Add support for S3-backed image storage

tests/fixtures/README.md

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+The two tiny ONNX models are deterministic classifiers used by the image upload API tests.
+They accept the same `[1, 224, 224, 3]` float input as the production nsfw model and return
+`[Drawings, Hentai, Neutral, Porn, Sexy]` scores. One always returns Neutral = 1 and the
+other always returns Porn = 1. They exercise the real nsfw inference path without requiring
+image fixtures.
+
+## Image fixtures for the bundled model
+
+Add image pairs to these project directories:
+
+* `tests/fixtures/nsfw/` — images expected to be rejected by moderation.
+* `tests/fixtures/non_nsfw/` — images expected to be accepted by moderation.
+
+Install the splitter's dependency with `python3 -m pip install Pillow`, then run from the
+project root:
+
+```sh
+python3 scripts/split_nsfw_test_image.py /path/to/input.jpg --output-dir tests/fixtures/nsfw
+python3 scripts/split_nsfw_test_image.py /path/to/safe.jpg --output-dir tests/fixtures/non_nsfw
+```
+
+Each command automatically selects the first unused positive index, starting with
+`image_1a.png` and `image_1b.png`. Indices with either part already present are skipped.
+You can override the selection with `--index 2`; existing parts are never overwritten.
+Each part contains random-looking
+RGBA pixels. The original decoded pixels, including alpha, are recovered by XORing the two
+parts. Only the first frame of animated input is used. Keep both files unchanged: resizing,
+re-encoding with loss or flattening their alpha channels prevents correct reconstruction.
+Both parts together reconstruct the original image, so splitting is not access control.
+
+Run the splitter's round-trip and overwrite checks with
+`python3 -m unittest discover -s scripts -p 'test_*.py'`.
+
+The directories initially contain no images. The two dataset tests are explicitly ignored
+until you provide fixtures. To run them after adding pairs to both directories:
+
+```sh
+SQLX_OFFLINE=true cargo test --locked directory_images -- --ignored
+```
+
+The tests load all numbered pairs, recombine them in memory and pass the reconstructed PNG
+through the production upload moderation function using the bundled model and its current
+threshold. They also check that disabling moderation accepts the reconstructed images.
+Empty directories, missing partners, mismatched dimensions, unexpected names and unexpected
+moderation results fail the tests. Recombined images are never written to disk.
+
+## Deterministic model fixtures
+
+To regenerate them with Python's `onnx` package:
+
+```python
+from pathlib import Path
+from onnx import TensorProto, checker, helper
+
+for name, scores in [("safe", [0., 0., 1., 0., 0.]), ("unsafe", [0., 0., 0., 1., 0.])]:
+    tensor = helper.make_tensor("probabilities", TensorProto.FLOAT, [1, 5], scores)
+    graph = helper.make_graph(
+        [helper.make_node("Identity", ["probabilities"], ["output"])],
+        "constant classifier for upload endpoint tests",
+        [helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 224, 224, 3])],
+        [helper.make_tensor_value_info("output", TensorProto.FLOAT, [1, 5])],
+        [tensor],
+    )
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)], ir_version=8)
+    checker.check_model(model)
+    Path(f"tests/fixtures/image-upload-{name}.onnx").write_bytes(model.SerializeToString())
+```