336 lines
10 KiB
Python
336 lines
10 KiB
Python
import torch
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from .tools import VariantSupport
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from comfy_execution.graph_utils import GraphBuilder
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class TestLazyMixImages:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image1": ("IMAGE",{"lazy": True}),
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"image2": ("IMAGE",{"lazy": True}),
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"mask": ("MASK",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "mix"
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CATEGORY = "Testing/Nodes"
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def check_lazy_status(self, mask, image1, image2):
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mask_min = mask.min()
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mask_max = mask.max()
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needed = []
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if image1 is None and (mask_min != 1.0 or mask_max != 1.0):
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needed.append("image1")
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if image2 is None and (mask_min != 0.0 or mask_max != 0.0):
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needed.append("image2")
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return needed
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# Not trying to handle different batch sizes here just to keep the demo simple
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def mix(self, mask, image1, image2):
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mask_min = mask.min()
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mask_max = mask.max()
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if mask_min == 0.0 and mask_max == 0.0:
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return (image1,)
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elif mask_min == 1.0 and mask_max == 1.0:
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return (image2,)
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if len(mask.shape) == 2:
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mask = mask.unsqueeze(0)
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if len(mask.shape) == 3:
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mask = mask.unsqueeze(3)
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if mask.shape[3] < image1.shape[3]:
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mask = mask.repeat(1, 1, 1, image1.shape[3])
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result = image1 * (1. - mask) + image2 * mask,
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return (result[0],)
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class TestVariadicAverage:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input1": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "variadic_average"
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CATEGORY = "Testing/Nodes"
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def variadic_average(self, input1, **kwargs):
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inputs = [input1]
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while 'input' + str(len(inputs) + 1) in kwargs:
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inputs.append(kwargs['input' + str(len(inputs) + 1)])
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return (torch.stack(inputs).mean(dim=0),)
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class TestCustomIsChanged:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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},
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"optional": {
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"should_change": ("BOOL", {"default": False}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "custom_is_changed"
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CATEGORY = "Testing/Nodes"
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def custom_is_changed(self, image, should_change=False):
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return (image,)
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@classmethod
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def IS_CHANGED(cls, should_change=False, *args, **kwargs):
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if should_change:
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return float("NaN")
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else:
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return False
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class TestCustomValidation1:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input1": ("IMAGE,FLOAT",),
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"input2": ("IMAGE,FLOAT",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "custom_validation1"
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CATEGORY = "Testing/Nodes"
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def custom_validation1(self, input1, input2):
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if isinstance(input1, float) and isinstance(input2, float):
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result = torch.ones([1, 512, 512, 3]) * input1 * input2
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else:
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result = input1 * input2
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return (result,)
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@classmethod
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def VALIDATE_INPUTS(cls, input1=None, input2=None):
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if input1 is not None:
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if not isinstance(input1, (torch.Tensor, float)):
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return f"Invalid type of input1: {type(input1)}"
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if input2 is not None:
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if not isinstance(input2, (torch.Tensor, float)):
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return f"Invalid type of input2: {type(input2)}"
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return True
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class TestCustomValidation2:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input1": ("IMAGE,FLOAT",),
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"input2": ("IMAGE,FLOAT",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "custom_validation2"
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CATEGORY = "Testing/Nodes"
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def custom_validation2(self, input1, input2):
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if isinstance(input1, float) and isinstance(input2, float):
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result = torch.ones([1, 512, 512, 3]) * input1 * input2
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else:
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result = input1 * input2
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return (result,)
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@classmethod
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def VALIDATE_INPUTS(cls, input_types, input1=None, input2=None):
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if input1 is not None:
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if not isinstance(input1, (torch.Tensor, float)):
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return f"Invalid type of input1: {type(input1)}"
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if input2 is not None:
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if not isinstance(input2, (torch.Tensor, float)):
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return f"Invalid type of input2: {type(input2)}"
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if 'input1' in input_types:
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if input_types['input1'] not in ["IMAGE", "FLOAT"]:
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return f"Invalid type of input1: {input_types['input1']}"
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if 'input2' in input_types:
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if input_types['input2'] not in ["IMAGE", "FLOAT"]:
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return f"Invalid type of input2: {input_types['input2']}"
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return True
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@VariantSupport()
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class TestCustomValidation3:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input1": ("IMAGE,FLOAT",),
