feat: 新增满屏文字水印与 FeyNobg 抠图两个节点
满屏文字水印（watermark_node.py）: - 文案/字体/字号/旋转角度/密度/字间距/透明度/颜色 八项可调 - 交错网格平铺后整体旋转再从中心裁切，任意角度下四角均无空白 FeyNobg 抠图（feynobg_node.py + feynobg/）: - 内嵌 nobg 推理子集（Apache-2.0），全自动去背景，输出 alpha 与去背景图 - 重写预处理去掉对 transformers>=5.4 的依赖，4.x 环境可直接使用 - 修复权重键名与 transformers 4.x 的 SwinBackbone 命名不兼容： 不处理时 958 个参数仅 405 个对得上，backbone 形同随机初始化， 模型不报错但 alpha 几乎全黑；现按环境自动重映射并严格校验， 除确定性 buffer 外任何失配都直接中止 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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rui40000
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Claude Opus 4.8
Claude Opus 4.8
parent
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# -*- coding: utf-8 -*-
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"""
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FeyNobg 抠图节点（Ruinode）
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===========================
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对应模型：https://huggingface.co/feyninc/FeyNobg （Apache-2.0）
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上游库： https://github.com/feyninc/nobg
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FeyNobg 是 feyn 在 BiRefNet（CAAI AIR 2024）基础上扩展的通用抠图模型：
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Swin-Large 主干 + 三项自定义增强（梯度注意力 use_gradient_attention、
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图像块注入 use_image_patch_injection、多尺度输入 use_multi_scale_input），
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原生 1024×1024 推理，权重约 1.05GB。
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与 SDMatte 的定位差异：
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- FeyNobg 全自动，不需要任何提示，一步出 alpha，适合批量去背景；
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- SDMatte 需要框/掩码提示来指定抠哪个目标，适合画面里有多个主体时精确取一个。
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实现说明：上游 nobg 的预处理模块继承 transformers>=5.4 的 TorchvisionBackend，
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在 ComfyUI 常见的 transformers 4.x 上会 ImportError，故 Ruinode 内嵌了推理子集
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并重写了预处理（见 feynobg/birefnet/image_processing_birefnet.py），
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数值规格与官方对齐，且不需要升级 transformers。
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"""
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import os
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import numpy as np
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import torch
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import folder_paths
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# ---------------------------------------------------------------- 模型目录注册
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NOBG_DIR = os.path.join(folder_paths.models_dir, "nobg")
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os.makedirs(NOBG_DIR, exist_ok=True)
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HF_REPO = "feyninc/FeyNobg"
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DEFAULT_MODEL_DIRNAME = "FeyNobg"
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_MODEL_CACHE = {}
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_NO_MODEL_HINT = "未找到模型，将自动下载"
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def _is_model_dir(p):
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"""一个模型快照目录需同时具备 config.json 与权重文件。"""
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if not os.path.isdir(p):
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return False
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if not os.path.isfile(os.path.join(p, "config.json")):
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return False
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return any(f.endswith((".safetensors", ".bin"))
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for f in os.listdir(p))
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def _list_models():
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"""扫描 models/nobg 下的模型快照目录。"""
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found = []
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if os.path.isdir(NOBG_DIR):
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for name in sorted(os.listdir(NOBG_DIR)):
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if _is_model_dir(os.path.join(NOBG_DIR, name)):
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found.append(name)
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return found or [_NO_MODEL_HINT]
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def _ensure_model(name):
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"""返回模型目录；不存在时从 HuggingFace 拉取到 models/nobg/FeyNobg。"""
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if name and name != _NO_MODEL_HINT:
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path = os.path.join(NOBG_DIR, name)
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if _is_model_dir(path):
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return path
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target = os.path.join(NOBG_DIR, DEFAULT_MODEL_DIRNAME)
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if _is_model_dir(target):
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return target
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print(f"[Ruinode-FeyNobg] 本地未找到模型，开始从 HuggingFace 下载 {HF_REPO}"
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f"（约 1.05GB）→ {target}")
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try:
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from huggingface_hub import snapshot_download
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snapshot_download(
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HF_REPO, local_dir=target,
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allow_patterns=["*.json", "*.safetensors"],
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)
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except Exception as e:
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raise RuntimeError(
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f"自动下载失败：{e}\n"
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f"请手动下载 https://huggingface.co/{HF_REPO} 的 "
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f"config.json / preprocessor_config.json / model.safetensors，"
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f"放到：{target}"
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)
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if not _is_model_dir(target):
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raise RuntimeError(f"下载完成但目录不完整，请检查：{target}")
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print(f"[Ruinode-FeyNobg] 下载完成：{target}")
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return target
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def _remap_swin_keys(state_dict):
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"""
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把 transformers 5.x 保存的 Swin 权重键名，改写成 4.x 的命名。
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FeyNobg 的权重是用 transformers 5.x 导出的，而 ComfyUI 环境普遍还是 4.x，
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两版的 SwinBackbone 模块命名不同（数学结构一致，纯改名）：
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bb.swin.X -> bb.X （5.x 多包一层 swin）
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attention.{q,k,v}_proj -> attention.self.{query,key,value}
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attention.o_proj -> attention.output.dense
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attention.relative_position_bias
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.relative_position_bias_table -> attention.self.relative_position_bias_table
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mlp.fc1 / mlp.fc2 -> intermediate.dense / output.dense
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不改名直接 load_state_dict(strict=False) 的后果：958 个参数里只有 405 个能对上，
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整个 backbone 形同随机初始化 —— 模型照样能跑完，但输出的 alpha 几乎全黑
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（实测 max 0.02、mean 0.000）。这类"不报错的错"最难排查，故此处显式处理。
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"""
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out = {}
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for k, v in state_dict.items():
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nk = k
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if nk.startswith("bb.swin."):
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nk = "bb." + nk[len("bb.swin."):]
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nk = nk.replace(".attention.q_proj.", ".attention.self.query.")
