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feat(i18n): 21-language pipeline, landing/docs/API wiring, landing+API translations
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.
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description: Fix fake transparent PNGs with AI matting (BiRefNet) to produce true alpha, plus defringe edge cleanup.
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---
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# PNG Transparency Fixer
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# PNG Transparency Fixer {#png-transparency-fixer}
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Fix fake transparent PNGs in one click. Uses AI matting (BiRefNet HR Matting model) to produce true alpha transparency, with defringe post-processing to clean up edges.
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## API Endpoint
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## API Endpoint {#api-endpoint}
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`POST /api/v1/tools/image/transparency-fixer`
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**Model bundle:** `background-removal` (4-5 GB)
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## Parameters
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## Parameters {#parameters}
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| Parameter | Type | Required | Default | Description |
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|-----------|------|----------|---------|-------------|
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| outputFormat | string | No | `"png"` | Output format: `png` or `webp` |
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| removeWatermark | boolean | No | `false` | Apply watermark removal pre-processing (median filter) |
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## Example Request
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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/transparency-fixer \
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-F 'settings={"defringe":40,"outputFormat":"png"}'
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```
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## Response
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## Response {#response}
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### Initial Response (202 Accepted)
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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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}
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```
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### Progress (SSE at `/api/v1/jobs/{jobId}/progress`)
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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":"Processing transparency...","percent":50}
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```
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### Final Result (via SSE)
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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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}
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```
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## Notes
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## Notes {#notes}
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- Requires the `background-removal` model bundle to be installed (4-5 GB).
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- Uses `birefnet-hr-matting` as the primary model for high-quality alpha matting. Falls back to `birefnet-general` if the HR model runs out of memory.
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