Docker Image Tags

Stirling Image ships three Docker image variants to fit different use cases.

Full (default)

docker pull stirlingimage/stirling-image:latest

Includes all tools: image processing, AI-powered background removal, upscaling, face blurring, object erasing, and OCR. Size is ~11 GB due to bundled ML models.

Lite

docker pull stirlingimage/stirling-image:lite

Includes all image processing tools (resize, crop, rotate, convert, compress, watermark, collage, and 20+ more) but excludes AI/ML tools. Size is ~1-2 GB.

Use this if you:

• Only need standard image processing (no AI features)
• Are running on constrained hardware (Raspberry Pi, small VPS)
• Want faster pulls and smaller disk footprint

Tools excluded from lite

Tool	What it does
Remove Background	AI-powered background removal
Upscale	AI super-resolution upscaling
Blur Faces	AI face detection and blurring
Erase Object	AI inpainting to remove objects
OCR	Optical character recognition

All other tools (27+) work identically in both variants.

CUDA (GPU acceleration)

docker pull stirlingimage/stirling-image:cuda

Same tools as the full image, but built with GPU-accelerated Python packages (onnxruntime-gpu, PyTorch CUDA, PaddlePaddle GPU). The image auto-detects your NVIDIA GPU at runtime and falls back to CPU if none is found.

Requires NVIDIA Container Toolkit on the host. Linux amd64 only.

Benchmarks

Tested on an NVIDIA RTX 4070 (12 GB VRAM) with a 572x1024 JPEG portrait. Both images ran on the same machine. "Warm" means the model is already loaded in memory (second request onward).

Warm performance

Tool	CPU	GPU	Speedup
Background removal (u2net)	2,415ms	879ms	2.7x
Background removal (isnet)	2,457ms	1,137ms	2.2x
Upscale 2x	350ms	309ms	1.1x
Upscale 4x	910ms	310ms	2.9x
OCR (PaddleOCR)	137ms	94ms	1.5x
Face blur	139ms	122ms	1.1x

Cold start (first request after container start)

Tool	CPU	GPU	Speedup
Background removal	22,286ms	4,792ms	4.7x
Upscale 2x	3,957ms	2,318ms	1.7x
OCR (PaddleOCR)	1,469ms	1,090ms	1.3x

Cold start includes loading the model into memory. GPU cold starts are faster because CUDA parallelizes the model loading.

Larger images show bigger speedups, especially for upscaling. Non-AI tools (resize, crop, convert, etc.) are unaffected since they use Sharp (CPU-based).

GPU health check

After the first AI request, the admin health endpoint reports GPU status:

GET /api/v1/admin/health
{"ai": {"gpu": true}}

Docker Compose

Full

services:
  stirling-image:
    image: stirlingimage/stirling-image:latest
    ports:
      - "1349:1349"
    volumes:
      - stirling-data:/data
      - stirling-workspace:/tmp/workspace

volumes:
  stirling-data:
  stirling-workspace:

Lite

services:
  stirling-image:
    image: stirlingimage/stirling-image:lite
    ports:
      - "1349:1349"
    volumes:
      - stirling-data:/data
      - stirling-workspace:/tmp/workspace

volumes:
  stirling-data:
  stirling-workspace:

CUDA

services:
  stirling-image:
    image: stirlingimage/stirling-image:cuda
    ports:
      - "1349:1349"
    volumes:
      - stirling-data:/data
      - stirling-workspace:/tmp/workspace
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

volumes:
  stirling-data:
  stirling-workspace:

Switching from lite to full

To upgrade from lite to full and unlock AI tools:

1. Stop your container
2. Pull the full image: docker pull stirlingimage/stirling-image:latest
3. Update your compose file or run command to use :latest instead of :lite
4. Start the container

Your data and settings are preserved in the volumes.

Version pinning

Both variants support semver tags for pinning:

Tag	Description
latest	Latest full release
lite	Latest lite release
cuda	Latest full release with GPU support
1.6.0	Exact full version
1.6.0-lite	Exact lite version
1.6.0-cuda	Exact CUDA version
1.6	Latest patch in 1.6.x (full)
1.6-lite	Latest patch in 1.6.x (lite)
1.6-cuda	Latest patch in 1.6.x (CUDA)