File size: 3,729 Bytes
f1e06e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | import torch
class StubImage:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"content": (['WHITE', 'BLACK', 'NOISE'],),
"height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}),
"width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "stub_image"
CATEGORY = "Testing/Stub Nodes"
def stub_image(self, content, height, width, batch_size):
if content == "WHITE":
return (torch.ones(batch_size, height, width, 3),)
elif content == "BLACK":
return (torch.zeros(batch_size, height, width, 3),)
elif content == "NOISE":
return (torch.rand(batch_size, height, width, 3),)
class StubConstantImage:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}),
"width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "stub_constant_image"
CATEGORY = "Testing/Stub Nodes"
def stub_constant_image(self, value, height, width, batch_size):
return (torch.ones(batch_size, height, width, 3) * value,)
class StubMask:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}),
"width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}),
},
}
RETURN_TYPES = ("MASK",)
FUNCTION = "stub_mask"
CATEGORY = "Testing/Stub Nodes"
def stub_mask(self, value, height, width, batch_size):
return (torch.ones(batch_size, height, width) * value,)
class StubInt:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("INT", {"default": 0, "min": -0xffffffff, "max": 0xffffffff, "step": 1}),
},
}
RETURN_TYPES = ("INT",)
FUNCTION = "stub_int"
CATEGORY = "Testing/Stub Nodes"
def stub_int(self, value):
return (value,)
class StubFloat:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("FLOAT", {"default": 0.0, "min": -1.0e38, "max": 1.0e38, "step": 0.01}),
},
}
RETURN_TYPES = ("FLOAT",)
FUNCTION = "stub_float"
CATEGORY = "Testing/Stub Nodes"
def stub_float(self, value):
return (value,)
TEST_STUB_NODE_CLASS_MAPPINGS = {
"StubImage": StubImage,
"StubConstantImage": StubConstantImage,
"StubMask": StubMask,
"StubInt": StubInt,
"StubFloat": StubFloat,
}
TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS = {
"StubImage": "Stub Image",
"StubConstantImage": "Stub Constant Image",
"StubMask": "Stub Mask",
"StubInt": "Stub Int",
"StubFloat": "Stub Float",
}
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