# -*- coding: utf-8 -*- """ 像素化节点（Ruinode） ===================== 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"。 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； 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。 三种工作模式： - 按目标宽度 给定输出宽度（如 64），普通图/照片/插画 → 像素画 - 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 - 自动检测网格 探测图像本身隐含的像素网格并还原 —— 专治「AI 生成的伪像素图」：模型输出的 1024×1024 图看着像素风， 实际网格歪斜、边缘带抗锯齿、颜色成千上万，塞进引擎会糊。 关于方案选型（研究后的结论）： Sprite Fusion 的 Pixel Snapper（MIT）解决的是「伪像素图 → 完美像素图」， 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， 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， 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 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， 详见 pixelart/grid.py。 """ import numpy as np import torch from .pixelart import (PALETTE_NAMES, PALETTES, apply_palette, detect_grid, downsample_grid, kmeans_palette, median_cut_palette) _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）"] _DOWN = { "主导色 dominant（像素画首选）": "dominant", "中位数 median": "median", "均值 mean（会糊边，慎用）": "mean", "中心像素 center（最锐）": "center", } _DITHER = { "无（默认）": "none", "Bayer 2×2": "bayer2", "Bayer 4×4": "bayer4", "Bayer 8×8": "bayer8", "Floyd-Steinberg": "floyd-steinberg", "随机噪声": "noise", } _PAL_ADAPTIVE = ["自适应 k-means（质量优先）", "自适应 median cut（速度优先）"] _PAL_OPTIONS = ["不量化"] + _PAL_ADAPTIVE + PALETTE_NAMES def _nearest_resize(img, tw, th): """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。""" h, w = img.shape[:2] if (w, h) == (tw, th): return img xi = np.clip((np.arange(tw) * (w / tw)).astype(np.int64), 0, w - 1) yi = np.clip((np.arange(th) * (h / th)).astype(np.int64), 0, h - 1) return img[yi][:, xi] class RuiPixelate: """图像 → 像素画（面向像素游戏资产）。""" @classmethod def INPUT_TYPES(cls): return { "required": { "image": ("IMAGE", { "tooltip": "待像素化的图像。批量输入时若各图比例不同，\n" "会统一到首张的输出尺寸（ComfyUI 的 IMAGE 必须同形状）。" }), "mode": (_MODES, { "default": "按目标宽度", "tooltip": "按目标宽度：普通图转像素画，直接给输出宽度\n" "按像素块大小：每 N×N 原像素合成一个像素\n" "自动检测网格：探测图中隐含的像素网格并还原，\n" " 专治 AI 生成的模糊伪像素图（网格歪、带抗锯齿）" }), "target_width": ("INT", { "default": 64, "min": 8, "max": 2048, "step": 1, "tooltip": "仅「按目标宽度」模式生效。高度按原图比例自动计算。\n" "常见 sprite 尺寸：16 / 32 / 48 / 64 / 96 / 128" }), "pixel_size": ("INT", { "default": 8, "min": 1, "max": 256, "step": 1, "tooltip": "仅「按像素块大小」模式生效：每 N×N 原像素 → 1 像素。" }), "downsample": (list(_DOWN.keys()), { "default": "主导色 dominant（像素画首选）", "tooltip": "每个单元如何定色。\n" "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" "中位数：抗噪，偶尔比主导色更稳\n" "均值：会产生新颜色并糊边，只在想要柔和过渡时用\n" "中心像素：等价最近邻，最锐利但受噪点影响" }), "palette": (_PAL_OPTIONS, { "default": "自适应 k-means（质量优先）", "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" "自适应：从画面自身聚类出调色板（k-means 在 CIELAB 空间，\n" " 比 RGB 更贴合人眼，暗部层次保留更好）\n" "固定盘：PICO-8 / Game Boy 等复古机型的真实调色板" }), "palette_size": ("INT", { "default": 16, "min": 2, "max": 256, "step": 1, "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" "参考：8~16 复古感强，32~64 细节保留更多。" }), "dither": (list(_DITHER.keys()), { "default": "无（默认）", "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" "Bayer：规则网点，复古感强、可平铺，像素画最常用\n" "Floyd-Steinberg：过渡最自然，但纹理不规则、不利于后期手改\n" "手绘风像素画通常不抖动，先试「无」。" }), "output_scale": ("INT", { "default": 1, "min": 1, "max": 32, "step": 1, "tooltip": "输出放大倍数（最近邻，不插值）。\n" "1 = 真实像素尺寸，直接可用作 sprite（推荐）\n" ">1 仅为了在 ComfyUI 里看清效果，导出素材前记得改回 1" }), }, "optional": { "mask": ("MASK", { "tooltip": "可选。抠图得到的 alpha 接进来会按同一网格降采样，\n" "并按 mask_threshold 二值化成硬边 —— sprite 需要硬边 alpha。" }), "dither_strength": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 3.0, "step": 0.05, "tooltip": "抖动幅度。已按调色板的平均色距归一化，\n" "所以同一数值在 4 色盘和 64 色盘上观感接近。" }), "mask_threshold": ("FLOAT", { "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "遮罩二值化阈值，高于它算不透明。\n" "设为 0 则保留灰度遮罩（不二值化）。" }), "seed": ("INT", { "default": 0, "min": 0, "max": 0xFFFFFFFF, "tooltip": "随机种子。影响 k-means 调色板的初始化与随机噪声抖动；\n" "同一张图换种子会得到略有差异的配色，可多试几个挑顺眼的。\n" "固定调色板与 Bayer 抖动不受它影响。" }), }, } RETURN_TYPES = ("IMAGE", "MASK", "STRING") RETURN_NAMES = ("image", "mask", "info") FUNCTION = "pixelate" CATEGORY = "Rui-Node🐶/图像调节🎨" @classmethod def VALIDATE_INPUTS(cls, **kwargs): return True # ---------------------------------------------------------- 单张处理 def _one(self, img, msk, mode, target_width, pixel_size, down, pal_opt, pal_size, dither, strength, mask_th, seed, notes): H, W = img.shape[:2] # ---- 1. 