feat: 新增去背景/PSD分层导出节点，优化聊天面板与重启逻辑
- 新增 O1keyRemoveBackground 节点（基于 rembg CPU 推理） - 新增 O1keyColorRemoveBG 节点（颜色距离去背景，支持多模式） - 新增 O1keySavePSD 节点（手写 PSD 二进制，零外部依赖） - GPT Image 批量输出不同尺寸时自动 resize 对齐 - 聊天面板大幅增强（多模态/交互优化） - 重启按钮绕过 beforeunload 弹窗强制刷新 - 默认路由切换为 CF加速 - http_error 新增 system error 友好文案 Co-Authored-By: Claude Opus 4.7 <[email protected]>
This commit is contained in:
o1key
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@@ -28,5 +28,8 @@ from .K3_video import K3Video
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from .K3_video_firstlast import K3VideoFirstLast
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from .K3_motion_control import K3MotionControl, K3MotionVideoCheck
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from .save_image_format import SaveImageFormat
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from .save_psd import O1keySavePSD
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from .remove_bg import O1keyRemoveBackground
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from .color_remove_bg import O1keyColorRemoveBG
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__all__ = ['NanoBananaV2', 'NanoBananaV2Batch', 'NanoBanana', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage', 'O1keyGrokImage', 'KVideoFirstLast', 'KVideoImage2Video', 'K3Video', 'K3VideoFirstLast', 'K3MotionControl', 'K3MotionVideoCheck', 'AsyncImageGenerator', 'BatchAsyncImageGenerator', 'SaveImageFormat']
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__all__ = ['NanoBananaV2', 'NanoBananaV2Batch', 'NanoBanana', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage', 'O1keyGrokImage', 'KVideoFirstLast', 'KVideoImage2Video', 'K3Video', 'K3VideoFirstLast', 'K3MotionControl', 'K3MotionVideoCheck', 'AsyncImageGenerator', 'BatchAsyncImageGenerator', 'SaveImageFormat', 'O1keySavePSD', 'O1keyRemoveBackground', 'O1keyColorRemoveBG']
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@@ -0,0 +1,93 @@
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"""
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o1key 颜色去背景节点
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基于颜色距离计算，精确可控，不依赖 AI 模型
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"""
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import numpy as np
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import torch
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from PIL import Image
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class O1keyColorRemoveBG:
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"""
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颜色去背景 - 精确移除纯色背景
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模式说明：
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- 白色(white): 移除白色背景，适合大多数场景
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- 白色保护(white-preserve): 移除白底但保护浅色前景物体
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- 自动检测(corner): 自动采样四角颜色作为背景色
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- 指定颜色(color): 手动指定要移除的背景颜色
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"""
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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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"模式": (["白色", "白色保护", "自动检测", "指定颜色"], {
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"default": "白色",
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}),
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"容差": ("FLOAT", {
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"default": 8.0,
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"min": 0.0,
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"max": 100.0,
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"step": 1.0,
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"tooltip": "颜色距离阈值，越大去除范围越广",
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}),
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"羽化": ("FLOAT", {
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"default": 45.0,
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"min": 0.0,
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"max": 200.0,
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"step": 1.0,
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"tooltip": "边缘过渡范围，越大边缘越柔和",
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}),
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},
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"optional": {
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"背景色R": ("INT", {"default": 255, "min": 0, "max": 255}),
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"背景色G": ("INT", {"default": 255, "min": 0, "max": 255}),
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"背景色B": ("INT", {"default": 255, "min": 0, "max": 255}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("RGBA图像",)
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FUNCTION = "remove_bg"
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CATEGORY = "o1key/image"
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_MODE_MAP = {
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"白色": "white",
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"白色保护": "white-preserve",
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"自动检测": "corner",
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"指定颜色": "color",
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}
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def remove_bg(self, image, 模式, 容差, 羽化, 背景色R=255, 背景色G=255, 背景色B=255):
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from ..utils.color_key import remove_background
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mode = self._MODE_MAP.get(模式, "white")
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bg_color = (背景色R, 背景色G, 背景色B)
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batch_size = image.shape[0]
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results = []
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for i in range(batch_size):
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frame = image[i] # [H, W, C]
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arr = (frame.cpu().numpy() * 255).clip(0, 255).astype(np.uint8)
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if arr.shape[2] == 4:
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pil_img = Image.fromarray(arr, mode="RGBA")
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else:
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pil_img = Image.fromarray(arr, mode="RGB")
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result = remove_background(
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pil_img, mode=mode, bg_color=bg_color,
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tolerance=容差, feather=羽化,
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)
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result_arr = np.array(result.convert("RGBA")).astype(np.float32) / 255.0
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results.append(torch.from_numpy(result_arr))
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output = torch.stack(results, dim=0)
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print(f"[o1key 颜色去背景] 模式={模式}, 容差={容差}, 羽化={羽化}, "
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f"处理 {batch_size} 张")
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return (output,)
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@@ -0,0 +1,36 @@
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"""
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o1key 去背景节点
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基于 rembg 实现，支持 CPU 推理
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"""
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import numpy as np
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import torch
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class O1keyRemoveBackground:
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"""
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移除图像背景，输出 RGBA 透明图层
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基于 rembg (ISNet-General-Use) 模型，支持 CPU 推理。
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首次运行会自动下载模型（约 170MB）。
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"""
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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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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("RGBA图像",)
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FUNCTION = "remove_bg"
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CATEGORY = "o1key/image"
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def remove_bg(self, image):
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from ..utils.rembg_utils import remove_background_tensor
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print("[o1key 去背景] 正在处理...")
