├── .github
└── workflows
│ └── publish.yml
├── .gitignore
├── LICENSE
├── README.md
├── README_EN.md
├── __init__.py
├── assets
├── 2de5b085-1125-46f9-8ef3-06706743f182.png
├── 9e6bf0f9-67a7-41ea-bc4b-d8352e4fac4a.png
├── d0a22b2a-ceb3-4205-9b4e-f6a68e4337c7.png
├── demo1.gif
├── demo2.gif
├── demo3.gif
├── demo4.gif
└── e21c32bf-ab98-444a-8055-54975ac47da3.png
├── example_workflows
├── image.webp
├── video_workflow.json
└── workflow.json
├── js
└── BiRefNet_Hugo.js
├── nodes.py
├── pyproject.toml
└── requirements.txt
/.github/workflows/publish.yml:
--------------------------------------------------------------------------------
1 | name: Publish to Comfy registry
2 | on:
3 | workflow_dispatch:
4 | push:
5 | branches:
6 | - main
7 | - master
8 | paths:
9 | - "pyproject.toml"
10 |
11 | permissions:
12 | issues: write
13 |
14 | jobs:
15 | publish-node:
16 | name: Publish Custom Node to registry
17 | runs-on: ubuntu-latest
18 | if: ${{ github.repository_owner == 'MoonHugo' }}
19 | steps:
20 | - name: Check out code
21 | uses: actions/checkout@v4
22 | - name: Publish Custom Node
23 | uses: Comfy-Org/publish-node-action@v1
24 | with:
25 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
26 |
--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
1 |
2 | *.pyc
3 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | ComfyUI-BiRefNet-Hugo
2 |
3 |
4 |
中文 | English
5 |
6 |
7 |
8 | ## 介绍
9 |
10 | 本仓库将BiRefNet最新模型封装为ComfyUI节点来使用，相较于旧模型，最新模型的抠图精度更高更好。
11 |
12 |
13 | ## 🔥🔥🔥 更新!!
14 |
15 | * 2025年02月17日: 🚀 支持多个版本BiRefNet模型，比如 [BiRefNet](https://huggingface.co/ZhengPeng7/BiRefNet) 、[BiRefNet_HR](https://huggingface.co/ZhengPeng7/BiRefNet_HR) 、 [BiRefNet-portrait](https://huggingface.co/ZhengPeng7/BiRefNet-portrait) 。
16 |
17 | ## 安装
18 |
19 | #### 方法1:
20 |
21 | 1. 进入节点目录, `ComfyUI/custom_nodes/`
22 | 2. `git clone https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo.git`
23 | 3. `cd ComfyUI-BiRefNet-Hugo`
24 | 4. `pip install -r requirements.txt`
25 | 5. 重启ComfyUI
26 |
27 | #### 方法2:
28 | 直接下载节点源码包，然后解压到custom_nodes目录下，最后重启ComfyUI
29 |
30 | #### 方法3：
31 | 通过ComfyUI-Manager安装，搜索“ComfyUI-BiRefNet-Hugo”进行安装
32 |
33 | ## 使用
34 |
35 | 示例工作流放置在`ComfyUI-BiRefNet-Hugo/workflow`中
36 |
37 | 加载模型支持两种方式，一种是自动下载远程模型并加载模型，另外一种是加载本地模型。加载本地模型的时候需要把load_local_model设置为true，并把local_model_path设置为本地模型所在路径，例如：H:\ZhengPeng7\BiRefNet
38 |
39 | 
40 | ___
41 |
42 | 
43 |
44 |
45 | 模型下载地址：https://huggingface.co/ZhengPeng7/BiRefNet/tree/main
46 |
47 | ___
48 | 工作流workflow.json的使用
49 |
50 | 
51 |
52 | ___
53 | 工作流video_workflow.json的使用
54 |
55 | 
56 |
57 | ## 效果演示
58 |
59 | 
60 |
61 | 
62 |
63 | 
64 |
65 | 
66 |
67 | ## 社交账号
68 | - Bilibili：[我的B站主页](https://space.bilibili.com/1303099255)
69 |
70 | ## 感谢
71 |
72 | 感谢BiRefNet仓库的所有作者 [ZhengPeng7/BiRefNet](https://github.com/zhengpeng7/birefnet)
73 |
74 | 部分代码参考了 [ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO) 感谢！
75 |
76 | ## 关注历史
77 |
78 | [](https://star-history.com/#MoonHugo/ComfyUI-BiRefNet-Hugo&Date)
--------------------------------------------------------------------------------
/README_EN.md:
--------------------------------------------------------------------------------
1 | ComfyUI-BiRefNet-Hugo
2 |
3 |
4 |
English | 中文
5 |
6 |
7 | ## Introduction
8 |
9 | This repository wraps the latest BiRefNet model as ComfyUI nodes. Compared to the previous model, the latest model offers higher and better matting accuracy.
