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Shared Claude Code translation pipeline (scripts/i18n, no API key) plus Astro/VitePress/Scalar i18n wiring. Landing and API reference translated into all 20 languages; docs i18n wiring + English source anchors. The translated docs markdown (apps/docs/<locale>/**, 3,620 files) follows in a companion PR because it exceeds GitHub's per-PR CI file limit.
83 lines
2.8 KiB
Markdown
83 lines
2.8 KiB
Markdown
---
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description: Restore and sharpen blurry or low-quality faces in images with GFPGAN and CodeFormer AI models.
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---
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# Face Enhancement {#face-enhancement}
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Restore and enhance faces in images using AI models (GFPGAN/CodeFormer).
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## API Endpoint {#api-endpoint}
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`POST /api/v1/tools/image/enhance-faces`
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**Processing:** Asynchronous (returns 202, poll `/api/v1/jobs/{jobId}/progress` for status via SSE)
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**Model bundles:** `upscale-enhance` (5-6 GB) and `face-detection` (200-300 MB)
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## Parameters {#parameters}
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| Parameter | Type | Required | Default | Description |
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|-----------|------|----------|---------|-------------|
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| file | file | Yes | - | Image file (multipart) |
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| model | string | No | `"auto"` | Model to use: `auto`, `gfpgan`, `codeformer` |
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| strength | number | No | `0.8` | Enhancement strength (0-1). Higher values produce stronger enhancement |
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| onlyCenterFace | boolean | No | `false` | Only enhance the most central/prominent face |
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| sensitivity | number | No | `0.5` | Face detection sensitivity (0-1) |
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## Example Request {#example-request}
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```bash
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curl -X POST http://localhost:1349/api/v1/tools/image/enhance-faces \
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-F "file=@portrait.jpg" \
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-F 'settings={"model":"codeformer","strength":0.7,"onlyCenterFace":false}'
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```
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## Response {#response}
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### Initial Response (202 Accepted) {#initial-response-202-accepted}
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```json
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{
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"jobId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
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"async": true
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}
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```
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### Progress (SSE at `/api/v1/jobs/{jobId}/progress`) {#progress-sse-at-api-v1-jobs-jobid-progress}
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```
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event: progress
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data: {"phase":"processing","stage":"Enhancing faces...","percent":60}
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```
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### Final Result (via SSE) {#final-result-via-sse}
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```json
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{
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"phase": "complete",
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"percent": 100,
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"result": {
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"jobId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
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"downloadUrl": "/api/v1/download/{jobId}/portrait_enhanced.png",
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"previewUrl": "/api/v1/download/{jobId}/preview.webp",
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"originalSize": 350000,
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"processedSize": 600000,
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"facesDetected": 2,
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"faces": [
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{"x": 120, "y": 80, "w": 100, "h": 100},
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{"x": 350, "y": 90, "w": 95, "h": 95}
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],
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"model": "codeformer"
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}
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}
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```
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## Notes {#notes}
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- Requires both the `upscale-enhance` model bundle (5-6 GB) and the `face-detection` model bundle (200-300 MB).
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- GFPGAN produces more aggressive enhancement; CodeFormer better preserves identity. `auto` selects the best model for the input.
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- Output is always PNG format for maximum quality.
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- A WebP preview is generated alongside the full-resolution output for faster frontend display.
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- The `strength` parameter blends the enhanced face with the original. Use lower values (0.3-0.5) for subtle improvements, higher values (0.7-1.0) for stronger restoration.
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- Supports HEIC/HEIF, RAW, TGA, PSD, EXR, and HDR input formats via automatic decoding.
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