189 lines
7.8 KiB
Python
189 lines
7.8 KiB
Python
import torch
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class PSBlendNode:
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@classmethod
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def INPUT_TYPES(s):
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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_mode": (["Normal", "Dissolve", "Darken", "Multiply", "Color Burn", "Linear Burn", "Darker Color", "Lighten", "Screen", "Color Dodge", "Linear Dodge", "Lighter Color", "Overlay", "Soft Light", "Hard Light", "Vivid Light", "Linear Light", "Pin Light", "Hard Mix", "Difference", "Exclusion", "Subtract", "Divide", "Hue", "Saturation", "Color", "Luminosity"],),
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"opacity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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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 = "image/blending"
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def blend_images(self, image1, image2, blend_mode, opacity):
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# GPU availability check
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if torch.cuda.is_available():
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print("PS Blend Node is using CUDA.")
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device = torch.device("cuda")
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elif torch.backends.mps.is_available():
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print("PS Blend Node is using MPS.")
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device = torch.device("mps")
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else:
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print("No GPU acceleration available, using CPU")
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device = torch.device("cpu")
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# Move tensors to the appropriate device
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img1 = image1.to(device)
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img2 = image2.to(device)
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# self.print_tensor_shape(img1, "img1")
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# self.print_tensor_shape(img2, "img2")
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# Ensure images have the same shape
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if img1.shape != img2.shape:
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raise ValueError("Images must have the same dimensions")
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# Separate color and alpha channels
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img1, img1_a = img1[..., :3], img1[..., 3:4]
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img2, img2_a = img2[..., :3], img2[..., 3:4]
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# Helper functions
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def lum(img):
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return 0.3 * img[..., 0] + 0.59 * img[..., 1] + 0.11 * img[..., 2]
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def sat(img):
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min_val, _ = torch.min(img, dim=-1)
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max_val, _ = torch.max(img, dim=-1)
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return max_val - min_val
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def set_lum(c, l):
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d = l - lum(c)
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c = c + d.unsqueeze(-1)
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return clip_color(c)
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def set_sat(c, s):
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c_min, _ = torch.min(c, dim=-1, keepdim=True)
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c_max, _ = torch.max(c, dim=-1, keepdim=True)
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c_mid = c - c_min
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c_mid *= s.unsqueeze(-1) / (c_max - c_min + 1e-6)
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return clip_color(c_mid + c_min)
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def clip_color(c):
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l = lum(c).unsqueeze(-1)
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n = torch.min(c, dim=-1, keepdim=True)[0]
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x = torch.max(c, dim=-1, keepdim=True)[0]
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c_clipped = torch.clamp(c, 0, 1)
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c_clipped = torch.where(n < 0, l + ((c - l) * l) / (l - n + 1e-6), c_clipped)
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c_clipped = torch.where(x > 1, l + ((c - l) * (1 - l)) / (x - l + 1e-6), c_clipped)
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return c_clipped
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def hue_blend(img1, img2):
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# Calculate luminance and saturation for both images
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lum1 = lum(img1)
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lum2 = lum(img2)
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sat1 = sat(img1)
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sat2 = sat(img2)
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# Combine the hue from img1 with the saturation and luminance from img2
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result = set_lum(set_sat(img1, sat2), lum2)
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return result
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# Blend mode calculations (only on RGB channels)
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if blend_mode == "Normal":
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result = img1
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elif blend_mode == "Dissolve":
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mask = torch.rand_like(img1) < opacity
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result = torch.where(mask, img1, img2)
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elif blend_mode == "Darken":
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result = torch.min(img1, img2)
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elif blend_mode == "Multiply":
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result = img1 * img2
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elif blend_mode == "Color Burn":
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result = torch.where(
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img1 == 1,
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img2, # If top layer (source) is white, no change
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torch.where(
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img1 > 0,
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1 - torch.clamp((1 - img2) / img1, 0, 1),
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torch.zeros_like(img2) # If top layer is black, result is black
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)
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)
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elif blend_mode == "Linear Burn":
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result = img1 + img2 - 1
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elif blend_mode == "Darker Color":
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result = torch.where(lum(img1).unsqueeze(-1) < lum(img2).unsqueeze(-1), img1, img2)
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elif blend_mode == "Lighten":
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result = torch.max(img1, img2)
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elif blend_mode == "Screen":
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result = 1 - (1 - img1) * (1 - img2)
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elif blend_mode == "Color Dodge":
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# result = torch.where(img2 < 1, torch.min(torch.ones_like(img1), img1 / (1 - img2)), torch.ones_like(img1))
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result = torch.where(img2 < 1, torch.min(torch.ones_like(img1), img1 / (1 - img2)), torch.ones_like(img1))
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elif blend_mode == "Linear Dodge":
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result = img1 + img2
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elif blend_mode == "Lighter Color":
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result = torch.where(lum(img1).unsqueeze(-1) > lum(img2).unsqueeze(-1), img1, img2)
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elif blend_mode == "Overlay":
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result = torch.where(img2 < 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
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elif blend_mode == "Soft Light":
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d = torch.where(img2 <= 0.25,
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((16 * img2 - 12) * img2 + 4) * img2,
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torch.sqrt(img2))
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result = torch.where(img1 <= 0.5,
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img2 - (1 - 2 * img1) * img2 * (1 - img2),
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img2 + (2 * img1 - 1) * (d - img2))
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elif blend_mode == "Hard Light":
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result = torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
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elif blend_mode == "Vivid Light":
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result = torch.where(img1 < 0.5,
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1 - (1 - img2) / (2 * img1 + 1e-8),
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img2 / (2 * (1 - img1) + 1e-8))
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elif blend_mode == "Linear Light":
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result = img2 + 2 * img1 - 1
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elif blend_mode == "Pin Light":
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result = torch.where(img1 < 0.5,
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torch.min(img2, 2 * img1),
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torch.max(img2, 2 * img1 - 1))
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elif blend_mode == "Hard Mix":
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# result = (img1 + img2).round()
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# Calculate the sum of the base and blend images
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sum_img = img1 + img2
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# Apply the Hard Mix formula
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result = torch.where(sum_img >= 1, torch.ones_like(img1), torch.zeros_like(img1))
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elif blend_mode == "Difference":
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result = torch.abs(img1 - img2)
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elif blend_mode == "Exclusion":
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result = img1 + img2 - 2 * img1 * img2
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elif blend_mode == "Subtract":
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result = img2 - img1
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elif blend_mode == "Divide":
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result = img2 / (img1 + 1e-8)
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elif blend_mode == "Hue":
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result = hue_blend(img1, img2)
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elif blend_mode == "Saturation":
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result = set_lum(set_sat(img2, sat(img1)), lum(img2))
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elif blend_mode == "Color":
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result = set_lum(img1, lum(img2))
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elif blend_mode == "Luminosity":
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result = set_lum(img2, lum(img1))
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# self.print_tensor_shape(result, "result before opacity")
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# Apply opacity
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opacity_tensor = torch.tensor(opacity, device=device).view(1, 1, 1, 1)
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# self.print_tensor_shape(opacity_tensor, "opacity_tensor")
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result_rgb = img2 * (1 - opacity_tensor) + result * opacity_tensor
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# Blend alpha channels
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result_a = img1_a * opacity_tensor + img2_a * (1 - opacity_tensor)
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# self.print_tensor_shape(result, "final result")
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# Combine RGB and alpha channels
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result = torch.cat([result_rgb, result_a], dim=-1)
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return (result,)
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# Add this method to print tensor shapes for debugging
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def print_tensor_shape(self, tensor, name):
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print(f"{name} shape: {tensor.shape}")
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