10 |
11 | ## 🔥🔥🔥 Update!!
12 |
13 | * 2025/02/17: 🚀 Multiple versions of BiRefNet models are supported, such as [BiRefNet](https://huggingface.co/ZhengPeng7/BiRefNet) 、[BiRefNet_HR](https://huggingface.co/ZhengPeng7/BiRefNet_HR) 、 [BiRefNet-portrait](https://huggingface.co/ZhengPeng7/BiRefNet-portrait) .
14 |
15 | ## Installation
16 |
17 | #### Method 1:
18 |
19 | 1. Go to comfyUI custom_nodes folder, `ComfyUI/custom_nodes/`
20 | 2. `git clone https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo.git`
21 | 3. `cd ComfyUI-BiRefNet-Hugo`
22 | 4. `pip install -r requirements.txt`
23 | 5. restart ComfyUI
24 |
25 | #### Method 2:
26 | Directly download the node source package, then extract it into the custom_nodes directory, and finally restart ComfyUI.
27 |
28 | #### Method 3：
29 | Install through ComfyUI-Manager by searching for 'ComfyUI-BiRefNet-Hugo' and installing it.
30 |
31 | ## Usage
32 |
33 | The demo workflow placed in `ComfyUI-BiRefNet-Hugo/workflow`
34 |
35 | Loading the model supports two methods: one is to automatically download and load a remote model, and the other is to load a local model. When loading a local model, you need to set 'load_local_model' to true and 'local_model_path' to the path where the local model is located, for example: H:\ZhengPeng7\BiRefNet.
36 |
37 | 
38 |
39 | ___
40 |
41 | 
42 |
43 |
44 | Model download address: https://huggingface.co/ZhengPeng7/BiRefNet/tree/main
45 |
46 | ___
47 | The use of workflow.json
48 |
49 | 
50 |
51 | ___
52 | The use of video_workflow.json
53 |
54 | 
55 |
56 | ## Sample Result
57 |
58 | 
59 |
60 | 
61 |
62 | 
63 |
64 | 
65 |
66 | ## Social Account Homepage
67 | - Bilibili：[My BILIBILI Homepage](https://space.bilibili.com/1303099255)
68 |
69 | ## Acknowledgments
70 |
71 | Thanks to BiRefNet repo owner [ZhengPeng7/BiRefNet](https://github.com/zhengpeng7/birefnet)
72 |
73 | Some of the code references [ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO) Thanks!
74 |
75 | ## star history
76 |
77 | [](https://star-history.com/#MoonHugo/ComfyUI-BiRefNet-Hugo&Date)
--------------------------------------------------------------------------------
/__init__.py:
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1 | from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
2 |
3 | WEB_DIRECTORY = "./js"
4 | __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS","WEB_DIRECTORY"]
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/example_workflows/video_workflow.json:
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1 | {
2 | "last_node_id": 15,
3 | "last_link_id": 16,
4 | "nodes": [
5 | {
6 | "id": 14,
7 | "type": "MaskToImage",
8 | "pos": [
9 | 1466,
10 | 93
11 | ],
12 | "size": [
13 | 210,
14 | 30
15 | ],
16 | "flags": {},
17 | "order": 3,
18 | "mode": 0,
19 | "inputs": [
20 | {
21 | "name": "mask",
22 | "label": "遮罩",
23 | "type": "MASK",
24 | "link": 15
25 | }
26 | ],
27 | "outputs": [
28 | {
29 | "name": "IMAGE",
30 | "label": "图像",
31 | "type": "IMAGE",
32 | "shape": 3,
33 | "links": [
34 | 16
35 | ],
36 | "slot_index": 0
37 | }
38 | ],
39 | "properties": {
40 | "Node name for S&R": "MaskToImage"
41 | },
42 | "widgets_values": []
43 | },
44 | {
45 | "id": 11,
46 | "type": "VHS_LoadVideo",
47 | "pos": [
48 | 826,
49 | -90
50 | ],
51 | "size": [
52 | 210,
53 | 262
54 | ],
55 | "flags": {},
