Allow RGB output of Voronoi
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+71
-34
@@ -4196,9 +4196,12 @@ class WAS_Image_Voronoi_Noise_Filter:
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"height": ("INT", {"default": 512, "max": 4096, "min": 64, "step": 1}),
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"density": ("INT", {"default": 50, "max": 256, "min": 10, "step": 2}),
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"modulator": ("INT", {"default": 0, "max": 8, "min": 0, "step": 1}),
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"flat": (["False", "True"],),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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},
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"optional": {
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"flat": (["False", "True"],),
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"RGB_output": (["True", "False"],),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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@@ -4207,11 +4210,16 @@ class WAS_Image_Voronoi_Noise_Filter:
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CATEGORY = "WAS Suite/Image/Generate/Noise"
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def voronoi_noise_filter(self, width, height, density, modulator, flat, seed):
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def voronoi_noise_filter(self, width, height, density, modulator, seed, flat="False", RGB_output="True"):
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WTools = WAS_Tools_Class()
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image = WTools.worley_noise(height=width, width=height, density=density, option=modulator, use_broadcast_ops=True, seed=seed, flat=(flat == "True")).image
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if RGB_output == "True":
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image = image.convert("RGB")
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else:
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image = image.convert("L")
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return (pil2tensor(image), )
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@@ -6407,6 +6415,65 @@ class WAS_Image_Select_Channel:
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return channel_img
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# IMAGES TO RGB
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class WAS_Images_To_RGB:
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def __init__(self):
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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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"images": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "image_to_rgb"
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CATEGORY = "WAS Suite/Image/Process"
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def image_to_rgb(self, images):
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if len(images) > 1:
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tensors = []
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for image in images:
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tensors.append(pil2tensor(tensor2pil(image).convert('RGB')))
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tensors = torch.cat(tensors, dim=0)
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return (tensors, )
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else:
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return (pil2tensor(tensor2pil(images).convert("RGB")), )
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# IMAGES TO LINEAR
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class WAS_Images_To_Linear:
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def __init__(self):
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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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"images": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "image_to_linear"
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CATEGORY = "WAS Suite/Image/Process"
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def image_to_linear(self, images):
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if len(images) > 1:
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tensors = []
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for image in images:
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tensors.append(pil2tensor(tensor2pil(image).convert('L')))
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tensors = torch.cat(tensors, dim=0)
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return (tensors, )
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else:
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return (pil2tensor(tensor2pil(images).convert("L")), )
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# IMAGE MERGE RGB CHANNELS
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@@ -7058,37 +7125,6 @@ class WAS_Load_Image:
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m.update(f.read())
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return m.digest().hex()
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# IMAGES TO RGB
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class WAS_Images_To_RGB:
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def __init__(self):
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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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"images": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "images_to_rgb"
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CATEGORY = "WAS Suite/Image"
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def images_to_rgb(self, images):
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tensors = []
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for image in images:
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tensors.append(pil2tensor(tensor2pil(image).convert("RGB")))
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tensors = torch.cat(tensors, dim=0)
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return (tensors,)
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# MASK BATCH TO MASK
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class WAS_Mask_Batch_to_Single_Mask:
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@@ -13038,8 +13074,9 @@ NODE_CLASS_MAPPINGS = {
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"Image fDOF Filter": WAS_Image_fDOF,
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"Image to Latent Mask": WAS_Image_To_Mask,
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"Image to Noise": WAS_Image_To_Noise,
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"Images to RGB": WAS_Images_To_RGB,
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"Image to Seed": WAS_Image_To_Seed,
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"Images to RGB": WAS_Images_To_RGB,
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"Images to Linear": WAS_Images_To_Linear,
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"Integer place counter": WAS_Integer_Place_Counter,
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"Image Voronoi Noise Filter": WAS_Image_Voronoi_Noise_Filter,
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"KSampler (Legacy)": WAS_KSampler,
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