feat: add all file
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duhaifeng
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GNU GENERAL PUBLIC LICENSE
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|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
not accept this License. Therefore, by modifying or propagating a
|
||||
covered work, you indicate your acceptance of this License to do so.
|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
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|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
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|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
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|
||||
purposes of this definition, "control" includes the right to grant
|
||||
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|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
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|
||||
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|
||||
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|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
|
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If, pursuant to or in connection with a single transaction or
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||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
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|
||||
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|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Use with the GNU Affero General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
section 13, concerning interaction through a network will apply to the
|
||||
combination as such.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
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|
||||
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|
||||
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|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
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|
||||
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|
||||
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|
||||
|
||||
Later license versions may give you additional or different
|
||||
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|
||||
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|
||||
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|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
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|
||||
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|
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|
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|
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|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
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|
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|
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|
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|
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|
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
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|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
above cannot be given local legal effect according to their terms,
|
||||
reviewing courts shall apply local law that most closely approximates
|
||||
an absolute waiver of all civil liability in connection with the
|
||||
Program, unless a warranty or assumption of liability accompanies a
|
||||
copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
state the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
Copyright (C) <year> <name of author>
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
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|
||||
(at your option) any later version.
|
||||
|
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This program is distributed in the hope that it will be useful,
|
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but WITHOUT ANY WARRANTY; without even the implied warranty of
|
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
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||||
|
||||
If the program does terminal interaction, make it output a short
|
||||
notice like this when it starts in an interactive mode:
|
||||
|
||||
<program> Copyright (C) <year> <name of author>
|
||||
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||
This is free software, and you are welcome to redistribute it
|
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|
||||
The hypothetical commands `show w' and `show c' should show the appropriate
|
