nodes/AIGODLIKE-AIGODLIKE-COMFYUI-TRANSLATION
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

更新中文 (#284)

Browse Source 
* 新增birefnet hugo
* Update ComfyUI-BrushNet.json
* Update comfyui-inpaint-nodes.json
* Update ComfyUI_IPAdapter_plus.json
* 更新kj
* Update ComfyUI-AutomaticCFG.json
* Update ComfyUI-Marigold.json
* Update sd-perturbed-attention.json
* Update comfy_mtb.json
* Update PuLID_ComfyUI.json
* Update internal.json
* Update ComfyUI-SUPIR.json
* Update ComfyUI-Tripo.json
This commit is contained in:
DorotaL
2024-09-12 17:19:14 +08:00
committed by GitHub
parent e1b196dd41
commit 0e30399787
14 changed files with 756 additions and 69 deletions
Download Patch File Download Diff File Expand all files Collapse all files
zh-CN/Menus/ComfyUI-BiRefNet-Hugo.json
+28
View File
@@ -0,0 +1,28 @@
{
"transparency": "透明",
"green": "绿",
"white": "白",
"red": "红",
"yellow": "黄",
"blue": "蓝",
"black": "黑",
"pink": "粉红",
"purple": "紫",
"brown": "棕",
"violet": "蓝紫",
"wheat": "小麦",
"whitesmoke": "烟白",
"yellowgreen": "黄绿",
"turquoise": "宝石绿",
"tomato": "番茄红",
"thistle": "苍紫",
"teal": "蓝绿",
"tan": "棕褐",
"steelblue": "钢蓝",
"springgreen": "春绿",
"snow": "雪白",
"slategrey": "灰石",
"slateblue": "灰蓝",
"skyblue": "天蓝",
"orange": "橙色"
}
zh-CN/Nodes/ComfyUI-AutomaticCFG.json
+9
View File
@@ -39,6 +39,15 @@
"MODEL": "模型"
}
},
"Automatic CFG - Warp Drive": {
"title": "自动CFG（Warp Drive）",
"inputs": {
"model": "模型"
},
"outputs": {
"MODEL": "模型"
}
},
"Automatic CFG - Preset Loader": {
"title": "自动CFG（预设组）",
"inputs": {
zh-CN/Nodes/ComfyUI-BiRefNet-Hugo.json
+16
View File
@@ -0,0 +1,16 @@
{
"BiRefNet_Hugo": {
"title": "🔥BiRefNet",
"inputs": {
"image": "图像"
},
"widgets": {
"background_color_name": "背景色",
"device": "设备"
},
"outputs": {
"image": "图像",
"mask": "遮罩"
}
}
}
zh-CN/Nodes/ComfyUI-BrushNet.json
+9
View File
@@ -95,6 +95,15 @@
"latent": "Latent"
}
},
"RAUNet": {
"title": "RAUNet",
"inputs": {
"model": "模型"
},
"outputs": {
"model": "模型"
}
},
"BrushNetInpaint": {
"title": "BrushNet局部重绘",
"inputs": {
zh-CN/Nodes/ComfyUI-KJNodes.json
+232 -46
View File
@@ -317,7 +317,7 @@
"mask": "遮罩",
"mask_inverted": "反转遮罩"
},
"description": "\n生成指定形状的遮罩或遮罩批次，位置参数为中心位置。"
"description": "生成指定形状的遮罩或遮罩批次，位置参数为中心位置。"
},
"CreateTextOnPath": {
"title": "创建文本",
@@ -385,7 +385,7 @@
"outputs": {
"masks": "遮罩"
},
"description": "\n合并多个遮罩为遮罩批次。可以通过“输入数量”设置输入接口数量，点击更新刷新接口。"
"description": "合并多个遮罩为遮罩批次。可以通过“输入数量”设置输入接口数量，点击更新刷新接口。"
},
"ColorToMask": {
"title": "图像颜色到遮罩",
@@ -414,7 +414,8 @@
},
"outputs": {
"image": "图像"
}
},
"description": "color-matcher（图像调色）可以在转换图像之间的色彩样式，用于照片、绘画、序列帧的自动调色以及光场定格修正。\n\n基于 Reinhard 等算法，Pitie 等人提出的 Monge-Kantorovich Linearization (MKL) 算法以及我们结合经典直方图匹配对 Multi-Variate Gaussian Distribution (MVGD)算法的分析解决方案。如下所示，我们的 HM-MVGD-HM 符合方法优于现有方法。\n\nhttps://github.com/hahnec/color-matcher/"
},
"BatchCropFromMask": {
"title": "遮罩裁剪",
@@ -614,7 +615,8 @@
},
"outputs": {
"mask": "遮罩"
}
},
"description": "用于桥接 AudioScheduler 插件的节点：\n\nhttps://github.com/a1lazydog/ComfyUI-AudioScheduler\n\n基于规格化振幅偏移遮罩。"
},
"RemapMaskRange": {
"title": "重映射遮罩范围",
@@ -657,7 +659,8 @@
},
"outputs": {
"image": "图像"
}
},
"description": "按屏幕坐标捕捉区域。\n\n用于实时采样。"
},
"PlotCoordinates": {
"title": "坐标图",
@@ -679,7 +682,7 @@
"bbox_height": "BBox高度",
"bbox_width": "BBox宽度"
},
"description": "\n使用 Matplotlib 制作坐标图表序列。"
"description": "使用 Matplotlib 制作坐标图表序列。"
},
"InterpolateCoords": {
"title": "坐标插值",
@@ -690,7 +693,32 @@
"outputs": {
"coordinates": "坐标"
},
"description": "\n依据曲线插入坐标"
"description": "依据曲线插入坐标"
},
"PointsEditor": {
"title": "点编辑器",
"inputs": {
"bg_image": "背景图"
},
"widgets": {
"points_store": "点位置",
