
||||
}
|
||||
|
||||
await utils.updateLibrary(admin.accessToken, library.id, {
|
||||
exclusionPatterns: ['**/directoryA/**'],
|
||||
});
|
||||
|
||||
await utils.scan(admin.accessToken, library.id);
|
||||
|
||||
{
|
||||
const { assets } = await utils.searchAssets(admin.accessToken, { libraryId: library.id });
|
||||
|
||||
expect(assets.count).toBe(1);
|
||||
|
||||
expect(assets.items).toEqual(
|
||||
expect.arrayContaining([
|
||||
expect.objectContaining({ originalPath: expect.stringContaining('directoryB/assetB.png') }),
|
||||
]),
|
||||
);
|
||||
}
|
||||
|
||||
await utils.updateLibrary(admin.accessToken, library.id, {
|
||||
exclusionPatterns: ['**/directoryA/**', '**/directoryB/**'],
|
||||
});
|
||||
|
||||
await utils.scan(admin.accessToken, library.id);
|
||||
|
||||
{
|
||||
const { assets } = await utils.searchAssets(admin.accessToken, { libraryId: library.id });
|
||||
|
||||
expect(assets.count).toBe(0);
|
||||
|
||||
expect(assets.items).toEqual([]);
|
||||
}
|
||||
});
|
||||
|
||||
it('should scan multiple import paths', async () => {
|
||||
|
||||
@@ -117,7 +117,7 @@ describe('/shared-links', () => {
|
||||
const resp = await request(shareUrl).get(`/${linkWithAssets.key}`);
|
||||
expect(resp.status).toBe(200);
|
||||
expect(resp.header['content-type']).toContain('text/html');
|
||||
expect(resp.text).toContain(`<meta property="og:image" content="http://`);
|
||||
expect(resp.text).toContain(`<meta property="og:image" content="https://my.immich.app`);
|
||||
});
|
||||
});
|
||||
|
||||
|
||||
@@ -22,7 +22,7 @@ const tests: Test[] = [
|
||||
},
|
||||
{
|
||||
test: 'should support paths with an asterisk',
|
||||
paths: [`/photos\*/image1.jpg`],
|
||||
paths: [`/photos*/image1.jpg`],
|
||||
files: {
|
||||
'/photos*/image1.jpg': true,
|
||||
'/photos*/image2.jpg': false,
|
||||
@@ -40,7 +40,7 @@ const tests: Test[] = [
|
||||
},
|
||||
{
|
||||
test: 'should support paths with a single quote',
|
||||
paths: [`/photos\'/image1.jpg`],
|
||||
paths: [`/photos'/image1.jpg`],
|
||||
files: {
|
||||
"/photos'/image1.jpg": true,
|
||||
"/photos'/image2.jpg": false,
|
||||
@@ -49,7 +49,7 @@ const tests: Test[] = [
|
||||
},
|
||||
{
|
||||
test: 'should support paths with a double quote',
|
||||
paths: [`/photos\"/image1.jpg`],
|
||||
paths: [`/photos"/image1.jpg`],
|
||||
files: {
|
||||
'/photos"/image1.jpg': true,
|
||||
'/photos"/image2.jpg': false,
|
||||
@@ -67,7 +67,7 @@ const tests: Test[] = [
|
||||
},
|
||||
{
|
||||
test: 'should support paths with an opening brace',
|
||||
paths: [`/photos\{/image1.jpg`],
|
||||
paths: [`/photos{/image1.jpg`],
|
||||
files: {
|
||||
'/photos{/image1.jpg': true,
|
||||
'/photos{/image2.jpg': false,
|
||||
@@ -76,7 +76,7 @@ const tests: Test[] = [
|
||||
},
|
||||
{
|
||||
test: 'should support paths with a closing brace',
|
||||
paths: [`/photos\}/image1.jpg`],
|
||||
paths: [`/photos}/image1.jpg`],
|
||||
files: {
|
||||
'/photos}/image1.jpg': true,
|
||||
'/photos}/image2.jpg': false,
|
||||
|
||||
@@ -537,6 +537,7 @@ export const utils = {
|
||||
},
|
||||
|
||||
waitForQueueFinish: (accessToken: string, queue: keyof AllJobStatusResponseDto, ms?: number) => {
|
||||
// eslint-disable-next-line no-async-promise-executor
|
||||
return new Promise<void>(async (resolve, reject) => {
|
||||
const timeout = setTimeout(() => reject(new Error('Timed out waiting for queue to empty')), ms || 10_000);
|
||||
|
||||
|
||||
@@ -44,7 +44,7 @@ test.describe('Photo Viewer', () => {
|
||||
const { x, y, width, height } = box!;
|
||||
await page.mouse.move(x + width / 2, y + height / 2);
|
||||
await page.mouse.wheel(0, -1);
|
||||
await expect.poll(async () => await imageLocator(page).getAttribute('src')).toContain('original');
|
||||
await expect.poll(async () => await imageLocator(page).getAttribute('src')).toContain('fullsize');
|
||||
});
|
||||
|
||||
test('reloads photo when checksum changes', async ({ page }) => {
|
||||
|
||||
@@ -987,6 +987,7 @@
|
||||
"permanently_deleted_asset": "تم حذف الأصل بشكل نهائي",
|
||||
"permanently_deleted_assets_count": "تم حذف {count, plural, one {# محتوى} other {# المحتويات}} نهائيًا",
|
||||
"person": "شخص",
|
||||
"person_birthdate": "تاريخ الميلاد {التاريخ}",
|
||||
"person_hidden": "{name}{hidden, select, true { (مخفي)} other {}}",
|
||||
"photo_shared_all_users": "يبدو أنك شاركت صورك مع جميع المستخدمين أو ليس لديك أي مستخدم للمشاركة معه.",
|
||||
"photos": "الصور",
|
||||
@@ -1078,6 +1079,8 @@
|
||||
"remove_from_album": "إزالة من الألبوم",
|
||||
"remove_from_favorites": "إزالة من المفضلة",
|
||||
"remove_from_shared_link": "إزالة من الرابط المشترك",
|
||||
"remove_memory": "إزالة الذاكرة",
|
||||
"remove_photo_from_memory": "إزالة الصورة من هذه الذكرى",
|
||||
"remove_url": "إزالة عنوان URL",
|
||||
"remove_user": "إزالة المستخدم",
|
||||
"removed_api_key": "تم إزالة مفتاح API: {name}",
|
||||
@@ -1148,6 +1151,7 @@
|
||||
"searching_locales": "جارٍ البحث في اللغات...",
|
||||
"second": "ثانية",
|
||||
"see_all_people": "عرض جميع الأشخاص",
|
||||
"select": "إختر",
|
||||
"select_album_cover": "تحديد غلاف الألبوم",
|
||||
"select_all": "تحديد الكل",
|
||||
"select_all_duplicates": "تحديد جميع النسخ المكررة",
|
||||
|
||||
@@ -65,8 +65,13 @@
|
||||
"forcing_refresh_library_files": "Forcing refresh of all library files",
|
||||
"image_format": "Format",
|
||||
"image_format_description": "WebP produces smaller files than JPEG, but is slower to encode.",
|
||||
"image_fullsize_enabled": "Enable full-size image generation",
|
||||
"image_fullsize_enabled_description": "Generate full-size image for non-web-friendly formats. When \"Prefer embedded preview\" is enabled, embedded previews are used directly without conversion. Does not affect web-friendly formats like JPEG.",
|
||||
"image_fullsize_quality_description": "Full-size image quality from 1-100. Higher is better, but produces larger files.",
|
||||
"image_fullsize_title": "Full-size Image Settings",
|
||||
"image_fullsize_description": "Full-size image with stripped metadata, used when zoomed in",
|
||||
"image_prefer_embedded_preview": "Prefer embedded preview",
|
||||
"image_prefer_embedded_preview_setting_description": "Use embedded previews in RAW photos as the input to image processing when available. This can produce more accurate colors for some images, but the quality of the preview is camera-dependent and the image may have more compression artifacts.",
|
||||
"image_prefer_embedded_preview_setting_description": "Use embedded previews in RAW photos as the input to image processing and when available. This can produce more accurate colors for some images, but the quality of the preview is camera-dependent and the image may have more compression artifacts.",
|
||||
"image_prefer_wide_gamut": "Prefer wide gamut",
|
||||
"image_prefer_wide_gamut_setting_description": "Use Display P3 for thumbnails. This better preserves the vibrance of images with wide colorspaces, but images may appear differently on old devices with an old browser version. sRGB images are kept as sRGB to avoid color shifts.",
|
||||
"image_preview_description": "Medium-size image with stripped metadata, used when viewing a single asset and for machine learning",
|
||||
|
||||
1
i18n/eu.json
Normal file
1
i18n/eu.json
Normal file
@@ -0,0 +1 @@
|
||||
{}
|
||||
1
i18n/gl.json
Normal file
1
i18n/gl.json
Normal file
@@ -0,0 +1 @@
|
||||
{}
|
||||
@@ -50,6 +50,7 @@
|
||||
"confirm_user_password_reset": "क्या आप वाकई {user} का पासवर्ड रीसेट करना चाहते हैं?",
|
||||
"create_job": "जॉब बनाएँ",
|
||||
"cron_expression": "क्रॉन अभिव्यक्ति",
|
||||
"cron_expression_description": "क्रॉन प्रारूप का उपयोग करके स्कैनिंग अंतराल सेट करें। अधिक जानकारी के लिए कृपया <link>क्रोनटैब गुरु</link> देखें",
|
||||
"disable_login": "लॉगिन अक्षम करें",
|
||||
"duplicate_detection_job_description": "समान छवियों का पता लगाने के लिए संपत्तियों पर मशीन लर्निंग चलाएं। यह कार्यक्षमता स्मार्ट खोज पर निर्भर करती है",
|
||||
"exclusion_pattern_description": "Exclusion पैटर्न आपको अपनी लाइब्रेरी को स्कैन करते समय फ़ाइलों और फ़ोल्डरों को अनदेखा करने देता है। यह उपयोगी है यदि आपके पास ऐसे फ़ोल्डर हैं जिनमें ऐसी फ़ाइलें हैं जिन्हें आप आयात नहीं करना चाहते हैं, जैसे RAW फ़ाइलें।",
|
||||
@@ -61,11 +62,14 @@
|
||||
"failed_job_command": "कार्य {job} के लिए आदेश {command} विफल",
|
||||
"force_delete_user_warning": "चेतावनी: इससे उपयोगकर्ता और सारा डेटा तुरंत हट जाएगा। इसे पूर्ववत नहीं किया जा सकता और फ़ाइलें पुनर्प्राप्त नहीं की जा सकतीं।",
|
||||
"forcing_refresh_library_files": "सभी लाइब्रेरी फ़ाइलों को जबरन सामयिक करें",
|
||||
"image_format": "प्रारूप",
|
||||
"image_format_description": "वेबपी, जेपीईजी की तुलना में छोटी फ़ाइलें बनाता है, लेकिन एनकोड करने में धीमा है।",
|
||||
"image_prefer_embedded_preview": "एम्बेडेड पूर्वावलोकन को प्राथमिकता दें",
|
||||
"image_prefer_embedded_preview_setting_description": "जब उपलब्ध हो तो RAW फ़ोटो में एम्बेडेड पूर्वावलोकन का उपयोग इमेज प्रोसेसिंग के इनपुट के रूप में करें। यह कुछ छवियों के लिए अधिक सटीक रंग उत्पन्न कर सकता है, लेकिन पूर्वावलोकन की गुणवत्ता कैमरे पर निर्भर करती है और छवि में अधिक संपीड़न कलाकृतियाँ हो सकती हैं।",
|
||||
"image_prefer_wide_gamut": "विस्तृत सरगम को प्राथमिकता दें",
|
||||
"image_prefer_wide_gamut_setting_description": "थंबनेल के लिए डिस्प्ले P3 का उपयोग करें। यह विस्तृत कलरस्पेस वाली छवियों की जीवंतता को बेहतर ढंग से संरक्षित करता है, लेकिन पुराने ब्राउज़र संस्करण वाले पुराने डिवाइस पर छवियां अलग-अलग दिखाई दे सकती हैं। रंग परिवर्तन से बचने के लिए sRGB छवियों को sRGB के रूप में रखा जाता है।",
|
||||
"image_preview_description": "मेटाडेटा रहित मध्यम आकार की छवि, जिसका उपयोग एकल संपत्ति देखने और मशीन लर्निंग के लिए होता है",
|
||||
"image_preview_title": "पूर्वदर्शन सेटिंग्स",
|
||||
"image_quality": "गुणवत्ता",
|
||||
"image_settings": "छवि सेटिंग्स",
|
||||
"image_settings_description": "उत्पन्न छवियों की गुणवत्ता और रिज़ॉल्यूशन प्रबंधित करें",
|
||||
|
||||
@@ -35,7 +35,7 @@
|
||||
"authentication_settings_disable_all": "Biztosan letiltod az összes bejelentkezési módot? A bejelentkezés teljesen le lesz tiltva.",
|
||||
"authentication_settings_reenable": "Az újbóli engedélyezéshez használj egy<link>Szerver Parancsot</link>.",
|
||||
"background_task_job": "Háttérfeladatok",
|
||||
"backup_database": "Tartalék Adatbázis",
|
||||
"backup_database": "Adatbázis Biztonsági Mentése",
|
||||
"backup_database_enable_description": "Adatbázis biztonsági mentések engedélyezése",
|
||||
"backup_keep_last_amount": "Megőrizendő korábbi biztonsági mentések száma",
|
||||
"backup_settings": "Biztonsági mentés beállításai",
|
||||
|
||||
145
i18n/ja.json
145
i18n/ja.json
@@ -7,7 +7,7 @@
|
||||
"actions": "アクション",
|
||||
"active": "アクティブ",
|
||||
"activity": "アクティビティ",
|
||||
"activity_changed": "アクティビティは{enabled, select, true {有効化} other {無効化}}されました",
|
||||
"activity_changed": "アクティビティは{enabled, select, true {有効} other {無効}}になりました",
|
||||
"add": "追加",
|
||||
"add_a_description": "説明を追加",
|
||||
"add_a_location": "場所を追加",
|
||||
@@ -20,20 +20,28 @@
|
||||
"add_partner": "パートナーを追加",
|
||||
"add_path": "パスを追加",
|
||||
"add_photos": "写真を追加",
|
||||
"add_to": "追加先...",
|
||||
"add_to": "追加先…",
|
||||
"add_to_album": "アルバムに追加",
|
||||
"add_to_shared_album": "共有アルバムに追加",
|
||||
"add_url": "URLを追加",
|
||||
"added_to_archive": "アーカイブに追加済",
|
||||
"added_to_favorites": "お気に入りに追加済",
|
||||
"added_to_favorites_count": "{count, number} 枚の画像をお気に入りに追加済",
|
||||
"admin": {
|
||||
"add_exclusion_pattern_description": "除外パターンを追加します。ワイルドカード「*」「**」「?」を使用できます。すべてのディレクトリで「Raw」と名前が付いたファイルを無視するには、「**/Raw/**」を使用します。また、「.tif」で終わるファイルをすべて無視するには、「**/*.tif」を使用します。さらに、絶対パスを無視するには「/path/to/ignore/**」を使用します。",
|
||||
"asset_offline_description": "この外部ライブラリのアセットはディスク上に見つからなくなってゴミ箱に移動されました。ファイルがライブラリの中で移動された場合はタイムラインで新しい対応するアセットを確認してください。このアセットを復元するには以下のファイルパスがImmichからアクセスできるか確認してライブラリをスキャンしてください。",
|
||||
"authentication_settings": "認証設定",
|
||||
"authentication_settings_description": "認証設定の管理（パスワード、OAuth、その他）",
|
||||
"authentication_settings_disable_all": "本当に全てのログイン方法を無効にしますか? ログインは完全に無効になります。",
|
||||
"authentication_settings_reenable": "再び有効にするには、<link>サーバーコマンド</link>を使用してください。",
|
||||
"background_task_job": "バックグラウンドタスク",
|
||||
"backup_database": "データベースのバックアップ",
|
||||
"backup_database_enable_description": "データベースのバックアップを有効にする",
|
||||
"backup_keep_last_amount": "過去のバックアップの保持数",
|
||||
"backup_settings": "バックアップ設定",
|
||||
"backup_settings_description": "データベースのバックアップ設定の管理",
|
||||
"check_all": "すべてを選択",
|
||||
"cleanup": "クリーンアップ",
|
||||
"cleared_jobs": "{job}のジョブをクリアしました",
|
||||
"config_set_by_file": "設定は現在 Config File で設定されている",
|
||||
"confirm_delete_library": "本当に {library} を削除しますか？",
|
||||
@@ -41,6 +49,10 @@
|
||||
"confirm_email_below": "確認のため、以下に \"{email}\" と入力してください",
|
||||
"confirm_reprocess_all_faces": "本当にすべての顔を再処理しますか? これにより名前が付けられた人物も消去されます。",
|
||||
"confirm_user_password_reset": "本当に {user} のパスワードをリセットしますか？",
|
||||
"create_job": "ジョブの作成",
|
||||
"cron_expression": "Cron式",
|
||||
"cron_expression_description": "cronのフォーマットを使ってスキャン間隔を設定します。詳しくは<link>Crontab Guru</link>などを参照してください",
|
||||
"cron_expression_presets": "Cron式のプリセット",
|
||||
"disable_login": "ログインを無効にする",
|
||||
"duplicate_detection_job_description": "機械学習を用いて類似画像の検出を行います。（スマートサーチに依存）",
|
||||
"exclusion_pattern_description": "除外パターンを使用すると、ライブラリをスキャンする際にファイルやフォルダを無視することができます。RAWファイルなど、インポートしたくないファイルを含むフォルダがある場合に便利です。",
|
||||
@@ -52,15 +64,25 @@
|
||||
"failed_job_command": "ジョブ {job}のコマンド {command}が失敗しました",
|
||||
"force_delete_user_warning": "警告：この操作を行うと、ユーザーとすべてのアセットが直ちに削除されます。これは元に戻せず、ファイルも復元できません。",
|
||||
"forcing_refresh_library_files": "すべてのライブラリファイルを強制更新",
|
||||
"image_format": "フォーマット",
|
||||
"image_format_description": "WebPはJPEGよりもファイルサイズが小さいですが、エンコードに時間がかかります。",
|
||||
"image_prefer_embedded_preview": "埋め込みプレビューを優先",
|
||||
"image_prefer_embedded_preview_setting_description": "RAW写真の埋め込みプレビューが利用可能な場合に画像処理の入力として使用します。これにより、いくつかの画像でより正確な色を得ることができますが、プレビューの品質はカメラによって異なり、画像により多くの圧縮アーティファクトが含まれる場合があります。",
|
||||
"image_prefer_wide_gamut": "広色域に対応させる",
|
||||
"image_prefer_wide_gamut_setting_description": "サムネイルにはDisplay P3を使用します。これにより、広色域の画像の鮮やかさをよりよく保つことができますが、古いデバイスや古いブラウザバージョンでは画像が異なって見える場合があります。sRGBの画像は、色の変化を避けるためにsRGBのままにします。",
|
||||
"image_preview_description": "単一のアセットを表示する時や機械学習に使われるメタデータを取り除いた中サイズの画像",
|
||||
"image_preview_quality_description": "プレビューの画質は1〜100で設定できます。値が高いほど品質は良くなりますがファイルサイズが大きくなってアプリの応答性が低下するおそれがあります。低い値を設定すると機械学習の品質に影響を与えるおそれがあります。",
|
||||
"image_preview_title": "プレビュー設定",
|
||||
"image_quality": "品質",
|
||||
"image_resolution": "解像度",
|
||||
"image_resolution_description": "解像度を上げるとより精細に保存できますが、エンコードに時間がかかりファイルサイズが大きくなってアプリの応答性が低下するおそれがあります。",
|
||||
"image_settings": "画像設定",
|
||||
"image_settings_description": "生成される画像の品質と解像度の設定",
|
||||
"image_thumbnail_description": "メインのタイムラインのような写真グループで表示する際に使われるメタデータを取り除いた小さなサムネイル",
|
||||
"image_thumbnail_quality_description": "サムネイルの画質を1〜100の間で設定できます。値が大きいほど良い品質ですがファイルサイズが大きくなりアプリの応答性が低下します。",
|
||||
"image_thumbnail_title": "サムネイル設定",
|
||||
"job_concurrency": "{job} の同時実行数",
|
||||
"job_created": "ジョブを作成しました",
|
||||
"job_not_concurrency_safe": "このジョブは安全に同時実行できません。",
|
||||
"job_settings": "ジョブ設定",
|
||||
"job_settings_description": "ジョブの同時実行を管理します",
|
||||
@@ -75,7 +97,7 @@
|
||||
"library_scanning_enable_description": "ライブラリ定期スキャンの有効化",
|
||||
"library_settings": "外部ライブラリ",
|
||||
"library_settings_description": "外部ライブラリ設定を管理します",
|
||||
"library_tasks_description": "ライブラリのタスクを実行する",
|
||||
"library_tasks_description": "アセットが追加または変更された外部ライブラリをスキャンする",
|
||||
"library_watching_enable_description": "外部ライブラリのファイル変更を監視",
|
||||
"library_watching_settings": "ライブラリ監視（実験的）",
|
||||
"library_watching_settings_description": "変更されたファイルを自動的に監視",
|
||||
@@ -110,7 +132,7 @@
|
||||
"machine_learning_smart_search_description": "CLIP埋め込みを使用して画像を意味的に検索します",
|
||||
"machine_learning_smart_search_enabled": "スマートサーチを有効にします",
|
||||
"machine_learning_smart_search_enabled_description": "無効にすると、画像はスマートサーチ用にエンコードされません。",
|
||||
"machine_learning_url_description": "機械学習サーバーのURL",
|
||||
"machine_learning_url_description": "機械学習サーバーのURL。複数のURLが設定された場合は1つずつサーバーが正常に応答するまで接続を試みます。応答のないサーバーはオンラインになるまで一時的に無視されます。",
|
||||
"manage_concurrency": "同時実行数の管理",
|
||||
"manage_log_settings": "ログ設定を管理します",
|
||||
"map_dark_style": "ダークモード",
|
||||
@@ -126,8 +148,14 @@
|
||||
"map_settings": "地図",
|
||||
"map_settings_description": "地図設定",
|
||||
"map_style_description": "マップテーマ（style.json）の参照先URL",
|
||||
"memory_cleanup_job": "メモリーのクリーンアップ",
|
||||
"memory_generate_job": "メモリーの生成",
|
||||
"metadata_extraction_job": "メタデータの展開",
|
||||
"metadata_extraction_job_description": "GPSや解像度などのメタデータを各アセットから抽出",
|
||||
"metadata_faces_import_setting": "顔のインポートを有効にする",
|
||||
"metadata_faces_import_setting_description": "画像のEXIFデータとサイドカーファイルから顔をインポート",
|
||||
"metadata_settings": "メタデータ設定",
|
||||
"metadata_settings_description": "メタデータの設定を管理します",
|
||||
"migration_job": "マイグレーション",
|
||||
"migration_job_description": "アセットおよび顔のサムネイルを最新のフォルダ構造に移行します",
|
||||
"no_paths_added": "パスが追加されていません",
|
||||
@@ -182,6 +210,7 @@
|
||||
"password_settings": "パスワード ログイン",
|
||||
"password_settings_description": "パスワード ログイン設定を管理します",
|
||||
"paths_validated_successfully": "すべてのパスが正常に検証されました",
|
||||
"person_cleanup_job": "人物のクリーンアップ",
|
||||
"quota_size_gib": "割り当て容量 (GiB)",
|
||||
"refreshing_all_libraries": "すべてのライブラリを更新",
|
||||
"registration": "管理者登録",
|
||||
@@ -192,9 +221,13 @@
|
||||
"require_password_change_on_login": "初回ログイン時にパスワード変更を要求する",
|
||||
"reset_settings_to_default": "設定をデフォルトにリセットします",
|
||||
"reset_settings_to_recent_saved": "前回の設定値に戻す",
|
||||
"scanning_library": "ライブラリのスキャン",
|
||||
"search_jobs": "ジョブを検索…",
|
||||
"send_welcome_email": "ウェルカム メール を送信します",
|
||||
"server_external_domain_settings": "外部ドメイン",
|
||||
"server_external_domain_settings_description": "公開共有リンク用のドメイン（ http(s):// を含める）",
|
||||
"server_public_users": "公開ユーザー",
|
||||
"server_public_users_description": "共有アルバムにユーザーを追加するとすべてのユーザー (名前とメールアドレス) がリスト化されます。無効にするとユーザーリストは管理者のみ利用可能になります。",
|
||||
"server_settings": "サーバー設定",
|
||||
"server_settings_description": "サーバー設定を管理します",
|
||||
"server_welcome_message": "ウェルカム メッセージ",
|
||||
@@ -210,7 +243,7 @@
|
||||
"storage_template_hash_verification_enabled_description": "ハッシュ検証の有効化（よくわからなければ、有効にしてください）",
|
||||
"storage_template_migration": "ストレージ テンプレート の移行",
|
||||
"storage_template_migration_description": "現在の<link>{template}</link>を以前にアップロードされたアセットに適用",
|
||||
"storage_template_migration_info": "テンプレートの変更は新しいアセットにのみ適用されます。 以前にアップロードしたアセットにテンプレートを遡って適用するには、<link>{job}</link> を実行してください。",
|
||||
"storage_template_migration_info": "ストレージテンプレートは全ての拡張子を小文字に変換します。テンプレートの変更は新しいアセットにのみ適用されます。 以前にアップロードしたアセットにテンプレートを遡って適用するには、<link>{job}</link> を実行してください。",
|
||||