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result = remove_background_tensor(image)
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print(f"[o1key 去背景] 完成，输出 {result.shape[0]} 张 RGBA")
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return (result,)
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@@ -0,0 +1,237 @@
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"""
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o1key SavePSD 节点
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将多个 IMAGE 图层合成为分层 PSD 文件
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手写 PSD 二进制格式，零外部依赖（仅 numpy + Pillow）
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"""
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import os
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import struct
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import time
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import numpy as np
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import torch
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from PIL import Image
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import folder_paths
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def _pad_even(data: bytes) -> bytes:
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if len(data) % 2:
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return data + b"\x00"
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return data
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def _pad4(data: bytes) -> bytes:
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return data + (b"\x00" * ((4 - (len(data) % 4)) % 4))
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def _pascal_name(name: str) -> bytes:
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raw = name.encode("macroman", errors="replace")[:255]
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data = bytes([len(raw)]) + raw
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return _pad4(data)
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def _unicode_name_block(name: str) -> bytes:
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payload = struct.pack(">I", len(name)) + name.encode("utf-16be")
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block = b"8BIM" + b"luni" + struct.pack(">I", len(payload)) + _pad_even(payload)
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return block
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def _layer_extra_data(name: str) -> bytes:
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data = b""
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data += struct.pack(">I", 0) # layer mask data length
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data += struct.pack(">I", 0) # layer blending ranges length
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data += _pascal_name(name)
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data += _unicode_name_block(name)
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return data
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def _alpha_bbox(rgba_arr: np.ndarray):
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"""找到 RGBA 数组中非透明区域的 bounding box。"""
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alpha = rgba_arr[:, :, 3]
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rows = np.any(alpha > 0, axis=1)
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cols = np.any(alpha > 0, axis=0)
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if not rows.any():
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return None
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top = int(np.argmax(rows))
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bottom = int(len(rows) - np.argmax(rows[::-1]))
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left = int(np.argmax(cols))
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right = int(len(cols) - np.argmax(cols[::-1]))
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return top, left, bottom, right
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def write_psd(filepath: str, layers: list, canvas_w: int, canvas_h: int):
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"""
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写入 PSD 文件。
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layers: [(name, rgba_array), ...] 从底到顶排列
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rgba_array: numpy uint8 [H, W, 4]
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"""
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records = []
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channel_data_blocks = []
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layers_top_to_bottom = list(reversed(layers))
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for name, rgba in layers_top_to_bottom:
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bbox = _alpha_bbox(rgba)
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if not bbox:
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continue
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top, left, bottom, right = bbox
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cropped = rgba[top:bottom, left:right]
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# PLACEHOLDER_CHANNELS
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channels = [
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(0, cropped[:, :, 0].tobytes(order="C")),
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(1, cropped[:, :, 1].tobytes(order="C")),
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(2, cropped[:, :, 2].tobytes(order="C")),
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(-1, cropped[:, :, 3].tobytes(order="C")),
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]
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channel_info = b""
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data_block = b""
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for channel_id, data in channels:
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channel_info += struct.pack(">hI", channel_id, 2 + len(data))
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data_block += struct.pack(">H", 0) + data # raw compression
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extra = _layer_extra_data(name)
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record = b""
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record += struct.pack(">iiii", top, left, bottom, right)
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record += struct.pack(">H", len(channels))
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record += channel_info
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record += b"8BIM" + b"norm"
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record += bytes([255, 0, 0, 0]) # opacity=255, clipping, flags, filler
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record += struct.pack(">I", len(extra)) + extra
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records.append(record)
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channel_data_blocks.append(data_block)
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if not records:
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raise ValueError("所有图层均为空（完全透明），无法生成 PSD")
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# Layer and Mask Information
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layer_info = struct.pack(">h", len(records))
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layer_info += b"".join(records) + b"".join(channel_data_blocks)
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layer_info = _pad_even(layer_info)
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layer_info_block = struct.pack(">I", len(layer_info)) + layer_info
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global_mask = struct.pack(">I", 0)
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layer_mask_payload = layer_info_block + global_mask