"bbox_store": "BBox位置",
"bbox_format": "BBox格式",
"width": "宽度",
"height": "高度",
"normalize": "规格化",
"New canvs": "新画布",
"Load Image": "加载图像",
"Clear Image": "清除图像"
},
"outputs": {
"positive_coords": "正面坐标",
"negative_coords": "负面坐标",
"bbox": "BBox",
"bbox_mask": "BBox遮罩",
"cropped_image": "裁剪图像"
},
"description": "# 开发中 \n尽量不要在工作流中使用这个节点，可能会遇到大量bug并且不稳定！！ \n\n## 创建坐标的图形编辑器\n\n**Shift + 左键** 添加正面（绿色）的坐标点。\n\n**Shift + 右键** 添加负面（红色）的坐标点。\n\n**Ctrl + 左键** 绘制方框。\n\n**右键坐标点** 删除该点，不能删除点数组的起点/终点。\n\n要添加图像，选择节点并粘贴或拖入图像。\n\n或从 背景图 接口输入，使用输入图像的第一帧。\n\n**图像会保存到节点和工作流中**\n右键菜单可以清除图像。\n"
},
"SoundReactive": {
"title": "音频反馈",
@@ -707,7 +735,8 @@
"outputs": {
"sound_level": "音量图",
"sound_level_int": "音量整数"
}
},
"description": "反馈输入音频的音量。\n\n使用浏览器音频输入。\n\n用于实时采样。"
},
@@ -953,7 +982,7 @@
"outputs": {
"string": "字符串"
},
"description": "\n合并多个字符串为字符串批次或字符串列表。可以通过“输入数量”设置输入接口数量，点击更新刷新接口。"
"description": "合并多个字符串为字符串批次或字符串列表。可以通过“输入数量”设置输入接口数量，点击更新刷新接口。"
},
"EmptyLatentImagePresets": {
"title": "空Latent预设",
@@ -971,15 +1000,18 @@
"GetImageRangeFromBatch": {
"title": "获取图像范围",
"inputs": {
"images": "图像"
"images": "图像",
"masks": "遮罩"
},
"widgets": {
"start_index": "起始编号",
"num_frames": "帧数"
},
"outputs": {
"IMAGE": "图像"
}
"IMAGE": "图像",
"MASK": "遮罩"
},
"description": "根据输入图像批次以及参数，创建新图像批次。"
},
"ImageAddMulti": {
"title": "图像混合（多重）",
@@ -1003,7 +1035,7 @@
"outputs": {
"images": "图像"
},
"description": "\n混合多个图像。可以通过“输入数量”设置输入接口数量，点击更新刷新接口。"
"description": "混合多个图像。可以通过“输入数量”设置输入接口数量，点击更新刷新接口。"
},
"ImageBatchMulti": {
"title": "图像组合批次（多重）",
@@ -1027,7 +1059,7 @@
"outputs": {
"images": "图像"
},
"description": "\n合并多个图像为图像批次。可以通过“输入数量”设置输入接口数量，点击更新刷新接口。"
"description": "合并多个图像为图像批次。可以通过“输入数量”设置输入接口数量，点击更新刷新接口。"
},
"ReverseImageBatch": {
"title": "反转图像批次",
@@ -1048,7 +1080,8 @@
},
"outputs": {
"IMAGE": "图像"
}
},
"description": "将 4 个输入图像合并为 2x2 表格。"
},
"ImageGridComposite3x3": {
"title": "图像栅格合并3x3",
@@ -1065,7 +1098,21 @@
},
"outputs": {
"IMAGE": "图像"
}
},
"description": "将 9 个输入图像合并为 3x3 表格。"
},
"ImageGridToBatch": {
"title": "图像表格到批次",
"inputs": {
"image": "图像"
},
"widgets": {
"columns": "列数"
},
"outputs": {
"IMAGE": "图像"
},
"description": "将图表转换为单个图像批次。"
},
"ImageConcanate": {
"title": "图像联结",
@@ -1078,7 +1125,40 @@
},
"outputs": {
"IMAGE": "图像"
}
},
"description": "按方向合并两个图像。"
},
"ImageConcatFromBatch": {
"title": "图像联结（批次）",
"inputs": {
"images": "图像"
},
"widgets": {
"num_columns": "列数",
"match_image_size": "匹配尺寸",
"max_resolution": "最大分辨率"
},
"outputs": {
"IMAGE": "图像"
},
"description": "从图像批次中获取图像，然后按照列数排列为单个图像。"
},
"ImageConcatMulti": {
"title": "图像联结（多个）",
"inputs": {
"image_1": "图像_1",
"image_2": "图像_2"
},
"widgets": {
"inputcount": "输入数量",
"direction": "方向",
"match_image_size": "匹配尺寸",
"Update inputs": "更新输入"
},
"outputs": {
"images": "图像"
},
"description": "按方向合并多个图像。单击更新以更新输入数量。"
},
"ImageBatchTestPattern": {
"title": "图像批次图案",
@@ -1137,7 +1217,7 @@
"image": "图像",
"mask": "遮罩"
},
"description": "\n生成指定形状的图像或图像批次，位置参数为中心位置。"
"description": "生成指定形状的图像或图像批次，位置参数为中心位置。"
},
"CreateGradientFromCoords": {
"title": "从坐标创建渐变图像",
@@ -1154,7 +1234,7 @@
"outputs": {
"image": "图像"
},
"description": "\n在坐标位置创建渐变图像。"
"description": "在坐标位置创建渐变图像。"
},
"GradientToFloat": {
"title": "渐变到浮点",
@@ -1168,7 +1248,7 @@
"float_x": "浮点_X",
"float_y": "浮点_Y"
},
"description": "\n计算图像浮点列表"
"description": "计算图像浮点列表"
},
"WidgetToString": {
"title": "组件到字符串",
@@ -1280,7 +1360,8 @@
"positive": "正面条件",
"negative": "负面条件",
"latent": "Latent"
}
},
"description": "使用插值调度安排 SV3D 的方位角和俯仰角数值。\n\n注意 SV3D 是视频模型，所以调度值必须递进。\n\nhttps://huggingface.co/stabilityai/sv3d"
},
"GLIGENTextBoxApplyBatch": {
