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.
125 lines
5.4 KiB
Python
125 lines
5.4 KiB
Python
# image_blend_pro.py
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import torch
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import numpy as np
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import cv2
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class ImageBlendPro:
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DESCRIPTION = "Blend two images with 15 blend modes and opacity."
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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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"image1": ("IMAGE",),
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"image2": ("IMAGE",),
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"blend_amount": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05, "display": "slider"}),
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"blend_mode": ([
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"normal", "overlay", "multiply", "screen", "soft_light",
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"hard_light", "linear_light", "difference", "color", "luminosity",
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"darken", "lighten", "color_dodge", "color_burn", "exclusion"
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], {"default": "normal"}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "blend_images"
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CATEGORY = "MachinePaintingNodes/Blend"
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def blend_images(self, image1, image2, blend_amount, blend_mode):
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img1 = (image1[0].cpu().numpy() * 255).astype(np.uint8)
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img2 = (image2[0].cpu().numpy() * 255).astype(np.uint8)
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h1, w1 = img1.shape[:2]
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h2, w2 = img2.shape[:2]
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# Resize to match
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if (h1, w1) != (h2, w2):
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img2 = cv2.resize(img2, (w1, h1))
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img1_bgr = cv2.cvtColor(img1, cv2.COLOR_RGB2BGR)
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img2_bgr = cv2.cvtColor(img2, cv2.COLOR_RGB2BGR)
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result_bgr = self.apply_blend_mode(img1_bgr, img2_bgr, blend_mode, blend_amount)
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result_rgb = cv2.cvtColor(result_bgr, cv2.COLOR_BGR2RGB)
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result_tensor = torch.from_numpy(result_rgb).float() / 255.0
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result_tensor = result_tensor.unsqueeze(0)
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return (result_tensor,)
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def apply_blend_mode(self, bottom, top, mode, opacity):
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bottom_f = bottom.astype(np.float32) / 255.0
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top_f = top.astype(np.float32) / 255.0
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if mode == "normal":
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result = top_f * opacity + bottom_f * (1 - opacity)
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elif mode == "overlay":
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screen = 1 - (1 - bottom_f) * (1 - top_f)
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multiply = bottom_f * top_f
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result = np.where(bottom_f < 0.5, 2 * screen * bottom_f, 2 * multiply)
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "multiply":
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result = bottom_f * top_f
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "screen":
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result = 1 - (1 - bottom_f) * (1 - top_f)
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "soft_light":
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result = (1 - 2 * top_f) * (bottom_f**2) + 2 * top_f * bottom_f
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "hard_light":
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result = np.where(top_f < 0.5, 2 * bottom_f * top_f, 1 - 2 * (1 - bottom_f) * (1 - top_f))
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "linear_light":
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result = np.clip(bottom_f + 2 * top_f - 1, 0, 1)
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "difference":
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result = np.abs(bottom_f - top_f)
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "color":
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result = self.blend_color(bottom_f, top_f)
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "luminosity":
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result = self.blend_luminosity(bottom_f, top_f)
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "darken":
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result = np.minimum(bottom_f, top_f)
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "lighten":
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result = np.maximum(bottom_f, top_f)
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "color_dodge":
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result = np.where(top_f == 0, 0, np.minimum(bottom_f / (1 - top_f), 1))
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "color_burn":
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result = np.where(top_f == 1, 1, np.maximum(1 - ((1 - bottom_f) / top_f), 0))
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result = bottom_f * (1 - opacity) + result * opacity
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elif mode == "exclusion":
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result = bottom_f + top_f - 2 * bottom_f * top_f
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result = bottom_f * (1 - opacity) + result * opacity
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else:
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result = top_f * opacity + bottom_f * (1 - opacity)
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return (np.clip(result, 0, 1) * 255).astype(np.uint8)
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def blend_color(self, bottom, top):
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bottom_uint8 = (bottom * 255).astype(np.uint8)
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top_uint8 = (top * 255).astype(np.uint8)
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hsv_bottom = cv2.cvtColor(bottom_uint8, cv2.COLOR_RGB2HSV)
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hsv_top = cv2.cvtColor(top_uint8, cv2.COLOR_RGB2HSV)
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hsv_result = hsv_top.copy()
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hsv_result[:, :, 2] = hsv_bottom[:, :, 2]
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rgb_result = cv2.cvtColor(hsv_result, cv2.COLOR_HSV2RGB)
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return rgb_result.astype(np.float32) / 255.0
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def blend_luminosity(self, bottom, top):
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bottom_uint8 = (bottom * 255).astype(np.uint8)
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top_uint8 = (top * 255).astype(np.uint8)
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hsv_bottom = cv2.cvtColor(bottom_uint8, cv2.COLOR_RGB2HSV)
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hsv_top = cv2.cvtColor(top_uint8, cv2.COLOR_RGB2HSV)
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hsv_result = hsv_bottom.copy()
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hsv_result[:, :, 2] = hsv_top[:, :, 2]
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rgb_result = cv2.cvtColor(hsv_result, cv2.COLOR_HSV2RGB)
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return rgb_result.astype(np.float32) / 255.0
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