MachinePainting
3a574494eb
None of the nodes showed description text in ComfyUI because ComfyUI reads obj_class.DESCRIPTION when it builds node_info - it does not read the class docstring, which is what these nodes had. Every registered node now carries a one-line DESCRIPTION, kept to 29-67 characters so it fits the limited space in ComfyUI Manager and the node search results. Image Transform Pro's four-sentence description is shortened to match. Descriptions are derived from each node's existing docstring, or from the README for the five classes that had no docstring (ImageBlendPro, ColorMatchBlend and the three Boolean switch nodes). README also updated for the recent changes: the New Nodes table now lists the real "Rotate & Scale" category, the XY placement direction is documented on both nodes (positive X right, positive Y up), and the v2.2.0 changelog records the placement convention, the node outputs and the MP_ImageTransform internal name.
63 lines
2.2 KiB
Python
63 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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DESCRIPTION = "Convert between IMAGE and MASK with a toggle."
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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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