rui40000andClaude Opus 4.8
b517c5714b
产出真实小分辨率、颜色数受控、边缘硬朗的 sprite，而非马赛克滤镜。 纯 numpy/PIL 自研实现，无额外依赖、无需模型权重。 三种模式：按目标宽度 / 按像素块大小 / 自动检测网格。第三种专治 AI 生成的伪像素图（看着像素风，实际网格歪斜、边缘抗锯齿、上千颜色）。 选型说明：Pixel Snapper（MIT）解决的是「伪像素图 → 完美像素图」， 而「普通图 → 像素画」是另一个问题，核心在降采样与调色板量化， 故两条路都做进同一节点。 网格检测处理了两类经典误判： - 谐波(八度)错误：取得最高分后回查真约数（octave killer） - 内容周期冒充像素周期：把最佳/最差相位的分差并入评分（anti-phase） 评分用单元内方差而非相邻差分——差分对模糊极敏感，实测会把 4 像素 网格判成 24~28；组内方差则天然压制过大的 s。 实测：干净放大图 k=2~16 共 27/27 全对零八度错误；非方形网格与相位 偏移全对；普通插画正确判为未检出；端到端把 32×32 放大 10 倍加模糊 噪点的图还原回 32×32，与真值 MAE 0.0049；整数倍放大为纯复制无插值。 调色板在 CIELAB 空间聚类（RGB 距离与人眼感受相差很远，会丢暗部层次）， 降采样默认主导色（均值会造出新颜色并糊边），内置 PICO-8/Game Boy 等 可确认取值的复古调色板。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
280 lines
14 KiB
Python
280 lines
14 KiB
Python
# -*- coding: utf-8 -*-
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"""
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像素化节点（Ruinode）
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=====================
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把普通图像转成**能直接当素材用的像素画**，而不是"马赛克滤镜"。
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两者的区别在于：滤镜只是把画面涂成方块，输出仍是原尺寸的大图；
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而像素游戏要的是真实小分辨率、颜色数受控、边缘硬朗的 sprite。
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三种工作模式：
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- 按目标宽度 给定输出宽度（如 64），普通图/照片/插画 → 像素画
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- 按像素块大小 每 N×N 原像素合成一个像素，适合已知放大倍数时精确还原
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- 自动检测网格 探测图像本身隐含的像素网格并还原
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—— 专治「AI 生成的伪像素图」：模型输出的 1024×1024 图看着像素风，
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实际网格歪斜、边缘带抗锯齿、颜色成千上万，塞进引擎会糊。
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关于方案选型（研究后的结论）：
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Sprite Fusion 的 Pixel Snapper（MIT）解决的是「伪像素图 → 完美像素图」，
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思路是检测网格 + 按主导色重采样；而「普通图 → 像素画」是另一个问题，
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核心在降采样方式与调色板量化。本节点把两条路都做进同一个节点，
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算法为纯 numpy/PIL 自研实现（无额外依赖、无需模型权重），其中网格检测
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按公开研究的要点处理了两类经典误判：谐波（八度）错误与内容周期冒充像素周期，
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详见 pixelart/grid.py。
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"""
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import numpy as np
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import torch
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from .pixelart import (PALETTE_NAMES, PALETTES, apply_palette, detect_grid,
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downsample_grid, kmeans_palette, median_cut_palette)
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_MODES = ["按目标宽度", "按像素块大小", "自动检测网格（AI伪像素图还原）"]
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_DOWN = {
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"主导色 dominant（像素画首选）": "dominant",
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"中位数 median": "median",
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"均值 mean（会糊边，慎用）": "mean",
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"中心像素 center（最锐）": "center",
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}
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_DITHER = {
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"无（默认）": "none",
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"Bayer 2×2": "bayer2",
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"Bayer 4×4": "bayer4",
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"Bayer 8×8": "bayer8",
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"Floyd-Steinberg": "floyd-steinberg",
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"随机噪声": "noise",
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}
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_PAL_ADAPTIVE = ["自适应 k-means（质量优先）", "自适应 median cut（速度优先）"]
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_PAL_OPTIONS = ["不量化"] + _PAL_ADAPTIVE + PALETTE_NAMES
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def _nearest_resize(img, tw, th):
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"""最近邻缩放。绝不插值 —— 插值会造出调色板外的新颜色并糊掉硬边。"""
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h, w = img.shape[:2]
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if (w, h) == (tw, th):
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return img
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xi = np.clip((np.arange(tw) * (w / tw)).astype(np.int64), 0, w - 1)
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yi = np.clip((np.arange(th) * (h / th)).astype(np.int64), 0, h - 1)
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return img[yi][:, xi]
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class RuiPixelate:
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"""图像 → 像素画（面向像素游戏资产）。"""
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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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"mode": (_MODES, {
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"default": "按目标宽度",
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"tooltip": "按目标宽度：普通图转像素画，直接给输出宽度\n"
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"按像素块大小：每 N×N 原像素合成一个像素\n"
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"自动检测网格：探测图中隐含的像素网格并还原，\n"
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" 专治 AI 生成的模糊伪像素图（网格歪、带抗锯齿）"
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}),
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"target_width": ("INT", {
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"default": 64, "min": 8, "max": 2048, "step": 1,
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"tooltip": "仅「按目标宽度」模式生效。高度按原图比例自动计算。\n"
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"常见 sprite 尺寸：16 / 32 / 48 / 64 / 96 / 128"
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}),
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"pixel_size": ("INT", {
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"default": 8, "min": 1, "max": 256, "step": 1,
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"tooltip": "仅「按像素块大小」模式生效：每 N×N 原像素 → 1 像素。"
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}),
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"downsample": (list(_DOWN.keys()), {
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"default": "主导色 dominant（像素画首选）",
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"tooltip": "每个单元如何定色。\n"
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"主导色：取块内出现最多的颜色，不会凭空造出新颜色（首选）\n"
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"中位数：抗噪，偶尔比主导色更稳\n"
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"均值：会产生新颜色并糊边，只在想要柔和过渡时用\n"
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"中心像素：等价最近邻，最锐利但受噪点影响"
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}),
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"palette": (_PAL_OPTIONS, {
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"default": "自适应 k-means（质量优先）",