"storage_template_migration_job": "ストレージテンプレート移行ジョブ",
|
||||
"storage_template_more_details": "この機能の詳細については、<template-link>ストレージテンプレート</template-link>とその<implications-link>影響</implications-link>を参照してください",
|
||||
"storage_template_onboarding_description": "この機能を有効にすると、ユーザー定義のテンプレートに基づいてファイルが自動で整理されます。 安定性の問題のため、この機能はデフォルトでオフになっています。 詳細については、<link>ドキュメント</link>を参照してください。",
|
||||
@@ -219,6 +252,17 @@
|
||||
"storage_template_settings_description": "アップロードしたアセットのフォルダ構造とファイル名を管理します",
|
||||
"storage_template_user_label": "<code>{label}</code>はユーザーのストレージラベルです",
|
||||
"system_settings": "システム設定",
|
||||
"tag_cleanup_job": "タグのクリーンアップ",
|
||||
"template_email_available_tags": "テンプレートで次の変数を使えます: {tags}",
|
||||
"template_email_if_empty": "テンプレートが空の場合はデフォルトのメールが使われます。",
|
||||
"template_email_invite_album": "アルバム招待のテンプレート",
|
||||
"template_email_preview": "プレビュー",
|
||||
"template_email_settings": "メールテンプレート",
|
||||
"template_email_settings_description": "通知のメールテンプレートのカスタムを管理します",
|
||||
"template_email_update_album": "アルバム更新のテンプレート",
|
||||
"template_email_welcome": "ウェルカムメールのテンプレート",
|
||||
"template_settings": "通知テンプレート",
|
||||
"template_settings_description": "通知のためのカスタムテンプレートを管理します。",
|
||||
"theme_custom_css_settings": "カスタムCSS",
|
||||
"theme_custom_css_settings_description": "CSS を使って Immich のデザインをカスタマイズできます。",
|
||||
"theme_settings": "テーマ設定",
|
||||
@@ -248,6 +292,8 @@
|
||||
"transcoding_constant_rate_factor": "CRF値 (-crf)",
|
||||
"transcoding_constant_rate_factor_description": "出力動画の品質レベル。H.264の場合は23、HEVCの場合は28、VP9の場合は31、AV1の場合は35が一般的な値です。値が低いほど品質が良くなりますが、ファイルサイズが大きくなります。",
|
||||
"transcoding_disabled_description": "動画をトランスコードしない設定にしますが、これにより一部のクライアントで再生ができなくなる可能性があります",
|
||||
"transcoding_encoding_options": "エンコードオプション",
|
||||
"transcoding_encoding_options_description": "エンコードされた動画のコーデック、解像度、画質、その他オプションの設定します",
|
||||
"transcoding_hardware_acceleration": "ハードウェアアクセラレーション",
|
||||
"transcoding_hardware_acceleration_description": "より高速ですが、同じビットレートではより低品質になります（実験的）",
|
||||
"transcoding_hardware_decoding": "ハードウェアデコード",
|
||||
@@ -260,6 +306,8 @@
|
||||
"transcoding_max_keyframe_interval": "最大キーフレーム間隔",
|
||||
"transcoding_max_keyframe_interval_description": "キーフレーム間の最大フレーム間隔を設定します。値を低くすると圧縮効率が悪化しますが、シーク時間が改善され、動きの速いシーンの品質が向上する場合があります。\"0\" に設定すると、この値が自動的に設定されます。",
|
||||
"transcoding_optimal_description": "設定解像度を超える動画、または容認されていない形式の動画",
|
||||
"transcoding_policy": "トランスコードポリシー",
|
||||
"transcoding_policy_description": "動画がいつトランスコードされるかを設定します",
|
||||
"transcoding_preferred_hardware_device": "推奨ハードウェアデバイス",
|
||||
"transcoding_preferred_hardware_device_description": "VAAPI と QSV のみに適用されます。 ハードウェアトランスコードに使用されるdriノードを設定します。",
|
||||
"transcoding_preset_preset": "プリセット (-preset)",
|
||||
@@ -268,7 +316,7 @@
|
||||
"transcoding_reference_frames_description": "特定のフレームを圧縮するときに参照するフレームの数。より高い値は圧縮効率を改善しますが、エンコードが遅くなります。\"0\" に設定すると、この値が自動的に設定されます。",
|
||||
"transcoding_required_description": "許容されていない動画形式のみ",
|
||||
"transcoding_settings": "動画トランスコード設定",
|
||||
"transcoding_settings_description": "動画ファイルの解像度とエンコード情報を管理します",
|
||||
"transcoding_settings_description": "トランスコードする動画とその処理方法を管理します",
|
||||
"transcoding_target_resolution": "解像度",
|
||||
"transcoding_target_resolution_description": "解像度を高くすると細かなディテールを保持できますが、エンコードに時間がかかり、ファイルサイズが大きくなり、アプリの応答性が低下する可能性があります。",
|
||||
"transcoding_temporal_aq": "適応的量子化(Temporal AQ)",
|
||||
@@ -290,6 +338,7 @@
|
||||
"trash_settings_description": "ごみ箱の設定を管理します",
|
||||
"untracked_files": "追跡されていないファイル",
|
||||
"untracked_files_description": "これらのファイルはアプリケーションによって追跡されていません。これらは移動の失敗、アップロードの中断、またはバグにより取り残されたものである可能性があります",
|
||||
"user_cleanup_job": "ユーザーのクリーンアップ",
|
||||
"user_delete_delay": "<b>{user}</b>のアカウントとアセットは{delay, plural, one {#日} other {#日}}後に完全に削除されるように予定されます。",
|
||||
"user_delete_delay_settings": "遅延削除",
|
||||
"user_delete_delay_settings_description": "削除実行後、ユーザーのアカウントとアセットが完全に削除されるまでの日数。 ユーザー削除ジョブは深夜に実行され、削除の準備ができているユーザーを確認します。 この設定への変更は、次回の実行時に反映されます。",
|
||||
@@ -345,6 +394,7 @@
|
||||
"allow_edits": "編集を許可",
|
||||
"allow_public_user_to_download": "一般ユーザーによるダウンロードを許可",
|
||||
"allow_public_user_to_upload": "一般ユーザーによるアップロードを許可",
|
||||
"alt_text_qr_code": "QRコード画像",
|
||||
"anti_clockwise": "反時計回り",
|
||||
"api_key": "APIキー",
|
||||
"api_key_description": "この値は一回のみ表示されます。 ウィンドウを閉じる前に必ずコピーしてください。",
|
||||
@@ -368,8 +418,9 @@
|
||||
"asset_offline": "アセットはオフラインです",
|
||||
"asset_offline_description": "このアセットはオフラインです。 Immichはファイルの場所にアクセスできません。 アセットが利用可能であることを確認しライブラリを再スキャンしてください。",
|
||||
"asset_skipped": "スキップ済",
|
||||
"asset_skipped_in_trash": "ゴミ箱の中",
|
||||
"asset_uploaded": "アップロード済",
|
||||
"asset_uploading": "アップロード中...",
|
||||
"asset_uploading": "アップロード中…",
|
||||
"assets": "アセット",
|
||||
"assets_added_count": "{count, plural, one {#個} other {#個}}のアセットを追加しました",
|
||||
"assets_added_to_album_count": "{count, plural, one {#個} other {#個}}のアセットをアルバムに追加しました",
|
||||
@@ -378,7 +429,7 @@
|
||||
"assets_moved_to_trash_count": "{count, plural, one {#個} other {#個}}のアセットをごみ箱に移動しました",
|
||||
"assets_permanently_deleted_count": "{count, plural, one {#個} other {#個}}のアセットを完全に削除しました",
|
||||
"assets_removed_count": "{count, plural, one {#個} other {#個}}のアセットを削除しました",
|
||||
"assets_restore_confirmation": "ごみ箱のアセットをすべて復元してもよろしいですか? この操作を元に戻すことはできません!",
|
||||
"assets_restore_confirmation": "ごみ箱のアセットをすべて復元してもよろしいですか? この操作を元に戻すことはできません! オフラインのアセットはこの方法では復元できません。",
|
||||
"assets_restored_count": "{count, plural, one {#個} other {#個}}のアセットを復元しました",
|
||||
"assets_trashed_count": "{count, plural, one {#個} other {#個}}のアセットをごみ箱に移動しました",
|
||||
"assets_were_part_of_album_count": "{count, plural, one {個} other {個}}のアセットは既にアルバムの一部です",
|
||||
@@ -389,6 +440,7 @@
|
||||
"birthdate_saved": "生年月日が正常に保存されました",
|
||||
"birthdate_set_description": "生年月日は、写真撮影時のこの人物の年齢を計算するために使用されます。",
|
||||
"blurred_background": "ぼやけた背景",
|
||||
"bugs_and_feature_requests": "バグと機能のリクエスト",
|
||||
"build": "ビルド",
|
||||
"build_image": "ビルドイメージ",
|
||||
"bulk_delete_duplicates_confirmation": "本当に {count, plural, one {#個} other {#個}}の重複したアセットを一括削除しますか?これにより各重複中の最大のアセットが保持され、他の全ての重複が削除されます。この操作を元に戻すことはできません!",
|
||||
@@ -433,7 +485,9 @@
|
||||
"comments_are_disabled": "コメントは無効化されています",
|
||||
"confirm": "確認",
|
||||
"confirm_admin_password": "管理者パスワードを確認",
|
||||
"confirm_delete_face": "本当に『{name}』の顔をアセットから削除しますか?",
|
||||
"confirm_delete_shared_link": "本当にこの共有リンクを削除しますか?",
|
||||
"confirm_keep_this_delete_others": "このアセット以外のアセットがスタックから削除されます。本当に削除しますか?",
|
||||
"confirm_password": "確認",
|
||||
"contain": "収める",
|
||||
"context": "状況",
|
||||
@@ -474,25 +528,33 @@
|
||||
"date_range": "日付",
|
||||
"day": "ライトモード",
|
||||
"deduplicate_all": "全て重複排除",
|
||||
"deduplication_criteria_1": "バイト単位の画像サイズ",
|
||||
"deduplication_criteria_2": "EXIFデータ数",
|
||||
"deduplication_info": "重複排除情報",
|
||||
"deduplication_info_description": "アセットを自動的に選択して重複を一括で削除するには次のようにします:",
|
||||
"default_locale": "デフォルトのロケール",
|
||||
"default_locale_description": "ブラウザのロケールに基づいて日付と数値をフォーマットします",
|
||||
"delete": "削除",
|
||||
"delete_album": "アルバムを削除",
|
||||
"delete_api_key_prompt": "本当にこのAPI キーを削除しますか?",
|
||||
"delete_duplicates_confirmation": "本当にこれらの重複を完全に削除しますか?",
|
||||
"delete_face": "顔の削除",
|
||||
"delete_key": "キーを削除",
|
||||
"delete_library": "ライブラリを削除",
|
||||
"delete_link": "リンクを削除",
|
||||
"delete_others": "ほかを削除",
|
||||
"delete_shared_link": "共有リンクを消す",
|
||||
"delete_tag": "タグを削除する",
|
||||
"delete_tag_confirmation_prompt": "本当に{tagName}タグを削除しますか?",
|
||||
"delete_user": "ユーザーを削除",
|
||||
"deleted_shared_link": "共有リンクを削除",
|
||||
"deletes_missing_assets": "ディスクからなくなったアセットを削除する",
|
||||
"description": "概要欄",
|
||||
"details": "詳細",
|
||||
"direction": "方向",
|
||||
"disabled": "無効",
|
||||
"disallow_edits": "編集を許可しない",
|
||||
"discord": "Discord",
|
||||
"discover": "探索",
|
||||
"dismiss_all_errors": "全てのエラーを無視",
|
||||
"dismiss_error": "エラーを無視",
|
||||
@@ -501,6 +563,7 @@
|
||||
"display_original_photos": "オリジナルの写真を表示",
|
||||
"display_original_photos_setting_description": "オリジナルのアセットが Web 互換である場合は、アセットを表示するときにサムネイルではなく元の写真を優先して表示します。これにより写真の表示速度が遅くなる可能性があります。",
|
||||
"do_not_show_again": "このメッセージを再び表示しない",
|
||||
"documentation": "ドキュメント",
|
||||
"done": "完了",
|
||||
"download": "ダウンロード",
|
||||
"download_include_embedded_motion_videos": "埋め込まれた動画",
|
||||
@@ -543,6 +606,7 @@
|
||||
"enabled": "有効",
|
||||
"end_date": "終了日",
|
||||
"error": "エラー",
|
||||
"error_delete_face": "アセットから顔の削除ができませんでした",
|
||||
"error_loading_image": "画像の読み込みエラー",
|
||||
"error_title": "エラー - 問題が発生しました",
|
||||
"errors": {
|
||||
@@ -570,6 +634,7 @@
|
||||
"failed_to_create_shared_link": "共有リンクを作成できませんでした",
|
||||
"failed_to_edit_shared_link": "共有リンクを編集できませんでした",
|
||||
"failed_to_get_people": "人物を取得できませんでした",
|
||||
"failed_to_keep_this_delete_others": "ほかのアセットを削除できませんでした",
|
||||
"failed_to_load_asset": "アセットを読み込めませんでした",
|
||||
"failed_to_load_assets": "アセットを読み込めませんでした",
|
||||
"failed_to_load_people": "人物を読み込めませんでした",
|
||||
@@ -621,6 +686,7 @@
|
||||
"unable_to_get_comments_number": "コメント数を取得できません",
|
||||
"unable_to_get_shared_link": "共有リンクの取得に失敗しました",
|
||||
"unable_to_hide_person": "人物を非表示にできません",
|
||||
"unable_to_link_motion_video": "モーションビデオをリンクできません",
|
||||
"unable_to_link_oauth_account": "OAuth アカウントをリンクできません",
|
||||
"unable_to_load_album": "アルバムを読み込めません",
|
||||
"unable_to_load_asset_activity": "アセットのアクティビティを読み込めません",
|
||||
@@ -659,6 +725,7 @@
|
||||
"unable_to_submit_job": "ジョブを送信できません",
|
||||
"unable_to_trash_asset": "アセットをゴミ箱に移動できません",
|
||||
"unable_to_unlink_account": "アカウントのリンクを解除できません",
|
||||
"unable_to_unlink_motion_video": "モーションビデオのリンクを解除できません",
|
||||
"unable_to_update_album_cover": "アルバムカバーを更新できません",
|
||||
"unable_to_update_album_info": "アルバム情報を更新できません",
|
||||
"unable_to_update_library": "ライブラリを更新できません",
|
||||
@@ -682,6 +749,7 @@
|
||||
"external": "外部",
|
||||
"external_libraries": "外部ライブラリ",
|
||||
"face_unassigned": "未割り当て",
|
||||
"failed_to_load_assets": "アセットのロードに失敗しました",
|
||||
"favorite": "お気に入り",
|
||||
"favorite_or_unfavorite_photo": "写真をお気に入りまたはお気に入り解除",
|
||||
"favorites": "お気に入り",
|
||||
@@ -702,10 +770,13 @@
|
||||
"get_help": "助けを求める",
|
||||
"getting_started": "はじめる",
|
||||
"go_back": "戻る",
|
||||
"go_to_folder": "フォルダへ",
|
||||
"go_to_search": "検索へ",
|
||||
"group_albums_by": "これでアルバムをグループ化…",
|
||||
"group_country": "国でグループ化",
|
||||
"group_no": "グループ化なし",
|
||||
"group_owner": "所有者でグループ化",
|
||||
"group_places_by": "グループ分け...",
|
||||
"group_year": "年でグループ化",
|
||||
"has_quota": "クォータ有り",
|
||||
"hi_user": "こんにちは、{name}( {email})さん",
|
||||
@@ -738,6 +809,7 @@
|
||||
"include_shared_albums": "共有アルバムを含める",
|
||||
"include_shared_partner_assets": "パートナーがシェアしたアセットを含める",
|
||||
"individual_share": "1枚の共有",
|
||||
"individual_shares": "個人の共有",
|
||||
"info": "情報",
|
||||
"interval": {
|
||||
"day_at_onepm": "毎日午後1時",
|
||||
@@ -751,6 +823,8 @@
|
||||
"jobs": "ジョブ",
|
||||
"keep": "保持",
|
||||
"keep_all": "全て保持",
|
||||
"keep_this_delete_others": "これを残してほかを削除する",
|
||||
"kept_this_deleted_others": "このアセットを残して{count, plural, other {#件のアセット}}を削除する",
|
||||
"keyboard_shortcuts": "キーボードショートカット",
|
||||
"language": "言語",
|
||||
"language_setting_description": "優先言語を選択してください",
|
||||
@@ -758,12 +832,14 @@
|
||||
"latest_version": "最新バージョン",
|
||||
"latitude": "緯度",
|
||||
"leave": "標高",
|
||||
"lens_model": "レンズモデル",
|
||||
"let_others_respond": "他のユーザーの返信を許可する",
|
||||
"level": "レベル",
|
||||
"library": "ライブラリ",
|
||||
"library_options": "ライブラリ設定",
|
||||
"light": "ライトモード",
|
||||
"like_deleted": "いいねが削除されました",
|
||||
"link_motion_video": "モーションビデオのリンク",
|
||||
"link_options": "リンクのオプション",
|
||||
"link_to_oauth": "OAuthへリンクする",
|
||||
"linked_oauth_account": "リンクされたOAuthアカウント",
|
||||
@@ -782,6 +858,7 @@
|
||||
"look": "見た目",
|
||||
"loop_videos": "動画をループ",
|
||||
"loop_videos_description": "有効にすると詳細表示で自動的に動画がループします。",
|
||||
"main_branch_warning": "開発版を使っているようです。リリース版の使用を強く推奨します!",
|
||||
"make": "メーカー",
|
||||
"manage_shared_links": "共有済みのリンクを管理",
|
||||
"manage_sharing_with_partners": "パートナーとの共有を管理します",
|
||||
@@ -814,6 +891,7 @@
|
||||
"month": "月",
|
||||
"more": "もっと表示",
|
||||
"moved_to_trash": "ゴミ箱に移動しました",
|
||||
"mute_memories": "メモリーのミュート",
|
||||
"my_albums": "私のアルバム",
|
||||
"name": "名前",
|
||||
"name_or_nickname": "名前またはニックネーム",
|
||||
@@ -843,7 +921,7 @@
|
||||
"no_results": "結果がありません",
|
||||
"no_results_description": "同義語やより一般的なキーワードを試してください",
|
||||
"no_shared_albums_message": "アルバムを作成して写真や動画を共有しましょう",
|
||||
"not_in_any_album": "どのアルバムにも入っていません",
|
||||
"not_in_any_album": "どのアルバムにも入っていない",
|
||||
"note_apply_storage_label_to_previously_uploaded assets": "注意: 以前にアップロードしたアセットにストレージラベルを適用するには以下を実行してください",
|
||||
"note_unlimited_quota": "注: 容量を無制限にするには0を入力してください",
|
||||
"notes": "注意",
|
||||
@@ -851,6 +929,7 @@
|
||||
"notifications": "通知",
|
||||
"notifications_setting_description": "通知を管理します",
|
||||
"oauth": "OAuth",
|
||||
"official_immich_resources": "公式Immichリソース",
|
||||
"offline": "オフライン",
|
||||
"offline_paths": "オフラインのパス",
|
||||
"offline_paths_description": "これらの結果は、外部ライブラリの一部ではないファイルを手動で削除したことが原因である可能性があります。",
|
||||
@@ -908,6 +987,7 @@
|
||||
"permanently_deleted_asset": "アセットを完全に削除しました",
|
||||
"permanently_deleted_assets_count": "{count, plural, one {#個} other {#個}}のアセットを完全に削除しました",
|
||||
"person": "人物",
|
||||
"person_birthdate": "{date}生まれ",
|
||||
"person_hidden": "{name}{hidden, select, true { (非表示)} other {}}",
|
||||
"photo_shared_all_users": "写真をすべてのユーザーと共有したか、共有するユーザーがいないようです。",
|
||||
"photos": "写真",
|
||||
@@ -917,6 +997,7 @@
|
||||
"pick_a_location": "場所を選択",
|
||||
"place": "場所",
|
||||
"places": "撮影場所",
|
||||
"places_count": "{count, plural, other {{count, number}箇所}}",
|
||||
"play": "再生",
|
||||
"play_memories": "メモリーを再生",
|
||||
"play_motion_photo": "モーションビデオを再生",
|
||||
@@ -976,14 +1057,17 @@
|
||||
"reassigned_assets_to_new_person": "{count, plural, one {#個} other {#個}}のアセットを新しい人物に割り当てました",
|
||||
"reassing_hint": "選択されたアセットを既存の人物に割り当て",
|
||||
"recent": "最近",
|
||||
"recent-albums": "最近のアルバム",
|
||||
"recent_searches": "最近の検索",
|
||||
"refresh": "更新",
|
||||
"refresh_encoded_videos": "エンコードされた動画を更新",
|
||||
"refresh_faces": "顔認識を更新",
|
||||
"refresh_metadata": "メタデータを更新",
|
||||
"refresh_thumbnails": "サムネイルを更新",
|
||||
"refreshed": "更新済",
|
||||
"refreshes_every_file": "すべてのファイルを更新",
|
||||
"refreshing_encoded_video": "エンコードされた動画を更新中",
|
||||
"refreshing_faces": "顔認識を更新中",
|
||||
"refreshing_metadata": "メタデータを更新中",
|
||||
"regenerating_thumbnails": "サムネイルを再生成中",
|
||||
"remove": "削除",
|
||||
@@ -995,11 +1079,16 @@
|
||||
"remove_from_album": "アルバムから削除",
|
||||
"remove_from_favorites": "お気に入りから削除",
|
||||
"remove_from_shared_link": "共有リンクから削除",
|
||||
"remove_memory": "メモリーの削除",
|
||||
"remove_photo_from_memory": "メモリーから写真を削除",
|
||||
"remove_url": "URLの削除",
|
||||
"remove_user": "ユーザーを削除",
|
||||
"removed_api_key": "削除されたAPI キー: {name}",
|
||||
"removed_from_archive": "アーカイブから削除されました",
|
||||
"removed_from_favorites": "お気に入りから削除しました",
|
||||
"removed_from_favorites_count": "{count, plural, other {#項目}}お気に入りから削除しました",
|
||||
"removed_memory": "削除されたメモリー",
|
||||
"removed_photo_from_memory": "メモリーから削除された写真",
|
||||
"removed_tagged_assets": "{count, plural, one {#個のアセット} other {#個のアセット}}からタグを削除しました",
|
||||
"rename": "リネーム",
|
||||
"repair": "修復",
|
||||
@@ -1008,6 +1097,7 @@
|
||||
"repository": "リポジトリ",
|
||||
"require_password": "パスワードを要求",
|
||||
"require_user_to_change_password_on_first_login": "ユーザーに初回ログイン時にパスワードの変更を要求する",
|
||||
"rescan": "再スキャン",
|
||||
"reset": "リセット",
|
||||
"reset_password": "パスワードをリセット",
|
||||
"reset_people_visibility": "人物の非表示設定をリセット",
|
||||
@@ -1030,22 +1120,29 @@
|
||||
"saved_settings": "設定を保存しました",
|
||||
"say_something": "何か書き込みましょう",
|
||||
"scan_all_libraries": "全てのライブラリをスキャン",
|
||||
"scan_library": "スキャン",
|
||||
"scan_settings": "スキャン設定",
|
||||
"scanning_for_album": "アルバムをスキャン中…",
|
||||
"search": "検索",
|
||||
"search_albums": "アルバムを検索",
|
||||
"search_by_context": "状況で検索",
|
||||
"search_by_description": "概要で検索",
|
||||
"search_by_description_example": "サパでハイキングした日",
|
||||
"search_by_filename": "ファイル名もしくは拡張子で検索",
|
||||
"search_by_filename_example": "例: IMG_1234.JPG もしくは PNG",
|
||||
"search_camera_make": "カメラメーカーを検索…",
|
||||
"search_camera_model": "カメラのモデルを検索…",
|
||||
"search_city": "市町村を検索…",
|
||||
"search_country": "国を検索…",
|
||||
"search_for": "検索",
|
||||
"search_for_existing_person": "既存の人物を検索",
|
||||
"search_no_people": "人物がいません",
|
||||
"search_no_people_named": "「{name}」という名前の人物がいません",
|
||||
"search_options": "検索オプション",
|
||||
"search_people": "人物を検索",
|
||||
"search_places": "場所を検索",
|
||||
"search_rating": "レートで検索...",
|
||||
"search_settings": "検索設定",
|
||||
"search_state": "都道府県を検索…",
|
||||
"search_tags": "タグを検索...",
|
||||
"search_timezone": "タイムゾーンを検索…",
|
||||
@@ -1054,6 +1151,7 @@
|
||||
"searching_locales": "ロケールを検索…",
|
||||
"second": "秒",
|
||||
"see_all_people": "全ての人物を見る",
|
||||
"select": "選択",
|
||||
"select_album_cover": "アルバムカバーを選択",
|
||||
"select_all": "全て選択",
|
||||
"select_all_duplicates": "全ての重複を選択",
|
||||
@@ -1076,6 +1174,7 @@
|
||||
"server_version": "サーバーバージョン",
|
||||
"set": "設定",
|
||||
"set_as_album_cover": "アルバムカバーとして設定",
|
||||
"set_as_featured_photo": "人物写真に設定",
|
||||
"set_as_profile_picture": "プロフィール画像として設定",
|
||||
"set_date_of_birth": "生年月日を設定",
|
||||
"set_profile_picture": "プロフィール画像を設定",
|
||||
@@ -1090,6 +1189,7 @@
|
||||
"shared_from_partner": "{partner} による写真",
|
||||
"shared_link_options": "共有リンクのオプション",
|
||||
"shared_links": "共有リンク",
|
||||
"shared_links_description": "写真や動画をリンクで共有",
|
||||
"shared_photos_and_videos_count": "{assetCount, plural, other {#個の共有された写真と動画}}",
|
||||
"shared_with_partner": "{partner} と共有しました",
|
||||
"sharing": "共有",
|
||||
@@ -1112,6 +1212,8 @@
|
||||
"show_person_options": "人物設定を表示",
|
||||
"show_progress_bar": "プログレスバーを表示",
|
||||
"show_search_options": "検索オプションを表示",
|
||||
"show_shared_links": "共有リンクを表示",
|
||||
"show_slideshow_transition": "スライドショーのトランジションを表示",
|
||||
"show_supporter_badge": "サポーターバッジ",
|
||||
"show_supporter_badge_description": "サポーターバッジを表示",
|
||||
"shuffle": "ランダム",
|
||||
@@ -1121,6 +1223,8 @@
|
||||
"sign_up": "登録",
|
||||
"size": "サイズ",
|
||||
"skip_to_content": "コンテンツへスキップ",
|
||||
"skip_to_folders": "フォルダへスキップ",
|
||||
"skip_to_tags": "タグへスキップ",
|
||||
"slideshow": "スライドショー",
|
||||
"slideshow_settings": "スライドショー設定",
|
||||
"sort_albums_by": "この順序でアルバムをソート…",
|
||||
@@ -1128,6 +1232,7 @@
|
||||
"sort_items": "アイテムの数",
|
||||
"sort_modified": "変更日",
|
||||
"sort_oldest": "古い写真",
|
||||
"sort_people_by_similarity": "似ている順に人物を並び替える",
|
||||
"sort_recent": "最新の写真",
|
||||
"sort_title": "タイトル",
|
||||
"source": "ソース",
|
||||
@@ -1151,12 +1256,17 @@
|
||||
"submit": "送信",
|
||||
"suggestions": "ユーザーリスト",