"title": "GLIGEN文本框应用(批次)",
@@ -1288,7 +1369,9 @@
"conditioning_to": "条件",
"clip": "CLIP",
"gligen_textbox_model": "GLIGEN模型",
"latents": "Latent"
"latents": "Latent",
"coordinates": "坐标",
"size_multiplier": "尺寸乘数"
},
"widgets": {
"text": "文本",
@@ -1333,7 +1416,72 @@
"conditioning": "条件",
"coord_preview": "坐标预览"
},
"description": "\n这个节点允许在批次中调度 GLIGEN 文本框的位置，和 AnimateDiff-Evolved 一起使用。建议和 曲线编辑器 节点一起使用。\n\n GLIGEN模型可以在 管理器 的 “安装模型” 中下载。或直接在：https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/tree/main 下载。 \n \n 输入： \n- **Latent** 输入用于计算批次大小 \n- **CLIP** 是文本编码器，与其他CLIP输入相同 \n- **GLIGEN模型** 连接到 GLIGEN加载器 节点 \n- **坐标** 接收坐标点json字符串，和 曲线编辑器 节点兼容。 \n- **文本** 文本框位置的提示词 \n- **宽度** 和 **高度** 设置 GLIGEN文本框 的尺寸 \n \n输出：\n- **条件** 连接到 CLIP文本编码 和 采样器 中间 \n- **坐标预览** 用于预览坐标和BBox \n\n"
"description": "这个节点允许在批次中调度 GLIGEN 文本框的位置，和 AnimateDiff-Evolved 一起使用。建议和 曲线编辑器 节点一起使用。\n\n GLIGEN模型可以在 管理器 的 “安装模型” 中下载。或直接在：https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/tree/main 下载。 \n \n 输入： \n- **Latent** 输入用于计算批次大小 \n- **CLIP** 是文本编码器，与其他CLIP输入相同 \n- **GLIGEN模型** 连接到 GLIGEN加载器 节点 \n- **坐标** 接收坐标点json字符串，和 曲线编辑器 节点兼容。 \n- **文本** 文本框位置的提示词 \n- **宽度** 和 **高度** 设置 GLIGEN文本框 的尺寸 \n \n输出：\n- **条件** 连接到 CLIP文本编码 和 采样器 中间 \n- **坐标预览** 用于预览坐标和BBox \n\n"
},
"CheckpointPerturbWeights": {
"title": "Checkpoint参数扰动",
"inputs": {
"model": "模型"
},
"widgets": {
"joint_blocks": "joint_blocks",
"final_layer": "final_layer",
"rest_of_the_blocks": "rest_of_the_blocks",
"seed": "随机种",
"control_before_generate": "运行前操作"
},
"outputs": {
"MODEL": "模型"
}
},
"Screencap_mss": {
"title": "屏幕捕捉MSS",
"widgets": {
"x": "X",
"y": "Y",
"width": "宽度",
"height": "高度",
"num_frames": "帧数",
"delay": "延迟"
},
"outputs": {
"images": "图像"
},
"description": "按屏幕坐标捕捉区域。\n\n用于实时采样。"
},
"WebcamCaptureCV2": {
"title": "相机捕捉CV2",
"widgets": {
"x": "X",
"y": "Y",
"width": "宽度",
"height": "高度",
"cam_index": "相机序号",
"release": "release"
},
"outputs": {
"image": "图像"
},
"description": "使用 CV2 调用网页相机捕捉屏幕。\n\n用于实时采样。"
},
"FluxBlockLoraLoader": {
"title": "Flux Block LoRA 加载器",
"inputs": {
"model": "模型",
"blocks": "block",
"opt_lora_path": "LoRA路径(可选)"
},
"widgets": {
"strength_model": "模型强度",
"lora_name": "LoRA名称"
},
"outputs": {
"model": "模型",
"rank": "rank"
}
},
"FluxBlockLoraSelect": {
"title": "Flux Block LoRA 选择"
},
"LoadICLightUnet": {
"title": "加载ICLightUnet",
@@ -1346,7 +1494,7 @@
"outputs": {
"MODEL": "模型"
},
"description": "\n加载ICLightUnet：加载一个 ICLightUnet 模型。（实验性）开发中 \n 另一种不好用但可以用的方法是在 https://huggingface.co/Kijai/iclight-comfy/blob/main/iclight_fc_converted.safetensors 下载模型，然后和 InstructPixToPix条件 节点一起使用。"
"description": "加载ICLightUnet：加载一个 ICLightUnet 模型。（实验性）开发中 \n 另一种不好用但可以用的方法是在 https://huggingface.co/Kijai/iclight-comfy/blob/main/iclight_fc_converted.safetensors 下载模型，然后和 InstructPixToPix条件 节点一起使用。"
},
"StabilityAPI_SD3": {
"title": "StabilityAPI SD3",
@@ -1367,7 +1515,8 @@
},
"outputs": {
"IMAGE": "图像"
}
},
"description": "# 调用 Stability API\n\n尽量使用同一个key调用API，无论账号有多少个key。\n\n在 https://platform.stability.ai/account/keys 获取key，推荐创建包含key的 config.json 文件然后放到插件目录下。\n\n# 警告：\n\n即使开启了 disable_metadata（禁用元数据），如果工作流程中包含与此节点不同的保存节点，API 密钥也可能被保存到元数据中。\n\nSD3 每次生成需要 6.5 点数\n\nSD3-Turbo 每次生成需要 4 点数\n\n如果不连接图像输入，则使用 文生图 模式"
},
"GetImagesFromBatchIndexed": {
"title": "从批次获取图像",
@@ -1379,7 +1528,8 @@
},
"outputs": {
"IMAGE": "图像"
}
},
"description": "按索引编号选择图像。"
},
"InsertImagesToBatchIndexed": {
"title": "插入图像到索引批次",
@@ -1425,7 +1575,7 @@
"blue": "蓝色",
"mask": "遮罩"
},
"description": "\n分离图像为单通道图像，alpha通道分离为遮罩。"
"description": "分离图像为单通道图像，alpha通道分离为遮罩。"
},
"MergeImageChannels": {
"title": "合并图像通道",
@@ -1438,7 +1588,7 @@