56 | "order": 0,
57 | "mode": 0,
58 | "inputs": [
59 | {
60 | "name": "meta_batch",
61 | "label": "批次管理",
62 | "type": "VHS_BatchManager",
63 | "shape": 7,
64 | "link": null
65 | },
66 | {
67 | "name": "vae",
68 | "type": "VAE",
69 | "shape": 7,
70 | "link": null
71 | }
72 | ],
73 | "outputs": [
74 | {
75 | "name": "IMAGE",
76 | "label": "图像",
77 | "type": "IMAGE",
78 | "shape": 3,
79 | "links": [
80 | 12
81 | ],
82 | "slot_index": 0
83 | },
84 | {
85 | "name": "frame_count",
86 | "label": "帧计数",
87 | "type": "INT",
88 | "shape": 3,
89 | "links": null
90 | },
91 | {
92 | "name": "audio",
93 | "label": "音频",
94 | "type": "AUDIO",
95 | "shape": 3,
96 | "links": null
97 | },
98 | {
99 | "name": "video_info",
100 | "label": "视频信息",
101 | "type": "VHS_VIDEOINFO",
102 | "shape": 3,
103 | "links": null
104 | }
105 | ],
106 | "properties": {
107 | "Node name for S&R": "VHS_LoadVideo"
108 | },
109 | "widgets_values": {
110 | "video": "219862_tiny.mp4",
111 | "force_rate": 0,
112 | "force_size": "Disabled",
113 | "custom_width": 512,
114 | "custom_height": 512,
115 | "frame_load_cap": 0,
116 | "skip_first_frames": 0,
117 | "select_every_nth": 1,
118 | "choose video to upload": "image",
119 | "videopreview": {
120 | "hidden": false,
121 | "paused": false,
122 | "params": {
123 | "frame_load_cap": 0,
124 | "skip_first_frames": 0,
125 | "force_rate": 0,
126 | "filename": "219862_tiny.mp4",
127 | "type": "input",
128 | "format": "video/mp4",
129 | "select_every_nth": 1
130 | },
131 | "muted": false
132 | }
133 | }
134 | },
135 | {
136 | "id": 15,
137 | "type": "VHS_VideoCombine",
138 | "pos": [
139 | 1450,
140 | 225
141 | ],
142 | "size": [
143 | 360,
144 | 515.25
145 | ],
146 | "flags": {},
147 | "order": 4,
148 | "mode": 0,
149 | "inputs": [
150 | {
151 | "name": "images",
152 | "label": "图像",
153 | "type": "IMAGE",
154 | "link": 16
155 | },
156 | {
157 | "name": "audio",
158 | "label": "音频",
159 | "type": "AUDIO",
160 | "shape": 7,
161 | "link": null
162 | },
163 | {
164 | "name": "meta_batch",
165 | "label": "批次管理",
166 | "type": "VHS_BatchManager",
167 | "shape": 7,
168 | "link": null
169 | },
170 | {
171 | "name": "vae",
172 | "type": "VAE",
173 | "shape": 7,
174 | "link": null
175 | }
176 | ],
177 | "outputs": [
178 | {
179 | "name": "Filenames",
180 | "label": "文件名",
181 | "type": "VHS_FILENAMES",
182 | "shape": 3,
183 | "links": null
184 | }
185 | ],
186 | "properties": {
187 | "Node name for S&R": "VHS_VideoCombine"
188 | },
189 | "widgets_values": {
190 | "frame_rate": 30,
191 | "loop_count": 0,
192 | "filename_prefix": "AnimateDiff",
193 | "format": "video/h265-mp4",
194 | "pix_fmt": "yuv420p10le",
195 | "crf": 22,
196 | "save_metadata": true,
197 | "pingpong": false,
198 | "save_output": true,
199 | "videopreview": {
200 | "hidden": false,
201 | "paused": false,
202 | "params": {
203 | "filename": "AnimateDiff_00306.mp4",
204 | "subfolder": "",
205 | "type": "output",
206 | "format": "video/h265-mp4",
207 | "frame_rate": 30,
208 | "workflow": "AnimateDiff_00306.png"
209 | },
210 | "muted": false
211 | }
212 | }
213 | },
214 | {
215 | "id": 13,
216 | "type": "VHS_VideoCombine",
217 | "pos": [
218 | 1800,
219 | -350
220 | ],
221 | "size": [
222 | 360,
223 | 515.25
224 | ],
225 | "flags": {},
226 | "order": 2,
227 | "mode": 0,
228 | "inputs": [
229 | {