|
||||
"sunrise_on_the_beach": "海岸の日の出",
|
||||
"support": "サポート",
|
||||
"support_and_feedback": "サポートとフィードバック",
|
||||
"support_third_party_description": "Immichのインストールはサードパーティーによってパッケージ化されています。遭遇した問題はそのパッケージに起因している可能性があるので以下のリンクを使って最初にそのパッケージに問題を提起してください。",
|
||||
"swap_merge_direction": "統合する方向を入れ替え",
|
||||
"sync": "同期",
|
||||
"tag": "タグ付けする",
|
||||
"tag_assets": "アセットにタグ付けする",
|
||||
"tag_created": "タグ: {tag} を作成しました",
|
||||
"tag_feature_description": "意味を持たせたタグトでグループ化して写真と動画を閲覧する",
|
||||
"tag_not_found_question": "タグが見つかりませんか? <link>こちら</link>からタグを作成できます",
|
||||
"tag_people": "人物タグ",
|
||||
"tag_updated": "タグ: {tag} を更新しました",
|
||||
"tagged_assets": "{count, plural, one {#個のアセット} other {#個のアセット}}をタグ付けしました",
|
||||
"tags": "タグ",
|
||||
@@ -1165,15 +1275,19 @@
|
||||
"theme_selection": "テーマ選択",
|
||||
"theme_selection_description": "ブラウザのシステム設定に基づいてテーマを明色または暗色に自動的に設定します",
|
||||
"they_will_be_merged_together": "これらは一緒に統合されます",
|
||||
"third_party_resources": "サードパーティーリソース",
|
||||
"time_based_memories": "時間によるメモリー",
|
||||
"timeline": "タイムライン",
|
||||
"timezone": "タイムゾーン",
|
||||
"to_archive": "アーカイブ",
|
||||
"to_change_password": "パスワードを変更",
|
||||
"to_favorite": "お気に入り",
|
||||
"to_login": "ログイン",
|
||||
"to_parent": "上位の階層へ",
|
||||
"to_trash": "ゴミ箱",
|
||||
"toggle_settings": "設定をトグル",
|
||||
"toggle_theme": "ダークテーマを切り替え",
|
||||
"total": "合計",
|
||||
"total_usage": "総使用量",
|
||||
"trash": "ゴミ箱",
|
||||
"trash_all": "全て削除",
|
||||
@@ -1187,10 +1301,13 @@
|
||||
"unfavorite": "お気に入りから外す",
|
||||
"unhide_person": "人物の非表示を解除",
|
||||
"unknown": "不明",
|
||||
"unknown_country": "不明な国",
|
||||
"unknown_year": "不明な年",
|
||||
"unlimited": "無制限",
|
||||
"unlink_motion_video": "モーションビデオのリンクを解除",
|
||||
"unlink_oauth": "OAuthのリンクを解除",
|
||||
"unlinked_oauth_account": "リンクが解除されたOAuthアカウント",
|
||||
"unmute_memories": "メモリーのミュートを解除",
|
||||
"unnamed_album": "無名のアルバム",
|
||||
"unnamed_album_delete_confirmation": "本当にこのアルバムを削除しますか?",
|
||||
"unnamed_share": "無名の共有",
|
||||
@@ -1231,7 +1348,9 @@
|
||||
"variables": "変数",
|
||||
"version": "バージョン",
|
||||
"version_announcement_closing": "あなたの友人、Alex",
|
||||
"version_announcement_message": "こんにちは、親愛なる皆様へ。アプリの新しいバージョンがありますので、構成の不整合を防ぐために<link>リリースノート</link>にアクセスし、<code>docker-compose.yml</code>、及び<code>.cnv</code>の設定が最新か確認してください。特に自動的にアプリの更新を制御するWatchTowerやその他システムを利用している場合に当てはまります。",
|
||||
"version_announcement_message": "こんにちは! 新しいバージョンのImmichがリリースされました。特にWatchTowerやImmichインスタンスを自動的に更新する仕組みを設けている場合は<link>リリースノート</link>をよく読んで設定が最新のものになっているか確認してください。",
|
||||
"version_history": "バージョン履歴",
|
||||
"version_history_item": "{date}に{version}をインストール",
|
||||
"video": "動画",
|
||||
"video_hover_setting": "ホバー時にサムネイルで動画を再生",
|
||||
"video_hover_setting_description": "マウスが項目の上にあるときに動画のサムネイルを再生します。無効時でも再生アイコンにカーソルを合わせると再生を開始できます。",
|
||||
@@ -1242,7 +1361,9 @@
|
||||
"view_all": "すべて見る",
|
||||
"view_all_users": "全てのユーザーを確認する",
|
||||
"view_in_timeline": "タイムラインで見る",
|
||||
"view_link": "リンクを見る",
|
||||
"view_links": "リンクを確認する",
|
||||
"view_name": "分類",
|
||||
"view_next_asset": "次のアセットを見る",
|
||||
"view_previous_asset": "前のアセットを見る",
|
||||
"view_stack": "ビュースタック",
|
||||
@@ -1251,10 +1372,10 @@
|
||||
"warning": "警告",
|
||||
"week": "週",
|
||||
"welcome": "ようこそ",
|
||||
"welcome_to_immich": "immichにようこそ",
|
||||
"welcome_to_immich": "Immichにようこそ",
|
||||
"year": "年",
|
||||
"years_ago": "{years, plural, one {#年} other {#年}}前",
|
||||
"yes": "はい",
|
||||
"you_dont_have_any_shared_links": "共有リンクはありません",
|
||||
"zoom_image": "画像を拡大"
|
||||
}
|
||||
}
|
||||
|
||||
1
i18n/ka.json
Normal file
1
i18n/ka.json
Normal file
@@ -0,0 +1 @@
|
||||
{}
|
||||
1
i18n/kk.json
Normal file
1
i18n/kk.json
Normal file
@@ -0,0 +1 @@
|
||||
{}
|
||||
1
i18n/kn.json
Normal file
1
i18n/kn.json
Normal file
@@ -0,0 +1 @@
|
||||
{}
|
||||
73
i18n/ko.json
73
i18n/ko.json
@@ -41,6 +41,7 @@
|
||||
"backup_settings": "백업 설정",
|
||||
"backup_settings_description": "데이터베이스 백업 설정 관리",
|
||||
"check_all": "모두 확인",
|
||||
"cleanup": "정리",
|
||||
"cleared_jobs": "작업 중단: {job}",
|
||||
"config_set_by_file": "현재 설정은 구성 파일에 의해 관리됩니다.",
|
||||
"confirm_delete_library": "{library} 라이브러리를 삭제하시겠습니까?",
|
||||
@@ -96,7 +97,7 @@
|
||||
"library_scanning_enable_description": "주기적인 라이브러리 스캔 활성화",
|
||||
"library_settings": "외부 라이브러리",
|
||||
"library_settings_description": "외부 라이브러리 설정 관리",
|
||||
"library_tasks_description": "라이브러리 구성 및 확인 작업 수행",
|
||||
"library_tasks_description": "외부 라이브러리에서 새 자산 및/또는 변경된 자산을 검색합니다",
|
||||
"library_watching_enable_description": "외부 라이브러리의 파일 변경 감시",
|
||||
"library_watching_settings": "라이브러리 감시 (실험 기능)",
|
||||
"library_watching_settings_description": "파일 변겅을 자동으로 감지",
|
||||
@@ -147,6 +148,8 @@
|
||||
"map_settings": "지도",
|
||||
"map_settings_description": "지도 설정 관리",
|
||||
"map_style_description": "지도 테마 style.json URL",
|
||||
"memory_cleanup_job": "메모리 정리",
|
||||
"memory_generate_job": "메모리 생성",
|
||||
"metadata_extraction_job": "메타데이터 추출",
|
||||
"metadata_extraction_job_description": "각 항목에서 GPS, 인물 및 해상도 등의 메타데이터 정보 추출",
|
||||
"metadata_faces_import_setting": "얼굴 가져오기 활성화",
|
||||
@@ -240,7 +243,7 @@
|
||||
"storage_template_hash_verification_enabled_description": "해시 검증을 활성화합니다. 이 설정의 결과를 확실히 이해하지 않는 한 비활성화하지 마세요.",
|
||||
"storage_template_migration": "스토리지 템플릿 마이그레이션",
|
||||
"storage_template_migration_description": "이전에 업로드된 항목에 현재 <link>{template}</link> 적용",
|
||||
"storage_template_migration_info": "템플릿 변경 사항은 새 업로드 항목부터 적용됩니다. 기존 항목에도 적용하려면 <link>{job}</link>을 실행하세요.",
|
||||
"storage_template_migration_info": "저장소 템플릿은 모든 확장자를 소문자로 변환합니다. 템플릿 변경 사항은 새 자산에만 적용됩니다. 이전에 업로드한 자산에 템플릿을 적용하려면 <link>{job}</link>를 실행하세요.",
|
||||
"storage_template_migration_job": "스토리지 템플릿 마이그레이션 작업",
|
||||
"storage_template_more_details": "이 기능에 대한 자세한 내용은 <template-link>스토리지 템플릿</template-link> 및 <implications-link>설명</implications-link>을 참조하세요.",
|
||||
"storage_template_onboarding_description": "이 기능을 활성화하면 사용자 정의 템플릿을 사용하여 파일을 자동으로 정리할 수 있습니다. 안정성 문제로 인해 해당 기능은 기본적으로 비활성화되어 있습니다. 자세한 내용은 <link>문서</link>를 참조하세요.",
|
||||
@@ -250,10 +253,16 @@
|
||||
"storage_template_user_label": "사용자의 스토리지 레이블: <code>{label}</code>",
|
||||
"system_settings": "시스템 설정",
|
||||
"tag_cleanup_job": "태그 정리",
|
||||
"template_email_available_tags": "템플릿에서 다음 변수를 사용할 수 있습니다: {tags}",
|
||||
"template_email_if_empty": "비어 있는 경우 기본 템플릿이 사용됩니다.",
|
||||
"template_email_invite_album": "앨범 템플릿 초대",
|
||||
"template_email_preview": "미리보기",
|
||||
"template_email_settings": "이메일 템플릿",
|
||||
"template_email_settings_description": "사용자 정의 이메일 템플릿 관리",
|
||||
"template_email_update_album": "앨범 템플릿 업데이트",
|
||||
"template_email_welcome": "이메일 템플릿에 오신것을 환영합니다",
|
||||
"template_settings": "알림 템플릿",
|
||||
"template_settings_description": "알림을 위한 사용자 지정 템플릿을 관리합니다.",
|
||||
"theme_custom_css_settings": "사용자 정의 CSS",
|
||||
"theme_custom_css_settings_description": "Immich에 적용할 사용자 정의 CSS(Cascading Style Sheets) 설정",
|
||||
"theme_settings": "테마 설정",
|
||||
@@ -278,11 +287,13 @@
|
||||
"transcoding_audio_codec_description": "Opus는 가장 좋은 품질의 옵션이지만 기기 및 소프트웨어가 오래된 경우 호환되지 않을 수 있습니다.",
|
||||
"transcoding_bitrate_description": "최대 비트레이트를 초과하는 동영상 또는 허용되지 않는 형식의 동영상",
|
||||
"transcoding_codecs_learn_more": "여기에서 사용되는 용어에 대한 자세한 내용은 FFmpeg 문서의 <h264-link>H.264 코덱</h264-link>, <hevc-link>HEVC 코덱</hevc-link> 및 <vp9-link>VP9 코덱</vp9-link> 항목을 참조하세요.",
|
||||
"transcoding_constant_quality_mode": "Constant quality mode",
|
||||
"transcoding_constant_quality_mode": "고정 품질 모드",
|
||||
"transcoding_constant_quality_mode_description": "ICQ는 CQP보다 나은 성능을 보이나 일부 기기의 하드웨어 가속에서 지원되지 않을 수 있습니다. 이 옵션을 설정하면 품질 기반 인코딩 시 지정된 모드를 우선적으로 사용합니다. NVENC에서는 ICQ를 지원하지 않아 이 설정이 적용되지 않습니다.",
|
||||
"transcoding_constant_rate_factor": "Constant rate factor (-crf)",
|
||||
"transcoding_constant_rate_factor": "상수 비율 계수(-CRF)",
|
||||
"transcoding_constant_rate_factor_description": "일반적으로 H.264는 23, HEVC는 28, VP9는 31, AV1는 35를 사용합니다. 값이 낮으면 품질이 향상되지만 파일 크기가 증가합니다.",
|
||||
"transcoding_disabled_description": "동영상을 트랜스코딩하지 않음. 일부 기기에서 재생이 불가능할 수 있습니다.",
|
||||
"transcoding_encoding_options": "인코딩 옵션",
|
||||
"transcoding_encoding_options_description": "인코딩된 동영상의 코덱, 해상도, 품질 및 기타 옵션을 설정합니다",
|
||||
"transcoding_hardware_acceleration": "하드웨어 가속",
|
||||
"transcoding_hardware_acceleration_description": "실험적인 기능입니다. 속도가 향상되지만 동일 비트레이트에서 품질이 상대적으로 낮을 수 있습니다.",
|
||||
"transcoding_hardware_decoding": "하드웨어 디코딩",
|
||||
@@ -295,6 +306,8 @@
|
||||
"transcoding_max_keyframe_interval": "최대 키프레임 간격",
|
||||
"transcoding_max_keyframe_interval_description": "키프레임 사이 최대 프레임 거리를 설정합니다. 값이 낮으면 압축 효율이 저하되지만 검색 시간이 개선되고 빠른 움직임이 있는 장면에서 품질이 향상됩니다. 0을 입력한 경우 자동으로 설정합니다.",
|
||||
"transcoding_optimal_description": "목표 해상도보다 높은 동영상 또는 허용되지 않는 형식의 동영상",
|
||||
"transcoding_policy": "트랜스코드 정책",
|
||||
"transcoding_policy_description": "동영상 트랜스코딩 시기 설정하기",
|
||||
"transcoding_preferred_hardware_device": "선호하는 하드웨어 기기",
|
||||
"transcoding_preferred_hardware_device_description": "하드웨어 트랜스코딩에 사용할 dri 노드를 설정합니다. (VAAPI와 QSV만 해당)",
|
||||
"transcoding_preset_preset": "프리셋 (-preset)",
|
||||
@@ -303,10 +316,10 @@
|
||||
"transcoding_reference_frames_description": "특정 프레임을 압축할 때 참조하는 프레임 수를 설정합니다. 값이 높으면 압축 효율이 향상되나 인코딩 속도가 저하됩니다. 0을 입력한 경우 자동으로 설정합니다.",
|
||||
"transcoding_required_description": "허용된 형식이 아닌 동영상만",
|
||||
"transcoding_settings": "동영상 트랜스코딩 설정",
|
||||
"transcoding_settings_description": "동영상 파일의 해상도 및 인코딩 정보 관리",
|
||||
"transcoding_settings_description": "트랜스코딩할 동영상과 처리 방법 관리하기",
|
||||
"transcoding_target_resolution": "목표 해상도",
|
||||
"transcoding_target_resolution_description": "높은 해상도를 선택한 경우 세부 묘사의 손실을 최소화할 수 있지만, 인코딩 시간과 파일 크기가 증가하여 앱의 반응 속도가 느려질 수 있습니다.",
|
||||
"transcoding_temporal_aq": "Temporal AQ",
|
||||
"transcoding_temporal_aq": "일시적 AQ",
|
||||
"transcoding_temporal_aq_description": "세부 묘사가 많고 움직임이 적은 장면의 품질이 향상됩니다. 오래된 기기와 호환되지 않을 수 있습니다. (NVENC만 해당)",
|
||||
"transcoding_threads": "스레드",
|
||||
"transcoding_threads_description": "값이 높으면 인코딩 속도가 향상되지만 리소스 사용량이 증가합니다. 값은 CPU 코어 수보다 작아야 하며, 설정하지 않으려면 0을 입력합니다.",
|
||||
@@ -381,6 +394,7 @@
|
||||
"allow_edits": "편집자로 설정",
|
||||
"allow_public_user_to_download": "모든 사용자의 다운로드 허용",
|
||||
"allow_public_user_to_upload": "모든 사용자의 업로드 허용",
|
||||
"alt_text_qr_code": "QR코드 이미지",
|
||||
"anti_clockwise": "반시계 방향",
|
||||
"api_key": "API 키",
|
||||
"api_key_description": "이 값은 한 번만 표시됩니다. 창을 닫기 전 반드시 복사해주세요.",
|
||||
@@ -471,7 +485,9 @@
|
||||
"comments_are_disabled": "댓글이 비활성화되었습니다.",
|
||||
"confirm": "확인",
|
||||
"confirm_admin_password": "관리자 비밀번호 확인",
|
||||
"confirm_delete_face": "에셋에서 {name} 얼굴을 삭제하시겠습니까?",
|
||||
"confirm_delete_shared_link": "이 공유 링크를 삭제하시겠습니까?",
|
||||
"confirm_keep_this_delete_others": "이 에셋을 제외한 스택의 다른 모든 에셋이 삭제됩니다. 계속하시겠습니까?",
|
||||
"confirm_password": "비밀번호 확인",
|
||||
"contain": "맞춤",
|
||||
"context": "내용",
|
||||
@@ -512,15 +528,21 @@
|
||||
"date_range": "날짜 범위",
|
||||
"day": "일",
|
||||
"deduplicate_all": "모두 삭제",
|
||||
"deduplication_criteria_1": "이미지 크기(바이트)",
|
||||
"deduplication_criteria_2": "EXIF 데이터 개수",
|
||||
"deduplication_info": "중복 제거 정보",
|
||||
"deduplication_info_description": "자산을 자동으로 미리 선택하고 일괄적으로 중복을 제거하려면 다음을 살펴보세요:",
|
||||
"default_locale": "기본 로케일",
|
||||
"default_locale_description": "브라우저 로케일에 따른 날짜 및 숫자 형식 지정",
|
||||
"delete": "삭제",
|
||||
"delete_album": "앨범 삭제",
|
||||
"delete_api_key_prompt": "API 키를 삭제하시겠습니까?",
|
||||
"delete_duplicates_confirmation": "비슷한 항목들을 영구적으로 삭제하시겠습니까?",
|
||||
"delete_face": "얼굴 삭제",
|
||||
"delete_key": "키 삭제",
|
||||
"delete_library": "라이브러리 삭제",
|
||||
"delete_link": "링크 삭제",
|
||||
"delete_others": "다른 사람 삭제",
|
||||
"delete_shared_link": "공유 링크 삭제",
|
||||
"delete_tag": "태그 삭제",
|
||||
"delete_tag_confirmation_prompt": "{tagName} 태그를 삭제하시겠습니까?",
|
||||
@@ -532,7 +554,7 @@
|
||||
"direction": "방향",
|
||||
"disabled": "비활성화됨",
|
||||
"disallow_edits": "뷰어로 설정",
|
||||
"discord": "Discord",
|
||||
"discord": "디스코드",
|
||||
"discover": "탐색",
|
||||
"dismiss_all_errors": "모든 오류 무시",
|
||||
"dismiss_error": "오류 무시",
|
||||
@@ -579,11 +601,12 @@
|
||||
"editor_crop_tool_h2_rotation": "회전",
|
||||
"email": "이메일",
|
||||
"empty_trash": "휴지통 비우기",
|
||||
"empty_trash_confirmation": "휴지통을 비우시겠습니까? 휴지통에 있는 모든 항목이 Immich에서 영구적으로 삭제됩니다. 이 작업은 되돌릴 수 없습니다!",
|
||||
"empty_trash_confirmation": "휴지통을 비우시겠습니까? 휴지통에 있는 모든 항목이 Immich에서 영구적으로 삭제됩니다.\n이 작업은 되돌릴 수 없습니다!",
|
||||
"enable": "활성화",
|
||||
"enabled": "활성화됨",
|
||||
"end_date": "종료일",
|
||||
"error": "오류",
|
||||
"error_delete_face": "에셋에서 얼굴 삭제 오류",
|
||||
"error_loading_image": "이미지 로드 오류",
|
||||
"error_title": "오류 - 문제가 발생했습니다",
|
||||
"errors": {
|
||||
@@ -611,6 +634,7 @@
|
||||
"failed_to_create_shared_link": "공유 링크를 생성하지 못했습니다.",
|
||||
"failed_to_edit_shared_link": "공유 링크를 수정하지 못했습니다.",
|
||||
"failed_to_get_people": "인물 로드 실패",
|
||||
"failed_to_keep_this_delete_others": "이 자산을 유지하고 다른 자산을 삭제하지 못했습니다",
|
||||
"failed_to_load_asset": "항목 로드 실패",
|
||||
"failed_to_load_assets": "항목 로드 실패",
|
||||
"failed_to_load_people": "인물 로드 실패",
|
||||
@@ -725,6 +749,7 @@
|
||||
"external": "외부",
|
||||
"external_libraries": "외부 라이브러리",
|
||||
"face_unassigned": "알 수 없음",
|
||||
"failed_to_load_assets": "에셋 로드에 실패했습니다",
|
||||
"favorite": "즐겨찾기",
|
||||
"favorite_or_unfavorite_photo": "즐겨찾기 추가/제거",
|
||||
"favorites": "즐겨찾기",
|
||||
@@ -745,10 +770,13 @@
|
||||
"get_help": "도움 요청",
|
||||
"getting_started": "시작하기",
|
||||
"go_back": "뒤로",
|
||||
"go_to_folder": "폴더로 이동",
|
||||
"go_to_search": "검색으로 이동",
|
||||
"group_albums_by": "다음으로 앨범 그룹화...",
|
||||
"group_country": "국가별 그룹화",
|
||||
"group_no": "그룹화 없음",
|
||||
"group_owner": "소유자로 그룹화",
|
||||
"group_places_by": "장소 그룹화 기준...",
|
||||
"group_year": "연도로 그룹화",
|
||||
"has_quota": "할당량",
|
||||
"hi_user": "안녕하세요 {name}님, ({email})",
|
||||
@@ -781,6 +809,7 @@
|
||||
"include_shared_albums": "공유 앨범 포함",
|
||||
"include_shared_partner_assets": "파트너가 공유한 항목 포함",
|
||||
"individual_share": "개인 공유",
|
||||
"individual_shares": "개별 공유",
|
||||
"info": "정보",
|
||||
"interval": {
|
||||
"day_at_onepm": "매일 오후 1시",
|
||||
@@ -794,6 +823,8 @@
|
||||
"jobs": "작업",
|
||||
"keep": "유지",
|
||||
"keep_all": "모두 유지",
|
||||
"keep_this_delete_others": "이 항목은 보관하고 다른 항목은 삭제",
|
||||
"kept_this_deleted_others": "이 자산을 유지하고 {count, plural, one {# asset} other {# assets}}을 삭제했습니다",
|
||||
"keyboard_shortcuts": "키보드 단축키",
|
||||
"language": "언어",
|
||||
"language_setting_description": "선호하는 언어 선택",
|
||||
@@ -801,6 +832,7 @@
|
||||
"latest_version": "최신 버전",
|
||||
"latitude": "위도",
|
||||
"leave": "나가기",
|
||||
"lens_model": "카메라 렌즈 모델",
|
||||
"let_others_respond": "다른 사용자의 반응 허용",
|
||||
"level": "레벨",
|
||||
"library": "라이브러리",
|
||||
@@ -859,6 +891,7 @@
|
||||
"month": "월",
|
||||
"more": "더보기",
|
||||
"moved_to_trash": "휴지통으로 이동되었습니다.",
|
||||
"mute_memories": "추억 음소거",
|
||||
"my_albums": "내 앨범",
|
||||
"name": "이름",
|
||||
"name_or_nickname": "이름 또는 닉네임",
|
||||
@@ -954,6 +987,7 @@
|
||||
"permanently_deleted_asset": "항목이 영구적으로 삭제되었습니다.",
|
||||
"permanently_deleted_assets_count": "항목 {count, plural, one {#개} other {#개}}가 영구적으로 삭제되었습니다.",
|
||||
"person": "인물",
|
||||
"person_birthdate": "{date} 출생",
|
||||
"person_hidden": "{name}{hidden, select, true { (숨김)} other {}}",
|
||||
"photo_shared_all_users": "이미 모든 사용자와 사진을 공유 중이거나 다른 사용자가 없는 것 같습니다.",
|
||||
"photos": "사진",
|
||||
@@ -963,6 +997,7 @@
|
||||
"pick_a_location": "위치 선택",
|
||||
"place": "장소",
|
||||
"places": "장소",
|
||||
"places_count": "{count, plural, one {{count, number} 장소} other {{count, number} 장소}}",
|
||||
"play": "재생",
|
||||
"play_memories": "추억 재생",
|
||||
"play_motion_photo": "모션 포토 재생",
|
||||
@@ -1022,6 +1057,7 @@
|
||||
"reassigned_assets_to_new_person": "항목 {count, plural, one {#개} other {#개}}가 새 인물에 할당되었습니다.",
|
||||
"reassing_hint": "기존 인물에 선택한 항목 할당",
|
||||
"recent": "최근",
|
||||
"recent-albums": "최근 앨범",
|
||||
"recent_searches": "최근 검색",
|
||||
"refresh": "새로고침",
|
||||
"refresh_encoded_videos": "동영상 재인코딩",
|
||||
@@ -1043,11 +1079,16 @@
|
||||
"remove_from_album": "앨범에서 제거",
|
||||
"remove_from_favorites": "즐겨찾기에서 제거",
|
||||
"remove_from_shared_link": "공유 링크에서 제거",
|
||||
"remove_memory": "추억 제거",
|
||||
"remove_photo_from_memory": "이 추억에서 사진 제거",
|
||||
"remove_url": "URL 제거",
|
||||
"remove_user": "사용자 삭제",
|
||||
"removed_api_key": "API 키 삭제: {name}",
|
||||
"removed_from_archive": "보관함에서 제거되었습니다.",
|
||||
"removed_from_favorites": "즐겨찾기에서 제거되었습니다.",
|
||||
"removed_from_favorites_count": "즐겨찾기에서 항목 {count, plural, other {#개}} 제거됨",
|
||||
"removed_memory": "추억 제거",
|
||||
"removed_photo_from_memory": "이 추억에서 사진 제거",
|
||||
"removed_tagged_assets": "항목 {count, plural, one {#개} other {#개}}에서 태그를 제거함",
|
||||
"rename": "이름 바꾸기",
|
||||
"repair": "수리",
|
||||
@@ -1056,6 +1097,7 @@
|
||||
"repository": "리포지터리",
|
||||
"require_password": "비밀번호 필요",
|
||||
"require_user_to_change_password_on_first_login": "사용자가 처음 로그인할 때 비밀번호를 변경하도록 요구",
|
||||
"rescan": "재검색",
|
||||
"reset": "초기화",
|
||||
"reset_password": "비밀번호 재설정",
|
||||
"reset_people_visibility": "인물 표시 여부 초기화",
|
||||
@@ -1092,12 +1134,14 @@
|
||||
"search_camera_model": "카메라 모델명 검색...",
|
||||
"search_city": "도시 검색...",
|
||||
"search_country": "국가 검색...",
|
||||
"search_for": "검색",
|
||||
"search_for_existing_person": "존재하는 인물 검색",
|
||||
"search_no_people": "인물이 없습니다.",
|
||||
"search_no_people_named": "\"{name}\" 인물을 찾을 수 없음",
|
||||
"search_options": "검색 옵션",
|
||||
"search_people": "인물 검색",
|
||||
"search_places": "장소 검색",
|
||||
"search_rating": "등급으로 검색...",
|
||||
"search_settings": "설정 검색",
|
||||
"search_state": "지역 검색...",
|
||||
"search_tags": "태그로 검색...",
|
||||
@@ -1107,6 +1151,7 @@
|
||||
"searching_locales": "로케일 검색...",
|
||||
"second": "초",
|
||||
"see_all_people": "모든 인물 보기",
|
||||
"select": "선택",
|
||||
"select_album_cover": "앨범 커버 변경",
|
||||
"select_all": "모두 선택",
|
||||
"select_all_duplicates": "모두 선택",
|
||||
@@ -1129,6 +1174,7 @@
|
||||
"server_version": "서버 버전",
|
||||
"set": "설정",
|
||||
"set_as_album_cover": "앨범 커버로 설정",
|
||||
"set_as_featured_photo": "추천 사진으로 설정",
|
||||
"set_as_profile_picture": "프로필 사진으로 설정",
|
||||
"set_date_of_birth": "생년월일 설정",
|
||||
"set_profile_picture": "프로필 사진으로 설정",
|
||||
@@ -1143,6 +1189,7 @@
|
||||
"shared_from_partner": "{partner}님의 사진",
|
||||
"shared_link_options": "공유 링크 옵션",
|
||||
"shared_links": "공유 링크",
|
||||
"shared_links_description": "링크를 통해 사진 및 동영상 공유",
|
||||
"shared_photos_and_videos_count": "사진 및 동영상 {assetCount, plural, other {#개를 공유했습니다.}}",
|
||||
"shared_with_partner": "{partner}님과 공유함",
|
||||
"sharing": "공유",
|
||||
@@ -1165,6 +1212,7 @@
|
||||
"show_person_options": "인물 옵션 표시",
|
||||
"show_progress_bar": "진행 표시줄 표시",
|
||||
"show_search_options": "검색 옵션 표시",
|
||||
"show_shared_links": "공유 링크 표시",
|
||||