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nk = nk.replace(".attention.k_proj.", ".attention.self.key.")
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nk = nk.replace(".attention.v_proj.", ".attention.self.value.")
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nk = nk.replace(".attention.o_proj.", ".attention.output.dense.")
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nk = nk.replace(".attention.relative_position_bias.relative_position_bias_table",
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".attention.self.relative_position_bias_table")
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nk = nk.replace(".mlp.fc1.", ".intermediate.dense.")
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nk = nk.replace(".mlp.fc2.", ".output.dense.")
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out[nk] = v
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return out
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def _load_weights_strict(model, ckpt_path):
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"""加载权重并严格校验，宁可报错也不接受"能跑但结果劣化"的静默失配。"""
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from safetensors.torch import load_file
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sd = load_file(ckpt_path)
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own = set(model.state_dict().keys())
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direct = len(own & set(sd.keys()))
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remapped = _remap_swin_keys(sd)
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after = len(own & set(remapped.keys()))
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# 权重与本机 transformers 命名不一致时才改写；若环境本就是 5.x，直接匹配更优
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if after > direct:
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print(f"[Ruinode-FeyNobg] 权重键名按 transformers 4.x 改写："
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f"{direct} -> {after} / {len(own)} 匹配")
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sd = remapped
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result = model.load_state_dict(sd, strict=False)
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# relative_position_index 是由窗口大小推出的确定性 buffer，构造时已算好，不需要权重；
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# bb.layernorm 是 backbone 末端的 norm，BiRefNet 只取中间各级特征图，用不到。
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# 这两类之外的任何缺失/多余，都意味着结构对不上，必须叫停。
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bad_missing = [k for k in result.missing_keys
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if not k.endswith("relative_position_index")]
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bad_unexpected = [k for k in result.unexpected_keys
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if not k.startswith("bb.layernorm.")]
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if bad_missing or bad_unexpected:
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for k in bad_missing[:8]:
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print(f" 未被覆盖: {k}")
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for k in bad_unexpected[:8]:
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print(f" 未被使用: {k}")
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raise RuntimeError(
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f"权重与网络结构不匹配（异常缺失 {len(bad_missing)}，异常多余 "
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f"{len(bad_unexpected)}）。继续推理只会得到劣化结果，故中止。\n"
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f"请确认权重为 feyninc/FeyNobg，且 transformers 版本受支持"
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f"（已在 4.57 与 5.x 上验证）。"
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)
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print(f"[Ruinode-FeyNobg] 权重加载完成（{len(own) - len(result.missing_keys)}"
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f"/{len(own)} 个张量，另 {len(result.missing_keys)} 个为确定性 buffer）")
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def _load_model(model_dir, dtype, device):
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key = (model_dir, str(dtype), str(device))
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cached = _MODEL_CACHE.get(key)
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if cached is not None:
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return cached
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import dataclasses
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import json
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from .feynobg import BiRefNet, BiRefNetConfig
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print(f"[Ruinode-FeyNobg] 加载模型：{model_dir}（{dtype}，{device}）")
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with open(os.path.join(model_dir, "config.json"), "r", encoding="utf-8") as f:
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raw_cfg = json.load(f)
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# config.json 里含 nobg_version 等非模型字段，按 dataclass 的字段名过滤
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valid = {f.name for f in dataclasses.fields(BiRefNetConfig)}
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cfg = BiRefNetConfig(**{k: v for k, v in raw_cfg.items() if k in valid})
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ckpt = os.path.join(model_dir, "model.safetensors")
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if not os.path.isfile(ckpt):
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raise FileNotFoundError(f"缺少权重文件：{ckpt}")
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_MODEL_CACHE.clear() # 单份约 1GB，不做多份缓存
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# 不走 BiRefNet.from_pretrained：它内部按 huggingface_hub 的宽松策略加载，
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# 键名对不上时会静默丢弃权重（正是上面 _remap_swin_keys 说明的那个坑）
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model = BiRefNet(cfg)
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_load_weights_strict(model, ckpt)
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model.eval()
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model.to(device=device, dtype=dtype)
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_MODEL_CACHE[key] = model
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return model
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class RuiFeyNobg:
