77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
#from nodes import MAX_RESOLUTION
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import torch
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#from custom_nodes.EternalKernelLiteGraphNodes.components import fields as field
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class LatentInterpolation:
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def __init__(self) -> None:
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pass
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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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"latent1": ("LATENT",),
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"latent2": ("LATENT",),
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"t": ("FLOAT", {"default": 0.5}),
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}
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}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "interpolate"
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CATEGORY = "ETK/Latent"
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def interpolate(self, latent1, latent2, t):
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# Ensure that the latents are dictionaries
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if not (isinstance(latent1, dict) and isinstance(latent2, dict)):
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raise ValueError("Latent values must be dictionaries.")
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# Ensure that both latents have the same keys
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if set(latent1.keys()) != set(latent2.keys()):
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raise ValueError("Latent dictionaries must have the same keys.")
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inter_latent = {}
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for key in latent1:
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if isinstance(latent1[key], (list, tuple)):
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inter_latent[key] = [(1 - t) * l1_val + t * l2_val for l1_val, l2_val in
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zip(latent1[key], latent2[key])]
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elif isinstance(latent1[key], torch.Tensor):
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inter_latent[key] = (1 - t) * latent1[key] + t * latent2[key]
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else:
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raise ValueError(f"Unsupported value type for key '{key}' in latent dictionaries.")
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return (inter_latent,)
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def test_latent_interpolation():
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import torch
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# Create an instance of the LatentInterpolation class
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li = LatentInterpolation()
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# Define test cases for the interpolate method
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# latents are at least 4 diminsional
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# latents are of the same length
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test_cases = [
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{
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"input": {
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"latent1": torch.tensor([[[[1.0, 2.0], [3.0, 4.0]]]]),
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"latent2": torch.tensor([[[[5.0, 6.0], [7.0, 8.0]]]]),
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"t": 0.5,
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},
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"expected": torch.tensor([[[[3.0, 4.0], [5.0, 6.0]]]]),
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},
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]
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# Run the test cases
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for test_case in test_cases:
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result = li.interpolate(**test_case["input"])
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if not (result[0][0] == test_case["expected"]).all().item():
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print(f"Expected {test_case['expected']} but got {result}")
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if __name__ == "__main__":
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test_latent_interpolation() |