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parts of the General Public License. Of course, your program's commands
|
||||
might be different; for a GUI interface, you would use an "about box".
|
||||
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||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU GPL, see
|
||||
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|
||||
|
||||
The GNU General Public License does not permit incorporating your program
|
||||
into proprietary programs. If your program is a subroutine library, you
|
||||
may consider it more useful to permit linking proprietary applications with
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||||
the library. If this is what you want to do, use the GNU Lesser General
|
||||
Public License instead of this License. But first, please read
|
||||
<https://www.gnu.org/licenses/why-not-lgpl.html>.
|
||||
@@ -0,0 +1,70 @@
|
||||
<h1 align="center">ComfyUI-BiRefNet-lite</h1>
|
||||
|
||||
<p align="center">
|
||||
<br> <font size=5>中文 | <a href="README_EN.md">English</a></font>
|
||||
</p>
|
||||
|
||||
|
||||
## 介绍
|
||||
|
||||
本仓库将BiRefNet最新模型封装为ComfyUI节点来使用，并支持cpu和gpu分块加载，模型缓存特性。<br>
|
||||
## 特性
|
||||
特性1: 支持cpu和gpu分块加载<br>
|
||||
在开启cuda的情况下，指定cpu_size，可将模型部分加载到cpu上
|
||||
<br>
|
||||
特性2: 模型缓存<br>
|
||||
特性3: 多种裁剪方式<br>
|
||||
支持putalpha, naive, alpha_matting三种裁剪方式
|
||||
<br>
|
||||
<br>
|
||||
|
||||
|
||||
## 安装
|
||||
|
||||
#### 方法1:
|
||||
|
||||
1. 进入节点目录, `ComfyUI/custom_nodes/`
|
||||
2. `git clone https://github.com/rubi-du/ComfyUI-BiRefNet-lite.git`
|
||||
3. `cd ComfyUI-BiRefNet-lite`
|
||||
4. `pip install -r requirements.txt`
|
||||
5. 重启ComfyUI
|
||||
|
||||
#### 方法2:
|
||||
直接下载节点源码包，然后解压到custom_nodes目录下，最后重启ComfyUI
|
||||
|
||||
#### 方法3：
|
||||
通过ComfyUI-Manager安装，搜索“ComfyUI-BiRefNet-lite”进行安装
|
||||
|
||||
## 使用
|
||||
|
||||
示例工作流放置在`ComfyUI-BiRefNet-lite/workflow`中<br/>
|
||||
|
||||
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<br/>
|
||||
|
||||
|
||||
___
|
||||
|
||||
|
||||
|
||||
|
||||
模型下载地址：<br/>
|
||||
BiRefNet: https://huggingface.co/ZhengPeng7/BiRefNet/tree/main<br/>
|
||||
BiRefNet_lite-2K: https://huggingface.co/ZhengPeng7/BiRefNet_lite-2K/tree/main<br/>
|
||||
BiRefNet-portrait: https://huggingface.co/ZhengPeng7/BiRefNet-portrait/tree/main<br/>
|
||||
BiRefNet-matting: https://huggingface.co/ZhengPeng7/BiRefNet-matting/tree/main<br/>
|
||||
RMBG-2.0: https://huggingface.co/briaai/RMBG-2.0/tree/main<br/>
|
||||
|
||||
___
|
||||
工作流workflow.json的使用<br/>
|
||||
|
||||
|
||||
|
||||
___
|
||||
工作流video_workflow.json的使用<br/>
|
||||
[workflow地址](./workflow/video_workflow.json)
|
||||
|
||||
## 感谢
|
||||
|
||||
感谢BiRefNet仓库的所有作者 [ZhengPeng7/BiRefNet](https://github.com/zhengpeng7/birefnet)
|
||||
|
||||
部分代码参考了 [MoonHugo/ComfyUI-BiRefNet-Hugo](https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo) 感谢！
|
||||
@@ -0,0 +1,73 @@
|
||||
<h1 align="center">ComfyUI-BiRefNet-Hugo</h1>
|
||||
|
||||
<p align="center">
|
||||
<br> <font size=5>English | <a href="README.md">中文</a></font>
|
||||
</p>
|
||||
|
||||
## Introduction
|
||||
|
||||
This repository wraps the latest BiRefNet model as ComfyUI nodes. Compared to the previous model, the latest model offers higher and better matting accuracy.
|
||||
|
||||
## Installation
|
||||
|
||||
#### Method 1:
|
||||
|
||||
1. Go to comfyUI custom_nodes folder, `ComfyUI/custom_nodes/`
|
||||
2. `git clone https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo.git`
|
||||
3. `cd ComfyUI-BiRefNet-Hugo`
|
||||
4. `pip install -r requirements.txt`
|
||||
5. restart ComfyUI
|
||||
|
||||
#### Method 2:
|
||||
Directly download the node source package, then extract it into the custom_nodes directory, and finally restart ComfyUI.
|
||||
|
||||
#### Method 3：
|
||||
Install through ComfyUI-Manager by searching for 'ComfyUI-BiRefNet-Hugo' and installing it.
|
||||
|
||||
## Usage
|
||||
|
||||
The demo workflow placed in `ComfyUI-BiRefNet-Hugo/workflow`
|
||||
|
||||
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.
|
||||
|
||||
|
||||
|
||||
___
|
||||
|
||||
|
||||
|
||||
|
||||
Model download address: https://huggingface.co/ZhengPeng7/BiRefNet/tree/main
|
||||
|
||||
___
|
||||
The use of workflow.json
|
||||
|
||||
|
||||
|
||||
___
|
||||
The use of video_workflow.json
|
||||
|
||||
|
||||
|
||||
## Sample Result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Social Account Homepage
|
||||
- Bilibili：[My BILIBILI Homepage](https://space.bilibili.com/1303099255)
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
Thanks to BiRefNet repo owner [ZhengPeng7/BiRefNet](https://github.com/zhengpeng7/birefnet)
|
||||
|
||||
Some of the code references [ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO) Thanks!