"outputs": {
"image": "图像"
},
"description": "\n合并通道为图像"
"description": "合并通道为图像"
},
"PreviewAnimation": {
"title": "预览动画",
@@ -1460,7 +1610,8 @@
},
"outputs": {
"IMAGE": "图像"
}
},
"description": "按指定次数复制批次中的图像。\n\n例如包含 5 个图像的批次：0,1,2,3,4\n\n重复 2 次后变为：0,0,1,1,2,2,3,3,4,4"
},
"ImageTransformByNormalizedAmplitude": {
"title": "按规格化幅度变换图像",
@@ -1474,7 +1625,8 @@
},
"outputs": {
"IMAGE": "图像"
}
},
"description": "用于桥接 AudioScheduler 插件的节点：\n\nhttps://github.com/a1lazydog/ComfyUI-AudioScheduler\n\n基于规格化振幅变换图像。"
},
"GetLatentsFromBatchIndexed": {
"title": "按索引获取Latent",
@@ -1534,7 +1686,8 @@
},
"outputs": {
"MASK": "遮罩"
}
},
"description": "用于桥接 AudioScheduler 插件的节点：\n\nhttps://github.com/a1lazydog/ComfyUI-AudioScheduler\n\n基于规格化振幅创建遮罩。"
},
"NormalizedAmplitudeToFloatList": {
"title": "规格化振幅到浮点列表",
@@ -1544,7 +1697,7 @@
"outputs": {
"FLOAT": "浮点"
},
"description": "\n连接“音频调度”的桥接节点：https://github.com/a1lazydog/ComfyUI-AudioScheduler \n，从规格化振幅建立一个浮点列表。"
"description": "用于桥接 AudioScheduler 插件的节点：\n\nhttps://github.com/a1lazydog/ComfyUI-AudioScheduler\n\n基于规格化振幅创建浮点列表。"
},
"ScaleBatchPromptSchedule": {
"title": "缩放提示词批次调度",
@@ -1573,7 +1726,8 @@
},
"outputs": {
"IMAGE": "图像"
}
},
"description": "规格化图像到 [-1,1]"
},
"ImagePass": {
"title": "图像桥接",
@@ -1583,13 +1737,13 @@
"outputs": {
"IMAGE": "图像"
},
"description": "\n传递图像到下一个节点"
"description": "传递图像到下一个节点"
},
"ImageResizeKJ": {
"title": "图像缩放（KJ）",
"inputs": {
"image": "图像",
"get_image_size": "参考图像大小",
"get_image_size": "参考图像",
"width_input": "宽度",
"height_input": "高度"
},
@@ -1598,14 +1752,16 @@
"height": "高度",
"upscale_method": "缩放方法",
"keep_proportions": "固定宽高比",
"divisible_by": "因数"
"keep_proportion": "固定宽高比",
"divisible_by": "因数",
"crop": "裁剪"
},
"outputs": {
"IMAGE": "图像",
"width": "宽度",
"height": "高度"
},
"description": "\n按指定宽高缩放图像。尺寸可由输入指定，最终输出优先级按以下规则排列：\n- 参考图像尺寸 \n- 宽度-输入 和 高度-输入 \n- 宽度 和 高度 \n \n固定宽高比会按最高尺寸固定宽高比。"
"description": "按指定宽高缩放图像。尺寸可由输入指定，最终输出优先级按以下规则排列：\n- 参考图像尺寸 \n- 宽度-输入 和 高度-输入 \n- 宽度 和 高度 \n \n固定宽高比会按最高尺寸固定宽高比。"
},
"CameraPoseVisualizer": {
"title": "相机姿态预览",
@@ -1666,7 +1822,7 @@
"outputs": {
"composite": "图像"
},
"description": "\n预览图像或遮罩，同时输入图像和遮罩时会合并遮罩到图像上。\n当 穿透 开启时将禁用合成，用于通过合并为视频节点的预览。"
"description": "预览图像或遮罩，同时输入图像和遮罩时会合并遮罩到图像上。\n当 穿透 开启时将禁用合成，用于通过合并为视频节点的预览。"
},
"MaskOrImageToWeight": {
"title": "遮罩或图像到权重",
@@ -1680,7 +1836,7 @@
"outputs": {
"FLOAT": "浮点"
},
"description": "\n获取遮罩或图像批次的平均值，并按 输出类型 输出结果。"
"description": "获取遮罩或图像批次的平均值，并按 输出类型 输出结果。"
},
"MaskOrImageToWeigh": {
"title": "遮罩或图像到权重",
@@ -1763,7 +1919,7 @@
"outputs": {
"clipseg_model": "CLIPSeg模型"
},
"description": "\n使用 huggingface_hub 下载 CLIPSeg 模型到 Comfy/models/clip_seg"
"description": "使用 huggingface_hub 下载 CLIPSeg 模型到 Comfy/models/clip_seg"
},
"GetMaskSizeAndCount": {
"title": "获取遮罩尺寸数量",
@@ -1776,7 +1932,7 @@
"height": "高度",
"count": "数量"
},
"description": "\n输出遮罩的宽度、高度、批次大小"
"description": "输出遮罩的宽度、高度、批次大小"
},
"GetImageSizeAndCount": {
"title": "获取图像尺寸数量",
@@ -1789,7 +1945,7 @@
"height": "高度",
"count": "数量"
},
"description": "\n输出图像的宽度、高度、批次大小"
"description": "输出图像的宽度、高度、批次大小"
},
"ModelPassThrough": {
"title": "模型桥接",
@@ -1799,7 +1955,7 @@
"outputs": {
"model": "模型"
},
"description": "\n将模型传递到下一个节点"
"description": "将模型传递到下一个节点"
},
"FloatToSigmas": {
"title": "浮点到Sigmas",
@@ -1809,7 +1965,7 @@
"outputs": {
"SIGMAS": "Sigmas"
},
"description": "\n从浮点列表创建sigmas tensor"
"description": "从浮点列表创建sigmas tensor"
},
"WeightScheduleExtend": {
"title": "权重调度扩展",
@@ -1823,7 +1979,7 @@
"outputs": {
"FLOAT": "浮点"
},
"description": "\n扩展不同的值列表/系列，并根据需要进行转换"
"description": "扩展不同的值列表/系列，并根据需要进行转换"
},
"SplineEditor": {
