本文主要是介绍基于【Lama Cleaner】一键秒去水印，轻松移除不想要的内容！，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
一、项目背景
革命性的AI图像编辑技术,让您的图片焕然一新!无论水印、logo、不想要的人物或物体,都能被神奇地移除,只留下纯净的画面。操作简单,效果出众,给你全新的视觉体验。开启图像编辑新纪元,尽在掌控!
利用去水印开源工具Lama Cleaner对照片中"杂质"进行去除！
可以去AI擦除一切应用体验!
先看效果：
二、Lama Cleaner是什么？
Lama Cleaner是一款开源且免费的人工学习图片去水印程序（个人主要学习用途），没有图片分辨率限制（个人使用暂未发现），并且保存的图片质量很高（个人觉得跟原图差不多），还能下载处理后的图片到本地。
三、操作
1、安装
In [1]
!pip install litelama==0.1.7Looking in indexes: https://mirror.baidu.com/pypi/simple/, https://mirrors.aliyun.com/pypi/simple/ Collecting litelama==0.1.7Downloading https://mirrors.aliyun.com/pypi/packages/6e/59/873f5cbaeae1f2b17e6d1ae6c74e1efde28783db4d7442346a77a6140673/litelama-0.1.7-py3-none-any.whl (21 kB) Collecting kornia>=0.7.0 (from litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/ac/fa/5612c4b1ad83b3044062e9dd0ca3c91937d8023cff0836269e18573655b0/kornia-0.7.2-py2.py3-none-any.whl (825 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 825.4/825.4 kB 1.1 MB/s eta 0:00:0000:0100:01 Requirement already satisfied: numpy>=1.24.4 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from litelama==0.1.7) (1.26.2) Collecting omegaconf>=2.3.0 (from litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/e3/94/1843518e420fa3ed6919835845df698c7e27e183cb997394e4a670973a65/omegaconf-2.3.0-py3-none-any.whl (79 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0:00:0000:0100:16 Collecting kornia-rs>=0.1.0 (from kornia>=0.7.0->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/7b/ef/eec16e87bc8893f608a42c96739ad0c35e30877b0f64bd19d95971534cef/kornia_rs-0.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.4/2.4 MB 1.3 MB/s eta 0:00:0000:0100:01 Requirement already satisfied: packaging in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from kornia>=0.7.0->litelama==0.1.7) (23.2) Collecting antlr4-python3-runtime==4.9.* (from omegaconf>=2.3.0->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz (117 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 117.0/117.0 kB 1.3 MB/s eta 0:00:00a 0:00:01Preparing metadata (setup.py) ... done Requirement already satisfied: PyYAML>=5.1.0 in 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torch>=2.0.1->litelama==0.1.7) (2023.10.0) Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/b6/9f/c64c03f49d6fbc56196664d05dba14e3a561038a81a638eeb47f4d4cfd48/nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 23.7/23.7 MB 1.3 MB/s eta 0:00:0000:0100:01 Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/eb/d5/c68b1d2cdfcc59e72e8a5949a37ddb22ae6cade80cd4a57a84d4c8b55472/nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 823.6/823.6 kB 1.2 MB/s eta 0:00:0000:0100:01 Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=2.0.1->litelama==0.1.7)Downloading 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already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from jinja2->torch>=2.0.1->litelama==0.1.7) (2.1.3) Collecting mpmath>=0.19 (from sympy->torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl (536 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 536.2/536.2 kB 1.3 MB/s eta 0:00:0000:0100:01 Building wheels for collected packages: antlr4-python3-runtimeBuilding wheel for antlr4-python3-runtime (setup.py) ... doneCreated wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=077a76af915c8b5e871c1a81a6cbda25ccce15c65326cd9d79be4d51a5141f99Stored in directory: /home/aistudio/.cache/pip/wheels/79/82/b1/b79d6e90f34257cd436860ed4f4a09f9e1ea8cd32da7046ea4 Successfully built antlr4-python3-runtime Installing collected packages: mpmath, antlr4-python3-runtime, triton, sympy, omegaconf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, kornia-rs, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, kornia, litelama Successfully installed antlr4-python3-runtime-4.9.3 kornia-0.7.2 kornia-rs-0.1.2 litelama-0.1.7 mpmath-1.3.0 networkx-3.2.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 omegaconf-2.3.0 sympy-1.12 torch-2.2.2 triton-2.2.0
2、clean_object
In [2]
from litelama import LiteLama
from litelama.model import download_file
import os
from fastapi import FastAPI, BodyMODEL_PATH = "work/models/"def clean_object_init_img_with_mask(init_img_with_mask):return clean_object(init_img_with_mask['image'],init_img_with_mask['mask'])def clean_object(image,mask):Lama = LiteLama2()init_image = imagemask_image = maskinit_image = init_image.convert("RGB")mask_image = mask_image.convert("RGB")device = "cuda:0"result = Nonetry:Lama.to(device)result = Lama.predict(init_image, mask_image)except:passfinally:Lama.to("cpu")return [result]class LiteLama2(LiteLama):_instance = Nonedef __new__(cls, *args, **kw):if cls._instance is None:cls._instance = object.__new__(cls, *args, **kw)return cls._instancedef __init__(self, checkpoint_path=None, config_path=None):self._checkpoint_path = checkpoint_pathself._config_path = config_pathself._model = Noneif self._checkpoint_path is None:checkpoint_path = os.path.join(MODEL_PATH, "big-lama.safetensors")if os.path.exists(checkpoint_path) and os.path.isfile(checkpoint_path):passelse:download_file("https://huggingface.co/anyisalin/big-lama/resolve/main/big-lama.safetensors", checkpoint_path)self._checkpoint_path = checkpoint_pathself.load(location="cpu")
3、去除标记物
In [3]
from PIL import Image
from work.scripts import lama
# 打开图片文件
image = Image.open("work/scripts/1.jpg")
mask = Image.open("work/scripts/image.png")
_output = clean_object(image,mask)
print(_output)/opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.htmlfrom .autonotebook import tqdm as notebook_tqdm
[<PIL.Image.Image image mode=RGB size=464x712 at 0x7F5007677B20>]
4、查看结果
In [4]
_output[0].show()
<PIL.Image.Image image mode=RGB size=464x712>
四、Gradio应用部署
本文开头所示的Gradio应用已经打包在work/scripts目录下的app.gradio.py文件内，大家可按照aistudio应用部署的方法进行在线部署，也可下载文件到本地进行本地运行。
具体步骤如下：
- 编辑器右上角找到部署按钮
- 选择Gradio部署
- 填写应用信息，执行文件选择 app.gradio.py，部署环境选择 GPU 即可，最后点击部署，接下来耐心等待部署完成。
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