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/support.py

@@ -57,13 +57,13 @@ def _make_fake_av(frame_count):
 
 
 class FakeModels:
-    def __init__(self, directory="model"):
+    def __init__(self, directory="BEN2-ONNX"):
         self.model_directory = Path(directory)
 
     def directory(self, _model):
         return self.model_directory
 
-    def get_ben2(self, _model_directory, _device):
+    def get_background(self, _model_directory, _device):
         from server.models.onnx_ben2 import create_session
 
         return create_session(self.model_directory, "cpu"), 0.0