|
||||
|
||||
## star history
|
||||
|
||||
[](https://star-history.com/#MoonHugo/ComfyUI-BiRefNet-Hugo&Date)
|
||||
@@ -0,0 +1,4 @@
|
||||
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
|
||||
|
||||
WEB_DIRECTORY = "./js"
|
||||
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS","WEB_DIRECTORY"]
|
||||
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|
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|
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|
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|
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|
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|
After Width: | Height: | Size: 241 KiB |
@@ -0,0 +1,119 @@
|
||||
import { app } from "../../scripts/app.js";
|
||||
|
||||
var localModelPathWidget = null;
|
||||
|
||||
app.registerExtension({
|
||||
name: "BiRefNet_Lite",
|
||||
async setup() {
|
||||
console.log("BiRefNet_Lite loaded");
|
||||
// function queue_button_pressed() {
|
||||
// console.log("Queue button was pressed!");
|
||||
// }
|
||||
// document.getElementById("queue-button").addEventListener("click", queue_button_pressed);
|
||||
},
|
||||
|
||||
async nodeCreated(node) {
|
||||
if (node.title === "🔥BiRefNet_Lite") {
|
||||
// 查找 load_local_model 和 local_model_path 的 widgets
|
||||
let loadLocalModelWidget = node.widgets.find(widget => widget.name === "load_local_model");
|
||||
localModelPathWidget = node.widgets.find(widget => widget.name === "local_model_path");
|
||||
// 确保找到相关控件
|
||||
if (!loadLocalModelWidget || !localModelPathWidget) {
|
||||
console.error("Required widgets not found: load_local_model or local_model_path.");
|
||||
return;
|
||||
}
|
||||
|
||||
// 初始状态控制：根据 load_local_model 的值决定是否显示 local_model_path
|
||||
if (!loadLocalModelWidget.value) {
|
||||
node.widgets = node.widgets.filter(widget => widget.name !== "local_model_path");
|
||||
}
|
||||
|
||||
// 当 load_local_model 变化时，动态更新 local_model_path 的显示或隐藏
|
||||
loadLocalModelWidget.callback = function() {
|
||||
console.log("load_local_model changed:", loadLocalModelWidget.value);
|
||||
|
||||
// 移除或添加 local_model_path 控件
|
||||
if (loadLocalModelWidget.value) {
|
||||
if (!node.widgets.includes(localModelPathWidget)) {
|
||||
node.widgets.push(localModelPathWidget); // 显示 local_model_path
|
||||
}
|
||||
} else {
|
||||
node.widgets = node.widgets.filter(widget => widget.name !== "local_model_path"); // 隐藏 local_model_path
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
},
|
||||
|
||||
async beforeRegisterNodeDef(nodeType, nodeData, app) {
|
||||
// 在注册节点前的逻辑
|
||||
console.log("beforeRegisterNodeDef called")
|
||||
},
|
||||
async init(nodeType, nodeData, app) {
|
||||
// 在注册节点前的逻辑
|
||||
console.log("init called")
|
||||
},
|
||||
async addCustomNodeDefs(nodeType, nodeData, app) {
|
||||
// 在注册节点前的逻辑
|
||||
console.log("addCustomNodeDefs called")
|
||||
},
|
||||
async getCustomWidgets(nodeType, nodeData, app) {
|
||||
// 在注册节点前的逻辑
|
||||
console.log("getCustomWidgets called")
|
||||
},
|
||||
async registerCustomNodes(nodeType, nodeData, app) {
|
||||
// 在注册节点前的逻辑
|
||||
console.log("registerCustomNodes called")
|
||||
},
|
||||
async beforeConfigureGraph(nodeType, nodeData, app) {
|
||||
// 在注册节点前的逻辑
|
||||
console.log("beforeConfigureGraph called")
|
||||
},
|
||||
loadedGraphNode(node) {
|
||||
// 在注册节点前的逻辑
|
||||
if (node.type === "BiRefNet_Lite") {
|
||||
// 查找 load_local_model 和 local_model_path 的 widgets
|
||||
console.log(node)
|
||||
