feat: Implement upscale functionality with ONNX models

- Added upscale operator to handle image upscaling by 4x using AI models.
- Introduced new jobs for upscale processing in the server, including debug and standard upscale jobs.
- Created a new ONNX model handler for upscale operations, supporting tiled inference and resource management.
- Updated tests to cover new upscale functionality, including input validation and output verification.
- Added documentation for the upscale feature, detailing usage and model options.

tests/test_download_progress.py

@@ -2,6 +2,7 @@ import os
 import sys
 import unittest
 from pathlib import Path
+import tempfile
 from types import SimpleNamespace
 from unittest.mock import patch
 
@@ -146,6 +147,26 @@ class DownloadProgressTest(unittest.TestCase):
 
         self.assertEqual(endpoints, ["https://hf.example"])
 
+    def test_declared_download_skips_hf_when_no_public_onnx_exists(self):
+        args = SimpleNamespace(
+            repository="",
+            progress=lambda *_args: None,
+            cancel_check=lambda: None,
+        )
+        with tempfile.TemporaryDirectory() as directory, patch.object(
+            self.download, "download_from_hf"
+        ) as hugging_face, patch.object(
+            self.download, "download_from_r2"
+        ) as mirror, patch.object(
+            self.download, "_verify_model_complete"
+        ):
+            self.download._download_declared_or_fail(
+                args, Path(directory), "Model", 0.05, 0.90
+            )
+
+        hugging_face.assert_not_called()
+        mirror.assert_called_once()
+
 
 if __name__ == "__main__":
     unittest.main()