Fix a few issues with the custom_nodes PR.
There only needs to be one example in the folder.
This commit is contained in:
comfyanonymous
parent
fa66ece26b
commit
1688f5024d
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@ -1,87 +0,0 @@
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from utils import waste_cpu_resource
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class ExampleFolder:
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"""
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A example node
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Class methods
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-------------
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INPUT_TYPES (dict):
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Tell the main program input parameters of nodes.
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Attributes
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----------
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RETURN_TYPES (`tuple`):
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The type of each element in the output tulple.
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FUNCTION (`str`):
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The name of the entry-point method which will return a tuple. For example, if `FUNCTION = "execute"` then it will run Example().execute()
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OUTPUT_NODE ([`bool`]):
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WIP
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CATEGORY (`str`):
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WIP
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execute(s) -> tuple || None:
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The entry point method. The name of this method must be the same as the value of property `FUNCTION`.
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For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`.
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"""
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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"""
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Return a dictionary which contains config for all input fields.
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The type can be a string indicate a type or a list indicate selection.
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Prebuilt types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
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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
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Argument: s (`None`): Useless ig
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Returns: `dict`:
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- Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
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- Value input_fields (`dict`): Contains input fields config:
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* Key field_name (`string`): Name of a entry-point method's argument
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* Value field_config (`tuple`):
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+ First value is a string indicate the type of field or a list for selection.
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+ Secound value is a config for type "INT", "STRING" or "FLOAT".
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"""
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return {
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"required": {
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"string_field": ("STRING", {
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"multiline": True, #Allow the input to be multilined
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"default": "Hello World!"
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}),
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"int_field": ("INT", {
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"default": 0,
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"min": 0, #Minimum value
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"max": 4096, #Maximum value
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"step": 64 #Slider's step
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}),
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#Like INT
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"print_to_screen": (["Enable", "Disable"], {"default": "Enable"})
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},
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#"hidden": {
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# "prompt": "PROMPT",
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# "extra_pnginfo": "EXTRA_PNGINFO"
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#},
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}
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RETURN_TYPES = ("STRING", "INT", "FLOAT", "STRING")
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FUNCTION = "test"
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#OUTPUT_NODE = True
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CATEGORY = "Example"
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def test(self, string_field, int_field, print_to_screen):
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rand_float = waste_cpu_resource()
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if print_to_screen == "Enable":
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print(f"""Your input contains:
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string_field aka input text: {string_field}
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int_field: {int_field}
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A random float number: {rand_float}
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""")
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return (string_field, int_field, rand_float, print_to_screen)
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NODE_CLASS_MAPPINGS = {
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"ExampleFolder": ExampleFolder
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}
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"""
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NODE_CLASS_MAPPINGS (dict): A dictionary contains all nodes you want to export
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"""
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@ -1,4 +0,0 @@
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import torch
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def waste_cpu_resource():
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x = torch.rand(1, 1e6, dtype=torch.float64).cpu()
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return x.numpy()[0, 1]
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@ -12,11 +12,13 @@ class Example:
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RETURN_TYPES (`tuple`):
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The type of each element in the output tulple.
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FUNCTION (`str`):
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The name of the entry-point method which will return a tuple. For example, if `FUNCTION = "execute"` then it will run Example().execute()
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The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute()
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OUTPUT_NODE ([`bool`]):
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WIP
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If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example.
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The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected.
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Assumed to be False if not present.
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CATEGORY (`str`):
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WIP
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The category the node should appear in the UI.
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execute(s) -> tuple || None:
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The entry point method. The name of this method must be the same as the value of property `FUNCTION`.
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For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`.
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@ -28,10 +30,10 @@ class Example:
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def INPUT_TYPES(s):
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"""
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Return a dictionary which contains config for all input fields.
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The type can be a string indicate a type or a list indicate selection.
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Prebuilt types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
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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
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Argument: s (`None`): Useless ig
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Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
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Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
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The type can be a list for selection.
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Returns: `dict`:
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- Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
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- Value input_fields (`dict`): Contains input fields config:
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@ -42,46 +44,43 @@ class Example:
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"""
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return {
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"required": {
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"string_field": ("STRING", {
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"multiline": True, #Allow the input to be multilined
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"default": "Hello World!"
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}),
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"image": ("IMAGE",),
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"int_field": ("INT", {
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"default": 0,
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"min": 0, #Minimum value
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"max": 4096, #Maximum value
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"step": 64 #Slider's step
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}),
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#Like INT
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"float_field": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"print_to_screen": (["Enable", "Disable"], {"default": "Enable"})
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"print_to_screen": (["enable", "disable"],),
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"string_field": ("STRING", {
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"multiline": False, #True if you want the field to look like the one on the ClipTextEncode node
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"default": "Hello World!"
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}),
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},
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#"hidden": {
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# "prompt": "PROMPT",
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# "extra_pnginfo": "EXTRA_PNGINFO"
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#},
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}
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RETURN_TYPES = ("STRING", "INT", "FLOAT", "STRING")
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "test"
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#OUTPUT_NODE = True
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#OUTPUT_NODE = False
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CATEGORY = "Example"
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def test(self, string_field, int_field, float_field, print_to_screen):
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if print_to_screen == "Enable":
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def test(self, image, string_field, int_field, float_field, print_to_screen):
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if print_to_screen == "enable":
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print(f"""Your input contains:
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string_field aka input text: {string_field}
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int_field: {int_field}
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float_field: {float_field}
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""")
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return (string_field, int_field, float_field, print_to_screen)
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#do some processing on the image, in this example I just invert it
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image = 1.0 - image
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return (image,)
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# A dictionary that contains all nodes you want to export with their names
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# NOTE: names should be globally unique
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NODE_CLASS_MAPPINGS = {
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"Example": Example
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}
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"""
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NODE_CLASS_MAPPINGS (dict): A dictionary contains all nodes you want to export
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"""
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22
nodes.py
22
nodes.py
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@ -5,6 +5,7 @@ import sys
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import json
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import hashlib
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import copy
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import traceback
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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@ -751,29 +752,28 @@ NODE_CLASS_MAPPINGS = {
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CUSTOM_NODE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "custom_nodes")
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def load_custom_nodes():
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possible_modules = os.listdir(CUSTOM_NODE_PATH)
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try:
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#Comment out these two lines if you want to test
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possible_modules.remove("example.py")
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possible_modules.remove("example_folder")
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if "__pycache__" in possible_modules:
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possible_modules.remove("__pycache__")
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except ValueError: pass
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for possible_module in possible_modules:
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module_path = os.path.join(CUSTOM_NODE_PATH, possible_module)
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if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
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module_name = "custom_node_module.{}".format(possible_module)
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try:
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if os.path.isfile(module_path):
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module_spec = importlib.util.spec_from_file_location(os.path.basename(module_path), module_path)
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module_spec = importlib.util.spec_from_file_location(module_name, module_path)
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else:
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module_spec = importlib.util.spec_from_file_location(module_path, "main.py")
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module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py"))
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module = importlib.util.module_from_spec(module_spec)
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sys.modules[module_name] = module
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module_spec.loader.exec_module(module)
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if getattr(module, "NODE_CLASS_MAPPINGS") is not None:
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if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None:
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NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
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else:
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print(f"Skip {possible_module} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
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except ImportError as e:
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print(f"Cannot import {possible_module} module for custom nodes.")
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print(e)
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except Exception as e:
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print(traceback.format_exc())
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print(f"Cannot import {possible_module} module for custom nodes:", e)
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load_custom_nodes()
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