"title": "曲线编辑器",
@@ -1848,6 +2004,36 @@
"count": "数量",
"normalized_str": "规格化字符串"
},
"description": "\n# 开发中 \n不要在工作流中经常使用这个节点，可能会遇到大量bug并且不稳定！！ \n \n##用于创建各类值调度的图形化编辑器 \n\n**Shift+左键单击** 添加新控制点，**Ctrl+左键单击** 创建中间控制点，**右键单击** 移除控制点。\n不能删除端点。\n\n 右键背景会调出菜单：一些视觉设置，不影响输出效果：\n - 控制柄可见性 \n - 显示采样点 \n\n**采样精度** 设置采样点数量，从样条线本身返回采样点，和实际控制点无关，由插值类型决定。\n采样方法：\n - 时间：沿时间轴采样，用于调度 \n - 路径：沿曲线路径采样，用于坐标 \n\n 输出：\n - 遮罩批次 \n 适用于任何接收遮罩的节点 \n - 浮点列表 \n 适用于 IPAdapter权重组 \n - pandas series \n 适用于 适用于接收 Fizz批次调度 节点 的节点 \n - torch tensor \n 适用于不明节点"
"description": "# 开发中 \n尽量不要在工作流中使用这个节点，可能会遇到大量bug并且不稳定！！ \n \n##用于创建各类值调度的图形化编辑器 \n\n**Shift+左键单击** 添加新控制点，**Ctrl+左键单击** 创建中间控制点，**右键单击** 移除控制点。\n不能删除端点。\n\n 右键背景会调出菜单：一些视觉设置，不影响输出效果：\n - 控制柄可见性 \n - 显示采样点 \n\n**采样精度** 设置采样点数量，从样条线本身返回采样点，和实际控制点无关，由插值类型决定。\n采样方法：\n - 时间：沿时间轴采样，用于调度 \n - 路径：沿曲线路径采样，用于坐标 \n\n 输出：\n - 遮罩批次 \n 适用于任何接收遮罩的节点 \n - 浮点列表 \n 适用于 IPAdapter权重组 \n - pandas series \n 适用于 适用于接收 Fizz批次调度 节点 的节点 \n - torch tensor \n 适用于不明节点"
},
"DifferentialDiffusionAdvanced": {
"title": "差异扩散（高级）",
"inputs": {
"model": "模型",
"samples": "Latent",
"mask": "遮罩"
},
"widgets": {
"multiplier": "乘数"
},
"outputs": {
"MODEL": "模型",
"LATENT": "Latent"
}
},
"LoadImagesFromFolderKJ": {
"title": "加载图像（文件夹）",
"widgets": {
"folder": "文件夹",
"image_load_cap": "加载上限",
"start_index": "起始编号"
},
"outputs": {
"image": "图像",
"mask": "遮罩",
"count": "数量",
"image_path": "图像路径"
}
}
}
zh-CN/Nodes/ComfyUI-Marigold.json
+4 -4
View File
@@ -7,7 +7,7 @@
"outputs": {
"marigold_model": "Marigold模型"
},
"description": "\n基于扩散的单目深度推算: \nhttps://github.com/prs-eth/Marigold  \n  \n使用 Diffusers 0.28.0 Marigold 管线。模型会自动下载到 ComfyUI/Models/diffusers 文件夹内"
"description": "\n基于扩散的单目深度推算: https://github.com/prs-eth/Marigold  \n\n 使用 Diffusers 0.28.0 Marigold 框架。模型会自动下载到 ComfyUI/Models/diffusers 文件夹内"
},
"MarigoldDepthEstimation_v2": {
"title": "Marigold_v2深度推算",
@@ -28,9 +28,9 @@
"outputs": {
"image": "图像"
},
"description": "\n基于扩散的单目深度推算: \nhttps://github.com/prs-eth/Marigold  \n  \n使用 Diffusers 0.28.0 Marigold 管线。"
"description": "\n基于扩散的单目深度推算: https://github.com/prs-eth/Marigold  \n\n 使用 Diffusers 0.28.0 Marigold 框架。"
},
"MarigoldDepthEstimation_v2_Video": {
"MarigoldDepthEstimation_v2_video": {
"title": "Marigold_v2深度推算视频",
"inputs": {
"marigold_model": "Marigold模型",
@@ -48,7 +48,7 @@
"outputs": {
"image": "图像"
},
"description": "\n基于扩散的单目深度推算: \nhttps://github.com/prs-eth/Marigold  \n  \n使用 Diffusers 0.28.0 Marigold 管线。\n这个节点使用上一帧作为初始Latent，用以平滑视频。"
"description": "\n基于扩散的单目深度推算: https://github.com/prs-eth/Marigold  \n\n使用 Diffusers 0.28.0 Marigold 框架。\n\n这个节点使用上一帧作为初始Latent，用以平滑视频。"
},
"MarigoldDepthEstimation": {
"title": "Marigold深度推算",
zh-CN/Nodes/ComfyUI-SUPIR.json
+19
View File
@@ -30,6 +30,25 @@
"SUPIR_VAE": "SUPIR_VAE"
}
},
"SUPIR_model_loader_v2_clip": {
"title": "SUPIR模型加载器_V2（）",
"inputs": {
"model": "模型",
"clip_l": "CLIP_L",
"clip_g": "CLIP_G",
"vae": "VAE"
},
"widgets": {
"supir_model": "SUPIR模型",
"fp8_unet": "fp8_unet",
"diffusion_dtype": "剪枝类型",
"high_vram": "高显存"
},
"outputs": {
"SUPIR_MODEL": "SUPIR模型",
"SUPIR_VAE": "SUPIR_VAE"
}
},
"SUPIR_encode": {
"title": "SUPIR编码",
"inputs": {
zh-CN/Nodes/ComfyUI-Tripo.json
+11
View File
@@ -45,6 +45,17 @@
"API_KEY": "APIKey"
}
},
"TripoRefineModel": {
"title": "TripoAPI优化模型",
"inputs": {
"apikey": "APIKey",
"model_task_id": "模型任务ID"
},
"outputs": {
"MESH": "网格",
"MODEL_TASK_ID": "模型任务ID"
}
},
"TripoAnimateRigNode": {
"title": "Tripo绑骨模型",
"inputs": {
zh-CN/Nodes/ComfyUI_IPAdapter_plus.json
+228 -12
View File
@@ -1,4 +1,83 @@
{
"IPAdapterMS": {
"title": "应用IPAdapter Mad Scientist",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
