Implement BiRefNet and HAT Sharper models with background removal capabilities

- Added validation documentation for AI model tiers.
- Introduced `background.py` for background removal logic, including inference functions for alpha and foreground extraction.
- Created `onnx_birefnet.py` to handle BiRefNet model sessions and alpha inference.
- Developed tests for background models, ensuring proper dispatch and session reuse.
- Added tests for BiRefNet ONNX model to validate alpha output and error handling for invalid values.
- Implemented model export scripts for BiRefNet and HAT Sharper, ensuring proper weight compression and licensing.
- Created `deform_sampling.py` for deformable convolution operations used in BiRefNet.
- Added utility functions for compressing ONNX weights without altering graph arithmetic.
- Introduced a reference plane node for depth field adjustments in geometry processing.

tests/anyimage/server/test_background_models.py

@@ -0,0 +1,75 @@
+import hashlib
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+from PIL import Image
+
+from server import model_catalog
+from server.jobs import remove_background
+from server.model_manager import ModelManager
+from server.models import background
+from tests.anyimage.server.support import FakeJobContext
+
+
+@pytest.mark.parametrize("key", model_catalog.BACKGROUND_MODELS)
+@pytest.mark.parametrize("kind", ["foreground", "frames", "alpha"])
+def test_models_dispatch_keep_alpha_and_reuse_sessions(tmp_path, key, kind):
+    source = tmp_path / "source.png"
+    Image.new("RGBA", (7, 5), (60, 120, 180, 128)).save(source)
+    if kind == "frames":
+        source = tmp_path / "frames"
+        source.mkdir()
+        for index in (1, 2):
+            Image.new("RGBA", (7, 5), (60, 120, 180, 128)).save(source / f"{index}.png")
+    manager = ModelManager(tmp_path / "models")
+    context = FakeJobContext(tmp_path / "job", "remove-background", manager)
+    adapter = background.model_adapter(manager.directory(key))
+    parameters = {"input": str(source), "model": key, "device": "directml", "video_frames": 2}
+    if kind == "alpha":
+        parameters["output_kind"] = kind
+    with patch.object(adapter, "create_session", return_value=object()) as create, patch.object(
+        adapter, "infer_alpha", return_value=np.full((5, 7), .4, np.float32)
+    ) as infer:
+        remove_background.run(context, parameters)
+    create.assert_called_once_with(manager.directory(key), "cpu" if key == "BIREFNET_HR_MATTING" else "directml")
+    assert infer.call_count == (2 if kind == "frames" else 1)
+    if kind == "alpha":
+        np.testing.assert_allclose(np.load(context.directory / "alpha.npy"), .4)
+    else:
+        outputs = sorted(context.directory.glob("*.png"))
+        assert len(outputs) == infer.call_count
+        with Image.open(outputs[0]) as result:
+            assert result.size == (7, 5)
+            assert result.getpixel((0, 0)) == (60, 120, 180, 51)
+    manager.close()
+    assert manager.snapshot()["loaded"]["background"] == ""
+
+
+@pytest.mark.parametrize("key", ["BIREFNET_LITE", "BIREFNET_HR_MATTING", "HAT_GAN_X4_SHARPER"])
+def test_readiness_checks_model_and_license_corruption(tmp_path, key):
+    from dataclasses import replace
+
+    content = b"verified"
+    files = tuple((name, len(content), hashlib.sha256(content).hexdigest()) for name in ("model.onnx", "LICENSE.txt"))
+    spec = replace(model_catalog.get_downloadable_model(key), r2_files=files)
+    directory = tmp_path / spec.directory_name
+    directory.mkdir()
+    manager = ModelManager(tmp_path)
+    with patch.object(model_catalog, "get_downloadable_model", return_value=spec):
+        assert not manager.ready(key)
+        for name, _, _ in files:
+            (directory / name).write_bytes(content)
+        assert manager.ready(key)
+        (directory / "LICENSE.txt").write_bytes(b"damaged!")
+        assert not manager.ready(key)
+
+
+@pytest.mark.parametrize("key", ["PISA_SR", "SAFMN_REAL_X4", "MOGE2_VITL_NORMAL"])
+def test_retired_job_keys_fail_before_loading(key):
+    from server.models import upscale
+
+    with pytest.raises(ValueError, match="Unknown upscale model"):
+        upscale.infer_one(None, {"model": key}, "unused.png")
+    with pytest.raises(ValueError, match="Unknown background removal model"):
+        remove_background.run(None, {"model": key})