230 | "name": "images",
231 | "label": "图像",
232 | "type": "IMAGE",
233 | "link": 14
234 | },
235 | {
236 | "name": "audio",
237 | "label": "音频",
238 | "type": "AUDIO",
239 | "shape": 7,
240 | "link": null
241 | },
242 | {
243 | "name": "meta_batch",
244 | "label": "批次管理",
245 | "type": "VHS_BatchManager",
246 | "shape": 7,
247 | "link": null
248 | },
249 | {
250 | "name": "vae",
251 | "type": "VAE",
252 | "shape": 7,
253 | "link": null
254 | }
255 | ],
256 | "outputs": [
257 | {
258 | "name": "Filenames",
259 | "label": "文件名",
260 | "type": "VHS_FILENAMES",
261 | "shape": 3,
262 | "links": null
263 | }
264 | ],
265 | "properties": {
266 | "Node name for S&R": "VHS_VideoCombine"
267 | },
268 | "widgets_values": {
269 | "frame_rate": 30,
270 | "loop_count": 0,
271 | "filename_prefix": "AnimateDiff",
272 | "format": "video/h265-mp4",
273 | "pix_fmt": "yuv420p10le",
274 | "crf": 22,
275 | "save_metadata": true,
276 | "pingpong": false,
277 | "save_output": true,
278 | "videopreview": {
279 | "hidden": false,
280 | "paused": false,
281 | "params": {
282 | "filename": "AnimateDiff_00305.mp4",
283 | "subfolder": "",
284 | "type": "output",
285 | "format": "video/h265-mp4",
286 | "frame_rate": 30,
287 | "workflow": "AnimateDiff_00305.png"
288 | },
289 | "muted": false
290 | }
291 | }
292 | },
293 | {
294 | "id": 1,
295 | "type": "BiRefNet_Hugo",
296 | "pos": [
297 | 1076,
298 | -81
299 | ],
300 | "size": [
301 | 319.6482238769531,
302 | 166
303 | ],
304 | "flags": {},
305 | "order": 1,
306 | "mode": 0,
307 | "inputs": [
308 | {
309 | "name": "image",
310 | "label": "图像",
311 | "type": "IMAGE",
312 | "link": 12,
313 | "slot_index": 0
314 | }
315 | ],
316 | "outputs": [
317 | {
318 | "name": "image",
319 | "label": "图像",
320 | "type": "IMAGE",
321 | "shape": 3,
322 | "links": [
323 | 14
324 | ],
325 | "slot_index": 0
326 | },
327 | {
328 | "name": "mask",
329 | "label": "遮罩",
330 | "type": "MASK",
331 | "shape": 3,
332 | "links": [
333 | 15
334 | ],
335 | "slot_index": 1
336 | }
337 | ],
338 | "properties": {
339 | "Node name for S&R": "BiRefNet_Hugo"
340 | },
341 | "widgets_values": [
342 | "ZhengPeng7/BiRefNet",
343 | false,
344 | "transparency",
345 | "auto"
346 | ]
347 | }
348 | ],
349 | "links": [
350 | [
351 | 12,
352 | 11,
353 | 0,
354 | 1,
355 | 0,
356 | "IMAGE"
357 | ],
358 | [
359 | 14,
360 | 1,
361 | 0,
362 | 13,
363 | 0,
364 | "IMAGE"
365 | ],
366 | [
367 | 15,
368 | 1,
369 | 1,
370 | 14,
371 | 0,
372 | "MASK"
373 | ],
374 | [
375 | 16,
376 | 14,
377 | 0,
378 | 15,
379 | 0,
380 | "IMAGE"
381 | ]
382 | ],
383 | "groups": [],
384 | "config": {},
385 | "extra": {
386 | "ds": {
387 | "scale": 0.7247295000000005,
388 | "offset": [
389 | -228.1400879488176,
390 | 455.8448667651046
391 | ]
392 | },
393 | "ue_links": []
394 | },
395 | "version": 0.4
396 | }
--------------------------------------------------------------------------------
/example_workflows/workflow.json:
--------------------------------------------------------------------------------
1 | {
2 | "last_node_id": 5,
3 | "last_link_id": 4,
4 | "nodes": [
5 | {
6 | "id": 2,
7 | "type": "LoadImage",
8 | "pos": [
9 | 561,
10 | 254
11 | ],
12 | "size": [
13 | 315,
14 | 314
15 | ],
16 | "flags": {},
17 | "order": 0,
18 | "mode": 0,
19 | "inputs": [],
20 | "outputs": [
21 | {
22 | "name": "IMAGE",
23 | "label": "图像",
