add image resolution
This commit is contained in:
jammyfu
+6
-2
@@ -2,6 +2,8 @@ from .image_scale_calculator import ImageScaleCalculator
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from .remove_empty_lines import RemoveEmptyLinesAndLeadingSpaces
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from .remove_empty_lines_advance import RemoveEmptyLinesAndLeadingSpacesAdvance
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from .dynamic_image_input import DynamicImageInputs
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from .image_resolution_adjuster import ImageResolutionAdjuster
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# 可以在此处添加任何初始化代码
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@@ -10,7 +12,8 @@ NODE_CLASS_MAPPINGS = {
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"ImageScaleCalculator": ImageScaleCalculator,
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"RemoveEmptyLinesAndLeadingSpaces": RemoveEmptyLinesAndLeadingSpaces,
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"RemoveEmptyLinesAndLeadingSpacesAdvance": RemoveEmptyLinesAndLeadingSpacesAdvance,
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"DynamicImageInputs": DynamicImageInputs
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"DynamicImageInputs": DynamicImageInputs,
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"ImageResolutionAdjuster": ImageResolutionAdjuster
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}
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# 友好/人类可读的节点标题映射
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@@ -18,7 +21,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"ImageScaleCalculator": "Image Scale Calculator Node",
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"RemoveEmptyLinesAndLeadingSpaces": "Remove Empty Lines & Leading Spaces",
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"RemoveEmptyLinesAndLeadingSpacesAdvance": "Remove Empty Lines & Leading Spaces Advance",
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"DynamicImageInputs": "Dynamic Image Inputs"
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"DynamicImageInputs": "Dynamic Image Inputs",
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"ImageResolutionAdjuster": "Image Resolution Adjuster"
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}
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# 你可以在此处添加更多的配置或注册逻辑
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@@ -0,0 +1,232 @@
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# -*- coding: utf-8 -*-
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# Filename: image_resolution_adjuster.py
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# Developer: jammyfu
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# Category: 🎨Painting👓Coder/🖼️Image
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import torch
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import numpy as np
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from PIL import Image, ImageOps
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from math import gcd
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def resize_image(image, target_width, target_height, method='contain'):
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"""Resize an image while maintaining aspect ratio.
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method: 'contain' (保持宽高比，缩放图像以完全适应容器),
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'cover' (保持宽高比，缩放图像以覆盖整个容器),
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'fill' (忽略宽高比，缩放图像以完全填充容器),
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'inside' (保持宽高比，缩小或不改变图像使其完全适合容器),
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'outside' (保持宽高比，放大或不改变图像使其完全覆盖容器).
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"""
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# 将ComfyUI的图像Tensor转换为PIL图像对象
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img = Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8))
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img_width, img_height = img.size
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if method == 'contain':
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# Contain: 缩放图像以适应目标尺寸，保持宽高比，可能出现黑边
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img_ratio = img_width / img_height
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target_ratio = target_width / target_height
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if img_ratio > target_ratio:
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new_width = target_width
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new_height = int(target_width / img_ratio)
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else:
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new_height = target_height
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new_width = int(target_height * img_ratio)
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resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
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padded_img = Image.new('RGB', (target_width, target_height), 'black')
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x_offset = (target_width - new_width) // 2
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y_offset = (target_height - new_height) // 2
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padded_img.paste(resized_img, (x_offset, y_offset))
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return np.array(padded_img).astype(np.float32)/255.0, new_width, new_height
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elif method == 'cover':
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# Cover: 缩放图像以覆盖目标尺寸，保持宽高比，可能裁剪
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img_ratio = img_width / img_height
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target_ratio = target_width / target_height
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if img_ratio > target_ratio:
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new_height = target_height
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new_width = int(target_height * img_ratio)
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else:
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new_width = target_width
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new_height = int(target_width / img_ratio)
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resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
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x_offset = (new_width - target_width) // 2
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y_offset = (new_height - target_height) // 2
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cropped_img = resized_img.crop((x_offset, y_offset, x_offset + target_width, y_offset + target_height))
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return np.array(cropped_img).astype(np.float32)/255.0 , target_width, target_height
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elif method == 'fill':
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# Fill: 拉伸图像以填充目标尺寸，忽略宽高比，可能变形
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resized_img = img.resize((target_width, target_height), Image.Resampling.LANCZOS)
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return np.array(resized_img).astype(np.float32)/255.0, target_width, target_height
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elif method == 'inside':
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# Inside: 和 contain 相同，保持宽高比，缩小或不改变图像使其完全适合容器
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img_ratio = img_width / img_height
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target_ratio = target_width / target_height
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if img_ratio > target_ratio:
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new_width = target_width
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new_height = int(target_width / img_ratio)
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else:
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new_height = target_height
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new_width = int(target_height * img_ratio)
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resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
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padded_img = Image.new('RGB', (target_width, target_height), 'black')
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x_offset = (target_width - new_width) // 2
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y_offset = (target_height - new_height) // 2
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padded_img.paste(resized_img, (x_offset, y_offset))
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return np.array(padded_img).astype(np.float32)/255.0, target_width, target_height
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elif method == 'outside':
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# Outside: 和 cover 相同，保持宽高比，放大或不改变图像使其完全覆盖容器
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img_ratio = img_width / img_height
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target_ratio = target_width / target_height
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if img_ratio > target_ratio:
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new_height = target_height
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new_width = int(target_height * img_ratio)
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else:
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new_width = target_width
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new_height = int(target_width / img_ratio)
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resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
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x_offset = (new_width - target_width) // 2
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y_offset = (new_height - target_height) // 2
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cropped_img = resized_img.crop((x_offset, y_offset, x_offset + target_width, y_offset + target_height))
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return np.array(cropped_img).astype(np.float32)/255.0 , target_width, target_height
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def pad_image(image, target_width, target_height, position='center'):
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"""Pad an image to the target dimensions.
