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
|
|
@ -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
|
||||
"""
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
import torch
|
||||
def waste_cpu_resource():
|
||||
x = torch.rand(1, 1e6, dtype=torch.float64).cpu()
|
||||
return x.numpy()[0, 1]
|
||||
|
|
@ -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.
|
||||
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
|
||||
Some 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.
|
||||
The type can be a list for selection.
|
||||
|
||||
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", {
|
||||
"multiline": True, #Allow the input to be multilined
|
||||
"default": "Hello World!"
|
||||
}),
|
||||
"image": ("IMAGE",),
|
||||
"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):
|
||||
if print_to_screen == "Enable":
|
||||
def test(self, image, string_field, int_field, float_field, print_to_screen):
|
||||
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
|
||||
"""
|
||||
24
nodes.py
24
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:
|
||||
#Comment out these two lines if you want to test
|
||||
possible_modules.remove("example.py")
|
||||
possible_modules.remove("example_folder")
|
||||
if "__pycache__" in possible_modules:
|
||||
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:
|
||||
print(f"Cannot import {possible_module} module for custom nodes.")
|
||||
print(e)
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
print(f"Cannot import {possible_module} module for custom nodes:", e)
|
||||
|
||||
load_custom_nodes()
|
||||
Loading…
Reference in New Issue