multipleof4andClaude Opus 5.5
967fedfecc
The README already credited AuroraAI-Research and described the ONNX conversion, but *.md is excluded from the Docker image, so the image shipped the license without the attribution or the statement of changes that Apache-2.0 asks for with modified files. model/NOTICE carries both and ships in the image alongside model/LICENSE. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
AuroraIMG-6M API
A small, authenticated text-to-image API for direct-img. It runs AuroraIMG-6M, a 6.8M-parameter model, on CPU with onnxruntime. An image takes about 0.4s and the container uses about 150-200 MB of RAM.
The output is 64×64 and upscaled, so images are soft and painterly: right colors, setting and mood, often not the exact object. That suits an abstract fallback when no real image exists.
API
GET /generate?prompt=frog+wizard+casting+a+spell
Authorization: Bearer $AURORA_API_KEY
| Param | Default | Range |
|---|---|---|
prompt |
required | 1-200 characters |
size |
512 |
64-1024 (output is square, upscaled from 64×64) |
format |
webp |
webp, png, jpeg |
steps |
12 |
1-50 (12 looks the same as 50, at a quarter of the time) |
cfg |
6 |
0-20 (guidance; ≤1 disables it) |
seed |
CRC32 of the lowercased prompt | 0-4294967295 |
The same prompt and params always give the same image. Responses include X-Seed and X-Generation-Ms.
Errors are JSON {"error": "..."}: 400 bad params, 401 missing or wrong key, 503 when the queue is full (Retry-After: 2).
GET /health returns ok without auth.
Config
| Env var | Default | |
|---|---|---|
AURORA_API_KEY |
none | Required. Without it /generate rejects every request |
PORT |
8080 |
|
THREADS |
1 |
onnxruntime threads per generation |
CONCURRENCY |
2 |
Generations at once; more requests queue |
MAX_QUEUE |
32 |
Queued requests beyond this get 503 |
Deploy
Pushing to main builds the image with GitHub Actions, pushes it to ghcr.io/direct-img/auroraimg-6m:main, then calls the Dokploy deploy webhook (repo secret DOKPLOY_WEBHOOK_URL). It runs as the AuroraIMG-6M app in the direct-img Dokploy project.
Run locally:
pip install -r requirements.txt
AURORA_API_KEY=dev python app.py
curl -H "Authorization: Bearer dev" "localhost:8080/generate?prompt=orange+cat" -o cat.webp
Model and license
model/ holds an ONNX conversion of AuroraAI-Research/AuroraIMG-6M at revision 3d66743, licensed under the Apache License 2.0 (copy in model/LICENSE, attribution and changes in model/NOTICE; both ship in the Docker image). The weights are unchanged; they were converted from the original safetensors to ONNX, and the latent un-normalization and output scaling were folded into decoder.onnx. Output matches the original pipeline to within 1/255 per pixel.
The model was trained on CC12M, which is distributed as image URLs and captions; the underlying images carry their own licenses.
scripts/export_onnx.py regenerates model/ from the original repo (needs torch, onnx, onnxscript, onnxruntime, safetensors).