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.

src/anyimage/server/media/input.py

@@ -144,11 +144,11 @@ def prepare_background_input(
 
 
 def copy_color_images(images, output_directory):
-    """Copy input images as the canonical numbered color layer."""
+    """Copy input images as the canonical numbered image layer."""
     for index, image_path in enumerate(images):
         suffix = Path(image_path).suffix.lower() or ".jpg"
         destination = (
-            Path(output_directory) / f"color.{index + 1:04d}{suffix}"
+            Path(output_directory) / f"image.{index + 1:04d}{suffix}"
         )
         if Path(image_path).resolve() == destination.resolve():
             continue