let loadLocalModelWidget = node.widgets.find(widget => widget.name === "load_local_model");
|
||||
// 确保找到相关控件
|
||||
if (!loadLocalModelWidget) {
|
||||
console.error("Required widgets not found: load_local_model.");
|
||||
return;
|
||||
}
|
||||
|
||||
if(node.widgets_values[3] != undefined){
|
||||
localModelPathWidget.value = node.widgets_values[3];
|
||||
}
|
||||
|
||||
// 初始状态控制：根据 load_local_model 的值决定是否显示 local_model_path
|
||||
if (!loadLocalModelWidget.value) {
|
||||
node.widgets = node.widgets.filter(widget => widget.name !== "local_model_path");
|
||||
}else{
|
||||
if (!node.widgets.includes(localModelPathWidget)) {
|
||||
node.widgets.push(localModelPathWidget); // 显示 local_model_path
|
||||
}
|
||||
}
|
||||
|
||||
// 当 load_local_model 变化时，动态更新 local_model_path 的显示或隐藏
|
||||
loadLocalModelWidget.callback = function() {
|
||||
console.log("load_local_model changed:", loadLocalModelWidget.value);
|
||||
|
||||
// 移除或添加 local_model_path 控件
|
||||
if (loadLocalModelWidget.value) {
|
||||
if (!node.widgets.includes(localModelPathWidget)) {
|
||||
node.widgets.push(localModelPathWidget); // 显示 local_model_path
|
||||
}
|
||||
} else {
|
||||
node.widgets = node.widgets.filter(widget => widget.name !== "local_model_path"); // 隐藏 local_model_path
|
||||
}
|
||||
};
|
||||
}
|
||||
},
|
||||
async afterConfigureGraph(node, nodeData, app) {
|
||||
|
||||
},
|
||||
async beforeConfigureGraph(nodeType, nodeData, app) {
|
||||
// 在注册节点前的逻辑
|
||||
console.log("beforeConfigureGraph called")
|
||||
},
|
||||
});
|
||||
@@ -0,0 +1,295 @@
|
||||
from transformers import AutoModelForImageSegmentation, AutoConfig
|
||||
from transformers.dynamic_module_utils import get_class_from_dynamic_module
|
||||
import torch
|
||||
from torchvision import transforms
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
import torch.nn.functional as F
|
||||
import os
|
||||
from scipy.ndimage import binary_erosion
|
||||
from pymatting.alpha.estimate_alpha_cf import estimate_alpha_cf
|
||||
from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
|
||||
from pymatting.util.util import stack_images
|
||||
from folder_paths import models_dir, add_model_folder_path, get_folder_paths
|
||||
|
||||
torch.set_float32_matmul_precision(["high", "highest"][0])
|
||||
|
||||
transform_image = transforms.Compose(
|
||||
[
|
||||
transforms.Resize((1024, 1024)),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
||||
]
|
||||
)
|
||||
|
||||
current_path = os.getcwd()
|
||||
|
||||
## ComfyUI portable standalone build for Windows
|
||||
|
||||
add_model_folder_path('birefnet', os.path.join(models_dir, "birefnet"))
|
||||
model_path = "BiRefNet"
|
||||
|
||||
max_gpu_size = 3
|
||||
|
||||
def clear_memory():
|
||||
import gc
|
||||
# Cleanup
|
||||
gc.collect()
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.ipc_collect()
|
||||
|
||||
def tensor2pil(image):
|
||||
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
|
||||
|
||||
def pil2tensor(image):
|
||||
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
|
||||
def resize_image(image):
|
||||
image = image.convert('RGB')
|
||||
model_input_size = (1024, 1024)
|
||||
image = image.resize(model_input_size, Image.BILINEAR)
|
||||
return image
|
||||
|
||||
|
||||
def alpha_matting_cutout(
|
||||
img: Image,
|
||||
mask: Image,
|
||||
foreground_threshold: int,
|
||||
background_threshold: int,
|
||||
erode_structure_size: int,
|
||||
) -> Image:
|
||||
"""
|
||||
Perform alpha matting on an image using a given mask and threshold values.
