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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>
58 lines
2.8 KiB
Markdown
58 lines
2.8 KiB
Markdown
# AuroraIMG-6M API
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A small, authenticated text-to-image API for direct-img. It runs [AuroraIMG-6M](https://huggingface.co/AuroraAI-Research/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.
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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.
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## API
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```
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GET /generate?prompt=frog+wizard+casting+a+spell
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Authorization: Bearer $AURORA_API_KEY
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```
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| Param | Default | Range |
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|---|---|---|
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| `prompt` | required | 1-200 characters |
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| `size` | `512` | 64-1024 (output is square, upscaled from 64×64) |
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| `format` | `webp` | `webp`, `png`, `jpeg` |
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| `steps` | `12` | 1-50 (12 looks the same as 50, at a quarter of the time) |
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| `cfg` | `6` | 0-20 (guidance; ≤1 disables it) |
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| `seed` | CRC32 of the lowercased prompt | 0-4294967295 |
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The same prompt and params always give the same image. Responses include `X-Seed` and `X-Generation-Ms`.
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Errors are JSON `{"error": "..."}`: 400 bad params, 401 missing or wrong key, 503 when the queue is full (`Retry-After: 2`).
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`GET /health` returns `ok` without auth.
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## Config
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| Env var | Default | |
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| `AURORA_API_KEY` | none | Required. Without it `/generate` rejects every request |
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| `PORT` | `8080` | |
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| `THREADS` | `1` | onnxruntime threads per generation |
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| `CONCURRENCY` | `2` | Generations at once; more requests queue |
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| `MAX_QUEUE` | `32` | Queued requests beyond this get 503 |
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## Deploy
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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.
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Run locally:
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```sh
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pip install -r requirements.txt
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AURORA_API_KEY=dev python app.py
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curl -H "Authorization: Bearer dev" "localhost:8080/generate?prompt=orange+cat" -o cat.webp
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```
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## Model and license
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`model/` holds an ONNX conversion of [AuroraAI-Research/AuroraIMG-6M](https://huggingface.co/AuroraAI-Research/AuroraIMG-6M) at revision `3d66743`, licensed under the Apache License 2.0 (copy in [`model/LICENSE`](model/LICENSE), attribution and changes in [`model/NOTICE`](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.
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The model was trained on [CC12M](https://huggingface.co/datasets/pixparse/cc12m-wds), which is distributed as image URLs and captions; the underlying images carry their own licenses.
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`scripts/export_onnx.py` regenerates `model/` from the original repo (needs torch, onnx, onnxscript, onnxruntime, safetensors).
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