Initial commit: Image Aspect Size and Multi Switch nodes

- ImageAspectSize: reads input image dimensions and outputs width/height scaled to a target longest-side, snapped to multiples of 8, with a flip toggle for portrait/landscape rotation - MultiSwitch: any-type switch node with dynamic slot pairs (JS-driven add/remove), colour-coded active/inactive sides, and clean labelling
2026-06-01 02:18:04 +01:00
commit e41eccf1a9
16 changed files with 257 additions and 0 deletions
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+import torch
+
+
+class ImageAspectSize:
+    TITLE = "Image Aspect Size"
+    CATEGORY = "JezzWTF/image"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "target_size": ("INT", {
+                    "default": 1024,
+                    "min": 64,
+                    "max": 8192,
+                    "step": 8,
+                    "tooltip": "Longest side in pixels. The other dimension is calculated to preserve aspect ratio, snapped to multiples of 8.",
+                }),
+                "flip": ("BOOLEAN", {
+                    "default": False,
+                    "label_on": "Flipped (portrait↔landscape)",
+                    "label_off": "Normal",
+                    "tooltip": "Swap width and height before scaling — useful for rotating orientation without changing the image.",
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT", "INT")
+    RETURN_NAMES = ("IMAGE", "WIDTH", "HEIGHT")
+    FUNCTION = "calculate"
+
+    def calculate(self, image: torch.Tensor, target_size: int, flip: bool) -> tuple[torch.Tensor, int, int]:
+        _, H, W, _ = image.shape
+
+        if flip:
+            W, H = H, W
+
+        scale = target_size / max(W, H)
+        width = round(W * scale / 8) * 8
+        height = round(H * scale / 8) * 8
+
+        return (image, width, height)