什么是 IP-Adapter？
看着画或照片想“希望用和这个一样的氛围画”，但只用文本详细说明细节几乎是不可能的。
于是，“不介由文本，直接让 AI 看图像”的机制被提出了几个。
其中，被用于 风格和被摄体的“转印” 的古典手法之一就是 IP-Adapter。
请认为是“reference2image”、“Subject 转印”的元祖定位。
必要的自定义节点
SD1.5 × IP-Adapter
IP-Adapter 有几种种类，首先试一下最标准的东西吧。
模型的下载
- IP-Adapter 本体（SD1.5 用）
- CLIP Vision 模型
- model.safetensors
- 很难懂所以请重命名为
OpenCLIP-ViT-H-14。
- 很难懂所以请重命名为
- model.safetensors
📂ComfyUI/
└── 📂models/
├── 📂clip_vision/
│ └── OpenCLIP-ViT-H-14.safetensors
└── 📂ip_adapter/
└── ip-adapter_sd15.safetensors
工作流
{
"id": "8b9f7796-0873-4025-be3c-0f997f67f866",
"revision": 0,
"last_node_id": 21,
"last_link_id": 26,
"nodes": [
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"type": "VAEDecode",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
"name": "samples",
"type": "LATENT",
"link": 7
},
{
"name": "vae",
"type": "VAE",
"link": 10
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"slot_index": 0,
"links": [
9
]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.33",
"Node name for S&R": "VAEDecode"
},
"widgets_values": []
},
{
"id": 9,
"type": "SaveImage",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 12,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 9
}
],
"outputs": [],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.33"
},
"widgets_values": [
"ComfyUI"
]
},
{
"id": 7,
"type": "CLIPTextEncode",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
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"name": "clip",
"type": "CLIP",
"link": 5
}
],
"outputs": [
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"name": "CONDITIONING",
"type": "CONDITIONING",
"slot_index": 0,
"links": [
6
]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.33",
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
"text, watermark"
]
},
{
"id": 5,
"type": "EmptyLatentImage",
"pos": [
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],
"size": [
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"flags": {},
"order": 0,
"mode": 0,
"inputs": [],
"outputs": [
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"links": [
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]
}
],
"properties": {
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"flags": {},
"order": 1,
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"inputs": [],
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"type": "MODEL",
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"links": [
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{
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{
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"type": "VAE",
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"links": []
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"properties": {
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"widgets_values": [
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"links": [
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]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.76",
"Node name for S&R": "VAELoader"
},
"widgets_values": [
"vae-ft-mse-840000-ema-pruned.safetensors"
]
},
{
"id": 11,
"type": "CLIPVisionLoader",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [],
"outputs": [
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"type": "CLIP_VISION",
"links": [
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]
}
],
"properties": {
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},
"widgets_values": [
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],
"color": "#232",
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{
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"type": "CLIPTextEncode",
"pos": [
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"size": [
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],
"flags": {},
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"mode": 0,
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],
"outputs": [
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"slot_index": 0,
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]
}
],
"properties": {
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"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
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]
},
{
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"type": "LoadImage",
"pos": [
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"mode": 0,
"inputs": [],
"outputs": [
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"type": "IMAGE",
"slot_index": 0,
"links": [
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]
},
{
"name": "MASK",
"type": "MASK",
"links": null
}
],
"properties": {
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"ver": "0.3.76",
"Node name for S&R": "LoadImage"
},
"widgets_values": [
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"image"
],
"color": "#232",
"bgcolor": "#353"
},
{
"id": 3,
"type": "KSampler",
"pos": [
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"flags": {},
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{
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"type": "LATENT",
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],
"outputs": [
{
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"type": "LATENT",
"slot_index": 0,
"links": [
7
]
}
],
"properties": {
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"widgets_values": [
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{
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"flags": {},
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],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
26
]
}
],
"properties": {
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"ver": "a0f451a5113cf9becb0847b92884cb10cbdec0ef",
"Node name for S&R": "PrepImageForClipVision"
},
"widgets_values": [
"LANCZOS",
"center",
0
],
"color": "#223",
"bgcolor": "#335"
},
{
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"type": "IPAdapterModelLoader",
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"size": [
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"flags": {},
"order": 5,
"mode": 0,
"inputs": [],
"outputs": [
{
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"type": "IPADAPTER",
"links": [
19
]
}
],
"properties": {
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"ver": "a0f451a5113cf9becb0847b92884cb10cbdec0ef",
"Node name for S&R": "IPAdapterModelLoader"
},
"widgets_values": [
"ip-adapter_sd15.safetensors"
],
"color": "#232",
"bgcolor": "#353"
},
{
"id": 19,
"type": "IPAdapterAdvanced",
"pos": [
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"flags": {},
"order": 9,
"mode": 0,
"inputs": [
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{
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"link": 19
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{
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"type": "IMAGE",
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{
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"shape": 7,
"type": "IMAGE",
"link": null
},
{
"name": "attn_mask",
"shape": 7,
"type": "MASK",
"link": null
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{
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"type": "CLIP_VISION",
"link": 22
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"outputs": [
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"type": "MODEL",
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}
],
"properties": {
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"ver": "a0f451a5113cf9becb0847b92884cb10cbdec0ef",
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},
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"groups": [],
"config": {},
"extra": {
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"offset": [