"image": "图像",
"image_negative": "负面图像",
"attn_mask": "关注层遮罩",
"clip_vision": "CLIP视觉",
"insightface": "InsightFace"
},
"widgets": {
"weight": "权重",
"weight_faceidv2": "FaceID_V2权重",
"weight_type": "权重类型",
"combine_embeds": "合并嵌入组",
"start_at": "开始应用位置",
"end_at": "结束应用位置",
"embeds_scaling": "嵌入组缩放",
"layer_weights": "层权重"
},
"outputs": {
"MODEL": "模型"
}
},
"IPAdapterClipVisionEnhancer": {
"title": "应用IPAdapter CLIP Vision 增强",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
"image": "图像",
"image_negative": "负面图像",
"attn_mask": "关注层遮罩",
"clip_vision": "CLIP视觉",
"insightface": "InsightFace"
},
"widgets": {
"weight": "权重",
"weight_faceidv2": "FaceID_V2权重",
"weight_type": "权重类型",
"combine_embeds": "合并嵌入组",
"start_at": "开始应用位置",
"end_at": "结束应用位置",
"embeds_scaling": "嵌入组缩放",
"enhance_tiles": "增强分块",
"enhance_ratio": "增强比率"
},
"outputs": {
"MODEL": "模型"
}
},
"IPAdapterClipVisionEnhancerBatch": {
"title": "应用IPAdapter CLIP Vision 批次增强",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
"image": "图像",
"image_negative": "负面图像",
"attn_mask": "关注层遮罩",
"clip_vision": "CLIP视觉",
"insightface": "InsightFace"
},
"widgets": {
"weight": "权重",
"weight_faceidv2": "FaceID_V2权重",
"weight_type": "权重类型",
"combine_embeds": "合并嵌入组",
"start_at": "开始应用位置",
"end_at": "结束应用位置",
"embeds_scaling": "嵌入组缩放",
"enhance_tiles": "增强分块",
"enhance_ratio": "增强比率"
},
"outputs": {
"MODEL": "模型"
}
},
"IPAdapterApply": {
"title": "IPAdapter应用",
"inputs": {
@@ -23,7 +102,7 @@
}
},
"IPAdapterApplyFaceID": {
"title": "IPAdapter应用(FaceID)",
"title": "IPAdapter应用（FaceID）",
"inputs": {
"ipadapter": "IPAdapter",
"clip_vision": "CLIP视觉",
@@ -47,7 +126,7 @@
}
},
"IPAdapterApplyEncoded": {
"title": "IPAdapter应用(已编码)",
"title": "IPAdapter应用（已编码）",
"inputs": {
"ipadapter": "IPAdapter",
"embeds": "嵌入组",
@@ -121,7 +200,7 @@
}
},
"IPAdapterAdvanced": {
"title": "应用IPAdapter(高级)",
"title": "应用IPAdapter（高级）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
@@ -143,7 +222,7 @@
}
},
"IPAdapterBatch": {
"title": "应用IPAdapter(批次)",
"title": "应用IPAdapter（批次）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
@@ -187,8 +266,32 @@
"MODEL": "模型"
}
},
"IPAdapterFaceIDKolors": {
"title": "应用IPAdapterFaceID（Kolors）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
"image": "正面图像",
"image_negative": "负面图像",
"attn_mask": "关注层遮罩",
"clip_vision": "CLIP视觉",
"insightface": "InsightFace"
},
"widgets": {
"weight": "权重",
"weight_faceidv2": "FaceID_V2权重",
"weight_type": "权重类型",
"combine_embeds": "合并嵌入组",
"start_at": "开始应用位置",
"end_at": "结束应用位置",
"embeds_scaling": "嵌入组缩放"
},
"outputs": {
"MODEL": "模型"
}
},
"IPAdapterFaceIDBatch": {
"title": "应用IPAdapterFaceID(批次)",
"title": "应用IPAdapterFaceID（批次）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
@@ -212,7 +315,7 @@
}
},
"IPAAdapterFaceIDBatch": {
"title": "应用IPAdapterFaceID(批次)",
"title": "应用IPAdapterFaceID（批次）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
@@ -236,7 +339,7 @@
}
},
"IPAdapterTiled": {
"title": "应用IPAdapter(分块)",
"title": "应用IPAdapter（分块）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
@@ -261,7 +364,7 @@
}
},
"IPAdapterTiledBatch": {
"title": "应用IPAdapter(分块批次)",
"title": "应用IPAdapter（分块批次）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
@@ -285,7 +388,7 @@
}
},
"IPAdapterEmbeds": {
"title": "应用IPAdapter(嵌入组)",
"title": "应用IPAdapter（嵌入组）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
@@ -305,6 +408,119 @@
"MODEL": "模型"
}
},
"IPAdapterEmbedsBatch": {
"title": "应用IPAdapter（批次嵌入组）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
"pos_embed": "正面嵌入组",
"neg_embed": "负面嵌入组",
"attn_mask": "关注层遮罩",