24 | "type": "IMAGE",
25 | "shape": 3,
26 | "links": [
27 | 1
28 | ]
29 | },
30 | {
31 | "name": "MASK",
32 | "label": "遮罩",
33 | "type": "MASK",
34 | "shape": 3,
35 | "links": null
36 | }
37 | ],
38 | "properties": {
39 | "Node name for S&R": "LoadImage"
40 | },
41 | "widgets_values": [
42 | "image.webp",
43 | "image"
44 | ]
45 | },
46 | {
47 | "id": 3,
48 | "type": "PreviewImage",
49 | "pos": [
50 | 1312,
51 | 140
52 | ],
53 | "size": [
54 | 410.6827087402344,
55 | 246
56 | ],
57 | "flags": {},
58 | "order": 2,
59 | "mode": 0,
60 | "inputs": [
61 | {
62 | "name": "images",
63 | "label": "图像",
64 | "type": "IMAGE",
65 | "link": 2
66 | }
67 | ],
68 | "outputs": [],
69 | "properties": {
70 | "Node name for S&R": "PreviewImage"
71 | },
72 | "widgets_values": []
73 | },
74 | {
75 | "id": 5,
76 | "type": "PreviewImage",
77 | "pos": [
78 | 1316,
79 | 425
80 | ],
81 | "size": [
82 | 430.8826599121094,
83 | 261.3667907714844
84 | ],
85 | "flags": {},
86 | "order": 4,
87 | "mode": 0,
88 | "inputs": [
89 | {
90 | "name": "images",
91 | "label": "图像",
92 | "type": "IMAGE",
93 | "link": 4
94 | }
95 | ],
96 | "outputs": [],
97 | "properties": {
98 | "Node name for S&R": "PreviewImage"
99 | },
100 | "widgets_values": []
101 | },
102 | {
103 | "id": 4,
104 | "type": "MaskToImage",
105 | "pos": [
106 | 925,
107 | 500
108 | ],
109 | "size": [
110 | 210,
111 | 26
112 | ],
113 | "flags": {},
114 | "order": 3,
115 | "mode": 0,
116 | "inputs": [
117 | {
118 | "name": "mask",
119 | "label": "遮罩",
120 | "type": "MASK",
121 | "link": 3
122 | }
123 | ],
124 | "outputs": [
125 | {
126 | "name": "IMAGE",
127 | "label": "图像",
128 | "type": "IMAGE",
129 | "shape": 3,
130 | "links": [
131 | 4
132 | ],
133 | "slot_index": 0
134 | }
135 | ],
136 | "properties": {
137 | "Node name for S&R": "MaskToImage"
138 | },
139 | "widgets_values": []
140 | },
141 | {
142 | "id": 1,
143 | "type": "BiRefNet_Hugo",
144 | "pos": [
145 | 921,
146 | 256
147 | ],
148 | "size": [
149 | 350,
150 | 175
151 | ],
152 | "flags": {},
153 | "order": 1,
154 | "mode": 0,
155 | "inputs": [
156 | {
157 | "name": "image",
158 | "label": "图像",
159 | "type": "IMAGE",
160 | "link": 1,
161 | "slot_index": 0
162 | }
163 | ],
164 | "outputs": [
165 | {
166 | "name": "image",
167 | "label": "图像",
168 | "type": "IMAGE",
169 | "shape": 3,
170 | "links": [
171 | 2
172 | ],
173 | "slot_index": 0
174 | },
175 | {
176 | "name": "mask",
177 | "label": "遮罩",
178 | "type": "MASK",
179 | "shape": 3,
180 | "links": [
181 | 3
182 | ],
183 | "slot_index": 1
184 | }
185 | ],
186 | "properties": {
187 | "Node name for S&R": "BiRefNet_Hugo"
188 | },
189 | "widgets_values": [
190 | "ZhengPeng7/BiRefNet",
191 | false,
192 | "transparency",
193 | "auto"
194 | ]
195 | }
196 | ],
197 | "links": [
198 | [
199 | 1,
200 | 2,
201 | 0,
202 | 1,
203 | 0,
204 | "IMAGE"
205 | ],
206 | [
207 | 2,
208 | 1,
209 | 0,
210 | 3,
211 | 0,
212 | "IMAGE"
213 | ],
214 | [
215 | 3,
216 | 1,
217 | 1,
218 | 4,
219 | 0,
220 | "MASK"
221 | ],
222 | [
223 | 4,
224 | 4,
225 | 0,
226 | 5,
227 | 0,
228 | "IMAGE"
229 | ]
230 | ],
231 | "groups": [],
232 | "config": {},
233 | "extra": {
234 | "ds": {
235 | "scale": 1.1671841070450009,
236 | "offset": [
237 | -422.7922693194354,
238 | -81.46496131912286
239 | ]
240 | },
241 | "ue_links": []
242 | },
243 | "version": 0.4
244 | }