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layer_and_mask = struct.pack(">I", len(layer_mask_payload)) + layer_mask_payload
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# PLACEHOLDER_COMPOSITE
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# Composite preview (flattened image for compatibility)
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comp = Image.new("RGBA", (canvas_w, canvas_h), (255, 255, 255, 255))
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for name, rgba in layers:
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layer_img = Image.fromarray(rgba, "RGBA")
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comp.alpha_composite(layer_img)
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comp_rgb = np.asarray(comp.convert("RGB"), dtype=np.uint8)
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composite_data = (
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struct.pack(">H", 0)
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+ comp_rgb[:, :, 0].tobytes(order="C")
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+ comp_rgb[:, :, 1].tobytes(order="C")
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+ comp_rgb[:, :, 2].tobytes(order="C")
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)
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# Write PSD file
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with open(filepath, "wb") as f:
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# Header
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f.write(b"8BPS")
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f.write(struct.pack(">H", 1)) # version
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f.write(b"\x00" * 6) # reserved
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f.write(struct.pack(">HIIHH", 3, canvas_h, canvas_w, 8, 3))
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# Color Mode Data
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f.write(struct.pack(">I", 0))
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# Image Resources
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f.write(struct.pack(">I", 0))
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# Layer and Mask
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f.write(layer_and_mask)
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# Composite Image Data
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f.write(composite_data)
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# PLACEHOLDER_NODE
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class O1keySavePSD:
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"""
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将多个 IMAGE 输入合成为分层 PSD 文件
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每个输入作为独立图层，支持 RGBA 透明通道。
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图层从下到上排列（图层1在最底部）。
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使用 bbox 裁剪优化文件大小，包含合成预览层。
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"""
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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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"批次图像": ("IMAGE", {
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"tooltip": "批次图像输入，每张图自动作为独立图层（支持RGBA透明）",
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}),
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},
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"optional": {
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"图层名称": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "每行一个图层名称，与图层顺序对应。留空则自动命名。",
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}),
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"文件名前缀": ("STRING", {
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"default": "o1key_layers",
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"tooltip": "输出 PSD 文件名前缀",
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}),
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("文件路径",)
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FUNCTION = "save_psd"
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CATEGORY = "o1key/image"
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OUTPUT_NODE = True
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def save_psd(self, 批次图像, 图层名称: str = "", 文件名前缀: str = "o1key_layers", **kwargs):
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# 将批次 tensor [B, H, W, C] 拆为单张列表
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if 批次图像.dim() == 3:
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layer_tensors = [批次图像]
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else:
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layer_tensors = [批次图像[i] for i in range(批次图像.shape[0])]
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names = [n.strip() for n in 图层名称.split("\n") if n.strip()]
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# 确定画布尺寸
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max_h, max_w = 0, 0
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for t in layer_tensors:
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h, w = t.shape[0], t.shape[1]
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max_h = max(max_h, h)
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max_w = max(max_w, w)
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# 转换为 [(name, rgba_array), ...] 格式
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layers = []
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for idx, tensor in enumerate(layer_tensors):
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arr = (tensor.cpu().numpy() * 255).clip(0, 255).astype(np.uint8)
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h, w = arr.shape[0], arr.shape[1]
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channels = arr.shape[2] if arr.ndim == 3 else 1
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if channels == 3:
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rgba = np.zeros((max_h, max_w, 4), dtype=np.uint8)
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rgba[:h, :w, :3] = arr
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rgba[:h, :w, 3] = 255
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elif channels == 4:
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rgba = np.zeros((max_h, max_w, 4), dtype=np.uint8)
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rgba[:h, :w] = arr
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else:
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rgba = np.zeros((max_h, max_w, 4), dtype=np.uint8)
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rgba[:h, :w, 0] = rgba[:h, :w, 1] = rgba[:h, :w, 2] = arr[:, :, 0] if arr.ndim == 3 else arr
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rgba[:h, :w, 3] = 255
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name = names[idx] if idx < len(names) else f"图层 {idx + 1}"
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layers.append((name, rgba))
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print(f"[o1key SavePSD] 图层 '{name}': {w}×{h}")
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# 写入 PSD
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output_dir = folder_paths.get_output_directory()
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timestamp = time.strftime("%Y%m%d_%H%M%S")
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filename = f"{文件名前缀}_{timestamp}.psd"
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filepath = os.path.join(output_dir, filename)
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write_psd(filepath, layers, max_w, max_h)
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size_kb = os.path.getsize(filepath) / 1024
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print(f"[o1key SavePSD] 完成: {filepath} ({size_kb:.0f}KB, "
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f"{len(layers)} 层, {max_w}×{max_h})")
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return (filepath,)
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