|
||||
|
||||
This function takes a PIL image `img` and a PIL image `mask` as input, along with
|
||||
the `foreground_threshold` and `background_threshold` values used to determine
|
||||
foreground and background pixels. The `erode_structure_size` parameter specifies
|
||||
the size of the erosion structure to be applied to the mask.
|
||||
|
||||
The function returns a PIL image representing the cutout of the foreground object
|
||||
from the original image.
|
||||
"""
|
||||
if img.mode == "RGBA" or img.mode == "CMYK":
|
||||
img = img.convert("RGB")
|
||||
|
||||
img_array = np.asarray(img)
|
||||
mask_array = np.asarray(mask)
|
||||
|
||||
is_foreground = mask_array > foreground_threshold
|
||||
is_background = mask_array < background_threshold
|
||||
|
||||
structure = None
|
||||
if erode_structure_size > 0:
|
||||
structure = np.ones(
|
||||
(erode_structure_size, erode_structure_size), dtype=np.uint8
|
||||
)
|
||||
|
||||
is_foreground = binary_erosion(is_foreground, structure=structure)
|
||||
is_background = binary_erosion(is_background, structure=structure, border_value=1)
|
||||
|
||||
trimap = np.full(mask_array.shape, dtype=np.uint8, fill_value=128)
|
||||
trimap[is_foreground] = 255
|
||||
trimap[is_background] = 0
|
||||
|
||||
img_normalized = img_array / 255.0
|
||||
trimap_normalized = trimap / 255.0
|
||||
|
||||
alpha = estimate_alpha_cf(img_normalized, trimap_normalized)
|
||||
foreground = estimate_foreground_ml(img_normalized, alpha)
|
||||
cutout = stack_images(foreground, alpha)
|
||||
|
||||
cutout = np.clip(cutout * 255, 0, 255).astype(np.uint8)
|
||||
cutout = Image.fromarray(cutout)
|
||||
|
||||
return cutout
|
||||
|
||||
|
||||
def naive_cutout(img: Image, mask: Image) -> Image:
|
||||
"""
|
||||
Perform a simple cutout operation on an image using a mask.
|
||||
|
||||
This function takes a PIL image `img` and a PIL image `mask` as input.
|
||||
It uses the mask to create a new image where the pixels from `img` are
|
||||
cut out based on the mask.
|
||||
|
||||
The function returns a PIL image representing the cutout of the original
|
||||
image using the mask.
|
||||
"""
|
||||
empty = Image.new("RGBA", (img.size), 0)
|
||||
cutout = Image.composite(img, empty, mask)
|
||||
return cutout
|
||||
|
||||
def putalpha_cutout(img: Image, mask: Image) -> Image:
|
||||
"""
|
||||
Apply the specified mask to the image as an alpha cutout.
|
||||
|
||||
Args:
|
||||
img (PILImage): The image to be modified.
|
||||
mask (PILImage): The mask to be applied.
|
||||
|
||||
Returns:
|
||||
PILImage: The modified image with the alpha cutout applied.