--------------------------------------------------------------------------------
/js/BiRefNet_Hugo.js:
--------------------------------------------------------------------------------
1 | import { app } from "../../scripts/app.js";
2 |
3 | var localModelPathWidget = null;
4 |
5 | app.registerExtension({
6 | name: "BiRefNet_Hugo",
7 | async setup() {
8 | console.log("BiRefNet_Hugo loaded");
9 | // function queue_button_pressed() {
10 | // console.log("Queue button was pressed!");
11 | // }
12 | // document.getElementById("queue-button").addEventListener("click", queue_button_pressed);
13 | },
14 |
15 | async nodeCreated(node) {
16 | if (node.title === "🔥BiRefNet") {
17 | // 查找 load_local_model 和 local_model_path 的 widgets
18 | let loadLocalModelWidget = node.widgets.find(widget => widget.name === "load_local_model");
19 | localModelPathWidget = node.widgets.find(widget => widget.name === "local_model_path");
20 | // 确保找到相关控件
21 | if (!loadLocalModelWidget || !localModelPathWidget) {
22 | console.error("Required widgets not found: load_local_model or local_model_path.");
23 | return;
24 | }
25 |
26 | // 初始状态控制：根据 load_local_model 的值决定是否显示 local_model_path
27 | if (!loadLocalModelWidget.value) {
28 | node.widgets = node.widgets.filter(widget => widget.name !== "local_model_path");
29 | }
30 |
31 | // 当 load_local_model 变化时，动态更新 local_model_path 的显示或隐藏
32 | loadLocalModelWidget.callback = function() {
33 | console.log("load_local_model changed:", loadLocalModelWidget.value);
34 |
35 | // 移除或添加 local_model_path 控件
36 | if (loadLocalModelWidget.value) {
37 | if (!node.widgets.includes(localModelPathWidget)) {
38 | node.widgets.push(localModelPathWidget); // 显示 local_model_path
39 | }
40 | } else {
41 | node.widgets = node.widgets.filter(widget => widget.name !== "local_model_path"); // 隐藏 local_model_path
42 | }
43 | };
44 | }
45 |
46 | },
47 |
48 | async beforeRegisterNodeDef(nodeType, nodeData, app) {
49 | // 在注册节点前的逻辑
50 | console.log("beforeRegisterNodeDef called")
51 | },
52 | async init(nodeType, nodeData, app) {
53 | // 在注册节点前的逻辑
54 | console.log("init called")
55 | },
56 | async addCustomNodeDefs(nodeType, nodeData, app) {
57 | // 在注册节点前的逻辑
58 | console.log("addCustomNodeDefs called")
59 | },
60 | async getCustomWidgets(nodeType, nodeData, app) {
61 | // 在注册节点前的逻辑
62 | console.log("getCustomWidgets called")
63 | },
64 | async registerCustomNodes(nodeType, nodeData, app) {
65 | // 在注册节点前的逻辑
66 | console.log("registerCustomNodes called")
67 | },
68 | async beforeConfigureGraph(nodeType, nodeData, app) {
69 | // 在注册节点前的逻辑
70 | console.log("beforeConfigureGraph called")
71 | },
72 | loadedGraphNode(node) {
73 | // 在注册节点前的逻辑
74 | if (node.type === "BiRefNet_Hugo") {
75 | // 查找 load_local_model 和 local_model_path 的 widgets
76 | console.log(node)
77 | let loadLocalModelWidget = node.widgets.find(widget => widget.name === "load_local_model");
78 | // 确保找到相关控件
79 | if (!loadLocalModelWidget) {
80 | console.error("Required widgets not found: load_local_model.");
81 | return;
82 | }
83 |
84 | if(node.widgets_values[4] != undefined){
85 | localModelPathWidget.value = node.widgets_values[4];
86 | }
87 |
88 | // 初始状态控制：根据 load_local_model 的值决定是否显示 local_model_path
89 | if (!loadLocalModelWidget.value) {
90 | node.widgets = node.widgets.filter(widget => widget.name !== "local_model_path");
