Fix a few issues with the custom_nodes PR.

There only needs to be one example in the folder.

custom_nodes/example_folder/main.py

@@ -1,87 +0,0 @@
-from utils import waste_cpu_resource
-class ExampleFolder:
-    """
-    A example node
-
-    Class methods
-    -------------
-    INPUT_TYPES (dict): 
-        Tell the main program input parameters of nodes.
-
-    Attributes
-    ----------
-    RETURN_TYPES (`tuple`): 
-        The type of each element in the output tulple.
-    FUNCTION (`str`):
-        The name of the entry-point method which will return a tuple. For example, if `FUNCTION = "execute"` then it will run Example().execute()
-    OUTPUT_NODE ([`bool`]):
-        WIP
-    CATEGORY (`str`):
-        WIP
-    execute(s) -> tuple || None:
-        The entry point method. The name of this method must be the same as the value of property `FUNCTION`.
-        For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`.
-    """
-    def __init__(self):
-        pass
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        """
-            Return a dictionary which contains config for all input fields.
-            The type can be a string indicate a type or a list indicate selection.
-            Prebuilt types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
-            Input in type "INT", "STRING" or "FLOAT" will be converted automatically from a string to the corresponse Python type before passing and have special config
-            Argument: s (`None`): Useless ig
-            Returns: `dict`:
-                - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
-                - Value input_fields (`dict`): Contains input fields config:
-                    * Key field_name (`string`): Name of a entry-point method's argument
-                    * Value field_config (`tuple`):
-                        + First value is a string indicate the type of field or a list for selection.
-                        + Secound value is a config for type "INT", "STRING" or "FLOAT".
-        """
-        return {
-            "required": {
-                "string_field": ("STRING", {
-                    "multiline": True, #Allow the input to be multilined
-                    "default": "Hello World!"
-                }),
-                "int_field": ("INT", {
-                    "default": 0, 
-                    "min": 0, #Minimum value
-                    "max": 4096, #Maximum value
-                    "step": 64 #Slider's step
-                }),
-                #Like INT
-                "print_to_screen": (["Enable", "Disable"], {"default": "Enable"})
-            },
-            #"hidden": {
-            #    "prompt": "PROMPT", 
-            #    "extra_pnginfo": "EXTRA_PNGINFO"
-            #},
-        }
-
-    RETURN_TYPES = ("STRING", "INT", "FLOAT", "STRING")
-    FUNCTION = "test"
-
-    #OUTPUT_NODE = True
-
-    CATEGORY = "Example"
-
-    def test(self, string_field, int_field, print_to_screen):
-        rand_float = waste_cpu_resource()
-        if print_to_screen == "Enable":
-            print(f"""Your input contains:
-                string_field aka input text: {string_field}
-                int_field: {int_field}
-                A random float number: {rand_float}
-            """)
-        return (string_field, int_field, rand_float, print_to_screen)
-
-NODE_CLASS_MAPPINGS = {
-    "ExampleFolder": ExampleFolder
-}
-"""
-NODE_CLASS_MAPPINGS (dict): A dictionary contains all nodes you want to export
-"""

custom_nodes/example_folder/utils.py

@@ -1,4 +0,0 @@
-import torch
-def waste_cpu_resource():
-    x = torch.rand(1, 1e6, dtype=torch.float64).cpu()
-    return x.numpy()[0, 1]