确定网格并降采样 ---- if mode.startswith("自动检测"): info = detect_grid(img) sx, sy = info["size_x"], info["size_y"] ox, oy = info["off_x"], info["off_y"] if info["detected"]: notes.append( f"检测到网格 {sx}×{sy}（相位 {ox},{oy}；置信度 " f"{info['conf_x']:.1f}/{info['conf_y']:.1f}）") else: notes.append( f"未检出可靠网格（置信度 {info['conf_x']:.1f}/" f"{info['conf_y']:.1f}，低于阈值）—— 该图可能本就不是" f"放大的像素图。已按检测到的 {sx}×{sy} 处理，" f"建议改用「按目标宽度」模式") small = downsample_grid(img, sx, sy, ox, oy, down) small_m = downsample_grid(msk[..., None], sx, sy, ox, oy, "mean")[..., 0] if msk is not None else None elif mode.startswith("按像素块"): s = int(pixel_size) small = downsample_grid(img, s, s, 0, 0, down) small_m = downsample_grid(msk[..., None], s, s, 0, 0, "mean")[..., 0] if msk is not None else None notes.append(f"块大小 {s}×{s}") else: ow = int(max(1, min(target_width, W))) oh = max(1, int(round(H * ow / W))) # 先最近邻对齐到整数倍，再走等距网格：这样输出尺寸精确， # 又不会像先做面积平均那样把颜色糊掉 k = max(1, int(round(W / ow))) im2 = _nearest_resize(img, ow * k, oh * k) small = downsample_grid(im2, k, k, 0, 0, down) if msk is not None: m2 = _nearest_resize(msk[..., None], ow * k, oh * k) small_m = downsample_grid(m2, k, k, 0, 0, "mean")[..., 0] else: small_m = None notes.append(f"目标宽度 {ow} → 输出 {small.shape[1]}×{small.shape[0]}") # ---- 2. 调色板量化 ---- if pal_opt != "不量化": if pal_opt in PALETTES: pal = PALETTES[pal_opt] / 255.0 elif pal_opt.startswith("自适应 k-means"): pal = kmeans_palette(small, int(pal_size), seed=int(seed)) else: pal = median_cut_palette(small, int(pal_size)) small = apply_palette(small, pal, _DITHER.get(dither, "none"), float(strength), int(seed)) notes.append(f"调色板 {pal_opt}（{pal.shape[0]} 色）" + (f" + {dither}" if _DITHER.get(dither) != "none" else "")) else: uniq = np.unique( (np.clip(small, 0, 1) * 255).astype(np.uint8).reshape(-1, 3), axis=0).shape[0] notes.append(f"未量化（实际 {uniq} 色）") if small_m is not None and mask_th > 0: small_m = (small_m >= float(mask_th)).astype(np.float32) return np.clip(small, 0.0, 1.0), small_m def pixelate(self, image, mode, target_width, pixel_size, downsample, palette, palette_size, dither, output_scale, mask=None, dither_strength=1.0, mask_threshold=0.5, seed=0): down = _DOWN.get(downsample, "dominant") B, H, W, C = image.shape if C == 4: image = image[..., :3] elif C == 1: image = image.repeat(1, 1, 1, 3) if mask is not None: if mask.dim() == 2: mask = mask.unsqueeze(0) if mask.shape[0] != B: mask = mask[:1].repeat(B, 1, 1) outs, masks, notes = [], [], [] for b in range(B): img = image[b].detach().cpu().float().numpy() msk = mask[b].detach().cpu().float().numpy() if mask is not None else None if msk is not None and msk.shape[:2] != img.shape[:2]: msk = _nearest_resize(msk[..., None], img.shape[1], img.shape[0])[..., 0] n = [] small, small_m = self._one( img, msk, mode, target_width, pixel_size, down, palette, palette_size, dither, dither_strength, mask_threshold, seed + b, n) if b == 0: notes = n k = int(max(1, output_scale)) if k > 1: small = np.repeat(np.repeat(small, k, axis=0), k, axis=1) if small_m is not None: small_m = np.repeat(np.repeat(small_m, k, axis=0), k, axis=1) outs.append(torch.from_numpy(np.ascontiguousarray(small))) masks.append(torch.from_numpy(np.ascontiguousarray( small_m if small_m is not None else np.ones(small.shape[:2], dtype=np.float32)))) # 批内各图尺寸可能不同（原图比例不一），此时只能退回逐张， # 但 ComfyUI 的 IMAGE 必须是同形状张量，故统一到首张尺寸 h0, w0 = outs[0].shape[:2] for i in range(1, len(outs)): if outs[i].shape[:2] != (h0, w0): outs[i] = torch.from_numpy(_nearest_resize( outs[i].numpy(), w0, h0)) masks[i] = torch.from_numpy(_nearest_resize( masks[i].numpy()[..., None], w0, h0)[..., 0]) img_out = torch.stack(outs) msk_out = torch.stack(masks) info = f"{W}×{H} → {w0}×{h0}" + (f"（预览放大 {output_scale}×）" if output_scale > 1 else "") info += "\n" + "\n".join(notes) print(f"[Ruinode-Pixelate] {info}") return (img_out, msk_out, info) NODE_CLASS_MAPPINGS = { "RuiPixelate": RuiPixelate, } NODE_DISPLAY_NAME_MAPPINGS = { "RuiPixelate": "像素化 / Pixelate", }