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/operators/test_upscale_undo.py

@@ -23,7 +23,7 @@ def test_upscale_result_replacement_undo_restores_source(tmp_path):
 
     def execute(operator, context):
         operator._image_target = ImageEditTarget.capture(SimpleNamespace(object=owner))
-        operator.model = "PISA_SR"
+        operator.model = "HAT_GAN_X4_SHARPER"
         operator.response(context, SimpleNamespace(directory=tmp_path))
         return {"FINISHED"}