custom_nodes/example_node.py.example

@@ -12,11 +12,13 @@ class Example:
     RETURN_TYPES (`tuple`): 
         The type of each element in the output tulple.
     FUNCTION (`str`):
-        The name of the entry-point method which will return a tuple. For example, if `FUNCTION = "execute"` then it will run Example().execute()
+        The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute()
     OUTPUT_NODE ([`bool`]):
-        WIP
+        If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example.
+        The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected.
+        Assumed to be False if not present.
     CATEGORY (`str`):
-        WIP
+        The category the node should appear in the UI.
     execute(s) -> tuple || None:
         The entry point method. The name of this method must be the same as the value of property `FUNCTION`.
         For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`.
@@ -28,10 +30,10 @@ class Example:
     def INPUT_TYPES(s):
         """
             Return a dictionary which contains config for all input fields.
-            The type can be a string indicate a type or a list indicate selection.
+            Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
-            Prebuilt types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
+            Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
-            Input in type "INT", "STRING" or "FLOAT" will be converted automatically from a string to the corresponse Python type before passing and have special config
+            The type can be a list for selection.
-            Argument: s (`None`): Useless ig
+
             Returns: `dict`:
                 - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
                 - Value input_fields (`dict`): Contains input fields config:
@@ -42,46 +44,43 @@ class Example:
         """
         return {
             "required": {
-                "string_field": ("STRING", {
+                "image": ("IMAGE",),
-                    "multiline": True, #Allow the input to be multilined
-                    "default": "Hello World!"
-                }),
                 "int_field": ("INT", {
                     "default": 0, 
                     "min": 0, #Minimum value
                     "max": 4096, #Maximum value
                     "step": 64 #Slider's step
                 }),
-                #Like INT
                 "float_field": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                "print_to_screen": (["Enable", "Disable"], {"default": "Enable"})
+                "print_to_screen": (["enable", "disable"],),
+                "string_field": ("STRING", {
+                    "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node
+                    "default": "Hello World!"
+                }),
             },
-            #"hidden": {
-            #    "prompt": "PROMPT", 
-            #    "extra_pnginfo": "EXTRA_PNGINFO"
-            #},
         }
 
-    RETURN_TYPES = ("STRING", "INT", "FLOAT", "STRING")
+    RETURN_TYPES = ("IMAGE",)
     FUNCTION = "test"
 
-    #OUTPUT_NODE = True
+    #OUTPUT_NODE = False
 
     CATEGORY = "Example"
 
-    def test(self, string_field, int_field, float_field, print_to_screen):
+    def test(self, image, string_field, int_field, float_field, print_to_screen):
-        if print_to_screen == "Enable":
+        if print_to_screen == "enable":
             print(f"""Your input contains:
                 string_field aka input text: {string_field}
                 int_field: {int_field}
                 float_field: {float_field}
             """)
-        return (string_field, int_field, float_field, print_to_screen)
+        #do some processing on the image, in this example I just invert it
+        image = 1.0 - image
+        return (image,)
 
 
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
 NODE_CLASS_MAPPINGS = {
     "Example": Example
 }
-"""
-NODE_CLASS_MAPPINGS (dict): A dictionary contains all nodes you want to export
-"""

nodes.py

@@ -5,6 +5,7 @@ import sys
 import json
 import hashlib
 import copy
+import traceback
 
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
@@ -751,29 +752,28 @@ NODE_CLASS_MAPPINGS = {
 CUSTOM_NODE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "custom_nodes")
 def load_custom_nodes():
     possible_modules = os.listdir(CUSTOM_NODE_PATH)
-    try:
+    if "__pycache__" in possible_modules:
-        #Comment out these two lines if you want to test
-        possible_modules.remove("example.py")
-        possible_modules.remove("example_folder")
         possible_modules.remove("__pycache__")
-    except ValueError: pass
+
     for possible_module in possible_modules:
         module_path = os.path.join(CUSTOM_NODE_PATH, possible_module)
         if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
-        
+
+        module_name = "custom_node_module.{}".format(possible_module)
         try:
             if os.path.isfile(module_path):
-                module_spec = importlib.util.spec_from_file_location(os.path.basename(module_path), module_path)
+                module_spec = importlib.util.spec_from_file_location(module_name, module_path)
             else:
-                module_spec = importlib.util.spec_from_file_location(module_path, "main.py")
+                module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py"))
             module = importlib.util.module_from_spec(module_spec)
+            sys.modules[module_name] = module
             module_spec.loader.exec_module(module)
-            if getattr(module, "NODE_CLASS_MAPPINGS") is not None:
+            if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None:
                 NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
             else:
                 print(f"Skip {possible_module} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
-        except ImportError as e:
+        except Exception as e:
-            print(f"Cannot import {possible_module} module for custom nodes.")
+            print(traceback.format_exc())
-            print(e)
+            print(f"Cannot import {possible_module} module for custom nodes:", e)
 
 load_custom_nodes()