Add new node asset scripts for geometry and shader operations

- Implemented `build_mesh_plane.py` to create a geometry node group for mesh planes with customizable parameters.
- Created `build_shadeless.py` for a shader node group that displays color without scene lighting while preserving shadows and reflections.
- Added `validate.py` to validate the integrity of the newly created node groups and their inputs.
- Introduced `validate_cutout.py` to ensure the functionality of cutout node groups and their dependencies.
- Developed `validate_mesh_plane.py` to test the mesh plane creation and its modifier settings.

src/anyimage/server/models/upscale.py

@@ -1,32 +1,4 @@
-import math
-
-
-SCALE = 4
-MAX_OUTPUT_SIZE = 8192
-MAX_OUTPUT_PIXELS = 32 * 1024 * 1024
-
-
-def _limited_input(image, max_output_size):
-    from PIL import Image
-
-    max_output_size = int(max_output_size)
-    if max_output_size < 1 or max_output_size > MAX_OUTPUT_SIZE:
-        raise ValueError(
-            f"Max Output Size must be between 1 and {MAX_OUTPUT_SIZE} pixels"
-        )
-    width, height = image.size
-    scale = min(
-        1.0,
-        max_output_size / (max(width, height) * SCALE),
-        math.sqrt(MAX_OUTPUT_PIXELS / (width * height * SCALE * SCALE)),
-    )
-    if scale >= 1.0:
-        return image
-    size = (
-        max(1, int(width * scale)),
-        max(1, int(height * scale)),
-    )
-    return image.resize(size, Image.Resampling.LANCZOS)
+from ..media.resolution import DEFAULT_MAX_AI_INPUT_SIZE, limit_image
 
 
 def _source_alpha(image):
@@ -53,9 +25,10 @@ def infer_one(
     )
     try:
         with Image.open(image_path) as opened:
-            source = opened
-            if "max_output_size" in parameters:
-                source = _limited_input(source, parameters["max_output_size"])
+            source = limit_image(
+                opened,
+                parameters.get("max_input_size", DEFAULT_MAX_AI_INPUT_SIZE),
+            )
             source_alpha = _source_alpha(source) if preserve_alpha else None
             prediction = infer(model, source, cancel_check=cancel_check)
             if source_alpha is None:
@@ -72,5 +45,5 @@ def infer_one(
         device = str(parameters.get("device", "selected device")).upper()
         raise RuntimeError(
             f"Upscale could not finish on {device}: {error}. "
-            "Reduce Max Output Size or select CPU, then try again."
+            "Reduce Maximum AI Input Size or select CPU, then try again."
         ) from error