"show_slideshow_transition": "슬라이드 전환 표시",
|
||||
"show_supporter_badge": "서포터 배지",
|
||||
"show_supporter_badge_description": "서포터 배지 표시",
|
||||
@@ -1184,6 +1232,7 @@
|
||||
"sort_items": "항목 수",
|
||||
"sort_modified": "수정된 날짜",
|
||||
"sort_oldest": "오래된 사진",
|
||||
"sort_people_by_similarity": "유사성을 기준으로 사람 정렬",
|
||||
"sort_recent": "최근 사진",
|
||||
"sort_title": "제목",
|
||||
"source": "소스",
|
||||
@@ -1217,6 +1266,7 @@
|
||||
"tag_created": "태그 생성됨: {tag}",
|
||||
"tag_feature_description": "사진 및 동영상을 주제별 그룹화된 태그로 탐색",
|
||||
"tag_not_found_question": "태그를 찾을 수 없나요? <link>새 태그를 생성하세요.</link>",
|
||||
"tag_people": "사람 태그",
|
||||
"tag_updated": "태그 업데이트됨: {tag}",
|
||||
"tagged_assets": "항목 {count, plural, one {#개} other {#개}}에 태그를 적용함",
|
||||
"tags": "태그",
|
||||
@@ -1237,6 +1287,7 @@
|
||||
"to_trash": "삭제",
|
||||
"toggle_settings": "설정 변경",
|
||||
"toggle_theme": "다크 모드 사용",
|
||||
"total": "합계",
|
||||
"total_usage": "총 사용량",
|
||||
"trash": "휴지통",
|
||||
"trash_all": "모두 삭제",
|
||||
@@ -1256,6 +1307,7 @@
|
||||
"unlink_motion_video": "모션 비디오 링크 해제",
|
||||
"unlink_oauth": "OAuth 연결 해제",
|
||||
"unlinked_oauth_account": "OAuth 계정 연결이 해제되었습니다.",
|
||||
"unmute_memories": "추억 음소거 해제",
|
||||
"unnamed_album": "이름 없는 앨범",
|
||||
"unnamed_album_delete_confirmation": "선텍한 앨범을 삭제하시겠습니까?",
|
||||
"unnamed_share": "이름 없는 공유",
|
||||
@@ -1287,6 +1339,7 @@
|
||||
"user_purchase_settings_description": "구매 및 제품 키 관리",
|
||||
"user_role_set": "{user}님에게 {role} 역할을 설정했습니다.",
|
||||
"user_usage_detail": "사용자 사용량 상세",
|
||||
"user_usage_stats": "계정 사용량 통계",
|
||||
"user_usage_stats_description": "계정 사용량 통계 보기",
|
||||
"username": "계정명",
|
||||
"users": "사용자",
|
||||
@@ -1308,7 +1361,9 @@
|
||||
"view_all": "모두 보기",
|
||||
"view_all_users": "모든 사용자 보기",
|
||||
"view_in_timeline": "타임라인에서 보기",
|
||||
"view_link": "링크 보기",
|
||||
"view_links": "링크 확인",
|
||||
"view_name": "보기",
|
||||
"view_next_asset": "다음 항목 보기",
|
||||
"view_previous_asset": "이전 항목 보기",
|
||||
"view_stack": "스택 보기",
|
||||
@@ -1323,4 +1378,4 @@
|
||||
"yes": "네",
|
||||
"you_dont_have_any_shared_links": "생성한 공유 링크가 없습니다.",
|
||||
"zoom_image": "이미지 확대"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -29,6 +29,7 @@
|
||||
"added_to_favorites_count": "Pievienots {count, number} izlasei",
|
||||
"admin": {
|
||||
"add_exclusion_pattern_description": "Pievienojiet izlaišanas shēmas. Aizstājējzīmju izmantoša *, **, un ? tiek atbalstīta. Lai ignorētu visus failus jebkurā direktorijā ar nosaukumu “RAW”, izmantojiet “**/RAW/**”. Lai ignorētu visus failus, kas beidzas ar “. tif”, izmantojiet “**/*. tif”. Lai ignorētu absolūto ceļu, izmantojiet “/path/to/ignore/**”.",
|
||||
"asset_offline_description": "Šis ārējās bibliotēkas resurss vairs nav atrodams diskā un ir pārvietots uz atkritumu grozu. Ja fails tika pārvietots bibliotēkas ietvaros, pārbaudiet, vai jūsu hronoloģijā ir jauns atbilstošais resurss. Lai atjaunotu šo resursu, pārliecinieties, vai Immich var piekļūt tālāk norādītajam faila ceļam un skenēt bibliotēku.",
|
||||
"authentication_settings": "Autentifikācijas iestatījumi",
|
||||
"authentication_settings_description": "Paroļu, OAuth un citu autentifikācijas iestatījumu pārvaldība",
|
||||
"authentication_settings_disable_all": "Vai tiešām vēlaties atspējot visas pieteikšanās metodes? Pieteikšanās tiks pilnībā atspējota.",
|
||||
@@ -316,6 +317,8 @@
|
||||
"birthdate_set_description": "Dzimšanas datums tiek izmantots, lai aprēķinātu šīs personas vecumu fotogrāfijas uzņemšanas brīdī.",
|
||||
"blurred_background": "",
|
||||
"bugs_and_feature_requests": "Kļūdas un funkciju pieprasījumi",
|
||||
"build": "Būvējums",
|
||||
"build_image": "Būvējuma attēls",
|
||||
"camera": "",
|
||||
"camera_brand": "",
|
||||
"camera_model": "",
|
||||
@@ -599,7 +602,7 @@
|
||||
"model": "Modelis",
|
||||
"month": "Mēnesis",
|
||||
"more": "Vairāk",
|
||||
"moved_to_trash": "",
|
||||
"moved_to_trash": "Pārvietots uz atkritni",
|
||||
"my_albums": "Mani albumi",
|
||||
"name": "Vārds",
|
||||
"name_or_nickname": "Vārds vai iesauka",
|
||||
@@ -824,7 +827,7 @@
|
||||
"sort_oldest": "Vecākā fotogrāfija",
|
||||
"sort_recent": "Nesenākā fotogrāfija",
|
||||
"sort_title": "Nosaukums",
|
||||
"source": "Avots",
|
||||
"source": "Pirmkods",
|
||||
"stack": "Apvienot kaudzē",
|
||||
"stack_selected_photos": "",
|
||||
"stacktrace": "",
|
||||
@@ -893,6 +896,7 @@
|
||||
"version": "Versija",
|
||||
"version_announcement_message": "Sveiki! Ir pieejama jauna Immich versija. Lūdzu, veltiet laiku, lai izlasītu <link>laidiena piezīmes</link> un pārliecinātos, ka jūsu iestatījumi ir atjaunināti, lai novērstu jebkādu nepareizu konfigurāciju, jo īpaši, ja izmantojat WatchTower vai citu mehānismu, kas automātiski atjaunina jūsu Immich instanci.",
|
||||
"version_history": "Versiju vēsture",
|
||||
"version_history_item": "{version} uzstādīta {date}",
|
||||
"video": "Videoklips",
|
||||
"video_hover_setting_description": "",
|
||||
"videos": "Videoklipi",
|
||||
|
||||
1
i18n/pa.json
Normal file
1
i18n/pa.json
Normal file
@@ -0,0 +1 @@
|
||||
{}
|
||||
@@ -1079,6 +1079,8 @@
|
||||
"remove_from_album": "Odstrániť z albumu",
|
||||
"remove_from_favorites": "Odstrániť z obľúbených",
|
||||
"remove_from_shared_link": "Odstrániť zo zdieľaného odkazu",
|
||||
"remove_memory": "Odstrániť spomienku",
|
||||
"remove_photo_from_memory": "Odstrániť fotografiu z tejto spomienky",
|
||||
"remove_url": "Odstrániť URL",
|
||||
"remove_user": "Odstrániť používateľa",
|
||||
"removed_api_key": "Odstrániť API kľúč: {name}",
|
||||
|
||||
1
i18n/sq.json
Normal file
1
i18n/sq.json
Normal file
@@ -0,0 +1 @@
|
||||
{}
|
||||
@@ -246,7 +246,7 @@
|
||||
"storage_template_migration_info": "Lagringsmallen kommer konvertera alla filändelser till gemena bokstäver. Ändringar gäller endast för nya resurser, för att retoaktivt tillämpa mallen på befintliga resurser kör <link>{job}</link>.",
|
||||
"storage_template_migration_job": "Lagringsmall migreringsjobb",
|
||||
"storage_template_more_details": "För mer information om den här funktionen se <template-link>Lagringsmall</template-link> och dess <implications-link>konsekvenser</implications-link>",
|
||||
"storage_template_onboarding_description": "Vid aktivering organiserar denna funktion automatiskt filer baserat på en användardefinierad mall. På grunda av stabilitetsproblem är denna funktion avstängd som standard, för mer information se <link>dokumentation</link>.",
|
||||
"storage_template_onboarding_description": "Vid aktivering organiserar denna funktion automatiskt filer baserat på en användardefinierad mall. På grund av stabilitetsproblem är denna funktion avstängd som standard, för mer information se <link>dokumentation</link>.",
|
||||
"storage_template_path_length": "Uppskattad längdbegränsning på sökväg: <b>{length, number}</b>/{limit, number}",
|
||||
"storage_template_settings": "Lagringsmall",
|
||||
"storage_template_settings_description": "Hantera mappstruktur och filnamn för uppladdade resurser",
|
||||
|
||||
949
i18n/te.json
949
i18n/te.json
File diff suppressed because it is too large
Load Diff
28
i18n/uk.json
28
i18n/uk.json
@@ -29,7 +29,7 @@
|
||||
"added_to_favorites_count": "Додано {count, number} до обраного",
|
||||
"admin": {
|
||||
"add_exclusion_pattern_description": "Додайте шаблони виключень. Підстановка з використанням *, ** та ? підтримується. Для ігнорування всіх файлів у будь-якому каталозі з ім'ям «Raw», використовуйте \"**/Raw/**\". Для ігнорування всіх файлів, що закінчуються на \".tif\", використовуйте \"**/*.tif\". Для ігнорування абсолютного шляху використовуйте \"/path/to/ignore/**\".",
|
||||
"asset_offline_description": "Цей зовнішній бібліотечний актив більше не знайдено на диску і був переміщений до кошика. Якщо файл був переміщений у межах бібліотеки, перевірте свій таймлайн на наявність нового відповідного активу. Щоб відновити цей актив, переконайтеся, що шлях файлу нижче доступний для Immich, і проскануйте бібліотеку.",
|
||||
"asset_offline_description": "Цей зовнішній бібліотечний актив більше не знайдено на диску і був переміщений до смітника. Якщо файл був переміщений у межах бібліотеки, перевірте свій таймлайн на наявність нового відповідного активу. Щоб відновити цей актив, переконайтеся, що шлях файлу нижче доступний для Immich, і проскануйте бібліотеку.",
|
||||
"authentication_settings": "Налаштування аутентифікації",
|
||||
"authentication_settings_description": "Управління паролями, OAuth та іншими налаштуваннями аутентифікації",
|
||||
"authentication_settings_disable_all": "Ви впевнені, що хочете вимкнути всі методи входу? Вхід буде повністю вимкнений.",
|
||||
@@ -331,11 +331,11 @@
|
||||
"transcoding_two_pass_encoding_setting_description": "Транскодування за двома проходами для отримання кращих закодованих відео. Коли ввімкнено максимальний бітрейт (необхідний для роботи з H.264 та HEVC), цей режим використовує діапазон бітрейту, заснований на максимальному бітрейті, і ігнорує CRF. Для VP9 можна використовувати CRF, якщо вимкнено максимальний бітрейт.",
|
||||
"transcoding_video_codec": "Відеокодек",
|
||||
"transcoding_video_codec_description": "VP9 має високу ефективність і сумісність з вебом, але потребує більше часу на транскодування. HEVC працює схоже, але має меншу сумісність з вебом. H.264 має широку сумісність і швидко транскодується, але створює значно більші файли. AV1 - найефективніший кодек, але не підтримується на старіших пристроях.",
|
||||
"trash_enabled_description": "Увімкнення кошика",
|
||||
"trash_enabled_description": "Увімкнення смітника",
|
||||
"trash_number_of_days": "Кількість днів",
|
||||
"trash_number_of_days_description": "Кількість днів, щоб залишити ресурси в кошику перед остаточним їх видаленням",
|
||||
"trash_settings": "Налаштування кошика",
|
||||
"trash_settings_description": "Керування налаштуваннями кошика",
|
||||
"trash_number_of_days_description": "Кількість днів, щоб залишити ресурси в смітнику перед остаточним їх видаленням",
|
||||
"trash_settings": "Налаштування смітника",
|
||||
"trash_settings_description": "Керування налаштуваннями смітника",
|
||||
"untracked_files": "Невідстежувані файли",
|
||||
"untracked_files_description": "Ці файли не відстежуються програмою. Вони можуть бути результатом невдалого переміщення, перерваного завантаження або залишитися через помилку програми",
|
||||
"user_cleanup_job": "Очищення користувача",
|
||||
@@ -426,12 +426,12 @@
|
||||
"assets_added_to_album_count": "Додано {count, plural, one {# ресурс} few {# ресурси} other {# ресурсів}} до альбому",
|
||||
"assets_added_to_name_count": "Додано {count, plural, one {# елемент} other {# елементів}} до {hasName, select, true {<b>{name}</b>} other {нового альбому}}",
|
||||
"assets_count": "{count, plural, one {# ресурс} few {# ресурси} other {# ресурсів}}",
|
||||
"assets_moved_to_trash_count": "Переміщено {count, plural, one {# ресурс} few {# ресурси} other {# ресурсів}} у кошик",
|
||||
"assets_moved_to_trash_count": "Переміщено {count, plural, one {# ресурс} few {# ресурси} other {# ресурсів}} у смітник",
|
||||
"assets_permanently_deleted_count": "Остаточно видалено {count, plural, one {# ресурс} few {# ресурси} other {# ресурсів}}",
|
||||
"assets_removed_count": "Вилучено {count, plural, one {# ресурс} few {# ресурси} other {# ресурсів}}",
|
||||
"assets_restore_confirmation": "Ви впевнені, що хочете відновити всі свої активи з кошика? Цю дію не можна скасувати! Зверніть увагу, що будь-які офлайн-активи не можуть бути відновлені таким чином.",
|
||||
"assets_restore_confirmation": "Ви впевнені, що хочете відновити всі свої активи з смітника? Цю дію не можна скасувати! Зверніть увагу, що будь-які офлайн-активи не можуть бути відновлені таким чином.",
|
||||
"assets_restored_count": "Відновлено {count, plural, one {# ресурс} few {# ресурси} other {# ресурсів}}",
|
||||
"assets_trashed_count": "Поміщено в кошик {count, plural, one {# ресурс} few {# ресурси} other {# ресурсів}}",
|
||||
"assets_trashed_count": "Поміщено в смітник {count, plural, one {# ресурс} few {# ресурси} other {# ресурсів}}",
|
||||
"assets_were_part_of_album_count": "{count, plural, one {Ресурс був} few {Ресурси були} other {Ресурси були}} вже частиною альбому",
|
||||
"authorized_devices": "Авторизовані пристрої",
|
||||
"back": "Назад",
|
||||
@@ -445,7 +445,7 @@
|
||||
"build_image": "Версія збірки",
|
||||
"bulk_delete_duplicates_confirmation": "Ви впевнені, що хочете масово видалити {count, plural, one {# дубльований ресурс} few {# дубльовані ресурси} other {# дубльованих ресурсів}}? Це дія залишить найбільший ресурс у кожній групі і остаточно видалить всі інші дублікати. Цю дію неможливо скасувати!",
|
||||
"bulk_keep_duplicates_confirmation": "Ви впевнені, що хочете залишити {count, plural, one {# дубльований ресурс} few {# дубльовані ресурси} other {# дубльованих ресурсів}}? Це дозволить вирішити всі групи дублікатів без видалення чого-небудь.",
|
||||
"bulk_trash_duplicates_confirmation": "Ви впевнені, що хочете викинути в кошик {count, plural, one {# дубльований ресурс} few {# дубльовані ресурси} other {# дубльованих ресурсів}} масово? Це залишить найбільший ресурс у кожній групі і викине в кошик всі інші дублікати.",
|
||||
"bulk_trash_duplicates_confirmation": "Ви впевнені, що хочете викинути в смітник {count, plural, one {# дубльований ресурс} few {# дубльовані ресурси} other {# дубльованих ресурсів}} масово? Це залишить найбільший ресурс у кожній групі і викине в смітник всі інші дублікати.",
|
||||
"buy": "Придбайте Immich",
|
||||
"camera": "Камера",
|
||||
"camera_brand": "Марка камери",
|
||||
@@ -600,8 +600,8 @@
|
||||
"editor_crop_tool_h2_aspect_ratios": "Пропорції зображення",
|
||||
"editor_crop_tool_h2_rotation": "Орієнтація",
|
||||
"email": "Електронна пошта",
|
||||
"empty_trash": "Очистити кошик",
|
||||
"empty_trash_confirmation": "Ви впевнені, що хочете очистити кошик? Це остаточно видалить всі ресурси в кошику з Immich.\nЦю дію не можна скасувати!",
|
||||
"empty_trash": "Очистити смітник",
|
||||
"empty_trash_confirmation": "Ви впевнені, що хочете очистити смітник? Це остаточно видалить всі ресурси в смітнику з Immich.\nЦю дію не можна скасувати!",
|
||||
"enable": "Увімкнути",
|
||||
"enabled": "Увімкнено",
|
||||
"end_date": "Дата завершення",
|
||||
@@ -680,7 +680,7 @@
|
||||
"unable_to_download_files": "Неможливо завантажити файли",
|
||||
"unable_to_edit_exclusion_pattern": "Не вдалося редагувати шаблон виключення",
|
||||
"unable_to_edit_import_path": "Неможливо відредагувати шлях імпорту",
|
||||
"unable_to_empty_trash": "Неможливо очистити кошик",
|
||||
"unable_to_empty_trash": "Неможливо очистити смітник",
|
||||
"unable_to_enter_fullscreen": "Неможливо увійти в повноекранний режим",
|
||||
"unable_to_exit_fullscreen": "Неможливо вийти з повноекранного режиму",
|
||||
"unable_to_get_comments_number": "Не вдалося отримати кількість коментарів",
|
||||
@@ -710,7 +710,7 @@
|
||||
"unable_to_reset_password": "Не вдається скинути пароль",
|
||||
"unable_to_resolve_duplicate": "Не вдається вирішити дублікат",
|
||||
"unable_to_restore_assets": "Неможливо відновити активи",
|
||||
"unable_to_restore_trash": "Неможливо відновити сміття",
|
||||
"unable_to_restore_trash": "Не вдалося відновити вміст",
|
||||
"unable_to_restore_user": "Не вдається відновити користувача",
|
||||
"unable_to_save_album": "Не вдається зберегти альбом",
|
||||
"unable_to_save_api_key": "Не вдається зберегти ключ API",
|
||||
@@ -1289,7 +1289,7 @@
|
||||
"toggle_theme": "Перемикання теми",
|
||||
"total": "Усього",
|
||||
"total_usage": "Загальне використання",
|
||||
"trash": "Кошик",
|
||||
"trash": "Смітник",
|
||||
"trash_all": "Видалити все",
|
||||
"trash_count": "Видалити {count, number}",
|
||||
"trash_delete_asset": "Смітник/Видалити ресурс",
|
||||
|
||||
@@ -51,7 +51,6 @@ ARG DEVICE
|
||||
ENV PYTHONDONTWRITEBYTECODE=1 \
|
||||
PYTHONUNBUFFERED=1 \
|
||||
VIRTUAL_ENV=/opt/venv
|
||||
WORKDIR /usr/src/app
|
||||
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends g++
|
||||
|
||||
@@ -66,7 +65,9 @@ RUN if [ "$DEVICE" = "rocm" ]; then \
|
||||
|
||||
FROM python:3.11-slim-bookworm@sha256:7029b00486ac40bed03e36775b864d3f3d39dcbdf19cd45e6a52d541e6c178f0 AS prod-cpu
|
||||
|
||||
FROM prod-cpu AS prod-openvino
|
||||
ENV LD_PRELOAD=/usr/lib/libmimalloc.so.2
|
||||
|
||||
FROM python:3.11-slim-bookworm@sha256:7029b00486ac40bed03e36775b864d3f3d39dcbdf19cd45e6a52d541e6c178f0 AS prod-openvino
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install --no-install-recommends -yqq ocl-icd-libopencl1 wget && \
|
||||
@@ -81,6 +82,8 @@ RUN apt-get update && \
|
||||
|
||||
FROM nvidia/cuda:12.2.2-runtime-ubuntu22.04@sha256:94c1577b2cd9dd6c0312dc04dff9cb2fdce2b268018abc3d7c2dbcacf1155000 AS prod-cuda
|
||||
|
||||
ENV LD_PRELOAD=/usr/lib/libmimalloc.so.2
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install --no-install-recommends -yqq libcudnn9-cuda-12 && \
|
||||
apt-get clean && \
|
||||
@@ -94,7 +97,8 @@ FROM rocm/dev-ubuntu-22.04:6.3.4-complete@sha256:1f7e92ca7e3a3785680473329ed1091
|
||||
|
||||
FROM prod-cpu AS prod-armnn
|
||||
|
||||
ENV LD_LIBRARY_PATH=/opt/armnn
|
||||
ENV LD_LIBRARY_PATH=/opt/armnn \
|
||||
LD_PRELOAD=/usr/lib/libmimalloc.so.2
|
||||
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends ocl-icd-libopencl1 mesa-opencl-icd libgomp1 && \
|
||||
rm -rf /var/lib/apt/lists/* && \
|
||||
@@ -114,6 +118,8 @@ COPY --from=builder-armnn \
|
||||
|
||||
FROM prod-cpu AS prod-rknn
|
||||
|
||||
ENV LD_PRELOAD=/usr/lib/libmimalloc.so.2
|
||||
|
||||
ADD --checksum=sha256:73993ed4b440460825f21611731564503cc1d5a0c123746477da6cd574f34885 https://github.com/airockchip/rknn-toolkit2/raw/refs/tags/v2.3.0/rknpu2/runtime/Linux/librknn_api/aarch64/librknnrt.so /usr/lib/
|
||||
|
||||
FROM prod-${DEVICE} AS prod
|
||||
@@ -126,14 +132,17 @@ RUN apt-get update && \
|
||||
apt-get clean && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
WORKDIR /usr/src/app
|
||||
RUN ln -s "/usr/lib/$(arch)-linux-gnu/libmimalloc.so.2" /usr/lib/libmimalloc.so.2
|
||||
|
||||
WORKDIR /usr/src
|
||||
ENV TRANSFORMERS_CACHE=/cache \
|
||||
PYTHONDONTWRITEBYTECODE=1 \
|
||||
PYTHONUNBUFFERED=1 \
|
||||
PATH="/opt/venv/bin:$PATH" \
|
||||
PYTHONPATH=/usr/src \
|
||||
DEVICE=${DEVICE} \
|
||||
VIRTUAL_ENV=/opt/venv
|
||||
VIRTUAL_ENV=/opt/venv \
|
||||
MACHINE_LEARNING_CACHE_FOLDER=/cache
|
||||
|
||||
# prevent core dumps
|
||||
RUN echo "hard core 0" >> /etc/security/limits.conf && \
|
||||
@@ -141,9 +150,8 @@ RUN echo "hard core 0" >> /etc/security/limits.conf && \
|
||||
echo 'ulimit -S -c 0 > /dev/null 2>&1' >> /etc/profile
|
||||
|
||||
COPY --from=builder /opt/venv /opt/venv
|
||||
COPY ann/ann.py /usr/src/ann/ann.py
|
||||
COPY start.sh log_conf.json gunicorn_conf.py ./
|
||||
COPY app .
