commit efe9971809829dbe3d5ae6bb0db65303cf8279bb Author: multipleof4 Date: Mon Oct 12 02:33:00 2026 +0000 Initial AuroraIMG-6M API An authenticated CPU text-to-image API for direct-img, serving an ONNX conversion of AuroraAI-Research/AuroraIMG-6M (Apache-2.0). About 0.4s per image and ~150-200 MB RAM. Output matches the original pipeline to within 1/255 per pixel. Pushes to main build ghcr.io/direct-img/auroraimg-6m:main and trigger the Dokploy deploy webhook. Co-Authored-By: Claude Opus 5.5 diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..e077007 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,6 @@ +.git +.github +.hf +.venv +scripts +*.md diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml new file mode 100644 index 0000000..79f9492 --- /dev/null +++ b/.github/workflows/docker.yml @@ -0,0 +1,45 @@ +name: Build and Deploy + +on: + push: + branches: ['main'] + workflow_dispatch: + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + IMAGE: ghcr.io/direct-img/auroraimg-6m + +jobs: + build-and-deploy: + runs-on: ubuntu-latest + permissions: + contents: read + packages: write + + steps: + - uses: actions/checkout@v4 + + - uses: docker/setup-buildx-action@v3 + + - uses: docker/login-action@v3 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + + - uses: docker/build-push-action@v6 + with: + context: . + push: true + tags: | + ${{ env.IMAGE }}:main + ${{ env.IMAGE }}:sha-${{ github.sha }} + labels: org.opencontainers.image.source=${{ github.server_url }}/${{ github.repository }} + cache-from: type=gha + cache-to: type=gha,mode=max + + - name: Trigger Dokploy deployment + run: curl -fsS -X POST "${{ secrets.DOKPLOY_WEBHOOK_URL }}" diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..134cf18 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +.hf/ +.venv/ +__pycache__/ diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..1909287 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,13 @@ +FROM python:3.12-slim +COPY --from=ghcr.io/astral-sh/uv:0.9 /uv /usr/local/bin/uv +WORKDIR /app +COPY requirements.txt . +RUN uv pip install --system --no-cache -r requirements.txt +COPY model model +COPY app.py . +RUN useradd --system --uid 10001 app +USER app +ENV PORT=8080 PYTHONUNBUFFERED=1 +EXPOSE 8080 +HEALTHCHECK --interval=30s --timeout=5s --start-period=15s CMD python -c "import os, urllib.request; urllib.request.urlopen(f'http://127.0.0.1:{os.environ[\"PORT\"]}/health', timeout=4)" +CMD ["python", "app.py"] diff --git a/README.md b/README.md new file mode 100644 index 0000000..0d5bb4e --- /dev/null +++ b/README.md @@ -0,0 +1,55 @@ +# AuroraIMG-6M API + +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. + +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: + +```sh +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](https://huggingface.co/AuroraAI-Research/AuroraIMG-6M) at revision `3d66743`, licensed under the Apache License 2.0 (copy in [`model/LICENSE`](model/LICENSE)). 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. + +`scripts/export_onnx.py` regenerates `model/` from the original repo (needs torch, onnx, onnxscript, onnxruntime, safetensors). diff --git a/app.py b/app.py new file mode 100644 index 0000000..925b34a --- /dev/null +++ b/app.py @@ -0,0 +1,104 @@ +# AuroraIMG-6M image API. GET /generate?prompt=... with "Authorization: Bearer $AURORA_API_KEY" returns an image. +# Runs the ONNX export of https://huggingface.co/AuroraAI-Research/AuroraIMG-6M on CPU with onnxruntime. +import os, re, io, json, hmac, time, zlib, threading +from http.server import ThreadingHTTPServer, BaseHTTPRequestHandler +from urllib.parse import urlparse, parse_qs +from pathlib import Path +import numpy as np, onnxruntime as ort +from PIL import Image +from tokenizers import Tokenizer + +KEY = os.environ.get("AURORA_API_KEY", "").encode() +PORT = int(os.environ.get("PORT", 8080)) +THREADS = int(os.environ.get("THREADS", 1)) # onnxruntime threads per generation +CONCURRENCY = int(os.environ.get("CONCURRENCY", 2)) # generations at once; the rest queue +MAX_QUEUE = int(os.environ.get("MAX_QUEUE", 32)) # queued requests beyond this get 503 +FORMATS = {"webp": ("WEBP", "image/webp", {"quality": 90}), "png": ("PNG", "image/png", {}), "jpeg": ("JPEG", "image/jpeg", {"quality": 90})} + +M = Path(__file__).parent / "model" +opts = ort.SessionOptions() +opts.intra_op_num_threads, opts.inter_op_num_threads = THREADS, 1 +S = {n: ort.InferenceSession(M / f"{n}.onnx", opts, providers=["CPUExecutionProvider"]) for n in ("text_encoder", "denoiser", "decoder")} +run = lambda n, **x: S[n].run(None, x)[0] +tok = Tokenizer.from_file(str(M / "tokenizer.json")) + +# Same cleanup and layout as the reference pipeline: =1, up to 62 tokens, =2, padded with 0 to 64 +def encode(p): + p = re.sub(r"https?://\S+|www\.