commit NanoBanana Image Scale node

py/nano_banana_image_scale.py

@@ -0,0 +1,66 @@
+import torch
+from PIL import Image
+from .imagefunc import log, tensor2pil, pil2tensor, fit_resize_image
+
+
+PREFERED_BANANA_RESOLUTIONS = [
+    (1024, 1024),
+    (1184, 864),
+    (864, 1184),
+    (832, 1248),
+    (1248, 832),
+    (768, 1344),
+    (1344, 768),
+]
+
+
+class LS_NanoBananaImageScale:
+    @classmethod
+    def INPUT_TYPES(s):
+        method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
+        return {"required": {"image": ("IMAGE", ),
+                             "method": (method_mode,),
+                            },
+               }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "scale"
+
+    CATEGORY = '😺dzNodes/LayerUtility'
+    DESCRIPTION = "This node resizes the image to one that is more optimal for nano-banana. For images with different aspect ratio, the scale will be adjusted appropriately to maintain all information"
+
+    def scale(self, image, method):
+        ret_images = []
+
+        width = image.shape[2]
+        height = image.shape[1]
+        aspect_ratio = width / height
+        _, target_width, target_height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_BANANA_RESOLUTIONS)
+
+        resize_sampler = Image.LANCZOS
+        if method == "bicubic":
+            resize_sampler = Image.BICUBIC
+        elif method == "hamming":
+            resize_sampler = Image.HAMMING
+        elif method == "bilinear":
+            resize_sampler = Image.BILINEAR
+        elif method == "box":
+            resize_sampler = Image.BOX
+        elif method == "nearest":
+            resize_sampler = Image.NEAREST
+
+        for img in  image:
+            _image = torch.unsqueeze(img, 0)
+            _image = tensor2pil(img).convert('RGB')
+            resized_image = fit_resize_image(_image, target_width, target_height, 'fill', resize_sampler)
+            ret_images.append(pil2tensor(resized_image))
+
+        return (torch.cat(ret_images, dim=0),)
+
+NODE_CLASS_MAPPINGS = {
+    "LayerUtility: NanoBananaImageScale": LS_NanoBananaImageScale
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayerUtility: NanoBananaImageScale": "LayerUtility: Nano Banana Image Scale"
+}

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui_layerstyle"
 description = "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress."
-version = "2.0.23"
+version = "2.0.24"
 license = {text = "MIT License"}
 dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "timm", "colour-science",  "transformers",  "blend_modes",  "huggingface_hub", "loguru"]