|
||||
COPY scripts/healthcheck.py .
|
||||
COPY immich_ml immich_ml
|
||||
|
||||
ARG BUILD_ID
|
||||
ARG BUILD_IMAGE
|
||||
@@ -161,6 +169,6 @@ ENV IMMICH_SOURCE_COMMIT=${BUILD_SOURCE_COMMIT}
|
||||
ENV IMMICH_SOURCE_URL=https://github.com/immich-app/immich/commit/${BUILD_SOURCE_COMMIT}
|
||||
|
||||
ENTRYPOINT ["tini", "--"]
|
||||
CMD ["./start.sh"]
|
||||
CMD ["python", "-m", "immich_ml"]
|
||||
|
||||
HEALTHCHECK CMD python3 healthcheck.py
|
||||
HEALTHCHECK CMD python3 healthcheck.py
|
||||
|
||||
@@ -8,9 +8,8 @@ from fastapi.testclient import TestClient
|
||||
from numpy.typing import NDArray
|
||||
from PIL import Image
|
||||
|
||||
from app.config import log
|
||||
|
||||
from .main import app
|
||||
from immich_ml.config import log
|
||||
from immich_ml.main import app
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
@@ -25,7 +24,7 @@ def cv_image(pil_image: Image.Image) -> NDArray[np.float32]:
|
||||
|
||||
@pytest.fixture
|
||||
def mock_get_model() -> Iterator[mock.Mock]:
|
||||
with mock.patch("app.models.cache.from_model_type", autospec=True) as mocked:
|
||||
with mock.patch("immich_ml.models.cache.from_model_type", autospec=True) as mocked:
|
||||
yield mocked
|
||||
|
||||
|
||||
@@ -104,14 +103,14 @@ def providers(request: pytest.FixtureRequest) -> Iterator[mock.Mock]:
|
||||
raise ValueError("Missing marker 'providers'")
|
||||
|
||||
providers = marker.args[0]
|
||||
with mock.patch("app.sessions.ort.ort.get_available_providers") as mocked:
|
||||
with mock.patch("immich_ml.sessions.ort.ort.get_available_providers") as mocked:
|
||||
mocked.return_value = providers
|
||||
yield providers
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def ort_pybind() -> Iterator[mock.Mock]:
|
||||
with mock.patch("app.sessions.ort.ort.capi._pybind_state") as mocked:
|
||||
with mock.patch("immich_ml.sessions.ort.ort.capi._pybind_state") as mocked:
|
||||
yield mocked
|
||||
|
||||
|
||||
@@ -126,25 +125,25 @@ def ov_device_ids(request: pytest.FixtureRequest, ort_pybind: mock.Mock) -> Iter
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def ort_session() -> Iterator[mock.Mock]:
|
||||
with mock.patch("app.sessions.ort.ort.InferenceSession") as mocked:
|
||||
with mock.patch("immich_ml.sessions.ort.ort.InferenceSession") as mocked:
|
||||
yield mocked
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def ann_session() -> Iterator[mock.Mock]:
|
||||
with mock.patch("app.sessions.ann.Ann") as mocked:
|
||||
with mock.patch("immich_ml.sessions.ann.Ann") as mocked:
|
||||
yield mocked
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def rknn_session() -> Iterator[mock.Mock]:
|
||||
with mock.patch("app.sessions.rknn.RknnPoolExecutor") as mocked:
|
||||
with mock.patch("immich_ml.sessions.rknn.RknnPoolExecutor") as mocked:
|
||||
yield mocked
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def rmtree() -> Iterator[mock.Mock]:
|
||||
with mock.patch("app.models.base.rmtree", autospec=True) as mocked:
|
||||
with mock.patch("immich_ml.models.base.rmtree", autospec=True) as mocked:
|
||||
mocked.avoids_symlink_attacks = True
|
||||
yield mocked
|
||||
|
||||
@@ -158,7 +157,7 @@ def path() -> Iterator[mock.Mock]:
|
||||
path.with_suffix.return_value = path
|
||||
path.return_value = path
|
||||
|
||||
with mock.patch("app.models.base.Path", return_value=path) as mocked:
|
||||
with mock.patch("immich_ml.models.base.Path", return_value=path) as mocked:
|
||||
yield mocked
|
||||
|
||||
|
||||
@@ -182,5 +181,5 @@ def exception() -> Iterator[mock.Mock]:
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def snapshot_download() -> Iterator[mock.Mock]:
|
||||
with mock.patch("app.models.base.snapshot_download") as mocked:
|
||||
with mock.patch("immich_ml.models.base.snapshot_download") as mocked:
|
||||
yield mocked
|
||||
@@ -1 +0,0 @@
|
||||
3.12
|
||||
@@ -1,165 +0,0 @@
|
||||
import json
|
||||
import resource
|
||||
from pathlib import Path
|
||||
|
||||
import typer
|
||||
from tenacity import retry, stop_after_attempt, wait_fixed
|
||||
from typing_extensions import Annotated
|
||||
|
||||
from .exporters.constants import DELETE_PATTERNS, SOURCE_TO_METADATA, ModelSource, ModelTask
|
||||
from .exporters.onnx import export as onnx_export
|
||||
from .exporters.rknn import export as rknn_export
|
||||
|
||||
app = typer.Typer(pretty_exceptions_show_locals=False)
|
||||
|
||||
|
||||
def generate_readme(model_name: str, model_source: ModelSource) -> str:
|
||||
(name, link, type) = SOURCE_TO_METADATA[model_source]
|
||||
match model_source:
|
||||
case ModelSource.MCLIP:
|
||||
tags = ["immich", "clip", "multilingual"]
|
||||
case ModelSource.OPENCLIP:
|
||||
tags = ["immich", "clip"]
|
||||
lowered = model_name.lower()
|
||||
if "xlm" in lowered or "nllb" in lowered:
|
||||
tags.append("multilingual")
|
||||
case ModelSource.INSIGHTFACE:
|
||||
tags = ["immich", "facial-recognition"]
|
||||
case _:
|
||||
raise ValueError(f"Unsupported model source {model_source}")
|
||||
|
||||
return f"""---
|
||||
tags:
|
||||
{" - " + "\n - ".join(tags)}
|
||||
---
|
||||
# Model Description
|
||||
|
||||
This repo contains ONNX exports for the associated {type} model by {name}. See the [{name}]({link}) repo for more info.
|
||||
|
||||
This repo is specifically intended for use with [Immich](https://immich.app/), a self-hosted photo library.
|
||||
"""
|
||||
|
||||
|
||||
def clean_name(model_name: str) -> str:
|
||||
hf_model_name = model_name.split("/")[-1]
|
||||
hf_model_name = hf_model_name.replace("xlm-roberta-large", "XLM-Roberta-Large")
|
||||
hf_model_name = hf_model_name.replace("xlm-roberta-base", "XLM-Roberta-Base")
|
||||
return hf_model_name
|
||||
|
||||
|
||||
@app.command()
|
||||
def export(model_name: str, model_source: ModelSource, output_dir: Path = Path("models"), cache: bool = True) -> None:
|
||||
hf_model_name = clean_name(model_name)
|
||||
output_dir = output_dir / hf_model_name
|
||||
match model_source:
|
||||
case ModelSource.MCLIP | ModelSource.OPENCLIP:
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
onnx_export(model_name, model_source, output_dir, cache=cache)
|
||||
case ModelSource.INSIGHTFACE:
|
||||
from huggingface_hub import snapshot_download
|
||||
|
||||
# TODO: start from insightface dump instead of downloading from HF
|
||||
snapshot_download(f"immich-app/{hf_model_name}", local_dir=output_dir)
|
||||
case _:
|
||||
raise ValueError(f"Unsupported model source {model_source}")
|
||||
|
||||
try:
|
||||
rknn_export(output_dir, cache=cache)
|
||||
except Exception as e:
|
||||
print(f"Failed to export model {model_name} to rknn: {e}")
|
||||
(output_dir / "rknpu").unlink(missing_ok=True)
|
||||
|
||||
readme_path = output_dir / "README.md"
|
||||
if not (cache or readme_path.exists()):
|
||||
with open(readme_path, "w") as f:
|
||||
f.write(generate_readme(model_name, model_source))
|
||||
|
||||
|
||||
@app.command()
|
||||
def profile(model_dir: Path, model_task: ModelTask, output_path: Path) -> None:
|
||||
from timeit import timeit
|
||||
|
||||
import numpy as np
|
||||
import onnxruntime as ort
|
||||
|
||||
np.random.seed(0)
|
||||
|
||||
sess_options = ort.SessionOptions()
|
||||
sess_options.enable_cpu_mem_arena = False
|
||||
providers = ["CPUExecutionProvider"]
|
||||
provider_options = [{"arena_extend_strategy": "kSameAsRequested"}]
|
||||
match model_task:
|
||||
case ModelTask.SEARCH:
|
||||
textual = ort.InferenceSession(
|
||||
model_dir / "textual" / "model.onnx",
|
||||
sess_options=sess_options,
|
||||
providers=providers,
|
||||
provider_options=provider_options,
|
||||
)
|
||||
tokens = {node.name: np.random.rand(*node.shape).astype(np.int32) for node in textual.get_inputs()}
|
||||
|
||||
visual = ort.InferenceSession(
|
||||
model_dir / "visual" / "model.onnx",
|
||||
sess_options=sess_options,
|
||||
providers=providers,
|
||||
provider_options=provider_options,
|
||||
)
|
||||
image = {node.name: np.random.rand(*node.shape).astype(np.float32) for node in visual.get_inputs()}
|
||||
|
||||
def predict() -> None:
|
||||
textual.run(None, tokens)
|
||||
visual.run(None, image)
|
||||
|
||||
case ModelTask.FACIAL_RECOGNITION:
|
||||
detection = ort.InferenceSession(
|
||||
model_dir / "detection" / "model.onnx",
|
||||
sess_options=sess_options,
|
||||
providers=providers,
|
||||
provider_options=provider_options,
|
||||
)
|
||||
image = {node.name: np.random.rand(1, 3, 640, 640).astype(np.float32) for node in detection.get_inputs()}
|
||||
|
||||
recognition = ort.InferenceSession(
|
||||
model_dir / "recognition" / "model.onnx",
|
||||
sess_options=sess_options,
|
||||
providers=providers,
|
||||
provider_options=provider_options,
|
||||
)
|
||||
face = {node.name: np.random.rand(1, 3, 112, 112).astype(np.float32) for node in recognition.get_inputs()}
|
||||
|
||||
def predict() -> None:
|
||||
detection.run(None, image)
|
||||
recognition.run(None, face)
|
||||
|
||||
case _:
|
||||
raise ValueError(f"Unsupported model task {model_task}")
|
||||
predict()
|
||||
ms = timeit(predict, number=100)
|
||||
rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
|
||||
json.dump({"pretrained_model": model_dir.name, "peak_rss": rss, "exec_time_ms": ms}, output_path.open("w"))
|
||||
print(f"Model {model_dir.name} took {ms:.2f}ms per iteration using {rss / 1024:.2f}MiB of memory")
|
||||
|
||||
|
||||
@app.command()
|
||||
def upload(
|
||||
model_dir: Path,
|
||||
hf_organization: str = "immich-app",
|
||||
hf_auth_token: Annotated[str | None, typer.Option(envvar="HF_AUTH_TOKEN")] = None,
|
||||
) -> None:
|
||||
from huggingface_hub import create_repo, upload_folder
|
||||
|
||||
repo_id = f"{hf_organization}/{model_dir.name}"
|
||||
|
||||
@retry(stop=stop_after_attempt(5), wait=wait_fixed(5))
|
||||
def upload_model() -> None:
|
||||
create_repo(repo_id, exist_ok=True, token=hf_auth_token)
|
||||
upload_folder(
|
||||
repo_id=repo_id,
|
||||
folder_path=model_dir,
|
||||
# remote repo files to be deleted before uploading
|
||||
# deletion is in the same commit as the upload, so it's atomic
|
||||
delete_patterns=DELETE_PATTERNS,
|
||||
token=hf_auth_token,
|
||||
)
|
||||
|
||||
upload_model()
|
||||
@@ -1,3 +0,0 @@
|
||||
from immich_model_exporter import app
|
||||
|
||||
app()
|
||||
@@ -1,54 +0,0 @@
|
||||
from enum import StrEnum
|
||||
from typing import NamedTuple
|
||||
|
||||
|
||||
class ModelSource(StrEnum):
|
||||
INSIGHTFACE = "insightface"
|
||||
MCLIP = "mclip"
|
||||
OPENCLIP = "openclip"
|
||||
|
||||
|
||||
class ModelTask(StrEnum):
|
||||
FACIAL_RECOGNITION = "facial-recognition"
|
||||
SEARCH = "clip"
|
||||
|
||||
|
||||
class SourceMetadata(NamedTuple):
|
||||
name: str
|
||||
link: str
|
||||
type: str
|
||||
|
||||
|
||||
SOURCE_TO_METADATA = {
|
||||
ModelSource.MCLIP: SourceMetadata("M-CLIP", "https://huggingface.co/M-CLIP", "CLIP"),
|
||||
ModelSource.OPENCLIP: SourceMetadata("OpenCLIP", "https://github.com/mlfoundations/open_clip", "CLIP"),
|
||||
ModelSource.INSIGHTFACE: SourceMetadata(
|
||||
"InsightFace", "https://github.com/deepinsight/insightface/tree/master", "facial recognition"
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
SOURCE_TO_TASK = {
|
||||
ModelSource.MCLIP: ModelTask.SEARCH,
|
||||
ModelSource.OPENCLIP: ModelTask.SEARCH,
|
||||
ModelSource.INSIGHTFACE: ModelTask.FACIAL_RECOGNITION,
|
||||
}
|
||||
|
||||
RKNN_SOCS = ["rk3566", "rk3568", "rk3576", "rk3588"]
|
||||
|
||||
|
||||
# glob to delete old UUID blobs when reuploading models
|
||||
_uuid_char = "[a-fA-F0-9]"
|
||||
_uuid_glob = _uuid_char * 8 + "-" + _uuid_char * 4 + "-" + _uuid_char * 4 + "-" + _uuid_char * 4 + "-" + _uuid_char * 12
|
||||
DELETE_PATTERNS = [
|
||||
"**/*onnx*",
|
||||
"**/Constant*",
|
||||
"**/*.weight",
|
||||
"**/*.bias",
|
||||
"**/*.proj",
|
||||
"**/*in_proj_bias",
|
||||
"**/*.npy",
|
||||
"**/*.latent",
|
||||
"**/*.pos_embed",
|
||||
f"**/{_uuid_glob}",
|
||||
]
|
||||
@@ -1,20 +0,0 @@
|
||||
from pathlib import Path
|
||||
|
||||
from ..constants import ModelSource
|
||||
from .models import mclip, openclip
|
||||
|
||||
|
||||
def export(
|
||||
model_name: str, model_source: ModelSource, output_dir: Path, opset_version: int = 19, cache: bool = True
|
||||
) -> None:
|
||||
visual_dir = output_dir / "visual"
|
||||
textual_dir = output_dir / "textual"
|
||||
match model_source:
|
||||
case ModelSource.MCLIP:
|
||||
mclip.to_onnx(model_name, opset_version, visual_dir, textual_dir, cache=cache)
|
||||
case ModelSource.OPENCLIP:
|
||||
name, _, pretrained = model_name.partition("__")
|
||||
config = openclip.OpenCLIPModelConfig(name, pretrained)
|
||||
openclip.to_onnx(config, opset_version, visual_dir, textual_dir, cache=cache)
|
||||
case _:
|
||||
raise ValueError(f"Unsupported model source {model_source}")
|
||||
@@ -1,77 +0,0 @@
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from .openclip import OpenCLIPModelConfig
|
||||
from .openclip import to_onnx as openclip_to_onnx
|
||||
from .util import get_model_path
|
||||
|
||||
_MCLIP_TO_OPENCLIP = {
|
||||
"M-CLIP/XLM-Roberta-Large-Vit-B-32": OpenCLIPModelConfig("ViT-B-32", "openai"),
|
||||
"M-CLIP/XLM-Roberta-Large-Vit-B-16Plus": OpenCLIPModelConfig("ViT-B-16-plus-240", "laion400m_e32"),
|
||||
"M-CLIP/LABSE-Vit-L-14": OpenCLIPModelConfig("ViT-L-14", "openai"),
|
||||
"M-CLIP/XLM-Roberta-Large-Vit-L-14": OpenCLIPModelConfig("ViT-L-14", "openai"),
|
||||
}
|
||||
|
||||
|
||||
def to_onnx(
|
||||
model_name: str,
|
||||
opset_version: int,
|
||||
output_dir_visual: Path | str,
|
||||
output_dir_textual: Path | str,
|
||||
cache: bool = True,
|
||||
) -> tuple[Path, Path]:
|
||||
textual_path = get_model_path(output_dir_textual)
|
||||
if not cache or not textual_path.exists():
|
||||
import torch
|
||||
from multilingual_clip.pt_multilingual_clip import MultilingualCLIP
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
torch.backends.mha.set_fastpath_enabled(False)
|
||||
|
||||
model = MultilingualCLIP.from_pretrained(model_name)
|
||||
AutoTokenizer.from_pretrained(model_name).save_pretrained(output_dir_textual)
|
||||
|
||||
model.eval()
|
||||
for param in model.parameters():
|
||||
param.requires_grad_(False)
|
||||
|
||||
_export_text_encoder(model, textual_path, opset_version)
|
||||
else:
|
||||
print(f"Model {textual_path} already exists, skipping")
|
||||
visual_path, _ = openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], opset_version, output_dir_visual, cache=cache)
|
||||
assert visual_path is not None, "Visual model export failed"
|
||||
return visual_path, textual_path
|
||||
|
||||
|
||||
def _export_text_encoder(model: Any, output_path: Path | str, opset_version: int) -> None:
|
||||
import torch
|
||||
from multilingual_clip.pt_multilingual_clip import MultilingualCLIP
|
||||
|
||||
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)
|
||||
torch.onnx.export(
|
||||
model,
|
||||
args,
|
||||
output_path.as_posix(),
|
||||
input_names=["input_ids", "attention_mask"],
|
||||
output_names=["embedding"],
|
||||
opset_version=opset_version,
|
||||
# dynamic_axes={
|
||||
# "input_ids": {0: "batch_size", 1: "sequence_length"},
|
||||
# "attention_mask": {0: "batch_size", 1: "sequence_length"},
|
||||
# },
|
||||
)
|
||||
@@ -1,151 +0,0 @@
|
||||
import warnings
|
||||
from dataclasses import dataclass
|
||||
from functools import cached_property
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from .util import get_model_path, save_config
|
||||
|
||||
|
||||
@dataclass
|
||||
class OpenCLIPModelConfig:
|
||||
name: str
|
||||
pretrained: str
|
||||
|
||||
@cached_property
|
||||
def model_config(self) -> dict[str, Any]:
|
||||
import open_clip
|
||||
|
||||
config: dict[str, Any] | None = open_clip.get_model_config(self.name)
|
||||
if config is None:
|
||||
raise ValueError(f"Unknown model {self.name}")
|
||||
return config
|
||||
|
||||
@property
|
||||
def image_size(self) -> int:
|
||||
image_size: int = self.model_config["vision_cfg"]["image_size"]
|
||||
return image_size
|
||||
|
||||
@property
|
||||
def sequence_length(self) -> int:
|
||||
context_length: int = self.model_config["text_cfg"].get("context_length", 77)
|
||||
return context_length
|
||||
|
||||
|
||||
def to_onnx(
|
||||
model_cfg: OpenCLIPModelConfig,
|
||||
opset_version: int,
|
||||
output_dir_visual: Path | str | None = None,
|
||||
output_dir_textual: Path | str | None = None,
|
||||
cache: bool = True,
|
||||
) -> tuple[Path | None, Path | None]:
|
||||
visual_path = None
|
||||
textual_path = None
|
||||
if output_dir_visual is not None:
|
||||
output_dir_visual = Path(output_dir_visual)
|
||||
visual_path = get_model_path(output_dir_visual)
|
||||
|
||||
if output_dir_textual is not None:
|
||||
output_dir_textual = Path(output_dir_textual)
|
||||
textual_path = get_model_path(output_dir_textual)
|
||||
|
||||
if cache and ((textual_path is None or textual_path.exists()) and (visual_path is None or visual_path.exists())):
|
||||
print(f"Models {textual_path} and {visual_path} already exist, skipping")
|
||||
return visual_path, textual_path
|
||||
|
||||
import open_clip
|
||||
import torch
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
torch.backends.mha.set_fastpath_enabled(False)
|
||||
|
||||
model = open_clip.create_model(
|
||||
model_cfg.name,
|
||||
pretrained=model_cfg.pretrained,
|
||||
jit=False,
|
||||
require_pretrained=True,
|
||||
)
|
||||
|
||||
text_vision_cfg = open_clip.get_model_config(model_cfg.name)
|
||||
|
||||
model.eval()
|
||||
for param in model.parameters():
|
||||
param.requires_grad_(False)
|
||||
|
||||
if visual_path is not None and output_dir_visual is not None:
|
||||
if not cache or not visual_path.exists():
|
||||
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, opset_version)
|
||||
else:
|
||||
print(f"Model {visual_path} already exists, skipping")
|
||||
|
||||
if textual_path is not None and output_dir_textual is not None:
|
||||
if not cache or not textual_path.exists():
|
||||
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, opset_version)
|
||||
else:
|
||||
print(f"Model {textual_path} already exists, skipping")
|
||||
return visual_path, textual_path
|
||||
|
||||
|
||||
def _export_image_encoder(
|
||||
model: Any, model_cfg: OpenCLIPModelConfig, output_path: Path | str, opset_version: int
|
||||
) -> None:
|
||||
import torch
|
||||
|
||||
output_path = Path(output_path)
|
||||
|
||||
def encode_image(image: torch.Tensor) -> torch.Tensor:
|
||||
output = model.encode_image(image, normalize=True)
|
||||
assert isinstance(output, torch.Tensor)
|
||||
return output
|
||||
|
||||
model.forward = encode_image
|
||||
|
||||
args = (torch.randn(1, 3, model_cfg.image_size, model_cfg.image_size),)
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore", UserWarning)
|
||||
torch.onnx.export(
|
||||
model,
|
||||
args,
|
||||
output_path.as_posix(),
|
||||
input_names=["image"],
|
||||
output_names=["embedding"],
|
||||
opset_version=opset_version,
|
||||
# dynamic_axes={"image": {0: "batch_size"}},
|
||||
)
|
||||
|
||||
|
||||
def _export_text_encoder(
|
||||
model: Any, model_cfg: OpenCLIPModelConfig, output_path: Path | str, opset_version: int
|
||||
) -> None:
|
||||
import torch
|
||||
|
||||
output_path = Path(output_path)
|
||||
|
||||
def encode_text(text: torch.Tensor) -> torch.Tensor:
|
||||
output = model.encode_text(text, normalize=True)
|
||||
assert isinstance(output, torch.Tensor)
|
||||
return output
|
||||
|
||||
model.forward = encode_text
|
||||
|
||||
args = (torch.ones(1, model_cfg.sequence_length, dtype=torch.int32),)
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore", UserWarning)
|
||||
torch.onnx.export(
|
||||
model,
|
||||
args,
|
||||
output_path.as_posix(),
|
||||
input_names=["text"],
|
||||
output_names=["embedding"],
|
||||
opset_version=opset_version,
|
||||
# dynamic_axes={"text": {0: "batch_size"}},
|
||||
)
|
||||
@@ -1,15 +0,0 @@
|
||||
import json
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
|
||||
def get_model_path(output_dir: Path | str) -> Path:
|
||||
output_dir = Path(output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
return output_dir / "model.onnx"
|
||||
|
||||
|
||||
def save_config(config: Any, output_path: Path | str) -> None:
|
||||
output_path = Path(output_path)
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
json.dump(config, output_path.open("w"))
|
||||
@@ -1,96 +0,0 @@
|
||||
from pathlib import Path
|
||||
|
||||
from .constants import RKNN_SOCS
|
||||
|
||||
|
||||
def _export_platform(
|
||||
model_dir: Path,
|
||||
target_platform: str,
|
||||
inputs: list[str] | None = None,
|
||||
input_size_list: list[list[int]] | None = None,
|
||||
fuse_matmul_softmax_matmul_to_sdpa: bool = True,
|
||||
cache: bool = True,
|
||||
) -> None:
|
||||
from rknn.api import RKNN
|
||||
|
||||
input_path = model_dir / "model.onnx"
|
||||
output_path = model_dir / "rknpu" / target_platform / "model.rknn"
|
||||
if cache and output_path.exists():
|
||||
print(f"Model {input_path} already exists at {output_path}, skipping")
|
||||
return
|
||||
|
||||
print(f"Exporting model {input_path} to {output_path}")
|
||||
|
||||
rknn = RKNN(verbose=False)
|
||||
|
||||
rknn.config(
|
||||
target_platform=target_platform,
|
||||
disable_rules=["fuse_matmul_softmax_matmul_to_sdpa"] if not fuse_matmul_softmax_matmul_to_sdpa else [],
|
||||
enable_flash_attention=False,
|
||||
model_pruning=True,
|
||||
)
|
||||
ret = rknn.load_onnx(model=input_path.as_posix(), inputs=inputs, input_size_list=input_size_list)
|
||||
|
||||
if ret != 0:
|
||||
raise RuntimeError("Load failed!")
