91 lines
3.6 KiB
Plaintext
91 lines
3.6 KiB
Plaintext
class Example:
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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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RETURN_NAMES (`tuple`):
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Optional: The name of each output in the output tulple.
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FUNCTION (`str`):
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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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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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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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"""
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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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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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* 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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"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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"float_field": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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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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}
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RETURN_TYPES = ("IMAGE",)
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#RETURN_NAMES = ("image_output_name",)
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FUNCTION = "test"
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#OUTPUT_NODE = False
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CATEGORY = "Example"
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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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#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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