Move to comfy_extras

comfy_extras/nodes_remote_images.py

@@ -0,0 +1,38 @@
+import torch
+from urllib import request
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+import numpy as np
+
+class LoadImageUrl:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "url": ("STRING", { "multiline": False, })
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "load_image"
+    CATEGORY = "image"
+
+    def load_image(self, url):
+        r = request.urlopen(url)
+        i = Image.open(r)
+        image = i.convert("RGB")
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        if 'A' in i.getbands():
+            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+            mask = 1. - torch.from_numpy(mask)
+        else:
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        return (image, mask)
+
+NODE_CLASS_MAPPINGS = {
+    "LoadImageUrl": LoadImageUrl,
+}

nodes.py

@@ -10,7 +10,6 @@ import traceback
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
 import numpy as np
-from urllib import request
 
 sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
 
@@ -937,35 +936,6 @@ class LoadImageMask:
             m.update(f.read())
         return m.digest().hex()
 
-class LoadImageUrl:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "url": ("STRING", { "multiline": False, })
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE", "MASK")
-    FUNCTION = "load_image"
-    CATEGORY = "image"
-
-    def load_image(self, url):
-        r = request.urlopen(url)
-        i = Image.open(r)
-        image = i.convert("RGB")
-        image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image)[None,]
-        if 'A' in i.getbands():
-            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
-            mask = 1. - torch.from_numpy(mask)
-        else:
-            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-        return (image, mask)
-
 class ImageScale:
     upscale_methods = ["nearest-exact", "bilinear", "area"]
     crop_methods = ["disabled", "center"]
@@ -1082,7 +1052,6 @@ NODE_CLASS_MAPPINGS = {
     "PreviewImage": PreviewImage,
     "LoadImage": LoadImage,
     "LoadImageMask": LoadImageMask,
-    "LoadImageUrl": LoadImageUrl,
     "ImageScale": ImageScale,
     "ImageInvert": ImageInvert,
     "ImagePadForOutpaint": ImagePadForOutpaint,
@@ -1144,4 +1113,5 @@ def load_custom_nodes():
 
 def init_custom_nodes():
     load_custom_nodes()
-    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_remote_images.py"))