"clip_vision": "CLIP视觉"
},
"widgets": {
"weight": "权重",
"weight_type": "权重类型",
"start_at": "开始应用位置",
"end_at": "结束应用位置",
"embeds_scaling": "嵌入组缩放"
},
"outputs": {
"MODEL": "模型"
}
},
"IPAdapterPreciseStyleTransfer": {
"title": "应用IPAdapter精确风格迁移",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
"image": "图像",
"image_negative": "负面图像",
"attn_mask": "关注层遮罩",
"clip_vision": "CLIP视觉",
"insightface": "InsightFace"
},
"widgets": {
"weight": "权重",
"style_boost": "风格增强",
"combine_embeds": "合并嵌入组",
"start_at": "开始应用位置",
"end_at": "结束应用位置",
"embeds_scaling": "嵌入组缩放"
},
"outputs": {
"MODEL": "模型"
}
},
"IPAdapterPreciseStyleTransferBatch": {
"title": "应用IPAdapter精确风格迁移（批次）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
"image": "图像",
"image_negative": "负面图像",
"attn_mask": "关注层遮罩",
"clip_vision": "CLIP视觉",
"insightface": "InsightFace"
},
"widgets": {
"weight": "权重",
"style_boost": "风格增强",
"combine_embeds": "合并嵌入组",
"start_at": "开始应用位置",
"end_at": "结束应用位置",
"embeds_scaling": "嵌入组缩放"
},
"outputs": {
"MODEL": "模型"
}
},
"IPAdapterPreciseComposition": {
"title": "应用IPAdapter精确合成",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
"image": "图像",
"image_negative": "负面图像",
"attn_mask": "关注层遮罩",
"clip_vision": "CLIP视觉",
"insightface": "InsightFace"
},
"widgets": {
"weight": "权重",
"composition_boost": "合成增强",
"combine_embeds": "合并嵌入组",
"start_at": "开始应用位置",
"end_at": "结束应用位置",
"embeds_scaling": "嵌入组缩放"
},
"outputs": {
"MODEL": "模型"
}
},
"IPAdapterPreciseCompositionBatch": {
"title": "应用IPAdapter精确合成（批次）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
"image": "图像",
"image_negative": "负面图像",
"attn_mask": "关注层遮罩",
"clip_vision": "CLIP视觉",
"insightface": "InsightFace"
},
"widgets": {
"weight": "权重",
"style_boost": "风格增强",
"combine_embeds": "合并嵌入组",
"start_at": "开始应用位置",
"end_at": "结束应用位置",
"embeds_scaling": "嵌入组缩放"
},
"outputs": {
"MODEL": "模型"
}
},
"IPAdapterUnifiedLoader": {
"title": "IPAdapter加载器",
"inputs": {
@@ -354,7 +570,7 @@
}
},
"IPAdapterUnifiedLoaderCommunity": {
"title": "IPAdapter加载器(社区)",
"title": "IPAdapter加载器（社区）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter"
@@ -368,7 +584,7 @@
}
},
"IPAdapterFromParams": {
"title": "应用IPAdapter(参数组)",
"title": "应用IPAdapter（参数组）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
@@ -441,7 +657,7 @@
}
},
"IPAdapterStyleCompositionBatch": {
"title": "IPAdapter风格合成SDXL(批次)",
"title": "IPAdapter风格合成SDXL（批次）",
"inputs": {
"model": "模型",
"ipadapter": "IPAdapter",
zh-CN/Nodes/PuLID_ComfyUI.json
+24 -1
View File
@@ -30,7 +30,8 @@
"pulid": "PuLID",
"eva_clip": "EVA_CLIP",
"face_analysis": "面部分析模型",
"image": "图像"
"image": "图像",
"attn_mask": "关注层遮罩"
},
"widgets": {
"method": "方法",
@@ -41,5 +42,27 @@
"outputs": {
"MODEL": "模型"
}
},
"ApplyPulidAdvanced": {
"title": "应用PuLID（高级）",
"inputs": {
"model": "模型",
"pulid": "PuLID",
"eva_clip": "EVA_CLIP",
"face_analysis": "面部分析模型",
"image": "图像",
"attn_mask": "关注层遮罩"
},
"widgets": {
"weight": "权重",
"projection": "预测",
"fidelity": "精确度",
"noise": "噪波",
"start_at": "开始应用位置",
"end_at": "结束应用位置"
},
"outputs": {
"MODEL": "模型"
}
}
}
zh-CN/Nodes/comfy_mtb.json
+108 -1
View File
@@ -37,6 +37,47 @@
"images": "图像"
}
},
"Audio Sequence (mtb)": {
"title": "音频序列",
"inputs": {
"audio_1": "音频_1"
},
"widgets": {
"silence_duration": "静音时长"
},
"outputs": {
"sequenced_audio": "音频序列"
},
"description": "按序排列所有输入的音频\n\n- 在每段之间添加静音时长，用于抑制片段重复以及控制时长。\n- 根据最高采样率重新采样所有音频，\n- 如果其中一个输入为立体声，则将所有音频转换为立体声。"
},
"Audio Stack (mtb)": {
"title": "音频堆栈",
"inputs": {
"audio_1": "音频_1"
},
"outputs": {
"stacked_audio": "音频堆栈"
},
"description": "堆叠/重叠所有输入的音频\n\n- 音频长度取决于最长的输入项。\n\n- 重采样为输入中的最高采样率。\n\n- 如果其中一个输入为立体声，则将所有音频转换为立体声。"