|
||||
|
||||
ret = rknn.build(do_quantization=False)
|
||||
|
||||
if ret != 0:
|
||||
raise RuntimeError("Build failed!")
|
||||
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
ret = rknn.export_rknn(output_path.as_posix())
|
||||
if ret != 0:
|
||||
raise RuntimeError("Export rknn model failed!")
|
||||
|
||||
|
||||
def _export_platforms(
|
||||
model_dir: Path,
|
||||
inputs: list[str] | None = None,
|
||||
input_size_list: list[list[int]] | None = None,
|
||||
cache: bool = True,
|
||||
) -> None:
|
||||
fuse_matmul_softmax_matmul_to_sdpa = True
|
||||
for soc in RKNN_SOCS:
|
||||
try:
|
||||
_export_platform(
|
||||
model_dir,
|
||||
soc,
|
||||
inputs=inputs,
|
||||
input_size_list=input_size_list,
|
||||
fuse_matmul_softmax_matmul_to_sdpa=fuse_matmul_softmax_matmul_to_sdpa,
|
||||
cache=cache,
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Failed to export model for {soc}: {e}")
|
||||
if "inputs or 'outputs' must be set" in str(e):
|
||||
print("Retrying without fuse_matmul_softmax_matmul_to_sdpa")
|
||||
fuse_matmul_softmax_matmul_to_sdpa = False
|
||||
_export_platform(
|
||||
model_dir,
|
||||
soc,
|
||||
inputs=inputs,
|
||||
input_size_list=input_size_list,
|
||||
fuse_matmul_softmax_matmul_to_sdpa=fuse_matmul_softmax_matmul_to_sdpa,
|
||||
cache=cache,
|
||||
)
|
||||
|
||||
|
||||
def export(model_dir: Path, cache: bool = True) -> None:
|
||||
textual = model_dir / "textual"
|
||||
visual = model_dir / "visual"
|
||||
detection = model_dir / "detection"
|
||||
recognition = model_dir / "recognition"
|
||||
|
||||
if textual.is_dir():
|
||||
_export_platforms(textual, cache=cache)
|
||||
|
||||
if visual.is_dir():
|
||||
_export_platforms(visual, cache=cache)
|
||||
|
||||
if detection.is_dir():
|
||||
_export_platforms(detection, inputs=["input.1"], input_size_list=[[1, 3, 640, 640]], cache=cache)
|
||||
|
||||
if recognition.is_dir():
|
||||
_export_platforms(recognition, inputs=["input.1"], input_size_list=[[1, 3, 112, 112]], cache=cache)
|
||||
@@ -1,22 +0,0 @@
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
models_dir = Path("models")
|
||||
model_to_embed_dim = {}
|
||||
for model_dir in models_dir.iterdir():
|
||||
if not model_dir.is_dir():
|
||||
continue
|
||||
|
||||
config_path = model_dir / "config.json"
|
||||
if not config_path.exists():
|
||||
print(f"Skipping {model_dir.name} as it does not have a config.json")
|
||||
continue
|
||||
with open(config_path) as f:
|
||||
config = json.load(f)
|
||||
embed_dim = config.get("embed_dim")
|
||||
if embed_dim is None:
|
||||
print(f"Skipping {model_dir.name} as it does not have an embed_dim")
|
||||
continue
|
||||
print(f"{model_dir.name}: {embed_dim}")
|
||||
model_to_embed_dim[model_dir.name] = {"dimSize": embed_dim}
|
||||
print(json.dumps(model_to_embed_dim))
|
||||
@@ -1,121 +0,0 @@
|
||||
import polars as pl
|
||||
|
||||
|
||||
def collapsed_table(language: str, df: pl.DataFrame) -> str:
|
||||
with pl.Config(
|
||||
tbl_formatting="ASCII_MARKDOWN",
|
||||
tbl_hide_column_data_types=True,
|
||||
tbl_hide_dataframe_shape=True,
|
||||
fmt_str_lengths=100,
|
||||
tbl_rows=1000,
|
||||
tbl_width_chars=1000,
|
||||
):
|
||||
return f"<details>\n<summary>{language}</summary>\n{str(df)}\n</details>"
|
||||
|
||||
|
||||
languages = {
|
||||
"en": "English",
|
||||
"ar": "Arabic",
|
||||
"bn": "Bengali",
|
||||
"zh": "Chinese (Simplified)",
|
||||
"hr": "Croatian",
|
||||
"quz": "Cusco Quechua",
|
||||
"cs": "Czech",
|
||||
"da": "Danish",
|
||||
"nl": "Dutch",
|
||||
"fil": "Filipino",
|
||||
"fi": "Finnish",
|
||||
"fr": "French",
|
||||
"de": "German",
|
||||
"el": "Greek",
|
||||
"he": "Hebrew",
|
||||
"hi": "Hindi",
|
||||
"hu": "Hungarian",
|
||||
"id": "Indonesian",
|
||||
"it": "Italian",
|
||||
"ja": "Japanese",
|
||||
"ko": "Korean",
|
||||
"mi": "Maori",
|
||||
"no": "Norwegian",
|
||||
"fa": "Persian",
|
||||
"pl": "Polish",
|
||||
"pt": "Portuguese",
|
||||
"ro": "Romanian",
|
||||
"ru": "Russian",
|
||||
"es": "Spanish",
|
||||
"sw": "Swahili",
|
||||
"sv": "Swedish",
|
||||
"te": "Telugu",
|
||||
"th": "Thai",
|
||||
"tr": "Turkish",
|
||||
"uk": "Ukrainian",
|
||||
"vi": "Vietnamese",
|
||||
}
|
||||
|
||||
profile_df = pl.scan_ndjson("profiling/*.json").select("pretrained_model", "peak_rss", "exec_time_ms")
|
||||
eval_df = pl.scan_ndjson("results/*.json").select("model", "pretrained", "language", "metrics")
|
||||
|
||||
eval_df = eval_df.with_columns(
|
||||
model=pl.col("model")
|
||||
.str.replace("xlm-roberta-base", "XLM-Roberta-Base")
|
||||
.str.replace("xlm-roberta-large", "XLM-Roberta-Large")
|
||||
)
|
||||
eval_df = eval_df.with_columns(pretrained_model=pl.concat_str(pl.col("model"), pl.col("pretrained"), separator="__"))
|
||||
eval_df = eval_df.drop("model", "pretrained")
|
||||
eval_df = eval_df.join(profile_df, on="pretrained_model")
|
||||
|
||||
eval_df = eval_df.with_columns(
|
||||
recall=(
|
||||
pl.col("metrics").struct.field("image_retrieval_recall@1")
|
||||
+ pl.col("metrics").struct.field("image_retrieval_recall@5")
|
||||
+ pl.col("metrics").struct.field("image_retrieval_recall@10")
|
||||
)
|
||||
* (100 / 3)
|
||||
)
|
||||
|
||||
pareto_front = eval_df.join_where(
|
||||
eval_df.select("language", "peak_rss", "exec_time_ms", "recall").rename(
|
||||
{
|
||||
"language": "language_other",
|
||||
"peak_rss": "peak_rss_other",
|
||||
"exec_time_ms": "exec_time_ms_other",
|
||||
"recall": "recall_other",
|
||||
}
|
||||
),
|
||||
(pl.col("language") == pl.col("language_other"))
|
||||
& (pl.col("peak_rss_other") <= pl.col("peak_rss"))
|
||||
& (pl.col("exec_time_ms_other") <= pl.col("exec_time_ms"))
|
||||
& (pl.col("recall_other") >= pl.col("recall"))
|
||||
& (
|
||||
(pl.col("peak_rss_other") < pl.col("peak_rss"))
|
||||
| (pl.col("exec_time_ms_other") < pl.col("exec_time_ms"))
|
||||
| (pl.col("recall_other") > pl.col("recall"))
|
||||
),
|
||||
)
|
||||
eval_df = eval_df.join(pareto_front, on=["pretrained_model", "language"], how="left")
|
||||
eval_df = eval_df.with_columns(is_pareto=pl.col("recall_other").is_null())
|
||||
eval_df = (
|
||||
eval_df.drop("peak_rss_other", "exec_time_ms_other", "recall_other", "language_other")
|
||||
.unique(subset=["pretrained_model", "language"])
|
||||
.collect()
|
||||
)
|
||||
eval_df.write_parquet("model_info.parquet")
|
||||
|
||||
eval_df = eval_df.drop("metrics")
|
||||
eval_df = eval_df.filter(pl.col("recall") >= 20)
|
||||
eval_df = eval_df.sort("recall", descending=True)
|
||||
eval_df = eval_df.select(
|
||||
pl.col("pretrained_model").alias("Model"),
|
||||
(pl.col("peak_rss") / 1024).round().cast(pl.UInt32).alias("Memory (MiB)"),
|
||||
pl.col("exec_time_ms").round(2).alias("Execution Time (ms)"),
|
||||
pl.col("language").alias("Language"),
|
||||
pl.col("recall").round(2).alias("Recall (%)"),
|
||||
pl.when(pl.col("is_pareto")).then(pl.lit("✅")).otherwise(pl.lit("❌")).alias("Pareto Optimal"),
|
||||
)
|
||||
|
||||
|
||||
for language in languages:
|
||||
lang_df = eval_df.filter(pl.col("Language") == language).drop("Language")
|
||||
if lang_df.shape[0] == 0:
|
||||
continue
|
||||
print(collapsed_table(languages[language], lang_df))
|
||||
@@ -1,171 +0,0 @@
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
|
||||
from exporters.constants import ModelSource
|
||||
|
||||
from immich_model_exporter import clean_name
|
||||
from immich_model_exporter.exporters.constants import SOURCE_TO_TASK
|
||||
|
||||
mclip = [
|
||||
"M-CLIP/LABSE-Vit-L-14",
|
||||
"M-CLIP/XLM-Roberta-Large-Vit-B-16Plus",
|
||||
"M-CLIP/XLM-Roberta-Large-Vit-B-32",
|
||||
"M-CLIP/XLM-Roberta-Large-Vit-L-14",
|
||||
]
|
||||
|
||||
openclip = [
|
||||
"RN101__openai",
|
||||
"RN101__yfcc15m",
|
||||
"RN50__cc12m",
|
||||
"RN50__openai",
|
||||
"RN50__yfcc15m",
|
||||
"RN50x16__openai",
|
||||
"RN50x4__openai",
|
||||
"RN50x64__openai",
|
||||
"ViT-B-16-SigLIP-256__webli",
|
||||
"ViT-B-16-SigLIP-384__webli",
|
||||
"ViT-B-16-SigLIP-512__webli",
|
||||
"ViT-B-16-SigLIP-i18n-256__webli",
|
||||
"ViT-B-16-SigLIP2__webli",
|
||||
"ViT-B-16-SigLIP__webli",
|
||||
"ViT-B-16-plus-240__laion400m_e31",
|
||||
"ViT-B-16-plus-240__laion400m_e32",
|
||||
"ViT-B-16__laion400m_e31",
|
||||
"ViT-B-16__laion400m_e32",
|
||||
"ViT-B-16__openai",
|
||||
"ViT-B-32-SigLIP2-256__webli",
|
||||
"ViT-B-32__laion2b-s34b-b79k",
|
||||
"ViT-B-32__laion2b_e16",
|
||||
"ViT-B-32__laion400m_e31",
|
||||
"ViT-B-32__laion400m_e32",
|
||||
"ViT-B-32__openai",
|
||||
"ViT-H-14-378-quickgelu__dfn5b",
|
||||
"ViT-H-14-quickgelu__dfn5b",
|
||||
"ViT-H-14__laion2b-s32b-b79k",
|
||||
"ViT-L-14-336__openai",
|
||||
"ViT-L-14-quickgelu__dfn2b",
|
||||
"ViT-L-14__laion2b-s32b-b82k",
|
||||
"ViT-L-14__laion400m_e31",
|
||||
"ViT-L-14__laion400m_e32",
|
||||
"ViT-L-14__openai",
|
||||
"ViT-L-16-SigLIP-256__webli",
|
||||
"ViT-L-16-SigLIP-384__webli",
|
||||
"ViT-L-16-SigLIP2-256__webli",
|
||||
"ViT-L-16-SigLIP2-384__webli",
|
||||
"ViT-L-16-SigLIP2-512__webli",
|
||||
"ViT-SO400M-14-SigLIP-384__webli",
|
||||
"ViT-SO400M-14-SigLIP2-378__webli",
|
||||
"ViT-SO400M-14-SigLIP2__webli",
|
||||
"ViT-SO400M-16-SigLIP2-256__webli",
|
||||
"ViT-SO400M-16-SigLIP2-384__webli",
|
||||
"ViT-SO400M-16-SigLIP2-512__webli",
|
||||
"ViT-gopt-16-SigLIP2-256__webli",
|
||||
"ViT-gopt-16-SigLIP2-384__webli",
|
||||
"nllb-clip-base-siglip__mrl",
|
||||
"nllb-clip-base-siglip__v1",
|
||||
"nllb-clip-large-siglip__mrl",
|
||||
"nllb-clip-large-siglip__v1",
|
||||
"xlm-roberta-base-ViT-B-32__laion5b_s13b_b90k",
|
||||
"xlm-roberta-large-ViT-H-14__frozen_laion5b_s13b_b90k",
|
||||
]
|
||||
|
||||
insightface = [
|
||||
"antelopev2",
|
||||
"buffalo_l",
|
||||
"buffalo_m",
|
||||
"buffalo_s",
|
||||
]
|
||||
|
||||
|
||||
def export_models(models: list[str], source: ModelSource) -> None:
|
||||
profiling_dir = Path("profiling")
|
||||
profiling_dir.mkdir(exist_ok=True)
|
||||
for model in models:
|
||||
try:
|
||||
model_dir = f"models/{clean_name(model)}"
|
||||
task = SOURCE_TO_TASK[source]
|
||||
|
||||
print(f"Processing model {model}")
|
||||
subprocess.check_call(["python", "-m", "immich_model_exporter", "export", model, source])
|
||||
subprocess.check_call(
|
||||
[
|
||||
"python",
|
||||
"-m",
|
||||
"immich_model_exporter",
|
||||
"profile",
|
||||
model_dir,
|
||||
task,
|
||||
"--output_path",
|
||||
profiling_dir / f"{model}.json",
|
||||
]
|
||||
)
|
||||
subprocess.check_call(["python", "-m", "immich_model_exporter", "upload", model_dir])
|
||||
except Exception as e:
|
||||
print(f"Failed to export model {model}: {e}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
export_models(mclip, ModelSource.MCLIP)
|
||||
export_models(openclip, ModelSource.OPENCLIP)
|
||||
export_models(insightface, ModelSource.INSIGHTFACE)
|
||||
|
||||
Path("results").mkdir(exist_ok=True)
|
||||
subprocess.check_call(
|
||||
[
|
||||
"python",
|
||||
"clip_benchmark",
|
||||
"eval",
|
||||
"--pretrained_model",
|
||||
*[name.replace("__", ",") for name in openclip],
|
||||
"--task",
|
||||
"zeroshot_retrieval",
|
||||
"--dataset",
|
||||
"crossmodal3600",
|
||||
"--batch_size",
|
||||
"64",
|
||||
"--language",
|
||||
"ar",
|
||||
"bn",
|
||||
"cs",
|
||||
"da",
|
||||
"de",
|
||||
"el",
|
||||
"en",
|
||||
"es",
|
||||
"fa",
|
||||
"fi",
|
||||
"fil",
|
||||
"fr",
|
||||
"he",
|
||||
"hi",
|
||||
"hr",
|
||||
"hu",
|
||||
"id",
|
||||
"it",
|
||||
"ja",
|
||||
"ko",
|
||||
"mi",
|
||||
"nl",
|
||||
"no",
|
||||
"pl",
|
||||
"pt",
|
||||
"quz",
|
||||
"ro",
|
||||
"ru",
|
||||
"sv",
|
||||
"sw",
|
||||
"te",
|
||||
"th",
|
||||
"tr",
|
||||
"uk",
|
||||
"vi",
|
||||
"zh",
|
||||
"--recall_k",
|
||||
"1",
|
||||
"5",
|
||||
"10",
|
||||
"--no_amp",
|
||||
"--output",
|
||||
"results/{dataset}_{language}_{model}_{pretrained}.json",
|
||||
]
|
||||
)
|
||||
@@ -1,60 +0,0 @@
|
||||
[project]
|
||||
name = "immich_model_exporter"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10, <4.0"
|
||||
dependencies = [
|
||||
"huggingface-hub>=0.29.3",
|
||||
"multilingual-clip>=1.0.10",
|
||||
"onnx>=1.14.1",
|
||||
"onnxruntime>=1.16.0",
|
||||
"open-clip-torch>=2.31.0",
|
||||
"typer>=0.15.2",
|
||||
"rknn-toolkit2>=2.3.0",
|
||||
"transformers>=4.49.0",
|
||||
"tenacity>=9.0.0",
|
||||
"clip-benchmark>=1.6.1",
|
||||
"polars>=1.25.2",
|
||||
]
|
||||
|
||||
[dependency-groups]
|
||||
dev = ["black>=23.3.0", "mypy>=1.3.0", "ruff>=0.0.272"]
|
||||
|
||||
[tool.uv]
|
||||
override-dependencies = [
|
||||
"onnx>=1.16.0,<2",
|
||||
"onnxruntime>=1.18.2,<2",
|
||||
"torch>=2.4",
|
||||
"torchvision>=0.21",
|
||||
]
|
||||
|
||||
[tool.hatch.build.targets.sdist]
|
||||
include = ["immich_model_exporter"]
|
||||
|
||||
[tool.hatch.build.targets.wheel]
|
||||
include = ["immich_model_exporter"]
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[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.ruff]
|
||||
line-length = 120
|
||||
target-version = "py312"
|
||||
|
||||
[tool.ruff.lint]
|
||||
select = ["E", "F", "I"]
|
||||
|
||||
[tool.black]
|
||||
line-length = 120
|
||||
target-version = ['py312']
|
||||
1924
machine-learning/export/uv.lock
generated
1924
machine-learning/export/uv.lock
generated
File diff suppressed because it is too large
Load Diff
43
machine-learning/immich_ml/__main__.py
Normal file
43
machine-learning/immich_ml/__main__.py
Normal file
@@ -0,0 +1,43 @@
|
||||
import os
|
||||
import signal
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
|
||||
from .config import log, non_prefixed_settings, settings
|
||||
|
||||
if source_ref := os.getenv("IMMICH_SOURCE_REF"):
|
||||
log.info(f"Initializing Immich ML [{source_ref}]")
|
||||
else:
|
||||
log.info("Initializing Immich ML")
|
||||
|
||||
module_dir = Path(__file__).parent
|
||||
|
||||
try:
|
||||
with subprocess.Popen(
|
||||
[
|
||||
"python",
|
||||
"-m",
|
||||
"gunicorn",
|
||||
"immich_ml.main:app",
|
||||
"-k",
|
||||
"immich_ml.config.CustomUvicornWorker",
|
||||
"-c",
|
||||
module_dir / "gunicorn_conf.py",
|
||||
"-b",
|
||||
f"{non_prefixed_settings.immich_host}:{non_prefixed_settings.immich_port}",
|
||||
"-w",
|
||||
str(settings.workers),
|
||||
"-t",
|
||||
str(settings.worker_timeout),
|
||||
"--log-config-json",
|
||||
module_dir / "log_conf.json",
|
||||
"--keep-alive",
|
||||
str(settings.http_keepalive_timeout_s),
|
||||
"--graceful-timeout",
|
||||
"10",
|
||||
],
|
||||
) as cmd:
|
||||
cmd.wait()
|
||||
except KeyboardInterrupt:
|
||||
cmd.send_signal(signal.SIGINT)
|
||||
exit(cmd.returncode)
|
||||
@@ -51,12 +51,12 @@ class Settings(BaseSettings):
|
||||
protected_namespaces=("settings_",),
|
||||
)
|
||||
|
||||
cache_folder: Path = Path("/cache")
|
||||
cache_folder: Path = (Path.home() / ".cache" / "immich_ml").resolve()
|
||||
model_ttl: int = 300
|
||||
model_ttl_poll_s: int = 10
|
||||
host: str = "0.0.0.0"
|
||||
port: int = 3003
|
||||
workers: int = 1
|
||||
worker_timeout: int = 300
|
||||
http_keepalive_timeout_s: int = 2
|
||||
test_full: bool = False
|
||||
request_threads: int = os.cpu_count() or 4
|
||||
model_inter_op_threads: int = 0
|
||||
@@ -74,9 +74,11 @@ class Settings(BaseSettings):
|
||||
return os.environ.get("MACHINE_LEARNING_DEVICE_ID", "0")
|
||||
|
||||
|
||||
class LogSettings(BaseSettings):
|
||||
class NonPrefixedSettings(BaseSettings):
|
||||
model_config = SettingsConfigDict(case_sensitive=False)
|
||||
|
||||
immich_host: str = "[::]"
|
||||
immich_port: int = 3003
|
||||
immich_log_level: str = "info"
|
||||
no_color: bool = False
|
||||
|
||||
@@ -100,14 +102,14 @@ LOG_LEVELS: dict[str, int] = {
|
||||
}
|
||||
|
||||
settings = Settings()
|
||||
log_settings = LogSettings()
|
||||
non_prefixed_settings = NonPrefixedSettings()
|
||||
|
||||
LOG_LEVEL = LOG_LEVELS.get(log_settings.immich_log_level.lower(), logging.INFO)
|
||||
LOG_LEVEL = LOG_LEVELS.get(non_prefixed_settings.immich_log_level.lower(), logging.INFO)
|
||||
|
||||
|
||||
class CustomRichHandler(RichHandler):
|
||||
def __init__(self) -> None:
|
||||
console = Console(color_system="standard", no_color=log_settings.no_color)
|
||||
console = Console(color_system="standard", no_color=non_prefixed_settings.no_color)
|
||||
self.excluded = ["uvicorn", "starlette", "fastapi"]
|
||||
super().__init__(
|
||||
show_path=False,
|
||||
21
machine-learning/immich_ml/log_conf.json
Normal file
21
machine-learning/immich_ml/log_conf.json
Normal file
@@ -0,0 +1,21 @@
|
||||
{
|
||||
"version": 1,
|
||||
"disable_existing_loggers": false,
|
||||
"handlers": {
|
||||
"console": {
|
||||
"class": "immich_ml.config.CustomRichHandler"
|
||||
}
|
||||
},
|
||||
"loggers": {
|
||||
"gunicorn.error": {
|
||||
"handlers": [
|
||||
"console"
|
||||
]
|
||||
}
|
||||
},
|
||||
"root": {
|
||||
"handlers": [
|
||||
"console"
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -18,9 +18,9 @@ from PIL.Image import Image
|
||||
from pydantic import ValidationError
|
||||
from starlette.formparsers import MultiPartParser
|
||||
|
||||
from app.models import get_model_deps
|
||||
from app.models.base import InferenceModel
|
||||
from app.models.transforms import decode_pil
|
||||
from immich_ml.models import get_model_deps
|
||||
from immich_ml.models.base import InferenceModel
|
||||
from immich_ml.models.transforms import decode_pil
|
||||
|
||||
from .config import PreloadModelData, log, settings
|
||||
from .models.cache import ModelCache
|
||||
@@ -1,9 +1,9 @@
|
||||
from typing import Any
|
||||
|
||||
from app.models.base import InferenceModel
|
||||
from app.models.clip.textual import MClipTextualEncoder, OpenClipTextualEncoder
|
||||
from app.models.clip.visual import OpenClipVisualEncoder
|
||||
from app.schemas import ModelSource, ModelTask, ModelType
|
||||
from immich_ml.models.base import InferenceModel
|
||||
from immich_ml.models.clip.textual import MClipTextualEncoder, OpenClipTextualEncoder
|
||||
from immich_ml.models.clip.visual import OpenClipVisualEncoder
|
||||
from immich_ml.schemas import ModelSource, ModelTask, ModelType
|
||||
|
||||
from .constants import get_model_source
|
||||
from .facial_recognition.detection import FaceDetector
|
||||
@@ -7,9 +7,9 @@ from typing import Any, ClassVar
|
||||
|
||||
from huggingface_hub import snapshot_download
|
||||
|
||||
import ann.ann
|
||||
import app.sessions.rknn as rknn
|
||||
from app.sessions.ort import OrtSession
|
||||
import immich_ml.sessions.ann.loader
|
||||
import immich_ml.sessions.rknn as rknn
|
||||
from immich_ml.sessions.ort import OrtSession
|
||||
|
||||
from ..config import clean_name, log, settings
|
||||
from ..schemas import ModelFormat, ModelIdentity, ModelSession, ModelTask, ModelType
|
||||
@@ -171,7 +171,7 @@ class InferenceModel(ABC):
|
||||
def _model_format_default(self) -> ModelFormat:
|
||||
if rknn.is_available:
|
||||
return ModelFormat.RKNN
|
||||
elif ann.ann.is_available and settings.ann:
|
||||
elif immich_ml.sessions.ann.loader.is_available and settings.ann:
|
||||
return ModelFormat.ARMNN
|
||||
else:
|
||||
return ModelFormat.ONNX
|
||||
@@ -4,8 +4,8 @@ from aiocache.backends.memory import SimpleMemoryCache
|
||||
from aiocache.lock import OptimisticLock
|
||||
from aiocache.plugins import TimingPlugin
|
||||
|
||||
from app.models import from_model_type
|
||||
from app.models.base import InferenceModel
|
||||
from immich_ml.models import from_model_type
|
||||
from immich_ml.models.base import InferenceModel
|
||||
|
||||
from ..schemas import ModelTask, ModelType, has_profiling
|
||||
|
||||
@@ -8,18 +8,20 @@ import numpy as np
|
||||
from numpy.typing import NDArray
|
||||
from tokenizers import Encoding, Tokenizer
|
||||
|
||||
from app.config import log
|
||||
from app.models.base import InferenceModel
|
||||
from app.models.transforms import clean_text, serialize_np_array
|
||||
from app.schemas import ModelSession, ModelTask, ModelType
|
||||
from immich_ml.config import log
|
||||
from immich_ml.models.base import InferenceModel
|
||||
from immich_ml.models.constants import WEBLATE_TO_FLORES200
|
||||
from immich_ml.models.transforms import clean_text, serialize_np_array
|
||||
from immich_ml.schemas import ModelSession, ModelTask, ModelType
|
||||
|
||||
|
||||
class BaseCLIPTextualEncoder(InferenceModel):
|
||||
depends = []
|
||||
identity = (ModelType.TEXTUAL, ModelTask.SEARCH)
|
||||
|
||||
def _predict(self, inputs: str, **kwargs: Any) -> str:
|
||||
res: NDArray[np.float32] = self.session.run(None, self.tokenize(inputs))[0][0]
|
||||
def _predict(self, inputs: str, language: str | None = None, **kwargs: Any) -> str:
|
||||
tokens = self.tokenize(inputs, language=language)
|
||||
res: NDArray[np.float32] = self.session.run(None, tokens)[0][0]
|
||||