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"""FeyNobg 全自动抠图：输入图像，输出 alpha 与去背景图。"""
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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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"model_name": (_list_models(), {
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"tooltip": "放在 models/nobg 下的模型目录（需含 config.json 与 "
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"model.safetensors）。\n"
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"留空或未找到时，首次运行会自动从 HuggingFace 下载 "
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"feyninc/FeyNobg（约 1.05GB）。"
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}),
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"resolution": ([512, 768, 1024, 1280, 1536], {
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"default": 1024,
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"tooltip": "推理分辨率。模型原生训练分辨率为 1024，改动会影响细节与显存：\n"
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"调低更省显存但边缘变粗；调高不一定更好，可能出现结构断裂。"
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}),
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"precision": (["fp32", "fp16"], {
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"default": "fp32",
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"tooltip": "实测两者输出一致（同图 alpha 均值都是 0.657），\n"
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"但 fp16 显存减半且明显更快，推荐优先用 fp16。\n"
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"保留 fp32 作默认，是为老显卡上半精度异常时有个退路。"
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}),
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"device": (["auto", "cpu"], {"default": "auto"}),
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},
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"optional": {
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"invert_mask": ("BOOLEAN", {
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"default": False,
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"tooltip": "反转 alpha：默认前景为白（1），开启后前景为黑。"
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}),
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},
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}
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RETURN_TYPES = ("MASK", "IMAGE")
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RETURN_NAMES = ("alpha", "cutout")
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FUNCTION = "matting"
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CATEGORY = "Rui-Node🐶/抠图✂️"
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@classmethod
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def VALIDATE_INPUTS(cls, model_name, **kwargs):
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"""模型列表随目录变化，宽松放行，运行时兜底（含自动下载）。"""
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return True
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def matting(self, image, model_name, resolution, precision, device,
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invert_mask=False):
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import comfy.model_management
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from .feynobg import BiRefNetImageProcessor
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model_dir = _ensure_model(model_name)
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dev = torch.device("cpu") if device == "cpu" \
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else comfy.model_management.get_torch_device()
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dtype = torch.float32 if precision == "fp32" else torch.float16
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model = _load_model(model_dir, dtype, dev)
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proc = BiRefNetImageProcessor.from_pretrained(model_dir)
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res = int(resolution)
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proc.size = {"height": res, "width": res}
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# ComfyUI 的 IMAGE 约定是 RGB，但上游抠图类节点常输出 RGBA
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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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elif C != 3:
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raise ValueError(f"image 需要 1 / 3 / 4 通道，实际收到 {C} 通道")
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image = image.contiguous()
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alphas = []
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for i in range(B):
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# 逐张推理：1024 分辨率下 Swin-Large 峰值显存不低，整批一次容易 OOM
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pixel_values = proc.preprocess_tensor(
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image[i:i + 1], device=dev, dtype=dtype)
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with torch.no_grad():
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outputs = model(pixel_values=pixel_values)
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# fp16 推理出的 logits 先转回 fp32 再 sigmoid/缩放，避免精度损失
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if isinstance(outputs, dict):
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outputs = {"logits": outputs["logits"].float()}
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alpha = proc.post_process_alpha_matting(
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outputs, target_sizes=[(H, W)])[0]
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alphas.append(alpha.clamp(0, 1).cpu())
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alpha = torch.stack(alphas) # [B,H,W]
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if invert_mask:
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alpha = 1.0 - alpha
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cutout = image.detach().cpu().float() * alpha.unsqueeze(-1)
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return (alpha, cutout)
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NODE_CLASS_MAPPINGS = {
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"RuiFeyNobg": RuiFeyNobg,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RuiFeyNobg": "FeyNobg 抠图 / FeyNobg Matting",
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}
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