91 | }else{
92 | if (!node.widgets.includes(localModelPathWidget)) {
93 | node.widgets.push(localModelPathWidget); // 显示 local_model_path
94 | }
95 | }
96 |
97 | // 当 load_local_model 变化时，动态更新 local_model_path 的显示或隐藏
98 | loadLocalModelWidget.callback = function() {
99 | console.log("load_local_model changed:", loadLocalModelWidget.value);
100 |
101 | // 移除或添加 local_model_path 控件
102 | if (loadLocalModelWidget.value) {
103 | if (!node.widgets.includes(localModelPathWidget)) {
104 | node.widgets.push(localModelPathWidget); // 显示 local_model_path
105 | }
106 | } else {
107 | node.widgets = node.widgets.filter(widget => widget.name !== "local_model_path"); // 隐藏 local_model_path
108 | }
109 | };
110 | }
111 | },
112 | async afterConfigureGraph(node, nodeData, app) {
113 |
114 | },
115 | async beforeConfigureGraph(nodeType, nodeData, app) {
116 | // 在注册节点前的逻辑
117 | console.log("beforeConfigureGraph called")
118 | },
119 | });
120 |
--------------------------------------------------------------------------------
/nodes.py:
--------------------------------------------------------------------------------
1 | from transformers import AutoModelForImageSegmentation
2 | import torch
3 | from torchvision import transforms
4 | import numpy as np
5 | from PIL import Image
6 | import torch.nn.functional as F
7 | import comfy.model_management as mm
8 | import os
9 |
10 | torch.set_float32_matmul_precision(["high", "highest"][0])
11 |
12 | transform_image = transforms.Compose(
13 | [
14 | transforms.Resize((1024, 1024)),
15 | transforms.ToTensor(),
16 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
17 | ]
18 | )
19 |
20 | current_path = os.getcwd()
21 |
22 | ## ComfyUI portable standalone build for Windows
23 | model_path = os.path.join(current_path, "ComfyUI"+os.sep+"models"+os.sep+"BiRefNet")
24 |
25 | def tensor2pil(image):
26 | return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
27 |
28 | def pil2tensor(image):
29 | return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
30 |
31 | def resize_image(image):
32 | image = image.convert('RGB')
33 | model_input_size = (1024, 1024)
34 | image = image.resize(model_input_size, Image.BILINEAR)
35 | return image
36 |
37 | colors = ["transparency", "green", "white", "red", "yellow", "blue", "black", "pink", "purple", "brown", "violet", "wheat", "whitesmoke", "yellowgreen", "turquoise", "tomato", "thistle", "teal", "tan", "steelblue", "springgreen", "snow", "slategrey", "slateblue", "skyblue", "orange"]
38 |
39 | def get_device_by_name(device):
40 | """
41 | "device": (["auto", "cuda", "cpu", "mps", "xpu", "meta"],{"default": "auto"}),
42 | """
43 | if device == 'auto':
44 | try:
45 | device = "cpu"
46 | if torch.cuda.is_available():
47 | device = "cuda"
48 | elif torch.backends.mps.is_available():
49 | device = "mps"
50 | elif torch.xpu.is_available():
51 | device = "xpu"
52 | except:
53 | raise AttributeError("What's your device(到底用什么设备跑的)？")
54 | print("\033[93mUse Device(使用设备):", device, "\033[0m")
55 | return device
56 |
57 |
58 | class BiRefNet_Hugo:
59 | def __init__(self):
60 | pass
61 |
62 | @classmethod
63 | def INPUT_TYPES(cls):
64 |
65 | return {
66 | "required": {
67 | "image": ("IMAGE",),
68 | "model": (["ZhengPeng7/BiRefNet", "ZhengPeng7/BiRefNet_HR", "ZhengPeng7/BiRefNet-portrait"],{"default": "ZhengPeng7/BiRefNet"}),