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position: 'center' (居中), 'top' (顶部), 'bottom' (底部), 'left' (左边), 'right' (右边)
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"""
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img = Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8))
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img_width, img_height = img.size
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padded_img = Image.new('RGB', (target_width, target_height), 'black') # 创建黑色背景
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if position == 'center':
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x_offset = (target_width - img_width) // 2
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y_offset = (target_height - img_height) // 2
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elif position == 'top':
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x_offset = (target_width - img_width) // 2
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y_offset = 0
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elif position == 'bottom':
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x_offset = (target_width - img_width) // 2
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y_offset = target_height - img_height
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elif position == 'left':
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x_offset = 0
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y_offset = (target_height - img_height) // 2
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elif position == 'right':
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x_offset = target_width - img_width
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y_offset = (target_height - img_height) // 2
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else:
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raise ValueError(f"Invalid pad position: {position}")
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padded_img.paste(img, (x_offset, y_offset))
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return np.array(padded_img).astype(np.float32) / 255.0, target_width, target_height
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def calculate_resolution(aspect_ratio, scale_factor, max_width, max_height, min_width, min_height):
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"""Calculate the target resolution based on aspect ratio and scale factor."""
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base_resolutions = {
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"1:1": (1024, 1024),
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"1152x896": (1152, 896),
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"896x1152": (896, 1152),
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"1216x832": (1216, 832),
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"832x1216": (832, 1216),
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"1344x768": (1344, 768),
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"768x1344": (768, 1344),
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"1536x640": (1536, 640),
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"640x1536": (640, 1536),
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}
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if aspect_ratio in base_resolutions:
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base_width, base_height = base_resolutions[aspect_ratio]
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else: # default to 1:1
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base_width = 1024
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base_height = 1024
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target_width = int(base_width * scale_factor)
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target_height = int(base_height * scale_factor)
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return target_width, target_height, base_width, base_height
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def get_aspect_ratio_string(width, height):
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"""Get the aspect ratio string from width and height"""
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common_divisor = gcd(width, height)
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aspect_width = width // common_divisor
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aspect_height = height // common_divisor
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return f"{aspect_width}:{aspect_height}"
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class ImageResolutionAdjuster:
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@classmethod
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def INPUT_TYPES(s):
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resolutions = ["1:1 (1024x1024)",
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f"{get_aspect_ratio_string(1152,896)} (1152x896)",
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f"{get_aspect_ratio_string(896,1152)} (896x1152)",
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f"{get_aspect_ratio_string(1216,832)} (1216x832)",
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f"{get_aspect_ratio_string(832,1216)} (832x1216)",
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f"{get_aspect_ratio_string(1344,768)} (1344x768)",
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f"{get_aspect_ratio_string(768,1344)} (768x1344)",
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f"{get_aspect_ratio_string(1536,640)} (1536x640)",
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f"{get_aspect_ratio_string(640,1536)} (640x1536)"]
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return {
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"required": {
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"images": ("IMAGE",),
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"target_resolution": (resolutions,),
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"extend_mode": (["contain", "cover", "fill", "inside", "outside", "top", "bottom", "left", "right", "center"],),
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"scale_factor": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}), # scale_factor 最小值保留小数点后1位
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"max_width": ("INT", {"default": 2048, "min": 1, "max": 8192, "step": 1}),
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"max_height": ("INT", {"default": 2048, "min": 1, "max": 8192, "step": 1}),
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"min_width": ("INT", {"default": 640, "min": 1, "max": 8192, "step": 1}), # 默认最小宽度
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"min_height": ("INT", {"default": 640, "min": 1, "max": 8192, "step": 1}), # 默认最小高度
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}
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}
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RETURN_TYPES = ("IMAGE", "INT", "INT")
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RETURN_NAMES = ("images", "width", "height")
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FUNCTION = "adjust_resolution"
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CATEGORY = "🎨Painting👓Coder/🖼️Image"
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def adjust_resolution(self, images, target_resolution, extend_mode, scale_factor, max_width, max_height, min_width, min_height):
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output_images = []
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output_width = 0
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output_height = 0
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# Extract aspect ratio from the target_resolution string
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aspect_ratio = target_resolution.split(" ")[0]
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target_width, target_height, base_width, base_height = calculate_resolution(aspect_ratio, scale_factor, max_width, max_height, min_width, min_height)
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for image in images:
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img = image
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# Calculate the initial target dimensions based on scale_factor
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# Apply maximum resolution constraint
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current_max = max(target_width, target_height)
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if current_max > max_width or current_max > max_height:
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scale = min(max_width / current_max, max_height / current_max)
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target_width = int(target_width * scale)
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target_height = int(target_height * scale)
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# Apply minimum resolution constraint
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current_min = min(target_width, target_height)
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if current_min < min_width or current_min < min_height:
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scale = max(min_width / current_min, min_height / current_min)
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target_width = int(target_width * scale)
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target_height = int(target_height * scale)
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if extend_mode in ["contain", "cover", "fill", "inside", "outside"]:
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scaled_image, width, height = resize_image(img, target_width, target_height, method=extend_mode)
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elif extend_mode in ["top", "bottom", "left", "right", "center"]:
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scaled_image, width, height = pad_image(img, target_width, target_height, position=extend_mode)
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else:
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raise ValueError(f"Invalid extend_mode: {extend_mode}")
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output_images.append(torch.from_numpy(scaled_image).unsqueeze(0))
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output_width = width
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output_height = height
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return (torch.cat(output_images, dim=0), output_width, output_height)
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