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feat: unified Docker image with GPU auto-detection (#37)

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Merge CPU, CUDA, and lite Docker images into a single unified image.
One tag (latest) works on all platforms: amd64 (NVIDIA CUDA) and arm64 (CPU).
GPU auto-detected at runtime. All ML models and packages baked in.

Key changes:
- Platform-conditional Dockerfile (nvidia/cuda on amd64, node on arm64)
- tini as PID 1 for proper signal handling
- Fix FILES_STORAGE_PATH data loss bug
- Fix RealESRGAN upscaler (was broken, always fell back to Lanczos)
- Fix PaddleOCR language codes and stdout corruption
- Simplified CI/CD (single build, single tag)
- Expanded model pre-download with verification
- Shutdown timeout, improved health endpoint
- Remove unused lama-cleaner
This commit is contained in:
stirling-image
2026-04-10 13:21:06 +08:00
committed by GitHub
parent 7bc979f677
commit b0083e2b08
15 changed files with 374 additions and 310 deletions
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apps/docs/api/ai.md
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@@ -4,12 +4,8 @@ The `@stirling-image/ai` package wraps Python ML models in TypeScript functions.
All model weights are bundled in the Docker image during the build. No downloads happen at runtime.
::: warning Lite image
AI tools are not available in the `:lite` Docker image. The API returns `501 Not Available` for these endpoints when running the lite variant. Use `:latest` for AI features. See [Docker Tags](/guide/docker-tags) for details.
:::
::: tip GPU acceleration
The `:cuda` Docker image includes GPU-accelerated versions of the ML libraries. Background removal, upscaling, and OCR all benefit from NVIDIA GPU acceleration. The image auto-detects your GPU and falls back to CPU if none is available. See [Docker Tags](/guide/docker-tags) for setup.
The Docker image includes CUDA-accelerated ML libraries on amd64. Add `--gpus all` to your Docker run command to enable GPU acceleration. The image auto-detects your GPU and falls back to CPU if none is available.
:::
## Background removal
@@ -73,7 +69,7 @@ Returns the blurred image along with metadata about each detected face region (b
Removes objects from images by filling in the area with generated content that matches the surroundings.
**Model:** [LaMa](https://github.com/advimman/lama) (Large Mask Inpainting)
**Model:** OpenCV TELEA algorithm
Takes an image and a mask (white = area to erase, black = keep). Returns the inpainted image.