69 | "load_local_model": ("BOOLEAN", {"default": False}),
70 | "background_color_name": (colors,{"default": "transparency"}),
71 | "device": (["auto", "cuda", "cpu", "mps", "xpu", "meta"],{"default": "auto"})
72 | },
73 | "optional": {
74 | "local_model_path": ("STRING", {"default":model_path}),
75 | }
76 | }
77 |
78 | RETURN_TYPES = ("IMAGE", "MASK",)
79 | RETURN_NAMES = ("image", "mask",)
80 | FUNCTION = "background_remove"
81 | CATEGORY = "🔥BiRefNet"
82 |
83 | def background_remove(self,
84 | image,
85 | model,
86 | load_local_model,
87 | device,
88 | background_color_name,
89 | *args, **kwargs
90 | ):
91 | processed_images = []
92 | processed_masks = []
93 |
94 | device = get_device_by_name(device)
95 |
96 | if load_local_model:
97 | local_model_path = kwargs.get("local_model_path", model_path)
98 | birefnet = AutoModelForImageSegmentation.from_pretrained(local_model_path,trust_remote_code=True)
99 | else:
100 | birefnet = AutoModelForImageSegmentation.from_pretrained(
101 | model, trust_remote_code=True
102 | )
103 |
104 | birefnet.to(device)
105 | for image in image:
106 | orig_image = tensor2pil(image)
107 | w,h = orig_image.size
108 | image = resize_image(orig_image)
109 | im_tensor = transform_image(image).unsqueeze(0)
110 | im_tensor=im_tensor.to(device)
111 | with torch.no_grad():
112 | result = birefnet(im_tensor)[-1].sigmoid().cpu()
113 | result = torch.squeeze(F.interpolate(result, size=(h,w)))
114 | ma = torch.max(result)
115 | mi = torch.min(result)
116 | result = (result-mi)/(ma-mi)
117 | im_array = (result*255).cpu().data.numpy().astype(np.uint8)
118 | pil_im = Image.fromarray(np.squeeze(im_array))
119 | if background_color_name == 'transparency':
120 | color = (0,0,0,0)
121 | mode = "RGBA"
122 | else:
123 | color = background_color_name
124 | mode = "RGB"
125 | new_im = Image.new(mode, pil_im.size, color)
126 | new_im.paste(orig_image, mask=pil_im)
127 | new_im_tensor = pil2tensor(new_im)
128 | pil_im_tensor = pil2tensor(pil_im)
129 | processed_images.append(new_im_tensor)
130 | processed_masks.append(pil_im_tensor)
131 |
132 | new_ims = torch.cat(processed_images, dim=0)
133 | new_masks = torch.cat(processed_masks, dim=0)
134 |
135 | return new_ims, new_masks
136 |
137 |
138 | NODE_CLASS_MAPPINGS = {
139 | "BiRefNet_Hugo": BiRefNet_Hugo
140 | }
141 |
142 | # A dictionary that contains the friendly/humanly readable titles for the nodes
143 | NODE_DISPLAY_NAME_MAPPINGS = {
144 | "BiRefNet_Hugo": "🔥BiRefNet"
145 | }
146 |
--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
1 | [project]
2 | name = "comfyui-birefnet-hugo"
3 | description = "This repository wraps the latest BiRefNet model as ComfyUI nodes. Compared to the previous model, the latest model offers higher and better matting accuracy."
4 | version = "1.0.0"
5 | license = {file = "LICENSE"}
6 | dependencies = ["numpy", "timm"]
7 |
8 | [project.urls]
9 | Repository = "https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo"
10 | # Used by Comfy Registry https://comfyregistry.org
11 |
12 | [tool.comfy]
13 | PublisherId = "moonhugo"
14 | DisplayName = "ComfyUI-BiRefNet-Hugo"
15 | Icon = ""
16 |
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | numpy
2 | timm
--------------------------------------------------------------------------------