87 lines
3.3 KiB
Python
87 lines
3.3 KiB
Python
import torch
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from PIL import Image, ImageChops
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from .imagefunc import log, tensor2pil, pil2tensor, chop_image_v2, gaussian_blur
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class HLFrequencyDetailRestore:
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def __init__(self):
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self.NODE_NAME = 'HLFrequencyDetailRestore'
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"image": ("IMAGE",),
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"detail_image": ("IMAGE",),
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"keep_high_freq": ("INT", {"default": 64, "min": 0, "max": 1023}),
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"erase_low_freq": ("INT", {"default": 32, "min": 0, "max": 1023}),
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"mask_blur": ("INT", {"default": 16, "min": 0, "max": 1023}),
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},
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"optional": {
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"mask": ("MASK",), #
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = 'hl_frequency_detail_restore'
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CATEGORY = '😺dzNodes/LayerUtility'
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def hl_frequency_detail_restore(self, image, detail_image, keep_high_freq, erase_low_freq, mask_blur, mask=None):
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b_images = []
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l_images = []
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l_masks = []
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ret_images = []
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for b in image:
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b_images.append(torch.unsqueeze(b, 0))
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for l in detail_image:
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l_images.append(torch.unsqueeze(l, 0))
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m = tensor2pil(l)
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if m.mode == 'RGBA':
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l_masks.append(m.split()[-1])
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else:
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l_masks.append(Image.new('L', m.size, 'white'))
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if mask is not None:
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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l_masks = []
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for m in mask:
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l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
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max_batch = max(len(b_images), len(l_images), len(l_masks))
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for i in range(max_batch):
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background_image = b_images[i] if i < len(b_images) else b_images[-1]
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background_image = tensor2pil(background_image).convert('RGB')
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detail_image = l_images[i] if i < len(l_images) else l_images[-1]
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detail_image = tensor2pil(detail_image).convert('RGB')
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_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
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high_ferq = chop_image_v2(ImageChops.invert(detail_image),
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gaussian_blur(detail_image, keep_high_freq),
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blend_mode='normal', opacity=50)
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high_ferq = ImageChops.invert(high_ferq)
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if erase_low_freq:
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low_freq = gaussian_blur(background_image, erase_low_freq)
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else:
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low_freq = background_image.copy()
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ret_image = chop_image_v2(low_freq, high_ferq, blend_mode="linear light", opacity=100)
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_mask = ImageChops.invert(_mask)
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if mask_blur > 0:
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_mask = gaussian_blur(_mask, mask_blur)
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ret_image.paste(background_image, _mask)
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ret_images.append(pil2tensor(ret_image))
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log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: HLFrequencyDetailRestore": HLFrequencyDetailRestore
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: HLFrequencyDetailRestore": "LayerUtility: H/L Frequency Detail Restore"
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} |