| import torch
|
|
|
| class SwarmLatentBlendMasked:
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| @classmethod
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| def INPUT_TYPES(s):
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| return {
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| "required": {
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| "samples0": ("LATENT",),
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| "samples1": ("LATENT",),
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| "mask": ("MASK",),
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| "blend_factor": ("FLOAT", { "default": 0.5, "min": 0, "max": 1, "step": 0.01, "tooltip": "The blend factor between the two samples. 0 means entirely use sample0, 1 means entirely sample1, 0.5 means 50/50 of each." }),
|
| }
|
| }
|
|
|
| RETURN_TYPES = ("LATENT",)
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| FUNCTION = "blend"
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| CATEGORY = "SwarmUI/images"
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| DESCRIPTION = "Blends two latent images together within a masked region."
|
|
|
| def blend(self, samples0, samples1, blend_factor, mask):
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| samples_out = samples0.copy()
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| samples0 = samples0["samples"]
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| samples1 = samples1["samples"]
|
| while mask.ndim < 4:
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| mask = mask.unsqueeze(0)
|
|
|
| if samples0.shape != samples1.shape:
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| samples1 = torch.nn.functional.interpolate(samples1, size=(samples0.shape[2], samples0.shape[3]), mode="bicubic")
|
| if samples0.shape != mask.shape:
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| mask = torch.nn.functional.interpolate(mask, size=(samples0.shape[2], samples0.shape[3]), mode="bicubic")
|
|
|
| samples_blended = samples0 * (1 - mask * blend_factor) + samples1 * (mask * blend_factor)
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| samples_out["samples"] = samples_blended
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| return (samples_out,)
|
|
|
|
|
| NODE_CLASS_MAPPINGS = {
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| "SwarmLatentBlendMasked": SwarmLatentBlendMasked,
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| }
|
|
|