return serialize_np_array(res)
|
||||
|
||||
def _load(self) -> ModelSession:
|
||||
@@ -28,6 +30,7 @@ class BaseCLIPTextualEncoder(InferenceModel):
|
||||
self.tokenizer = self._load_tokenizer()
|
||||
tokenizer_kwargs: dict[str, Any] | None = self.text_cfg.get("tokenizer_kwargs")
|
||||
self.canonicalize = tokenizer_kwargs is not None and tokenizer_kwargs.get("clean") == "canonicalize"
|
||||
self.is_nllb = self.model_name.startswith("nllb")
|
||||
log.debug(f"Loaded tokenizer for CLIP model '{self.model_name}'")
|
||||
|
||||
return session
|
||||
@@ -37,7 +40,7 @@ class BaseCLIPTextualEncoder(InferenceModel):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
|
||||
def tokenize(self, text: str, language: str | None = None) -> dict[str, NDArray[np.int32]]:
|
||||
pass
|
||||
|
||||
@property
|
||||
@@ -92,14 +95,23 @@ class OpenClipTextualEncoder(BaseCLIPTextualEncoder):
|
||||
|
||||
return tokenizer
|
||||
|
||||
def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
|
||||
def tokenize(self, text: str, language: str | None = None) -> dict[str, NDArray[np.int32]]:
|
||||
text = clean_text(text, canonicalize=self.canonicalize)
|
||||
if self.is_nllb and language is not None:
|
||||
flores_code = WEBLATE_TO_FLORES200.get(language)
|
||||
if flores_code is None:
|
||||
no_country = language.split("-")[0]
|
||||
flores_code = WEBLATE_TO_FLORES200.get(no_country)
|
||||
if flores_code is None:
|
||||
log.warning(f"Language '{language}' not found, defaulting to 'en'")
|
||||
flores_code = "eng_Latn"
|
||||
text = f"{flores_code}{text}"
|
||||
tokens: Encoding = self.tokenizer.encode(text)
|
||||
return {"text": np.array([tokens.ids], dtype=np.int32)}
|
||||
|
||||
|
||||
class MClipTextualEncoder(OpenClipTextualEncoder):
|
||||
def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
|
||||
def tokenize(self, text: str, language: str | None = None) -> dict[str, NDArray[np.int32]]:
|
||||
text = clean_text(text, canonicalize=self.canonicalize)
|
||||
tokens: Encoding = self.tokenizer.encode(text)
|
||||
return {
|
||||
@@ -8,9 +8,9 @@ import numpy as np
|
||||
from numpy.typing import NDArray
|
||||
from PIL import Image
|
||||
|
||||
from app.config import log
|
||||
from app.models.base import InferenceModel
|
||||
from app.models.transforms import (
|
||||
from immich_ml.config import log
|
||||
from immich_ml.models.base import InferenceModel
|
||||
from immich_ml.models.transforms import (
|
||||
crop_pil,
|
||||
decode_pil,
|
||||
get_pil_resampling,
|
||||
@@ -19,7 +19,7 @@ from app.models.transforms import (
|
||||
serialize_np_array,
|
||||
to_numpy,
|
||||
)
|
||||
from app.schemas import ModelSession, ModelTask, ModelType
|
||||
from immich_ml.schemas import ModelSession, ModelTask, ModelType
|
||||
|
||||
|
||||
class BaseCLIPVisualEncoder(InferenceModel):
|
||||
@@ -1,5 +1,5 @@
|
||||
from app.config import clean_name
|
||||
from app.schemas import ModelSource
|
||||
from immich_ml.config import clean_name
|
||||
from immich_ml.schemas import ModelSource
|
||||
|
||||
_OPENCLIP_MODELS = {
|
||||
"RN101__openai",
|
||||
@@ -86,6 +86,66 @@ RKNN_SUPPORTED_SOCS = ["rk3566", "rk3568", "rk3576", "rk3588"]
|
||||
RKNN_COREMASK_SUPPORTED_SOCS = ["rk3576", "rk3588"]
|
||||
|
||||
|
||||
WEBLATE_TO_FLORES200 = {
|
||||
"af": "afr_Latn",
|
||||
"ar": "arb_Arab",
|
||||
"az": "azj_Latn",
|
||||
"be": "bel_Cyrl",
|
||||
"bg": "bul_Cyrl",
|
||||
"ca": "cat_Latn",
|
||||
"cs": "ces_Latn",
|
||||
"da": "dan_Latn",
|
||||
"de": "deu_Latn",
|
||||
"el": "ell_Grek",
|
||||
"en": "eng_Latn",
|
||||
"es": "spa_Latn",
|
||||
"et": "est_Latn",
|
||||
"fa": "pes_Arab",
|
||||
"fi": "fin_Latn",
|
||||
"fr": "fra_Latn",
|
||||
"he": "heb_Hebr",
|
||||
"hi": "hin_Deva",
|
||||
"hr": "hrv_Latn",
|
||||
"hu": "hun_Latn",
|
||||
"hy": "hye_Armn",
|
||||
"id": "ind_Latn",
|
||||
"it": "ita_Latn",
|
||||
"ja": "jpn_Hira",
|
||||
"kmr": "kmr_Latn",
|
||||
"ko": "kor_Hang",
|
||||
"lb": "ltz_Latn",
|
||||
"lt": "lit_Latn",
|
||||
"lv": "lav_Latn",
|
||||
"mfa": "zsm_Latn",
|
||||
"mk": "mkd_Cyrl",
|
||||
"mn": "khk_Cyrl",
|
||||
"mr": "mar_Deva",
|
||||
"ms": "zsm_Latn",
|
||||
"nb-NO": "nob_Latn",
|
||||
"nn": "nno_Latn",
|
||||
"nl": "nld_Latn",
|
||||
"pl": "pol_Latn",
|
||||
"pt-BR": "por_Latn",
|
||||
"pt": "por_Latn",
|
||||
"ro": "ron_Latn",
|
||||
"ru": "rus_Cyrl",
|
||||
"sk": "slk_Latn",
|
||||
"sl": "slv_Latn",
|
||||
"sr-Cyrl": "srp_Cyrl",
|
||||
"sv": "swe_Latn",
|
||||
"ta": "tam_Taml",
|
||||
"te": "tel_Telu",
|
||||
"th": "tha_Thai",
|
||||
"tr": "tur_Latn",
|
||||
"uk": "ukr_Cyrl",
|
||||
"ur": "urd_Arab",
|
||||
"vi": "vie_Latn",
|
||||
"zh-CN": "zho_Hans",
|
||||
"zh-Hans": "zho_Hans",
|
||||
"zh-TW": "zho_Hant",
|
||||
}
|
||||
|
||||
|
||||
def get_model_source(model_name: str) -> ModelSource | None:
|
||||
cleaned_name = clean_name(model_name)
|
||||
|
||||
@@ -4,9 +4,9 @@ import numpy as np
|
||||
from insightface.model_zoo import RetinaFace
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from app.models.base import InferenceModel
|
||||
from app.models.transforms import decode_cv2
|
||||
from app.schemas import FaceDetectionOutput, ModelSession, ModelTask, ModelType
|
||||
from immich_ml.models.base import InferenceModel
|
||||
from immich_ml.models.transforms import decode_cv2
|
||||
from immich_ml.schemas import FaceDetectionOutput, ModelSession, ModelTask, ModelType
|
||||
|
||||
|
||||
class FaceDetector(InferenceModel):
|
||||
@@ -10,10 +10,17 @@ from numpy.typing import NDArray
|
||||
from onnx.tools.update_model_dims import update_inputs_outputs_dims
|
||||
from PIL import Image
|
||||
|
||||
from app.config import log, settings
|
||||
from app.models.base import InferenceModel
|
||||
from app.models.transforms import decode_cv2, serialize_np_array
|
||||
from app.schemas import FaceDetectionOutput, FacialRecognitionOutput, ModelFormat, ModelSession, ModelTask, ModelType
|
||||
from immich_ml.config import log, settings
|
||||
from immich_ml.models.base import InferenceModel
|
||||
from immich_ml.models.transforms import decode_cv2, serialize_np_array
|
||||
from immich_ml.schemas import (
|
||||
FaceDetectionOutput,
|
||||
FacialRecognitionOutput,
|
||||
ModelFormat,
|
||||
ModelSession,
|
||||
ModelTask,
|
||||
ModelType,
|
||||
)
|
||||
|
||||
|
||||
class FaceRecognizer(InferenceModel):
|
||||
@@ -6,10 +6,10 @@ from typing import Any, NamedTuple
|
||||
import numpy as np
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from ann.ann import Ann
|
||||
from app.schemas import SessionNode
|
||||
from immich_ml.config import log, settings
|
||||
from immich_ml.schemas import SessionNode
|
||||
|
||||
from ..config import log, settings
|
||||
from .loader import Ann
|
||||
|
||||
|
||||
class AnnSession:
|
||||
@@ -7,7 +7,7 @@ from typing import Any, Protocol, TypeVar
|
||||
import numpy as np
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from app.config import log
|
||||
from immich_ml.config import log
|
||||
|
||||
try:
|
||||
CDLL("libmali.so") # fail if libmali.so is not mounted into container
|
||||
@@ -7,8 +7,8 @@ import numpy as np
|
||||
import onnxruntime as ort
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from app.models.constants import SUPPORTED_PROVIDERS
|
||||
from app.schemas import SessionNode
|
||||
from immich_ml.models.constants import SUPPORTED_PROVIDERS
|
||||
from immich_ml.schemas import SessionNode
|
||||
|
||||
from ..config import log, settings
|
||||
|
||||
@@ -6,8 +6,8 @@ from typing import Any, NamedTuple
|
||||
import numpy as np
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from app.config import log, settings
|
||||
from app.schemas import SessionNode
|
||||
from immich_ml.config import log, settings
|
||||
from immich_ml.schemas import SessionNode
|
||||
|
||||
from .rknnpool import RknnPoolExecutor, is_available, soc_name
|
||||
|
||||
@@ -10,8 +10,8 @@ from typing import Callable
|
||||
import numpy as np
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from app.config import log
|
||||
from app.models.constants import RKNN_COREMASK_SUPPORTED_SOCS, RKNN_SUPPORTED_SOCS
|
||||
from immich_ml.config import log
|
||||
from immich_ml.models.constants import RKNN_COREMASK_SUPPORTED_SOCS, RKNN_SUPPORTED_SOCS
|
||||
|
||||
|
||||
def get_soc(device_tree_path: Path | str) -> str | None:
|
||||
@@ -1,15 +0,0 @@
|
||||
{
|
||||
"version": 1,
|
||||
"disable_existing_loggers": false,
|
||||
"handlers": {
|
||||
"console": {
|
||||
"class": "app.config.CustomRichHandler"
|
||||
}
|
||||
},
|
||||
"loggers": {
|
||||
"gunicorn.error": {
|
||||
"handlers": ["console"]
|
||||
}
|
||||
},
|
||||
"root": { "handlers": ["console"] }
|
||||
}
|
||||
@@ -1,5 +1,5 @@
|
||||
[project]
|
||||
name = "machine-learning"
|
||||
name = "immich-ml"
|
||||
version = "1.129.0"
|
||||
description = ""
|
||||
authors = [{ name = "Hau Tran", email = "alex.tran1502@gmail.com" }]
|
||||
@@ -66,10 +66,10 @@ explicit = true
|
||||
onnxruntime-gpu = { index = "cuda12" }
|
||||
|
||||
[tool.hatch.build.targets.sdist]
|
||||
include = ["app"]
|
||||
include = ["immich_ml"]
|
||||
|
||||
[tool.hatch.build.targets.wheel]
|
||||
include = ["app"]
|
||||
include = ["immich_ml"]
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
|
||||
@@ -1,31 +0,0 @@
|
||||
#!/usr/bin/env sh
|
||||
|
||||
echo "Initializing Immich ML $IMMICH_SOURCE_REF"
|
||||
|
||||
if ! [ "$DEVICE" = "openvino" ]; then
|
||||
: "${MACHINE_LEARNING_WORKER_TIMEOUT:=120}"
|
||||
else
|
||||
: "${MACHINE_LEARNING_WORKER_TIMEOUT:=300}"
|
||||
fi
|
||||
|
||||
# mimalloc seems to increase memory usage dramatically with openvino, need to investigate
|
||||
if ! [ "$DEVICE" = "openvino" ] && ! [ "$DEVICE" = "rocm" ]; then
|
||||
lib_path="/usr/lib/$(arch)-linux-gnu/libmimalloc.so.2"
|
||||
export LD_PRELOAD="$lib_path"
|
||||
export LD_BIND_NOW=1
|
||||
fi
|
||||
|
||||
: "${IMMICH_HOST:=[::]}"
|
||||
: "${IMMICH_PORT:=3003}"
|
||||
: "${MACHINE_LEARNING_WORKERS:=1}"
|
||||
: "${MACHINE_LEARNING_HTTP_KEEPALIVE_TIMEOUT_S:=2}"
|
||||
|
||||
gunicorn app.main:app \
|
||||
-k app.config.CustomUvicornWorker \
|
||||
-c gunicorn_conf.py \
|
||||
-b "$IMMICH_HOST":"$IMMICH_PORT" \
|
||||
-w "$MACHINE_LEARNING_WORKERS" \
|
||||
-t "$MACHINE_LEARNING_WORKER_TIMEOUT" \
|
||||
--log-config-json log_conf.json \
|
||||
--keep-alive "$MACHINE_LEARNING_HTTP_KEEPALIVE_TIMEOUT_S" \
|
||||
--graceful-timeout 0
|
||||
@@ -18,19 +18,18 @@ from PIL import Image
|
||||
from pytest import MonkeyPatch
|
||||
from pytest_mock import MockerFixture
|
||||
|
||||
from app.main import load, preload_models
|
||||
from app.models.clip.textual import MClipTextualEncoder, OpenClipTextualEncoder
|
||||
from app.models.clip.visual import OpenClipVisualEncoder
|
||||
from app.models.facial_recognition.detection import FaceDetector
|
||||
from app.models.facial_recognition.recognition import FaceRecognizer
|
||||
from app.sessions.ann import AnnSession
|
||||
from app.sessions.ort import OrtSession
|
||||
from app.sessions.rknn import RknnSession, run_inference
|
||||
|
||||
from .config import Settings, settings
|
||||
from .models.base import InferenceModel
|
||||
from .models.cache import ModelCache
|
||||
from .schemas import ModelFormat, ModelTask, ModelType
|
||||
from immich_ml.config import Settings, settings
|
||||
from immich_ml.main import load, preload_models
|
||||
from immich_ml.models.base import InferenceModel
|
||||
from immich_ml.models.cache import ModelCache
|
||||
from immich_ml.models.clip.textual import MClipTextualEncoder, OpenClipTextualEncoder
|
||||
from immich_ml.models.clip.visual import OpenClipVisualEncoder
|
||||
from immich_ml.models.facial_recognition.detection import FaceDetector
|
||||
from immich_ml.models.facial_recognition.recognition import FaceRecognizer
|
||||
from immich_ml.schemas import ModelFormat, ModelTask, ModelType
|
||||
from immich_ml.sessions.ann import AnnSession
|
||||
from immich_ml.sessions.ort import OrtSession
|
||||
from immich_ml.sessions.rknn import RknnSession, run_inference
|
||||
|
||||
|
||||
class TestBase:
|
||||
@@ -47,7 +46,7 @@ class TestBase:
|
||||
|
||||
def test_sets_default_model_format(self, mocker: MockerFixture) -> None:
|
||||
mocker.patch.object(settings, "ann", True)
|
||||
mocker.patch("ann.ann.is_available", False)
|
||||
mocker.patch("immich_ml.sessions.ann.loader.is_available", False)
|
||||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai")
|
||||
|
||||
@@ -55,7 +54,7 @@ class TestBase:
|
||||
|
||||
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)
|
||||
mocker.patch("immich_ml.sessions.ann.loader.is_available", True)
|
||||
path.suffix = ".armnn"
|
||||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=path)
|
||||
@@ -64,7 +63,7 @@ class TestBase:
|
||||
|
||||
def test_sets_model_format_kwarg(self, mocker: MockerFixture) -> None:
|
||||
mocker.patch.object(settings, "ann", False)
|
||||
mocker.patch("ann.ann.is_available", False)
|
||||
mocker.patch("immich_ml.sessions.ann.loader.is_available", False)
|
||||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", model_format=ModelFormat.ARMNN)
|
||||
|
||||
@@ -72,7 +71,7 @@ class TestBase:
|
||||
|
||||
def test_sets_default_model_format_to_rknn_if_available(self, mocker: MockerFixture) -> None:
|
||||
mocker.patch.object(settings, "rknn", True)
|
||||
mocker.patch("app.sessions.rknn.is_available", True)
|
||||
mocker.patch("immich_ml.sessions.rknn.is_available", True)
|
||||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai")
|
||||
|
||||
@@ -294,7 +293,7 @@ class TestOrtSession:
|
||||
assert session.sess_options.intra_op_num_threads == 0
|
||||
|
||||
def test_sets_default_sess_options_sets_threads_if_non_cpu_and_set_threads(self, mocker: MockerFixture) -> None:
|
||||
mock_settings = mocker.patch("app.sessions.ort.settings", autospec=True)
|
||||
mock_settings = mocker.patch("immich_ml.sessions.ort.settings", autospec=True)
|
||||
mock_settings.model_inter_op_threads = 2
|
||||
mock_settings.model_intra_op_threads = 4
|
||||
|
||||
@@ -373,8 +372,8 @@ class TestRknnSession:
|
||||
def test_creates_rknn_session(self, rknn_session: mock.Mock, info: mock.Mock, mocker: MockerFixture) -> None:
|
||||
model_path = mock.MagicMock(spec=Path)
|
||||
tpe = 1
|
||||
mocker.patch("app.sessions.rknn.soc_name", "rk3566")
|
||||
mocker.patch("app.sessions.rknn.is_available", True)
|
||||
mocker.patch("immich_ml.sessions.rknn.soc_name", "rk3566")
|
||||
mocker.patch("immich_ml.sessions.rknn.is_available", True)
|
||||
RknnSession(model_path)
|
||||
|
||||
rknn_session.assert_called_once_with(model_path=model_path.as_posix(), tpes=tpe, func=run_inference)
|
||||
@@ -384,7 +383,7 @@ class TestRknnSession:
|
||||
def test_run_rknn(self, rknn_session: mock.Mock, mocker: MockerFixture) -> None:
|
||||
rknn_session.return_value.load.return_value = 123
|
||||
np_spy = mocker.spy(np, "ascontiguousarray")
|
||||
mocker.patch("app.sessions.rknn.soc_name", "rk3566")
|
||||
mocker.patch("immich_ml.sessions.rknn.soc_name", "rk3566")
|
||||
session = RknnSession(Path("ViT-B-32__openai"))
|
||||
[input1, input2] = [np.random.rand(1, 3, 224, 224).astype(np.float32) for _ in range(2)]
|
||||
input_feed = {"input.1": input1, "input.2": input2}
|
||||
@@ -434,7 +433,7 @@ class TestCLIP:
|
||||
|
||||
mocked = mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
|
||||
mocked.run.return_value = [[self.embedding]]
|
||||
mocker.patch("app.models.clip.textual.Tokenizer.from_file", autospec=True)
|
||||
mocker.patch("immich_ml.models.clip.textual.Tokenizer.from_file", autospec=True)
|
||||
|
||||
clip_encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir="test_cache")
|
||||
embedding_str = clip_encoder.predict("test search query")
|
||||
@@ -454,7 +453,7 @@ class TestCLIP:
|
||||
mocker.patch.object(OpenClipTextualEncoder, "model_cfg", clip_model_cfg)
|
||||
mocker.patch.object(OpenClipTextualEncoder, "tokenizer_cfg", clip_tokenizer_cfg)
|
||||
mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
|
||||
mock_tokenizer = mocker.patch("app.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
|
||||
mock_tokenizer = mocker.patch("immich_ml.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
|
||||
mock_ids = [randint(0, 50000) for _ in range(77)]
|
||||
mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids)
|
||||
|
||||
@@ -480,7 +479,7 @@ class TestCLIP:
|
||||
mocker.patch.object(OpenClipTextualEncoder, "model_cfg", clip_model_cfg)
|
||||
mocker.patch.object(OpenClipTextualEncoder, "tokenizer_cfg", clip_tokenizer_cfg)
|
||||
mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
|
||||
mock_tokenizer = mocker.patch("app.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
|
||||
mock_tokenizer = mocker.patch("immich_ml.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
|
||||
mock_ids = [randint(0, 50000) for _ in range(77)]
|
||||
mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids)
|
||||
|
||||
@@ -495,6 +494,88 @@ class TestCLIP:
|
||||
assert np.allclose(tokens["text"], np.array([mock_ids], dtype=np.int32), atol=0)
|
||||
mock_tokenizer.encode.assert_called_once_with("test search query")
|
||||
|
||||
def test_openclip_tokenizer_adds_flores_token_for_nllb(
|
||||
self,
|
||||
mocker: MockerFixture,
|
||||
clip_model_cfg: dict[str, Any],
|
||||
clip_tokenizer_cfg: Callable[[Path], dict[str, Any]],
|
||||
) -> None:
|
||||
mocker.patch.object(OpenClipTextualEncoder, "download")
|
||||
mocker.patch.object(OpenClipTextualEncoder, "model_cfg", clip_model_cfg)
|
||||
mocker.patch.object(OpenClipTextualEncoder, "tokenizer_cfg", clip_tokenizer_cfg)
|
||||
mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
|
||||
mock_tokenizer = mocker.patch("immich_ml.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
|
||||
mock_ids = [randint(0, 50000) for _ in range(77)]
|
||||
mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids)
|
||||
|
||||
clip_encoder = OpenClipTextualEncoder("nllb-clip-base-siglip__mrl", cache_dir="test_cache")
|
||||
clip_encoder._load()
|
||||
clip_encoder.tokenize("test search query", language="de")
|
||||
|
||||
mock_tokenizer.encode.assert_called_once_with("deu_Latntest search query")
|
||||
|
||||
def test_openclip_tokenizer_removes_country_code_from_language_for_nllb_if_not_found(
|
||||
self,
|
||||
mocker: MockerFixture,
|
||||
clip_model_cfg: dict[str, Any],
|
||||
clip_tokenizer_cfg: Callable[[Path], dict[str, Any]],
|
||||
) -> None:
|
||||
mocker.patch.object(OpenClipTextualEncoder, "download")
|
||||
mocker.patch.object(OpenClipTextualEncoder, "model_cfg", clip_model_cfg)
|
||||
mocker.patch.object(OpenClipTextualEncoder, "tokenizer_cfg", clip_tokenizer_cfg)
|
||||
mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
|
||||
mock_tokenizer = mocker.patch("immich_ml.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
|
||||
mock_ids = [randint(0, 50000) for _ in range(77)]
|
||||
mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids)
|
||||
|
||||
clip_encoder = OpenClipTextualEncoder("nllb-clip-base-siglip__mrl", cache_dir="test_cache")
|
||||
clip_encoder._load()
|
||||
clip_encoder.tokenize("test search query", language="de-CH")
|
||||
|
||||
mock_tokenizer.encode.assert_called_once_with("deu_Latntest search query")
|
||||
|
||||
def test_openclip_tokenizer_falls_back_to_english_for_nllb_if_language_code_not_found(
|
||||
self,
|
||||
mocker: MockerFixture,
|
||||
clip_model_cfg: dict[str, Any],
|
||||
clip_tokenizer_cfg: Callable[[Path], dict[str, Any]],
|
||||
warning: mock.Mock,
|
||||
) -> None:
|
||||
mocker.patch.object(OpenClipTextualEncoder, "download")
|
||||
mocker.patch.object(OpenClipTextualEncoder, "model_cfg", clip_model_cfg)
|
||||
mocker.patch.object(OpenClipTextualEncoder, "tokenizer_cfg", clip_tokenizer_cfg)
|
||||
mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
|
||||
mock_tokenizer = mocker.patch("immich_ml.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
|
||||
mock_ids = [randint(0, 50000) for _ in range(77)]
|
||||
mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids)
|
||||
|
||||
clip_encoder = OpenClipTextualEncoder("nllb-clip-base-siglip__mrl", cache_dir="test_cache")
|
||||
clip_encoder._load()
|
||||
clip_encoder.tokenize("test search query", language="unknown")
|
||||
|
||||
mock_tokenizer.encode.assert_called_once_with("eng_Latntest search query")
|
||||
warning.assert_called_once_with("Language 'unknown' not found, defaulting to 'en'")
|
||||
|
||||
def test_openclip_tokenizer_does_not_add_flores_token_for_non_nllb_model(
|
||||
self,
|
||||
mocker: MockerFixture,
|
||||
clip_model_cfg: dict[str, Any],
|
||||
