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_model_birefnet_onnx.py

@@ -0,0 +1,36 @@
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+from PIL import Image
+
+from server.models import onnx_birefnet
+
+
+@pytest.mark.parametrize("size", [1024, 2048])
+def test_probability_alpha_keeps_transparency_and_source_dimensions(size):
+    session = SimpleNamespace(
+        get_inputs=lambda: [SimpleNamespace(name="image", shape=[1, 3, size, size])],
+        get_outputs=lambda: [SimpleNamespace(name="alpha")],
+    )
+    mask = np.full((1, 1, size, size), 0.37, dtype=np.float32)
+    with patch.object(onnx_birefnet, "run_session", return_value=[mask]) as run:
+        result = onnx_birefnet.infer_alpha(session, Image.new("RGB", (31, 17), (64, 128, 192)), release_memory=False)
+    assert result.shape == (17, 31)
+    assert result.dtype == np.float32
+    np.testing.assert_allclose(result, 0.37, atol=1e-6)
+    assert run.call_args.kwargs["release_memory"] is False
+    feed = run.call_args.args[2]["image"]
+    np.testing.assert_allclose(feed[0, :, 0, 0], (np.array([64, 128, 192]) / 255 - [.485, .456, .406]) / [.229, .224, .225], atol=1e-6)
+
+
+@pytest.mark.parametrize("value", [np.nan, np.inf, -0.1, 1.1])
+def test_invalid_alpha_is_rejected(value):
+    session = SimpleNamespace(
+        get_inputs=lambda: [SimpleNamespace(name="image", shape=[1, 3, 1024, 1024])],
+        get_outputs=lambda: [SimpleNamespace(name="alpha")],
+    )
+    with patch.object(onnx_birefnet, "run_session", return_value=[np.full((1, 1, 1024, 1024), value)]):
+        with pytest.raises(RuntimeError, match="invalid alpha"):
+            onnx_birefnet.infer_alpha(session, Image.new("RGB", (2, 3)))