|
||||
"""
|
||||
img.putalpha(mask)
|
||||
return img
|
||||
|
||||
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"]
|
||||
|
||||
def get_device_by_name(device):
|
||||
"""
|
||||
"device": (["auto", "cuda", "cpu", "mps", "xpu", "meta"],{"default": "auto"}),
|
||||
"""
|
||||
if device == 'auto':
|
||||
try:
|
||||
device = "cpu"
|
||||
if torch.cuda.is_available():
|
||||
device = "cuda"
|
||||
elif torch.backends.mps.is_available():
|
||||
device = "mps"
|
||||
elif torch.xpu.is_available():
|
||||
device = "xpu"
|
||||
except:
|
||||
raise AttributeError("What's your device(到底用什么设备跑的)？")
|
||||
print("\033[93mUse Device(使用设备):", device, "\033[0m")
|
||||
return device
|
||||
|
||||
_birefnet_model = None
|
||||
|
||||
class BiRefNet_Lite:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
"load_local_model": ("BOOLEAN", {"default": True}),
|
||||
"device": (["auto", "cuda", "cpu", "mps", "xpu", "meta"],{"default": "auto"}),
|
||||
"cutout_func": (["putalpha", "naive", "alpha_matting"],{"default": "putalpha"}),
|
||||
"cached": ("BOOLEAN", {"default": True}),
|
||||
"cpu_size": ("FLOAT",{"default": 0}),
|
||||
"alpha_matting_foreground_threshold": ("INT", {"default": 240}),
|
||||
"alpha_matting_background_threshold": ("INT", {"default": 10}),
|
||||
"alpha_matting_erode_size": ("INT", {"default": 10}),
|
||||
},
|
||||
"optional": {
|
||||
"local_model_path": ("STRING", {"default": model_path}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "MASK",)
|
||||
RETURN_NAMES = ("image", "mask",)
|
||||
FUNCTION = "background_remove"
|
||||
CATEGORY = "🔥BiRefNet"
|
||||
|
||||
def background_remove(self,
|
||||
image,
|
||||
load_local_model,
|
||||
device,
|
||||
cutout_func,
|
||||
cpu_size,
|
||||
cached,
|
||||
*args, **kwargs
|
||||
):
|
||||
processed_images = []
|
||||
processed_masks = []
|
||||
|
||||
device = get_device_by_name(device)
|
||||
birefnet = None
|
||||
global _birefnet_model
|
||||
if cached and _birefnet_model is not None:
|
||||
birefnet = _birefnet_model
|
||||
else:
|
||||
if _birefnet_model is not None:
|
||||
_birefnet_model = None
|
||||
clear_memory()
|
||||
|
||||
if load_local_model:
|
||||
local_model_path = kwargs.get("local_model_path", model_path)
|
||||
local_model_path = os.path.join(get_folder_paths('birefnet')[-1], local_model_path)
|
||||
# 判断是否开启双卡支持
|
||||
# 获取不同显卡的内存可用量
|
||||
# 显卡不足时，自动往CPU调度
|
||||
spare_params = {}
|
||||
config, kwargs = AutoConfig.from_pretrained(
|
||||
local_model_path,
|
||||
return_unused_kwargs=True,
|
||||
trust_remote_code=True,
|
||||
code_revision=None,
|
||||
_commit_hash=None,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
class_ref = config.auto_map["AutoModelForImageSegmentation"]
|
||||
model_class = get_class_from_dynamic_module(
|
||||
class_ref, local_model_path, code_revision=None, **kwargs
|
||||
)
|
||||
if cpu_size > 0 and device == 'cuda':
|
||||
spare_params['device_map'] = 'auto'
|
||||
spare_params['max_memory'] = {0: "${max_gpu_size}GiB", "cpu": "${cpu_size}GiB"}
|
||||
setattr(model_class, '_no_split_modules', ["Decoder", "SwinTransformer"])
|
||||
else:
|
||||
if hasattr(model_class, "_no_split_modules"):
|
||||
delattr(model_class, "_no_split_modules")
|
||||
|
||||
birefnet = AutoModelForImageSegmentation.from_pretrained(local_model_path,trust_remote_code=True)
|
||||
else:
|
||||
birefnet = AutoModelForImageSegmentation.from_pretrained(
|
||||
"ZhengPeng7/BiRefNet", trust_remote_code=True
|
||||
)
|
||||
# 远程加载不启动分块
|
||||
cpu_size = 0
|
||||
if cpu_size == 0:
|
||||
birefnet.to(device)
|
||||
|
||||
for image in image:
|
||||
