MachinePainting
95e4b9e2ce
New Nodes: - Blur Pro: 5 blur types (Gaussian, Surface, Box, Median, Motion) with dynamic UI, mask support, large radius optimization, preview - Noise Grain Pro: Film grain with size, sharpness, color/mono, 6 blend modes, seed, mask support - Frequency Separate: Split image into high/low frequency layers for retouching (subtract/divide modes) - Frequency Combine: Recombine edited frequency layers - Inpaint Mask Pro: Inpaint mask management with mask cropping, SEGS support, multi-color overlay preview - Image <> Mask Switch: Toggle conversion between IMAGE and MASK types Also: updated README, version bump to 2.1.0, added .claude/ and .codex/ to .gitignore
61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
import torch
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import numpy as np
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class ImageMaskSwitch:
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"""
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Converts between IMAGE and MASK types with a boolean toggle.
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Image to Mask: converts RGB image to single-channel grayscale mask.
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Mask to Image: converts single-channel mask to 3-channel RGB image.
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"""
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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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"switch_mode": ("BOOLEAN", {"default": False, "label_on": "mask_to_image", "label_off": "image_to_mask"}),
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},
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"optional": {
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"image": ("IMAGE",),
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"mask": ("MASK",),
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}
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}
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RETURN_TYPES = ("MASK", "IMAGE")
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RETURN_NAMES = ("mask", "image")
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FUNCTION = "switch"
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CATEGORY = "MachinePaintingNodes/Util"
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def switch(self, switch_mode, image=None, mask=None):
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if not switch_mode:
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# Image to Mask
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if image is None:
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# No image input — output blank
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out_mask = torch.zeros(1, 64, 64, dtype=torch.float32)
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out_image = torch.zeros(1, 64, 64, 3, dtype=torch.float32)
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return (out_mask, out_image)
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img = image[0].cpu().numpy().astype(np.float32)
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# Convert to grayscale using Rec. 601 luma
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gray = 0.299 * img[..., 0] + 0.587 * img[..., 1] + 0.114 * img[..., 2]
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out_mask = torch.from_numpy(gray).unsqueeze(0)
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# Pass through the original image
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return (out_mask, image)
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else:
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# Mask to Image
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if mask is None:
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out_mask = torch.zeros(1, 64, 64, dtype=torch.float32)
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out_image = torch.zeros(1, 64, 64, 3, dtype=torch.float32)
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return (out_mask, out_image)
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if len(mask.shape) == 3:
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mask_np = mask[0].cpu().numpy().astype(np.float32)
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else:
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mask_np = mask.cpu().numpy().astype(np.float32)
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# Convert single channel to 3-channel RGB
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img_3ch = np.stack([mask_np, mask_np, mask_np], axis=-1)
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out_image = torch.from_numpy(img_3ch).unsqueeze(0)
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# Pass through the original mask
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return (mask, out_image)
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