jaidaken
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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
2.9 KiB
Python
90 lines
2.9 KiB
Python
import torch
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import numpy as np
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from typing import Tuple
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class RegionOverlayVisualizer:
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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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"region_preview": ("IMAGE",),
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"opacity": ("FLOAT", {
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"default": 0.3,
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"min": 0.0,
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"max": 1.0,
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"step": 0.1,
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"display": "Overlay Opacity"
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}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "visualize_regions"
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CATEGORY = "ControlAltAI Nodes/Flux Region"
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def visualize_regions(
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self,
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image: torch.Tensor,
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region_preview: torch.Tensor,
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opacity: float,
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) -> Tuple[torch.Tensor]:
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try:
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print("\n=== Starting Region Overlay Visualization ===")
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print(f"Initial shapes - Image: {image.shape}, Preview: {region_preview.shape}")
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# Ensure input tensors are in [B, H, W, C] format
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if len(image.shape) == 3:
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image = image.unsqueeze(0)
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if len(region_preview.shape) == 3:
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region_preview = region_preview.unsqueeze(0)
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# Get working copies
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base_image = image.clone()
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preview = region_preview.clone()
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# Convert to numpy for mask creation (keeping batch and HWC format)
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preview_np = (preview * 255).byte().cpu().numpy()
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# Create mask based on preview content (operating on the last dimension - channels)
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color_sum = np.sum(preview_np, axis=-1) # Sum across color channels
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max_channel = np.max(preview_np, axis=-1)
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min_channel = np.min(preview_np, axis=-1)
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# Create binary mask where content exists
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mask = (
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(color_sum > 50) &
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(max_channel > 30) &
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((max_channel - min_channel) > 10)
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)
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# Expand mask to match input dimensions
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mask = mask[..., None] # Add channel dimension back
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mask = torch.from_numpy(mask).to(image.device)
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print(f"Mask shape: {mask.shape}")
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print(f"Masked pixels: {mask.sum().item()}/{mask.numel()} ({mask.sum().item()/mask.numel()*100:.2f}%)")
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# Apply blending only where mask is True
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result = torch.where(
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mask.bool(),
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(1 - opacity) * base_image + opacity * preview,
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base_image
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)
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print(f"Final shape: {result.shape}")
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return (result,)
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except Exception as e:
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print(f"Error in visualization: {str(e)}")
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import traceback
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traceback.print_exc()
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return (image,)
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NODE_CLASS_MAPPINGS = {
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"RegionOverlayVisualizer": RegionOverlayVisualizer
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RegionOverlayVisualizer": "Region Overlay Visualizer"
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} |