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"input2": ("IMAGE,FLOAT",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "custom_validation3"
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CATEGORY = "Testing/Nodes"
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def custom_validation3(self, input1, input2):
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if isinstance(input1, float) and isinstance(input2, float):
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result = torch.ones([1, 512, 512, 3]) * input1 * input2
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else:
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result = input1 * input2
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return (result,)
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class TestCustomValidation4:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input1": ("FLOAT",),
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"input2": ("FLOAT",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "custom_validation4"
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CATEGORY = "Testing/Nodes"
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def custom_validation4(self, input1, input2):
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result = torch.ones([1, 512, 512, 3]) * input1 * input2
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return (result,)
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@classmethod
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def VALIDATE_INPUTS(cls, input1, input2):
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if input1 is not None:
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if not isinstance(input1, float):
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return f"Invalid type of input1: {type(input1)}"
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if input2 is not None:
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if not isinstance(input2, float):
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return f"Invalid type of input2: {type(input2)}"
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return True
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class TestCustomValidation5:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input1": ("FLOAT", {"min": 0.0, "max": 1.0}),
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"input2": ("FLOAT", {"min": 0.0, "max": 1.0}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "custom_validation5"
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CATEGORY = "Testing/Nodes"
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def custom_validation5(self, input1, input2):
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value = input1 * input2
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return (torch.ones([1, 512, 512, 3]) * value,)
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@classmethod
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def VALIDATE_INPUTS(cls, **kwargs):
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if kwargs['input2'] == 7.0:
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return "7s are not allowed. I've never liked 7s."
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return True
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class TestDynamicDependencyCycle:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input1": ("IMAGE",),
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"input2": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "dynamic_dependency_cycle"
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CATEGORY = "Testing/Nodes"
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def dynamic_dependency_cycle(self, input1, input2):
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g = GraphBuilder()
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mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
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mix1 = g.node("TestLazyMixImages", image1=input1, mask=mask.out(0))
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mix2 = g.node("TestLazyMixImages", image1=mix1.out(0), image2=input2, mask=mask.out(0))
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# Create the cyle
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mix1.set_input("image2", mix2.out(0))
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return {
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"result": (mix2.out(0),),
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"expand": g.finalize(),
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}
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class TestMixedExpansionReturns:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input1": ("FLOAT",),
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},
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}
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RETURN_TYPES = ("IMAGE","IMAGE")
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FUNCTION = "mixed_expansion_returns"
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CATEGORY = "Testing/Nodes"
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def mixed_expansion_returns(self, input1):
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white_image = torch.ones([1, 512, 512, 3])
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if input1 <= 0.1:
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return (torch.ones([1, 512, 512, 3]) * 0.1, white_image)
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elif input1 <= 0.2:
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return {
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"result": (torch.ones([1, 512, 512, 3]) * 0.2, white_image),
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}
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else:
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g = GraphBuilder()
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mask = g.node("StubMask", value=0.3, height=512, width=512, batch_size=1)
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black = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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white = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
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mix = g.node("TestLazyMixImages", image1=black.out(0), image2=white.out(0), mask=mask.out(0))
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return {
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"result": (mix.out(0), white_image),
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"expand": g.finalize(),
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}
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TEST_NODE_CLASS_MAPPINGS = {
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"TestLazyMixImages": TestLazyMixImages,
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"TestVariadicAverage": TestVariadicAverage,
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"TestCustomIsChanged": TestCustomIsChanged,
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"TestCustomValidation1": TestCustomValidation1,
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"TestCustomValidation2": TestCustomValidation2,
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"TestCustomValidation3": TestCustomValidation3,
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"TestCustomValidation4": TestCustomValidation4,
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"TestCustomValidation5": TestCustomValidation5,
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"TestDynamicDependencyCycle": TestDynamicDependencyCycle,
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"TestMixedExpansionReturns": TestMixedExpansionReturns,
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}
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TEST_NODE_DISPLAY_NAME_MAPPINGS = {
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"TestLazyMixImages": "Lazy Mix Images",
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"TestVariadicAverage": "Variadic Average",
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"TestCustomIsChanged": "Custom IsChanged",
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"TestCustomValidation1": "Custom Validation 1",
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"TestCustomValidation2": "Custom Validation 2",
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"TestCustomValidation3": "Custom Validation 3",
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"TestCustomValidation4": "Custom Validation 4",
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"TestCustomValidation5": "Custom Validation 5",
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"TestDynamicDependencyCycle": "Dynamic Dependency Cycle",
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"TestMixedExpansionReturns": "Mixed Expansion Returns",
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}
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