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>
89 lines
3.6 KiB
Python
89 lines
3.6 KiB
Python
import torch
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import numpy as np
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from PIL import Image, ImageColor
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from typing import Union, List
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import logging
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# Utility functions from mtb nodes: https://github.com/melMass/comfy_mtb
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def pil2tensor(image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor:
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if isinstance(image, list):
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return torch.cat([pil2tensor(img) for img in image], dim=0)
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor:
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if isinstance(img_np, list):
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return torch.cat([np2tensor(img) for img in img_np], dim=0)
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return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0)
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def tensor2np(tensor: torch.Tensor):
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if len(tensor.shape) == 3: # Single image
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return np.clip(255.0 * tensor.cpu().numpy(), 0, 255).astype(np.uint8)
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else: # Batch of images
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return [np.clip(255.0 * t.cpu().numpy(), 0, 255).astype(np.uint8) for t in tensor]
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def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
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batch_count = image.size(0) if len(image.shape) > 3 else 1
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if batch_count > 1:
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out = []
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for i in range(batch_count):
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out.extend(tensor2pil(image[i]))
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return out
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return [
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Image.fromarray(
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np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
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)
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]
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def string_to_color(color_string: str) -> List[int]:
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color_list = [0, 0, 0] # Default fallback (black)
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if ',' in color_string:
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# Handle CSV format (e.g., "255, 0, 0" or "255, 0, 0, 128" or "1.0, 0.5, 0.0")
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try:
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values = [float(channel.strip()) for channel in color_string.split(',')]
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# Convert to 0-255 range if values are in 0-1 range
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if all(0 <= v <= 1 for v in values):
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color_list = [int(v * 255) for v in values]
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else:
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color_list = [int(v) for v in values]
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except ValueError:
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logging.warning(f"Invalid color format: {color_string}. Using default black.")
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elif color_string.lstrip('#').isalnum() and not color_string.lstrip('#').replace('.', '', 1).isdigit():
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# Could be Hex format or color name
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color_string_stripped = color_string.lstrip('#')
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# Try hex first
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if len(color_string_stripped) in [6, 8] and all(c in '0123456789ABCDEFabcdef' for c in color_string_stripped):
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if len(color_string_stripped) == 6: # #RRGGBB
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color_list = [int(color_string_stripped[i:i+2], 16) for i in (0, 2, 4)]
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elif len(color_string_stripped) == 8: # #RRGGBBAA
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color_list = [int(color_string_stripped[i:i+2], 16) for i in (0, 2, 4, 6)]
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else:
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# Try color name (e.g., "red", "blue", "cyan")
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try:
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rgb = ImageColor.getrgb(color_string)
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color_list = list(rgb)
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except ValueError:
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logging.warning(f"Invalid color name or hex format: {color_string}. Using default black.")
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else:
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# Handle single value (grayscale) - can be int or float
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try:
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value = float(color_string.strip())
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# Convert to 0-255 range if it's a float between 0-1
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if 0 <= value <= 1:
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value = int(value * 255)
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else:
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value = int(value)
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color_list = [value, value, value]
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except ValueError:
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logging.warning(f"Invalid color format: {color_string}. Using default black.")
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# Clip values to valid range
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color_list = np.clip(color_list, 0, 255).tolist()
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return color_list
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