new default module

__init__.py

@@ -14,6 +14,7 @@ NODE_MODULES = [
     ".comfyui_datetime",
     ".comfyui_image_sequence",
     ".comfyui_mask_sequence_ops",
+    ".comfyui_default",
 ]
 
 # Extra nodes for my own use

comfyui_default.py

@@ -0,0 +1,90 @@
+import json
+import os
+from pathlib import Path
+
+import numpy as np
+import torch
+import torchvision.transforms.functional as F
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+from torchvision.transforms import InterpolationMode
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+
+def register_node(identifier: str, display_name: str):
+    def decorator(cls):
+        NODE_CLASS_MAPPINGS[identifier] = cls
+        NODE_DISPLAY_NAME_MAPPINGS[identifier] = display_name
+
+        return cls
+
+    return decorator
+
+
+def load_image(path, convert="RGB"):
+    img = Image.open(path).convert(convert)
+    img = np.array(img).astype(np.float32) / 255.0
+    img = torch.from_numpy(img).unsqueeze(0)
+    return img
+
+
+def save_image(img: torch.Tensor, path, prompt=None, extra_pnginfo: dict = None):
+    path = str(path)
+
+    if len(img.shape) != 3:
+        raise ValueError(f"can't take image batch as input, got {img.shape[0]} images")
+
+    img = img.permute(2, 0, 1)
+    if img.shape[0] != 3:
+        raise ValueError(f"image must have 3 channels, but got {img.shape[0]} channels")
+
+    img = img.clamp(0, 1)
+    img = F.to_pil_image(img)
+
+    metadata = PngInfo()
+
+    if prompt is not None:
+        metadata.add_text("prompt", json.dumps(prompt))
+
+    if extra_pnginfo is not None:
+        for k, v in extra_pnginfo.items():
+            metadata.add_text(k, json.dumps(v))
+
+    img.save(path, pnginfo=metadata, compress_level=4)
+
+    subfolder, filename = os.path.split(path)
+
+    return {"filename": filename, "subfolder": subfolder, "type": "output"}
+
+
+@register_node("JWImageLoadRGBIfExists", "Image Load RGB If Exists")
+class _:
+    CATEGORY = "jamesWalker55"
+    INPUT_TYPES = lambda: {
+        "required": {
+            "default": ("IMAGE",),
+            "path": ("STRING", {"default": "./image.png"}),
+        }
+    }
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    def execute(self, path: str, default: torch.Tensor):
+        assert isinstance(path, str)
+        assert isinstance(default, torch.Tensor)
+
+        if not os.path.exists(path):
+            return (default,)
+
+        img = load_image(path)
+        return (img,)
+
+    @classmethod
+    def IS_CHANGED(cls, path: str, default: torch.Tensor):
+        if os.path.exists(path):
+            mtime = os.path.getmtime(path)
+        else:
+            mtime = None
+        return (mtime, default.__hash__())