},
"Audio Cut (mtb)": {
"title": "音频裁剪",
"inputs": {
"audio": "音频"
},
"widgets": {
"length": "长度",
"offset": "偏移"
},
"outputs": {
"cut_audio": "裁剪音频"
},
"description": "裁剪音频，单位为毫秒ms。"
},
"Batch Float (mtb)": {
"title": "浮点批次",
"widgets": {
@@ -263,6 +304,36 @@
"STRING": "字符串"
}
},
"Extract Coordinates From Image (mtb)": {
"title": "从图像解析坐标",
"inputs": {
"image": "图像",
"mask": "遮罩"
},
"widgets": {
"threshold": "阈值",
"max_points": "坐标点上限"
},
"outputs": {
"BATCH_COORDNATES": "坐标批次",
"IMAGE": "图像"
},
"description": "从图像批次中按照阈值解析出 2D 坐标点。"
},
"Coordinates To String (mtb)": {
"title": "坐标批次到字符串",
"inputs": {
"coordinates": "坐标批次"
},
"widgets": {
"frame": "帧数"
},
"outputs": {
"STRING": "字符串"
}
},
"Bbox From Mask (mtb)": {
"title": "遮罩提取BBox",
"inputs": {
@@ -322,6 +393,19 @@
"IMAGE": "图像"
}
},
"Split Bbox (mtb)": {
"title": "解析BBox",
"inputs": {
"bbox": "BBox"
},
"outputs": {
"x": "X",
"y": "Y",
"width": "宽度",
"height": "高度"
},
"description": "解析bbox"
},
"Curve (mtb)": {
"title": "浮点曲线",
"outputs": {
@@ -626,7 +710,30 @@
},
"outputs": {
"IMAGE": "图像"
}
},
"description": "各类颜色校正方案"
},
"Color Correct GPU (mtb)": {
"title": "颜色校正GPU",
"inputs": {
"image": "图像",
"mask": "遮罩"
},
"widgets": {
"force_gpu": "强制GPU",
"clamp": "钳制",
"gamma": "伽马",
"contrast": "对比度",
"exposure": "曝光",
"offset": "偏移",
"hue": "色相",
"saturation": "饱和度",
"value": "明度"
},
"outputs": {
"IMAGE": "图像"
},
"description": "各类颜色校正方案（使用torch）"
},
"Blur (mtb)": {
"title": "模糊",
zh-CN/Nodes/comfyui-inpaint-nodes.json
+13
View File
@@ -101,5 +101,18 @@
"outputs": {
"MASK": "遮罩"
}
},
"INPAINT_ExpandMask": {
"title": "扩展遮罩",
"inputs": {
"mask": "遮罩"
},
"widgets": {
"grow": "生长",
"blur": "模糊"
},
"outputs": {
"MASK": "遮罩"
}
}
}
zh-CN/Nodes/internal.json
+34 -4
View File
@@ -227,6 +227,12 @@
"SAMPLER": "采样器"
}
},
"SamplerDPMPP_2S_Ancestral": {
"title": "DPMPP_2S_Ancestral采样器",
"outputs": {
"SAMPLER": "采样器"
}
},
"SamplerEulerAncestral": {
"title": "Euler_A采样器",
"widgets": {
@@ -465,7 +471,8 @@
},
"outputs": {
"MODEL": "模型"
}
},
"description": "用于微调扩散模型和 CLIP 模型，改变 Latent 降噪的效果，实现控制风格等功能。多个 LoRA 可以直接连接到一起。"
},
@@ -1229,7 +1236,8 @@
"outputs": {
"IMAGE": "图像",
"The decoded image.": "解码后的图像"
}
},
"description": "将 Latent 图像解码为像素空间的图像。"
},
"VAEEncode": {
"title": "VAE编码",
@@ -1674,6 +1682,17 @@
"IMAGE": "图像"
}
},
"WebcamCapture": {
"title": "网页镜头捕捉",
"widgets": {
"width": "宽度",
"height": "高度",
"capture_on_queue": "执行时捕捉"
},
"outputs": {
"IMAGE": "图像"
}
},
"SaveImageWithAlpha": {
"title": "保存图像(Alpha)",
"inputs": {
@@ -2089,7 +2108,8 @@
},
"widgets": {
"samples": "Latent",
"latent": "Latent"
"latent": "Latent",
"filename_prefix": "文件名前缀"
}
},
"LoadLatent": {
@@ -2293,7 +2313,8 @@
"CLIPLoader": {
"title": "CLIP加载器",
"widgets": {
"clip_name": "CLIP名称"
"clip_name": "CLIP名称",
"type": "类型"
},
"outputs": {
"CLIP": "CLIP"
@@ -2559,6 +2580,15 @@
"filename_prefix": "文件名前缀"
}
},
"ModelSave": {
"title": "保存模型",
"inputs": {
"model": "模型"
},
"widgets": {
"filename_prefix": "文件名前缀"
}
},
"CLIPSave": {
"title": "保存CLIP",
"inputs": {
zh-CN/Nodes/sd-perturbed-attention.json
+21 -1
View File
@@ -1,6 +1,6 @@
{
"PerturbedAttention": {
"title": "PAG注意力引导(高级)",
"title": "PAG注意力引导（高级）",
"inputs": {
"model": "模型"
},
@@ -13,5 +13,25 @@
"outputs": {
"MODEL": "模型"
}
},
"SmoothedEnergyGuidanceAdvanced": {
"title": "SEG引导（高级）",
"inputs": {
"model": "模型"
},
"widgets": {
"scale": "缩放",
"blur_sigma": "模糊Sigma",
"unet_block": "UNet块",
"unet_block_id": "UNet块ID",
"sigma_start": "Sigma初始",
"sigma_end": "Sigma结束",
"rescale": "重缩放",
"rescale_mode": "重缩放模式",
"unet_block_list": "UNet块列表"
},
"outputs": {
"MODEL": "模型"
}
}
}