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/tools/nodes/test_shadeless.py

@@ -1,6 +1,7 @@
 """Render the bundled Shadeless group's opacity and HDR color endpoints."""
 
 import bpy
+import addon_utils
 import numpy as np
 
 from anyimage.common.node import load_node_group
@@ -8,6 +9,7 @@ from anyimage.common.node import load_node_group
 
 def test_shadeless_alpha_is_mixed_inside_the_group(tmp_path):
     bpy.ops.wm.read_factory_settings(use_empty=True)
+    addon_utils.enable("cycles", default_set=False)
     group = load_node_group("O Shadeless")
     inputs = {s.name: s for s in group.interface.items_tree if s.item_type == "SOCKET" and s.in_out == "INPUT"}
     assert list(inputs) == ["Color", "Alpha"] and inputs["Alpha"].default_value == 1