clip_tokenizer_cfg: Callable[[Path], dict[str, Any]],
|
||||
) -> None:
|
||||
mocker.patch.object(OpenClipTextualEncoder, "download")
|
||||
mocker.patch.object(OpenClipTextualEncoder, "model_cfg", clip_model_cfg)
|
||||
mocker.patch.object(OpenClipTextualEncoder, "tokenizer_cfg", clip_tokenizer_cfg)
|
||||
mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
|
||||
mock_tokenizer = mocker.patch("immich_ml.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
|
||||
mock_ids = [randint(0, 50000) for _ in range(77)]
|
||||
mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids)
|
||||
|
||||
clip_encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir="test_cache")
|
||||
clip_encoder._load()
|
||||
clip_encoder.tokenize("test search query", language="de")
|
||||
|
||||
mock_tokenizer.encode.assert_called_once_with("test search query")
|
||||
|
||||
def test_mclip_tokenizer(
|
||||
self,
|
||||
mocker: MockerFixture,
|
||||
@@ -505,7 +586,7 @@ class TestCLIP:
|
||||
mocker.patch.object(MClipTextualEncoder, "model_cfg", clip_model_cfg)
|
||||
mocker.patch.object(MClipTextualEncoder, "tokenizer_cfg", clip_tokenizer_cfg)
|
||||
mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
|
||||
mock_tokenizer = mocker.patch("app.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
|
||||
mock_tokenizer = mocker.patch("immich_ml.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
|
||||
mock_ids = [randint(0, 50000) for _ in range(77)]
|
||||
mock_attention_mask = [randint(0, 1) for _ in range(77)]
|
||||
mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids, attention_mask=mock_attention_mask)
|
||||
@@ -597,12 +678,12 @@ class TestFaceRecognition:
|
||||
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)
|
||||
onnx = mocker.patch("immich_ml.models.facial_recognition.recognition.onnx", autospec=True)
|
||||
update_dims = mocker.patch(
|
||||
"app.models.facial_recognition.recognition.update_inputs_outputs_dims", autospec=True
|
||||
"immich_ml.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")
|
||||
mocker.patch("immich_ml.models.base.InferenceModel.download")
|
||||
mocker.patch("immich_ml.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"
|
||||
@@ -631,12 +712,12 @@ class TestFaceRecognition:
|
||||
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)
|
||||
onnx = mocker.patch("immich_ml.models.facial_recognition.recognition.onnx", autospec=True)
|
||||
update_dims = mocker.patch(
|
||||
"app.models.facial_recognition.recognition.update_inputs_outputs_dims", autospec=True
|
||||
"immich_ml.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")
|
||||
mocker.patch("immich_ml.models.base.InferenceModel.download")
|
||||
mocker.patch("immich_ml.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))]
|
||||
@@ -655,12 +736,12 @@ class TestFaceRecognition:
|
||||
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)
|
||||
onnx = mocker.patch("immich_ml.models.facial_recognition.recognition.onnx", autospec=True)
|
||||
update_dims = mocker.patch(
|
||||
"app.models.facial_recognition.recognition.update_inputs_outputs_dims", autospec=True
|
||||
"immich_ml.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")
|
||||
mocker.patch("immich_ml.models.base.InferenceModel.download")
|
||||
mocker.patch("immich_ml.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))]
|
||||
@@ -679,12 +760,12 @@ class TestFaceRecognition:
|
||||
def test_recognition_does_not_add_batch_axis_for_openvino(
|
||||
self, ort_session: mock.Mock, path: mock.Mock, mocker: MockerFixture
|
||||
) -> None:
|
||||
onnx = mocker.patch("app.models.facial_recognition.recognition.onnx", autospec=True)
|
||||
onnx = mocker.patch("immich_ml.models.facial_recognition.recognition.onnx", autospec=True)
|
||||
update_dims = mocker.patch(
|
||||
"app.models.facial_recognition.recognition.update_inputs_outputs_dims", autospec=True
|
||||
"immich_ml.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")
|
||||
mocker.patch("immich_ml.models.base.InferenceModel.download")
|
||||
mocker.patch("immich_ml.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))]
|
||||
@@ -733,13 +814,13 @@ class TestCache:
|
||||
)
|
||||
assert len(model_cache.cache._cache) == 2
|
||||
|
||||
@mock.patch("app.models.cache.OptimisticLock", autospec=True)
|
||||
@mock.patch("immich_ml.models.cache.OptimisticLock", autospec=True)
|
||||
async def test_model_ttl(self, mock_lock_cls: mock.Mock, mock_get_model: mock.Mock) -> None:
|
||||
model_cache = ModelCache()
|
||||
await model_cache.get("test_model_name", ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION, ttl=100)
|
||||
mock_lock_cls.return_value.__aenter__.return_value.cas.assert_called_with(mock.ANY, ttl=100)
|
||||
|
||||
@mock.patch("app.models.cache.SimpleMemoryCache.expire")
|
||||
@mock.patch("immich_ml.models.cache.SimpleMemoryCache.expire")
|
||||
async def test_revalidate_get(self, mock_cache_expire: mock.Mock, mock_get_model: mock.Mock) -> None:
|
||||
model_cache = ModelCache(revalidate=True)
|
||||
await model_cache.get("test_model_name", ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION, ttl=100)
|
||||
@@ -784,7 +865,7 @@ class TestCache:
|
||||
assert settings.preload.clip.visual == "ViT-B-32__openai"
|
||||
|
||||
model_cache = ModelCache()
|
||||
monkeypatch.setattr("app.main.model_cache", model_cache)
|
||||
monkeypatch.setattr("immich_ml.main.model_cache", model_cache)
|
||||
|
||||
await preload_models(settings.preload)
|
||||
mock_get_model.assert_has_calls(
|
||||
@@ -807,7 +888,7 @@ class TestCache:
|
||||
assert settings.preload.facial_recognition.recognition == "buffalo_s"
|
||||
|
||||
model_cache = ModelCache()
|
||||
monkeypatch.setattr("app.main.model_cache", model_cache)
|
||||
monkeypatch.setattr("immich_ml.main.model_cache", model_cache)
|
||||
|
||||
await preload_models(settings.preload)
|
||||
mock_get_model.assert_has_calls(
|
||||
@@ -832,7 +913,7 @@ class TestCache:
|
||||
assert settings.preload.facial_recognition.detection == "buffalo_s"
|
||||
|
||||
model_cache = ModelCache()
|
||||
monkeypatch.setattr("app.main.model_cache", model_cache)
|
||||
monkeypatch.setattr("immich_ml.main.model_cache", model_cache)
|
||||
|
||||
await preload_models(settings.preload)
|
||||
mock_get_model.assert_has_calls(
|
||||
298
machine-learning/uv.lock
generated
298
machine-learning/uv.lock
generated
@@ -927,155 +927,7 @@ wheels = [
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "iniconfig"
|
||||
version = "2.0.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d7/4b/cbd8e699e64a6f16ca3a8220661b5f83792b3017d0f79807cb8708d33913/iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3", size = 4646 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374", size = 5892 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "insightface"
|
||||
version = "0.7.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "albumentations" },
|
||||
{ name = "cython" },
|
||||
{ name = "easydict" },
|
||||
{ name = "matplotlib" },
|
||||
{ name = "numpy" },
|
||||
{ name = "onnx" },
|
||||
{ name = "pillow" },
|
||||
{ name = "prettytable" },
|
||||
{ name = "requests" },
|
||||
{ name = "scikit-image" },
|
||||
{ name = "scikit-learn" },
|
||||
{ name = "scipy" },
|
||||
{ name = "tqdm" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/0b/8d/0f4af90999ca96cf8cb846eb5ae27c5ef5b390f9c090dd19e4fa76364c13/insightface-0.7.3.tar.gz", hash = "sha256:f191f719612ebb37018f41936814500544cd0f86e6fcd676c023f354c668ddf7", size = 439490 }
|
||||
|
||||
[[package]]
|
||||
name = "itsdangerous"
|
||||
version = "2.1.2"
|
||||
source = { registry = "https://pypi.org/simple" }
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||||
sdist = { url = "https://files.pythonhosted.org/packages/7f/a1/d3fb83e7a61fa0c0d3d08ad0a94ddbeff3731c05212617dff3a94e097f08/itsdangerous-2.1.2.tar.gz", hash = "sha256:5dbbc68b317e5e42f327f9021763545dc3fc3bfe22e6deb96aaf1fc38874156a", size = 56143 }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/68/5f/447e04e828f47465eeab35b5d408b7ebaaaee207f48b7136c5a7267a30ae/itsdangerous-2.1.2-py3-none-any.whl", hash = "sha256:2c2349112351b88699d8d4b6b075022c0808887cb7ad10069318a8b0bc88db44", size = 15749 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jinja2"
|
||||
version = "3.1.4"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "markupsafe" },
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||||
]
|
||||
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wheels = [
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||||
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|
||||
|
||||
[[package]]
|
||||
name = "joblib"
|
||||
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||||
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||||
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
source = { registry = "https://pypi.org/simple" }
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||||
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{ name = "gevent", marker = "python_full_version != '3.13.*'" },
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||||
{ name = "geventhttpclient" },
|
||||
{ name = "msgpack" },
|
||||
{ name = "psutil" },
|
||||
{ name = "pywin32", marker = "sys_platform == 'win32'" },
|
||||
{ name = "pyzmq" },
|
||||
{ name = "requests" },
|
||||
{ name = "setuptools" },
|
||||
{ name = "tomli", marker = "python_full_version < '3.11'" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.11'" },
|
||||
{ name = "werkzeug" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a2/9e/09ee87dc12b240248731080bfd460c7d384aadb3171f6d03a4e7314cd0e1/locust-2.33.2.tar.gz", hash = "sha256:e626ed0156f36cec94c3c6b030fc91046469e7e2f5c2e91a99aab0f28b84977e", size = 2237716 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/9c/c7/bb55ac53173d3e92b1b2577d0f36439500406ca5be476a27b7bc01ae8a75/locust-2.33.2-py3-none-any.whl", hash = "sha256:a2f3b53dcd5ed22cecee874cd989912749663d82ec9b030637d3e43044e5878e", size = 2254591 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "markdown-it-py"
|
||||
version = "3.0.0"
|
||||
|
||||
@@ -35,8 +35,8 @@ platform :android do
|
||||
task: 'bundle',
|
||||
build_type: 'Release',
|
||||
properties: {
|
||||
"android.injected.version.code" => 190,
|
||||
"android.injected.version.name" => "1.130.2",
|
||||
"android.injected.version.code" => 192,
|
||||
"android.injected.version.name" => "1.131.2",
|
||||
}
|
||||
)
|
||||
upload_to_play_store(skip_upload_apk: true, skip_upload_images: true, skip_upload_screenshots: true, aab: '../build/app/outputs/bundle/release/app-release.aab')
|
||||
|
||||
@@ -201,41 +201,41 @@ EXTERNAL SOURCES:
|
||||
:path: ".symlinks/plugins/wakelock_plus/ios"
|
||||
|
||||
SPEC CHECKSUMS:
|
||||
background_downloader: 3ca0e156ad83a9fc1c8300f5f7c38e94e2d0bf51
|
||||
connectivity_plus: 2a701ffec2c0ae28a48cf7540e279787e77c447d
|
||||
device_info_plus: bf2e3232933866d73fe290f2942f2156cdd10342
|
||||
background_downloader: b42a56120f5348bff70e74222f0e9e6f7f1a1537
|
||||
connectivity_plus: cb623214f4e1f6ef8fe7403d580fdad517d2f7dd
|
||||
device_info_plus: 21fcca2080fbcd348be798aa36c3e5ed849eefbe
|
||||
DKImagePickerController: 946cec48c7873164274ecc4624d19e3da4c1ef3c
|
||||
DKPhotoGallery: b3834fecb755ee09a593d7c9e389d8b5d6deed60
|
||||
file_picker: b159e0c068aef54932bb15dc9fd1571818edaf49
|
||||
file_picker: a0560bc09d61de87f12d246fc47d2119e6ef37be
|
||||
Flutter: e0871f40cf51350855a761d2e70bf5af5b9b5de7
|
||||
flutter_local_notifications: 4cde75091f6327eb8517fa068a0a5950212d2086
|
||||
flutter_native_splash: df59bb2e1421aa0282cb2e95618af4dcb0c56c29
|
||||
flutter_udid: b2417673f287ee62817a1de3d1643f47b9f508ab
|
||||
flutter_web_auth_2: 06d500582775790a0d4c323222fcb6d7990f9603
|
||||
fluttertoast: 21eecd6935e7064cc1fcb733a4c5a428f3f24f0f
|
||||
geolocator_apple: 9bcea1918ff7f0062d98345d238ae12718acfbc1
|
||||
image_picker_ios: c560581cceedb403a6ff17f2f816d7fea1421fc1
|
||||
integration_test: 252f60fa39af5e17c3aa9899d35d908a0721b573
|
||||
isar_flutter_libs: fdf730ca925d05687f36d7f1d355e482529ed097
|
||||
flutter_local_notifications: ad39620c743ea4c15127860f4b5641649a988100
|
||||
flutter_native_splash: c32d145d68aeda5502d5f543ee38c192065986cf
|
||||
flutter_udid: f7c3884e6ec2951efe4f9de082257fc77c4d15e9
|
||||
flutter_web_auth_2: 5c8d9dcd7848b5a9efb086d24e7a9adcae979c80
|
||||
fluttertoast: 2c67e14dce98bbdb200df9e1acf610d7a6264ea1
|
||||
geolocator_apple: 1560c3c875af2a412242c7a923e15d0d401966ff
|
||||
image_picker_ios: 7fe1ff8e34c1790d6fff70a32484959f563a928a
|
||||
integration_test: 4a889634ef21a45d28d50d622cf412dc6d9f586e
|
||||
isar_flutter_libs: bc909e72c3d756c2759f14c8776c13b5b0556e26
|
||||
MapLibre: 0ebfa9329d313cec8bf0a5ba5a336a1dc903785e
|
||||
maplibre_gl: be7b98f1c3ed75bf77f321eec04df359d0ff6f62
|
||||
native_video_player: d12af78a1a4a8cf09775a5177d5b392def6fd23c
|
||||
network_info_plus: 6613d9d7cdeb0e6f366ed4dbe4b3c51c52d567a9
|
||||
package_info_plus: c0502532a26c7662a62a356cebe2692ec5fe4ec4
|
||||
path_provider_foundation: 2b6b4c569c0fb62ec74538f866245ac84301af46
|
||||
permission_handler_apple: 9878588469a2b0d0fc1e048d9f43605f92e6cec2
|
||||
photo_manager: ff695c7a1dd5bc379974953a2b5c0a293f7c4c8a
|
||||
maplibre_gl: eab61cca6e1cfa9187249bacd3f08b51e8cd8ae9
|
||||
native_video_player: b65c58951ede2f93d103a25366bdebca95081265
|
||||
network_info_plus: cf61925ab5205dce05a4f0895989afdb6aade5fc
|
||||
package_info_plus: af8e2ca6888548050f16fa2f1938db7b5a5df499
|
||||
path_provider_foundation: 080d55be775b7414fd5a5ef3ac137b97b097e564
|
||||
permission_handler_apple: 4ed2196e43d0651e8ff7ca3483a069d469701f2d
|
||||
photo_manager: d2fbcc0f2d82458700ee6256a15018210a81d413
|
||||
SAMKeychain: 483e1c9f32984d50ca961e26818a534283b4cd5c
|
||||
SDWebImage: f84b0feeb08d2d11e6a9b843cb06d75ebf5b8868
|
||||
share_handler_ios: 6dd3a4ac5ca0d955274aec712ba0ecdcaf583e7c
|
||||
share_handler_ios: e2244e990f826b2c8eaa291ac3831569438ba0fb
|
||||
share_handler_ios_models: fc638c9b4330dc7f082586c92aee9dfa0b87b871
|
||||
share_plus: 8b6f8b3447e494cca5317c8c3073de39b3600d1f
|
||||
shared_preferences_foundation: fcdcbc04712aee1108ac7fda236f363274528f78
|
||||
sqflite_darwin: 5a7236e3b501866c1c9befc6771dfd73ffb8702d
|
||||
share_plus: 50da8cb520a8f0f65671c6c6a99b3617ed10a58a
|
||||
shared_preferences_foundation: 9e1978ff2562383bd5676f64ec4e9aa8fa06a6f7
|
||||
sqflite_darwin: 20b2a3a3b70e43edae938624ce550a3cbf66a3d0
|
||||
SwiftyGif: 706c60cf65fa2bc5ee0313beece843c8eb8194d4
|
||||
url_launcher_ios: 5334b05cef931de560670eeae103fd3e431ac3fe
|
||||
wakelock_plus: 373cfe59b235a6dd5837d0fb88791d2f13a90d56
|
||||
url_launcher_ios: 694010445543906933d732453a59da0a173ae33d
|
||||
wakelock_plus: 04623e3f525556020ebd4034310f20fe7fda8b49
|
||||
|
||||
PODFILE CHECKSUM: 03b7eead4ee77b9e778179eeb0f3b5513617451c
|
||||
|
||||
COCOAPODS: 1.15.2
|
||||
COCOAPODS: 1.16.2
|
||||
|
||||
@@ -541,7 +541,7 @@
|
||||
CODE_SIGN_ENTITLEMENTS = Runner/RunnerProfile.entitlements;
|
||||
CODE_SIGN_IDENTITY = "Apple Development";
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 198;
|
||||
CURRENT_PROJECT_VERSION = 200;
|
||||
CUSTOM_GROUP_ID = group.app.immich.share;
|
||||
DEVELOPMENT_TEAM = 2F67MQ8R79;
|
||||
ENABLE_BITCODE = NO;
|
||||
@@ -685,7 +685,7 @@
|
||||
CODE_SIGN_ENTITLEMENTS = Runner/Runner.entitlements;
|
||||
CODE_SIGN_IDENTITY = "Apple Development";
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 198;
|
||||
CURRENT_PROJECT_VERSION = 200;
|
||||
CUSTOM_GROUP_ID = group.app.immich.share;
|
||||
DEVELOPMENT_TEAM = 2F67MQ8R79;
|
||||
ENABLE_BITCODE = NO;
|
||||
@@ -715,7 +715,7 @@
|
||||
CODE_SIGN_ENTITLEMENTS = Runner/Runner.entitlements;
|
||||
CODE_SIGN_IDENTITY = "Apple Development";
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 198;
|
||||
CURRENT_PROJECT_VERSION = 200;
|
||||
CUSTOM_GROUP_ID = group.app.immich.share;
|
||||
DEVELOPMENT_TEAM = 2F67MQ8R79;
|
||||
ENABLE_BITCODE = NO;
|
||||
@@ -748,7 +748,7 @@
|
||||
CODE_SIGN_ENTITLEMENTS = ShareExtension/ShareExtension.entitlements;
|
||||
CODE_SIGN_IDENTITY = "Apple Development";
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 198;
|
||||
CURRENT_PROJECT_VERSION = 200;
|
||||
CUSTOM_GROUP_ID = group.app.immich.share;
|
||||
DEVELOPMENT_TEAM = 2F67MQ8R79;
|
||||
ENABLE_USER_SCRIPT_SANDBOXING = YES;
|
||||
@@ -791,7 +791,7 @@
|
||||
CODE_SIGN_ENTITLEMENTS = ShareExtension/ShareExtension.entitlements;
|
||||
CODE_SIGN_IDENTITY = "Apple Development";
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 198;
|
||||
CURRENT_PROJECT_VERSION = 200;
|
||||
CUSTOM_GROUP_ID = group.app.immich.share;
|
||||
DEVELOPMENT_TEAM = 2F67MQ8R79;
|
||||
ENABLE_USER_SCRIPT_SANDBOXING = YES;
|
||||
@@ -831,7 +831,7 @@
|
||||
CODE_SIGN_ENTITLEMENTS = ShareExtension/ShareExtension.entitlements;
|
||||
CODE_SIGN_IDENTITY = "Apple Development";
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 198;
|
||||
CURRENT_PROJECT_VERSION = 200;
|
||||
CUSTOM_GROUP_ID = group.app.immich.share;
|
||||
DEVELOPMENT_TEAM = 2F67MQ8R79;
|
||||
ENABLE_USER_SCRIPT_SANDBOXING = YES;
|
||||
|
||||
@@ -78,7 +78,7 @@
|
||||
<key>CFBundlePackageType</key>
|
||||
<string>APPL</string>
|
||||
<key>CFBundleShortVersionString</key>
|
||||
<string>1.130.0</string>
|
||||
<string>1.131.0</string>
|
||||
<key>CFBundleSignature</key>
|
||||
<string>????</string>
|
||||
<key>CFBundleURLTypes</key>
|
||||
@@ -93,7 +93,7 @@
|
||||
</dict>
|
||||
</array>
|
||||
<key>CFBundleVersion</key>
|
||||
<string>198</string>
|
||||
<string>200</string>
|
||||
<key>FLTEnableImpeller</key>
|
||||
<true/>
|
||||
<key>ITSAppUsesNonExemptEncryption</key>
|
||||
|
||||
@@ -19,7 +19,7 @@ platform :ios do
|
||||
desc "iOS Release"
|
||||
lane :release do
|
||||
increment_version_number(
|
||||
version_number: "1.130.2"
|
||||
version_number: "1.131.2"
|
||||
)
|
||||
increment_build_number(
|
||||
build_number: latest_testflight_build_number + 1,
|
||||
|
||||
@@ -236,6 +236,7 @@ class SearchFilter {
|
||||
String? context;
|
||||
String? filename;
|
||||
String? description;
|
||||
String? language;
|
||||
Set<Person> people;
|
||||
SearchLocationFilter location;
|
||||
SearchCameraFilter camera;
|
||||
@@ -249,6 +250,7 @@ class SearchFilter {
|
||||
this.context,
|
||||
this.filename,
|
||||
this.description,
|
||||
this.language,
|
||||
required this.people,
|
||||
required this.location,
|
||||
required this.camera,
|
||||
@@ -279,6 +281,7 @@ class SearchFilter {
|
||||
String? context,
|
||||
String? filename,
|
||||
String? description,
|
||||
String? language,
|
||||
Set<Person>? people,
|
||||
SearchLocationFilter? location,
|
||||
SearchCameraFilter? camera,
|
||||
@@ -290,6 +293,7 @@ class SearchFilter {
|
||||
context: context ?? this.context,
|
||||
filename: filename ?? this.filename,
|
||||
description: description ?? this.description,
|
||||
language: language ?? this.language,
|
||||
people: people ?? this.people,
|
||||
location: location ?? this.location,
|
||||
camera: camera ?? this.camera,
|
||||
@@ -301,7 +305,7 @@ class SearchFilter {
|
||||
|
||||
@override
|
||||
String toString() {
|
||||
return 'SearchFilter(context: $context, filename: $filename, description: $description, people: $people, location: $location, camera: $camera, date: $date, display: $display, mediaType: $mediaType)';
|
||||
return 'SearchFilter(context: $context, filename: $filename, description: $description, language: $language, people: $people, location: $location, camera: $camera, date: $date, display: $display, mediaType: $mediaType)';
|
||||
}
|
||||
|
||||
@override
|
||||
@@ -311,6 +315,7 @@ class SearchFilter {
|
||||
return other.context == context &&
|
||||
other.filename == filename &&
|
||||
other.description == description &&
|
||||
other.language == language &&
|
||||
other.people == people &&
|
||||
other.location == location &&
|
||||
other.camera == camera &&
|
||||
@@ -324,6 +329,7 @@ class SearchFilter {
|
||||
return context.hashCode ^
|
||||
filename.hashCode ^
|
||||
description.hashCode ^
|
||||
language.hashCode ^
|
||||
people.hashCode ^
|
||||
location.hashCode ^
|
||||
camera.hashCode ^
|
||||
|
||||
@@ -263,10 +263,6 @@ class GalleryViewerPage extends HookConsumerWidget {
|
||||
PhotoViewGalleryPageOptions buildAsset(BuildContext context, int index) {
|
||||
var newAsset = loadAsset(index);
|
||||
|
||||
WidgetsBinding.instance.addPostFrameCallback((_) {
|
||||
ref.read(currentAssetProvider.notifier).set(newAsset);
|
||||
});
|
||||
|
||||
final stackId = newAsset.stackId;
|
||||
if (stackId != null && currentIndex.value == index) {
|
||||
final stackElements =
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user