RelightSimple
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@@ -1,5 +1,6 @@
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import os
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import sys
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import math
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import copy
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import torch
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import torchvision.transforms
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@@ -886,6 +887,39 @@ class OffsetLatentImage:
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latent[:,3,:,:] = offset_3
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return ({"samples":latent}, )
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class RelightSimple:
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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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"image": ("IMAGE",),
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"normals": ("IMAGE",),
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"x_dir": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01}),
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"y_dir": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01}),
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"brightness": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100, "step": 0.01}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "relight"
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CATEGORY = "image/filters"
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def relight(self, image, normals, x_dir, y_dir, brightness):
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if image.shape[0] != normals.shape[0]:
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raise Exception("Batch size for image and normals must match")
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norm = normals.detach().clone() * 2 - 1
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norm = torch.nn.functional.interpolate(norm.movedim(-1,1), size=(image.shape[1], image.shape[2]), mode='bilinear').movedim(1,-1)
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light = torch.tensor([x_dir, y_dir, abs(1 - math.sqrt(x_dir ** 2 + y_dir ** 2) * 0.7)])
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light = torch.nn.functional.normalize(light, dim=0)
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diffuse = norm[:,:,:,0] * light[0] + norm[:,:,:,1] * light[1] + norm[:,:,:,2] * light[2]
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diffuse = torch.clip(diffuse.unsqueeze(3).repeat(1,1,1,3), 0, 1)
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relit = image.detach().clone()
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relit[:,:,:,:3] = torch.clip(relit[:,:,:,:3] * diffuse * brightness, 0, 1)
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return (relit,)
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class LatentStats:
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@classmethod
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def INPUT_TYPES(s):
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@@ -1413,6 +1447,7 @@ NODE_CLASS_MAPPINGS = {
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"LatentStats": LatentStats,
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"NormalMapSimple": NormalMapSimple,
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"OffsetLatentImage": OffsetLatentImage,
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"RelightSimple": RelightSimple,
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"RemapRange": RemapRange,
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"ShuffleChannels": ShuffleChannels,
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"Tonemap": Tonemap,
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@@ -1448,6 +1483,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"LatentStats": "Latent Stats",
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"NormalMapSimple": "Normal Map (Simple)",
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"OffsetLatentImage": "Offset Latent Image",
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"RelightSimple": "Relight (Simple)",
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"RemapRange": "Remap Range",
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"ShuffleChannels": "Shuffle Channels",
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"Tonemap": "Tonemap",
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