orig_image = tensor2pil(image)
|
||||
w,h = orig_image.size
|
||||
image = resize_image(orig_image)
|
||||
im_tensor = transform_image(image).unsqueeze(0)
|
||||
im_tensor=im_tensor.to(device)
|
||||
with torch.no_grad():
|
||||
result = birefnet(im_tensor)[-1].sigmoid().cpu()
|
||||
result = torch.squeeze(F.interpolate(result, size=(h,w)))
|
||||
ma = torch.max(result)
|
||||
mi = torch.min(result)
|
||||
result = (result-mi)/(ma-mi)
|
||||
im_array = (result*255).cpu().data.numpy().astype(np.uint8)
|
||||
pil_im = Image.fromarray(np.squeeze(im_array))
|
||||
if cutout_func == 'putalpha':
|
||||
new_im = putalpha_cutout(orig_image, pil_im)
|
||||
elif cutout_func == 'naive':
|
||||
new_im = naive_cutout(orig_image, pil_im)
|
||||
elif cutout_func == 'alpha_matting':
|
||||
new_im = alpha_matting_cutout(
|
||||
orig_image,
|
||||
pil_im,
|
||||
foreground_threshold=kwargs.get('alpha_matting_foreground_threshold'),
|
||||
background_threshold=kwargs.get('alpha_matting_background_threshold'),
|
||||
erode_structure_size=kwargs.get('alpha_matting_erode_size')
|
||||
)
|
||||
new_im_tensor = pil2tensor(new_im)
|
||||
pil_im_tensor = pil2tensor(pil_im)
|
||||
|
||||
processed_images.append(new_im_tensor)
|
||||
processed_masks.append(pil_im_tensor)
|
||||
|
||||
clear_memory()
|
||||
|
||||
new_ims = torch.cat(processed_images, dim=0)
|
||||
new_masks = torch.cat(processed_masks, dim=0)
|
||||
|
||||
return new_ims, new_masks
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"BiRefNet_Lite": BiRefNet_Lite
|
||||
}
|
||||
|
||||
# A dictionary that contains the friendly/humanly readable titles for the nodes
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"BiRefNet_Lite": "🔥BiRefNet_Lite"
|
||||
}
|
||||
@@ -0,0 +1,14 @@
|
||||
[project]
|
||||
name = "comfyui-birefnet-lite"
|
||||
description = ""
|
||||
version = "1.0.0"
|
||||
license = {file = "LICENSE"}
|
||||
|
||||
[project.urls]
|
||||
Repository = "https://github.com/rubi-du/ComfyUI-BiRefNet-lite"
|
||||
# Used by Comfy Registry https://comfyregistry.org
|
||||
|
||||
[tool.comfy]
|
||||
PublisherId = ""
|
||||
DisplayName = "ComfyUI-BiRefNet-lite"
|
||||
Icon = ""
|
||||
@@ -0,0 +1,5 @@
|
||||
numpy
|
||||
timm
|
||||
pymatting
|
||||
scikit-image
|
||||
scipy
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 259 KiB |
@@ -0,0 +1,389 @@
|
||||
{
|
||||
"last_node_id": 15,
|
||||
"last_link_id": 16,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 13,
|
||||
"type": "VHS_VideoCombine",
|
||||
"pos": {
|
||||
"0": 1830,
|
||||
"1": -180
|
||||
},
|
||||
"size": [
|
||||
360,
|
||||
928.4444444444445
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 14,
|
||||
"label": "图像"
|
||||
},
|
||||
{
|
||||
"name": "audio",
|
||||
"type": "AUDIO",
|
||||
"link": null,
|
||||
"label": "音频"
|
||||
},
|
||||
{
|
||||
"name": "meta_batch",
|
||||
"type": "VHS_BatchManager",
|
||||
"link": null,
|
||||
"label": "批次管理"
|
||||
},
|
||||
{
|
||||
"name": "vae",
|
||||
"type": "VAE",
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "Filenames",
|
||||
"type": "VHS_FILENAMES",
|
||||
"links": null,
|
||||
"shape": 3,
|
||||
"label": "文件名"
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "VHS_VideoCombine"
|
||||
},
|
||||
"widgets_values": {
|
||||
"frame_rate": 30,
|
||||
"loop_count": 0,
|
||||
"filename_prefix": "AnimateDiff",
|
||||
"format": "video/h265-mp4",
|
||||
"pix_fmt": "yuv420p10le",
|
||||
"crf": 22,
|
||||
"save_metadata": true,
|
||||
"pingpong": false,
|
||||
"save_output": true,
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Reference in New Issue
Block a user