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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>
120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
from PIL import Image, ImageDraw, ImageFont
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import numpy as np
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import torch
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def pil2tensor(image):
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"""Convert PIL image to tensor in the correct format"""
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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class HiDreamResolutionNode:
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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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"resolution": ([
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"1:1 (Perfect Square)",
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"3:4 (Standard Portrait)",
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"2:3 (Classic Portrait)",
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"9:16 (Widescreen Portrait)",
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"4:3 (Standard Landscape)",
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"3:2 (Classic Landscape)",
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"16:9 (Widescreen Landscape)",
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], {"default": "1:1 (Perfect Square)"}),
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}
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}
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RETURN_TYPES = ("INT", "INT", "STRING", "IMAGE")
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RETURN_NAMES = ("width", "height", "resolution", "preview")
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FUNCTION = "get_dimensions"
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CATEGORY = "ControlAltAI Nodes/HiDream"
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OUTPUT_NODE = True
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def create_preview_image(self, width, height, resolution):
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# 1024x1024 preview size
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preview_size = (1024, 1024)
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image = Image.new('RGB', preview_size, (0, 0, 0)) # Black background
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draw = ImageDraw.Draw(image)
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# Draw grid with grey lines
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grid_color = '#333333' # Dark grey for grid
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grid_spacing = 50 # Adjusted grid spacing
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for x in range(0, preview_size[0], grid_spacing):
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draw.line([(x, 0), (x, preview_size[1])], fill=grid_color)
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for y in range(0, preview_size[1], grid_spacing):
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draw.line([(0, y), (preview_size[0], y)], fill=grid_color)
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# Calculate preview box dimensions
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preview_width = 800 # Increased size
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preview_height = int(preview_width * (height / width))
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# Adjust if height is too tall
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if preview_height > 800: # Adjusted for larger preview
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preview_height = 800
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preview_width = int(preview_height * (width / height))
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# Calculate center position
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x_offset = (preview_size[0] - preview_width) // 2
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y_offset = (preview_size[1] - preview_height) // 2
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# Draw the aspect ratio box with thicker outline
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draw.rectangle(
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[(x_offset, y_offset), (x_offset + preview_width, y_offset + preview_height)],
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outline='red',
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width=4 # Thicker outline
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)
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# Add text with larger font sizes
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try:
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# Draw text (centered)
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text_y = y_offset + preview_height//2
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# Resolution text in red
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draw.text((preview_size[0]//2, text_y),
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f"{width}x{height}",
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fill='red',
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anchor="mm",
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font=ImageFont.truetype("arial.ttf", 48))
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except:
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# Fallback if font loading fails
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draw.text((preview_size[0]//2, text_y), f"{width}x{height}", fill='red', anchor="mm")
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# Convert to tensor using the helper function
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return pil2tensor(image)
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def get_dimensions(self, resolution):
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# Map from aspect ratio to actual dimensions
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resolution_map = {
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"1:1 (Perfect Square)": (1024, 1024),
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"3:4 (Standard Portrait)": (880, 1168),
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"2:3 (Classic Portrait)": (832, 1248),
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"9:16 (Widescreen Portrait)": (768, 1360),
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"4:3 (Standard Landscape)": (1168, 880),
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"3:2 (Classic Landscape)": (1248, 832),
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"16:9 (Widescreen Landscape)": (1360, 768)
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}
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# Get dimensions from the map
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width, height = resolution_map[resolution]
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# Resolution as string
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resolution_str = f"{width} x {height}"
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# Generate preview image
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preview = self.create_preview_image(width, height, resolution_str)
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return width, height, resolution_str, preview
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def gcd(a, b):
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"""Calculate the Greatest Common Divisor of a and b."""
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while b:
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a, b = b, a % b
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return a
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NODE_CLASS_MAPPINGS = {
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"HiDreamResolutionNode": HiDreamResolutionNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"HiDreamResolutionNode": "HiDream Resolution",
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} |