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},
"frontendVersion": "1.34.6",
"VHS_latentpreview": false,
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"VHS_MetadataImage": true,
"VHS_KeepIntermediate": true
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}
- 🟩 将各种模型和想参考的图像连接到
IPAdapter Advanced节点。 - 🟦 在
Prep Image For ClipVision节点裁剪参考图像。- 详情在下面
在“看”哪里
相当于 IP-Adapter “眼睛”的 CLIP ViT-H-14，基本只看 224 × 224 的范围。
因此，如果原样传递纵长的人物照片，脸和脚会被切掉，或者只以身体中间附近为线索获取特征。
如果想决定以哪个部分为基准，请像上面的工作流那样先进行缩放・裁剪。
IP-Adapter 的主要模型
虽然有几个派生模型，但从参照图像“借来什么到什么程度”，每个模型的性格相当不同。
ip-adapter-plus_sd15
强力转印构图和对象位置的模型。
{
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"revision": 0,
"last_node_id": 22,
"last_link_id": 27,
"nodes": [
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"type": "VAEDecode",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
"name": "samples",
"type": "LATENT",
"link": 7
},
{
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"type": "VAE",
"link": 10
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"slot_index": 0,
"links": [
9
]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.33",
"Node name for S&R": "VAEDecode"
},
"widgets_values": []
},
{
"id": 9,
"type": "SaveImage",
"pos": [
1451,
189
],
"size": [
354.2876035004722,
433.23967321788405
],
"flags": {},
"order": 12,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 9
}
],
"outputs": [],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.33"
},
"widgets_values": [
"ComfyUI"
]
},
{
"id": 7,
"type": "CLIPTextEncode",
"pos": [
416.1970166015625,
392.37848510742185
],
"size": [
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],
"flags": {},
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"mode": 0,
"inputs": [
{
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"type": "CLIP",
"link": 5
}
],
"outputs": [
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"type": "CONDITIONING",
"slot_index": 0,
"links": [
6
]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.33",
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
"text, watermark"
]
},
{
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"size": [
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"inputs": [],
"outputs": [
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2
]
}
],
"properties": {
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"widgets_values": [
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},
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"id": 4,
"type": "CheckpointLoaderSimple",
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],
"size": [
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"flags": {},
"order": 1,
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"inputs": [],
"outputs": [
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20
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{
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"inputs": [],
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"links": [
10
]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.76",
"Node name for S&R": "VAELoader"
},
"widgets_values": [
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]
},
{
"id": 11,
"type": "CLIPVisionLoader",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [],
"outputs": [
{
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"links": [
22
]
}
],
"properties": {
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},
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],
"color": "#232",
"bgcolor": "#353"
},
{
"id": 6,
"type": "CLIPTextEncode",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 6,
"mode": 0,
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],
"outputs": [
{
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4
]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.33",
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
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]
},
{
"id": 12,
"type": "LoadImage",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [],
"outputs": [
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"slot_index": 0,
"links": [
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]
},
{
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"type": "MASK",
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}
],
"properties": {
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"ver": "0.3.76",
"Node name for S&R": "LoadImage"
},
"widgets_values": [
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"image"
],
"color": "#232",
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},
{
"id": 3,
"type": "KSampler",
"pos": [
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"size": [
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"flags": {},
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"name": "negative",
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"link": 6
},
{
"name": "latent_image",
"type": "LATENT",
"link": 2
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"slot_index": 0,
"links": [
7
]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.33",
"Node name for S&R": "KSampler"
},
"widgets_values": [
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]
},
{
"id": 19,
"type": "IPAdapterAdvanced",
"pos": [
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],
"size": [
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"flags": {},
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"mode": 0,
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},
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"type": "MASK",
"link": null
},
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"type": "CLIP_VISION",
"link": 22
}
],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
23
]
}
],
"properties": {
"cnr_id": "comfyui_ipadapter_plus",
"ver": "a0f451a5113cf9becb0847b92884cb10cbdec0ef",
"Node name for S&R": "IPAdapterAdvanced"
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- ip-adapter-plus_sd15.safetensors
- 变成与参照图像相当接近的构图
ip-adapter_sd15_light
偏向文本提示词优先的模型。
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- ip-adapter_sd15_light.safetensors
- 强力保留文本的指示
- 参照图像主要只是“风格・氛围”的提示程度
ip-adapter-plus-face_sd15
专注于脸（头部）的 IP-Adapter。
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},
{
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}
],
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{
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}
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"outputs": [
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]
}
],
"properties": {
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},
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"linear",
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0.8,
"V only"
],
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}
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"links": [
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3,
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],
[
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],
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6,
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- ip-adapter-plus-face_sd15.safetensors
- 相当强地固定长相・轮廓・五官等
ip-adapter-faceid-plusv2_sd15
不仅是 CLIP，也组合了 insightface 的人脸识别模型的模型。
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{
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{
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"links": []
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"properties": {
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- ip-adapter-faceid-plusv2_sd15.bin
- 比 plus-face 更灵活地转印 ID。
- 🟨 使用
IPAdapter FaceID节点。
SDXL 用模型链接
面向也想试试 SDXL 的人，这里是 SDXL 用模型链接的一览。