\S+", " ", re.sub(r"<[^>]+>", " ", p)) + seq = [1] + tok.encode(re.sub(r"\s+", " ", p.lower()).strip(), add_special_tokens=False).ids[:62] + [2] + t = np.array([seq + [0] * (64 - len(seq))], np.int64) + return t, t != 0 + +NULL_TOK, NULL_MASK = encode("") +NULL_CTX = run("text_encoder", tok=NULL_TOK, mask=NULL_MASK) + +def generate(prompt, steps, cfg, seed): + t, m = encode(prompt) + ctx = run("text_encoder", tok=t, mask=m) + z = np.random.default_rng(seed).standard_normal((1, 8, 16, 16), dtype=np.float32) + ts = np.linspace(1, 0, steps + 1, dtype=np.float32) + for a, b in zip(ts[:-1], ts[1:]): + if cfg > 1: + v_c, v_u = run("denoiser", x=np.concatenate([z, z]), t=np.full(2, a * 1000, np.float32), + ctx=np.concatenate([ctx, NULL_CTX]), mask=np.concatenate([m, NULL_MASK])) + v = v_u + cfg * (v_c - v_u) + v *= np.linalg.norm(v_c) / max(np.linalg.norm(v), 1e-8) # the pipeline's default cfg_rescale=1.0 + else: + v = run("denoiser", x=z, t=np.full(1, a * 1000, np.float32), ctx=ctx, mask=m) + z = z + (b - a) * v + img = run("decoder", z=z)[0].transpose(1, 2, 0) + return Image.fromarray((img * 255).round().astype(np.uint8)) + +def params(q): + def num(k, cast, lo, hi, default): + try: v = cast(q[k]) if k in q else default + except ValueError: raise ValueError(f"{k} must be a number") + if not lo <= v <= hi: raise ValueError(f"{k} must be {lo}-{hi}") + return v + prompt = q.get("prompt", "").strip() + if not 1 <= len(prompt) <= 200: raise ValueError("prompt must be 1-200 characters") + fmt = q.get("format", "webp").lower() + if fmt not in FORMATS: raise ValueError(f"format must be one of {', '.join(FORMATS)}") + return dict(prompt=prompt, steps=num("steps", int, 1, 50, 12), cfg=num("cfg", float, 0, 20, 6.0), + seed=num("seed", int, 0, 2**32 - 1, zlib.crc32(prompt.lower().encode())), size=num("size", int, 64, 1024, 512), fmt=fmt) + +gen_slots, queue_lock, queued = threading.BoundedSemaphore(CONCURRENCY), threading.Lock(), 0 + +class Handler(BaseHTTPRequestHandler): + protocol_version = "HTTP/1.1" + + def reply(s, code, body, ctype="application/json", **headers): + s.send_response(code) + for k, v in {"Content-Type": ctype, "Content-Length": len(body), "Cache-Control": "no-store", **headers}.items(): s.send_header(k, str(v)) + s.end_headers(); s.wfile.write(body) + + err = lambda s, code, msg, **h: s.reply(code, json.dumps({"error": msg}).encode(), **h) + + def do_GET(s): + global queued + u = urlparse(s.path) + if u.path == "/health": return s.reply(200, b"ok", "text/plain") + if u.path != "/generate": return s.err(404, "not found") + if not KEY or not hmac.compare_digest(s.headers.get("Authorization", "").encode(), b"Bearer " + KEY): return s.err(401, "unauthorized") + try: p = params({k: v[-1] for k, v in parse_qs(u.query).items()}) + except ValueError as e: return s.err(400, str(e)) + with queue_lock: + if queued >= CONCURRENCY + MAX_QUEUE: return s.err(503, "busy, retry shortly", **{"Retry-After": 2}) + queued += 1 + try: + t0 = time.perf_counter() + with gen_slots: img = generate(p["prompt"], p["steps"], p["cfg"], p["seed"]) + if p["size"] != 64: img = img.resize((p["size"],) * 2, Image.LANCZOS) + fmt, ctype, save = FORMATS[p["fmt"]] + buf = io.BytesIO(); img.save(buf, fmt, **save) + s.reply(200, buf.getvalue(), ctype, **{"X-Seed": p["seed"], "X-Generation-Ms": round((time.perf_counter() - t0) * 1000)}) + finally: + with queue_lock: queued -= 1 + +if __name__ == "__main__": + if not KEY: print("warning: AURORA_API_KEY is not set, so /generate rejects every request", flush=True) + generate("warmup", 2, 6.0, 0) + server = ThreadingHTTPServer(("0.0.0.0", PORT), Handler) + server.daemon_threads = True + print(f"listening on :{PORT} (threads={THREADS}, concurrency={CONCURRENCY})", flush=True) + server.serve_forever() diff --git a/model/LICENSE b/model/LICENSE new file mode 100644 index 0000000..d645695 --- /dev/null +++ b/model/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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+onnxruntime==1.31.0 +numpy==2.5.4 +pillow==12.3.0 +tokenizers==0.23.3 diff --git a/scripts/export_onnx.py b/scripts/export_onnx.py new file mode 100644 index 0000000..d8ff8b9 --- /dev/null +++ b/scripts/export_onnx.py @@ -0,0 +1,63 @@ +# Converts AuroraIMG-6M (https://huggingface.co/AuroraAI-Research/AuroraIMG-6M, Apache-2.0) to the ONNX files in model/. +# Only needed to regenerate them. Needs: torch onnx onnxscript onnxruntime safetensors tokenizers numpy +# python scripts/export_onnx.py +# Weights are read from the .safetensors files only; the repo's pickle .pt files are never downloaded. +import sys, types, json, shutil, urllib.request, importlib.util +from pathlib import Path +import numpy as np, torch, onnxruntime as ort +from safetensors.torch import load_file +torch.set_grad_enabled(False) + +REPO, REV = "AuroraAI-Research/AuroraIMG-6M", "3d66743c3139d034b832a670d067221f5d544bbb" +FILES = ["pipeline.py", "tokenizer/tokenizer.json", "vae/config.json"] + [f"{m}/diffusion_pytorch_model.safetensors" for m in ("text_encoder", "unet", "vae")] +ROOT = Path(__file__).resolve().parent.parent +HF, OUT = ROOT / ".hf", ROOT / "model" + +for f in FILES: + p = HF / f + if not p.exists(): + p.parent.mkdir(parents=True, exist_ok=True) + urllib.request.urlretrieve(f"https://huggingface.co/{REPO}/resolve/{REV}/{f}", p) + +# pipeline.py imports diffusers/transformers only for base classes; these stubs let it load with plain torch +class ConfigMixin: + def register_to_config(self, **kw): self.__dict__["config"] = types.SimpleNamespace(**kw) +stub = lambda name, **attrs: types.SimpleNamespace(__name__=name, **attrs) +sys.modules |= {"diffusers": stub("diffusers", DiffusionPipeline=object, ModelMixin=type("ModelMixin", (), {}), ConfigMixin=ConfigMixin), + "diffusers.utils": stub("diffusers.utils", BaseOutput=object), "transformers": stub("transformers", PreTrainedTokenizerFast=object)} +spec = importlib.util.spec_from_file_location("aurora_pipeline", HF / "pipeline.py") +P = importlib.util.module_from_spec(spec); spec.loader.exec_module(P) + +def load(m, sub): + m.load_state_dict(load_file(HF / sub / "diffusion_pytorch_model.safetensors"), strict=True); return m.eval() +vae_cfg = json.loads((HF / "vae/config.json").read_text()) +te, den = load(P.TextEncoder(vocab=4096, d=128, layers=4), "text_encoder"), load(P.Denoiser(), "unet") +vae = load(P.VAE(lat_mu=vae_cfg["lat_mu"], lat_sd=vae_cfg["lat_sd"]), "vae") + +class Decode(torch.nn.Module): + """Un-normalizes latents and maps the VAE output to 0..1 RGB, as the original pipeline does after sampling.""" + def __init__(s): + super().__init__(); s.vae = vae + s.register_buffer("sd", torch.tensor(vae_cfg["lat_sd"]).view(1, 8, 1, 1)); s.register_buffer("mu", torch.tensor(vae_cfg["lat_mu"]).view(1, 8, 1, 1)) + def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) +dec = Decode().eval() + +B = torch.export.Dim("batch", min=1, max=64) +tok = torch.randint(3, 4096, (2, 64)); tok[:, 10:] = 0; tok[:, 0] = 1; mask = tok != 0 +ctx, x, t = te(tok, mask), torch.randn(2, 8, 16, 16), torch.tensor([500.0, 500.0]) +OUT.mkdir(exist_ok=True) +graphs = { + "text_encoder": (te, (tok, mask), ["tok", "mask"], ["ctx"]), + "denoiser": (den, (x, t, ctx, mask), ["x", "t", "ctx", "mask"], ["v"]), + "decoder": (dec, (x,), ["z"], ["image"]), +} +for name, (m, args, ins, outs) in graphs.items(): + shapes = tuple({0: B} for _ in args) + torch.onnx.export(m, args, OUT / f"{name}.onnx", input_names=ins, output_names=outs, dynamic_shapes=shapes, dynamo=True, external_data=False) + sess = ort.InferenceSession(OUT / f"{name}.onnx", providers=["CPUExecutionProvider"]) + want = m(*args).numpy() + got = sess.run(None, {k: a.numpy() for k, a in zip(ins, args)})[0] + err = float(np.abs(got - want).max()) + print(f"{name}.onnx: {(OUT / f'{name}.onnx').stat().st_size / 1e6:.1f} MB, max abs diff vs torch {err:.2e}") + assert err < 1e-3, name +shutil.copyfile(HF / "tokenizer/tokenizer.json", OUT / "tokenizer.json")