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"tooltip": "颜色数受控是像素画风格的一部分，也方便整套素材统一改色。\n"
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"自适应：从画面自身聚类出调色板（k-means 在 CIELAB 空间，\n"
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" 比 RGB 更贴合人眼，暗部层次保留更好）\n"
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"固定盘：PICO-8 / Game Boy 等复古机型的真实调色板"
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}),
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"palette_size": ("INT", {
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"default": 16, "min": 2, "max": 256, "step": 1,
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"tooltip": "仅自适应调色板生效。选固定调色板时其颜色数已定，此项忽略。\n"
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"参考：8~16 复古感强，32~64 细节保留更多。"
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}),
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"dither": (list(_DITHER.keys()), {
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"default": "无（默认）",
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"tooltip": "颜色数很少时用抖动能换回一些层次，代价是引入噪点。\n"
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"Bayer：规则网点，复古感强、可平铺，像素画最常用\n"
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"Floyd-Steinberg：过渡最自然，但纹理不规则、不利于后期手改\n"
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"手绘风像素画通常不抖动，先试「无」。"
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}),
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"output_scale": ("INT", {
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"default": 1, "min": 1, "max": 32, "step": 1,
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"tooltip": "输出放大倍数（最近邻，不插值）。\n"
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"1 = 真实像素尺寸，直接可用作 sprite（推荐）\n"
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">1 仅为了在 ComfyUI 里看清效果，导出素材前记得改回 1"
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}),
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},
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"optional": {
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"mask": ("MASK", {
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"tooltip": "可选。抠图得到的 alpha 接进来会按同一网格降采样，\n"
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"并按 mask_threshold 二值化成硬边 —— sprite 需要硬边 alpha。"
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}),
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"dither_strength": ("FLOAT", {
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"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.05,
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"tooltip": "抖动幅度。已按调色板的平均色距归一化，\n"
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"所以同一数值在 4 色盘和 64 色盘上观感接近。"
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}),
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"mask_threshold": ("FLOAT", {
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"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01,
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"tooltip": "遮罩二值化阈值，高于它算不透明。\n"
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"设为 0 则保留灰度遮罩（不二值化）。"
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}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFF}),
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},
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}
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RETURN_TYPES = ("IMAGE", "MASK", "STRING")
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RETURN_NAMES = ("image", "mask", "info")
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FUNCTION = "pixelate"
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CATEGORY = "Rui-Node🐶/图像调节🎨"
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@classmethod
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def VALIDATE_INPUTS(cls, **kwargs):
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return True
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# ---------------------------------------------------------- 单张处理
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def _one(self, img, msk, mode, target_width, pixel_size, down, pal_opt,
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pal_size, dither, strength, mask_th, seed, notes):
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H, W = img.shape[:2]
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# ---- 1. 确定网格并降采样 ----
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if mode.startswith("自动检测"):
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info = detect_grid(img)
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sx, sy = info["size_x"], info["size_y"]
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ox, oy = info["off_x"], info["off_y"]
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if info["detected"]:
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notes.append(
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f"检测到网格 {sx}×{sy}（相位 {ox},{oy}；置信度 "
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f"{info['conf_x']:.1f}/{info['conf_y']:.1f}）")
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else:
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notes.append(
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f"未检出可靠网格（置信度 {info['conf_x']:.1f}/"
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f"{info['conf_y']:.1f}，低于阈值）—— 该图可能本就不是"
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f"放大的像素图。已按检测到的 {sx}×{sy} 处理，"
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f"建议改用「按目标宽度」模式")
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small = downsample_grid(img, sx, sy, ox, oy, down)
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small_m = downsample_grid(msk[..., None], sx, sy, ox, oy,
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"mean")[..., 0] if msk is not None else None
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elif mode.startswith("按像素块"):
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s = int(pixel_size)
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small = downsample_grid(img, s, s, 0, 0, down)
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small_m = downsample_grid(msk[..., None], s, s, 0, 0,
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"mean")[..., 0] if msk is not None else None
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notes.append(f"块大小 {s}×{s}")
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else:
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ow = int(max(1, min(target_width, W)))
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oh = max(1, int(round(H * ow / W)))
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# 先最近邻对齐到整数倍，再走等距网格：这样输出尺寸精确，
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# 又不会像先做面积平均那样把颜色糊掉
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k = max(1, int(round(W / ow)))
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im2 = _nearest_resize(img, ow * k, oh * k)
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small = downsample_grid(im2, k, k, 0, 0, down)
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if msk is not None:
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m2 = _nearest_resize(msk[..., None], ow * k, oh * k)
