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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
import numpy as np
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from PIL import Image, ImageChops
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import torch
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class NoisePlusBlend:
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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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"image": ("IMAGE",),
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"noise_scale": ("FLOAT", {"default": 0.40, "min": 0.00, "max": 100.00, "step": 0.01}),
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"blend_opacity": ("INT", {"default": 20, "min": 0, "max": 100}),
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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", "IMAGE")
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RETURN_NAMES = ("blended_image_output", "noise_output")
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FUNCTION = "noise_plus_blend"
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CATEGORY = "ControlAltAI Nodes/Image"
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def tensor_to_pil(self, tensor_image):
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"""Converts tensor to a PIL Image"""
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tensor_image = tensor_image.squeeze(0) # Remove batch dimension if it exists
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pil_image = Image.fromarray((tensor_image.cpu().numpy() * 255).astype(np.uint8))
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return pil_image
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def pil_to_tensor(self, pil_image):
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"""Converts a PIL image back to a tensor"""
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return torch.from_numpy(np.array(pil_image).astype(np.float32) / 255).unsqueeze(0)
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def generate_gaussian_noise(self, width, height, noise_scale=0.05):
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"""Generates Gaussian noise with a given scale."""
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noise = np.random.normal(128, 128 * noise_scale, (height, width, 3)).astype(np.uint8)
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return Image.fromarray(noise)
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def soft_light_blend(self, base_image, noise_image, mask=None, opacity=15):
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"""Blends noise over the base image using soft light, applying mask if present."""
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# Resize noise to match base image size
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noise_image = noise_image.resize(base_image.size)
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base_image = base_image.convert('RGB')
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noise_image = noise_image.convert('RGB')
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noise_blended = ImageChops.soft_light(base_image, noise_image)
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blended_image = Image.blend(base_image, noise_blended, opacity / 100)
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# Apply mask only if it's provided, valid, and contains more than a single value
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if mask is not None:
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mask_pil = self.tensor_to_pil(mask).convert('L')
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mask_resized = mask_pil.resize(base_image.size)
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# Invert the mask by subtracting from 255
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inverted_mask = ImageChops.invert(mask_resized)
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# Apply the inverted mask to the composite blending
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blended_image = Image.composite(base_image, blended_image, inverted_mask)
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return blended_image
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def noise_plus_blend(self, image, noise_scale=0.05, blend_opacity=15, mask=None):
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"""Main function to generate noise, blend, and return results."""
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# Convert Tensor image to PIL
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base_image = self.tensor_to_pil(image)
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image_size = base_image.size
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# Generate Gaussian noise with the size of the input image
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noise_image = self.generate_gaussian_noise(image_size[0], image_size[1], noise_scale)
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# Blend the noise with the base image using soft light
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blended_image = self.soft_light_blend(base_image, noise_image, mask, blend_opacity)
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# Convert the final blended image back to tensor
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noise_tensor = self.pil_to_tensor(noise_image)
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blended_tensor = self.pil_to_tensor(blended_image)
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# Return both the noise and blended image as tensors
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return blended_tensor, noise_tensor
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NODE_CLASS_MAPPINGS = {
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"NoisePlusBlend": NoisePlusBlend,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"NoisePlusBlend": "Noise Plus Blend",
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}
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