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small_m = downsample_grid(m2, k, k, 0, 0, "mean")[..., 0]
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else:
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small_m = None
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notes.append(f"目标宽度 {ow} → 输出 {small.shape[1]}×{small.shape[0]}")
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# ---- 2. 调色板量化 ----
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if pal_opt != "不量化":
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if pal_opt in PALETTES:
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pal = PALETTES[pal_opt] / 255.0
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elif pal_opt.startswith("自适应 k-means"):
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pal = kmeans_palette(small, int(pal_size), seed=int(seed))
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else:
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pal = median_cut_palette(small, int(pal_size))
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small = apply_palette(small, pal, _DITHER.get(dither, "none"),
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float(strength), int(seed))
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notes.append(f"调色板 {pal_opt}（{pal.shape[0]} 色）"
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+ (f" + {dither}" if _DITHER.get(dither) != "none" else ""))
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else:
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uniq = np.unique(
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(np.clip(small, 0, 1) * 255).astype(np.uint8).reshape(-1, 3),
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axis=0).shape[0]
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notes.append(f"未量化（实际 {uniq} 色）")
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if small_m is not None and mask_th > 0:
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small_m = (small_m >= float(mask_th)).astype(np.float32)
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return np.clip(small, 0.0, 1.0), small_m
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def pixelate(self, image, mode, target_width, pixel_size, downsample,
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palette, palette_size, dither, output_scale,
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mask=None, dither_strength=1.0, mask_threshold=0.5, seed=0):
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down = _DOWN.get(downsample, "dominant")
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B, H, W, C = image.shape
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if C == 4:
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image = image[..., :3]
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elif C == 1:
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image = image.repeat(1, 1, 1, 3)
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if mask is not None:
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if mask.dim() == 2:
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mask = mask.unsqueeze(0)
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if mask.shape[0] != B:
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mask = mask[:1].repeat(B, 1, 1)
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outs, masks, notes = [], [], []
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for b in range(B):
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img = image[b].detach().cpu().float().numpy()
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msk = mask[b].detach().cpu().float().numpy() if mask is not None else None
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if msk is not None and msk.shape[:2] != img.shape[:2]:
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msk = _nearest_resize(msk[..., None], img.shape[1],
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img.shape[0])[..., 0]
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n = []
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small, small_m = self._one(
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img, msk, mode, target_width, pixel_size, down, palette,
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palette_size, dither, dither_strength, mask_threshold,
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seed + b, n)
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if b == 0:
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notes = n
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k = int(max(1, output_scale))
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if k > 1:
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small = np.repeat(np.repeat(small, k, axis=0), k, axis=1)
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if small_m is not None:
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small_m = np.repeat(np.repeat(small_m, k, axis=0), k, axis=1)
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outs.append(torch.from_numpy(np.ascontiguousarray(small)))
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masks.append(torch.from_numpy(np.ascontiguousarray(
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small_m if small_m is not None
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else np.ones(small.shape[:2], dtype=np.float32))))
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# 批内各图尺寸可能不同（原图比例不一），此时只能退回逐张，
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# 但 ComfyUI 的 IMAGE 必须是同形状张量，故统一到首张尺寸
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h0, w0 = outs[0].shape[:2]
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for i in range(1, len(outs)):
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if outs[i].shape[:2] != (h0, w0):
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outs[i] = torch.from_numpy(_nearest_resize(
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outs[i].numpy(), w0, h0))
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masks[i] = torch.from_numpy(_nearest_resize(
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masks[i].numpy()[..., None], w0, h0)[..., 0])
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img_out = torch.stack(outs)
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msk_out = torch.stack(masks)
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info = f"{W}×{H} → {w0}×{h0}" + (f"（预览放大 {output_scale}×）"
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if output_scale > 1 else "")
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info += "\n" + "\n".join(notes)
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print(f"[Ruinode-Pixelate] {info}")
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return (img_out, msk_out, info)
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NODE_CLASS_MAPPINGS = {
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"RuiPixelate": RuiPixelate,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RuiPixelate": "像素化 / Pixelate",
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}
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