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[有手就行]点几下开始画出写实的小姐姐

更新功能列表

修复了一些问题

同时使用多个 lora

支持控制网络（ControlNet）

增加了超分类型 可以选择 无 和 高清修复 两种

移植了隔壁大佬项目的[高清修复v2]版,如果喜欢[高清修复]或[高清修复v2]的话请去支持一下,! 文生图LoRA 融合版

增加切片大小控制，影响推理时的注意力切片大小，切片越小越省显存推理速度越慢，切片越大推理越快显存消耗越多

整体Fork的@天堂进制 的[多loRA,ControlNet预处理,高清v2,中文TAG]文生图v9.8版为基础修改，可以去支持下

感谢 @风飏 开发者开发的UI界面！

感谢 [文生图LoRA 融合版]项目,的高清修复方案,本想的高清修复均采用此项目实现.

感谢 [PPDiffusers:支持ControlNet和多LoRA融合,不定期更新]的图片骨骼算法,本项目控制网络预处理动作骨骼抽取采用此项目算法.

一、点“运行一下”，稍后，再点“启动环境”，选合适的GPU点开始"确定"！

点击“运行一下”稍等（Fork项目），再点击“启动环境"，点击选择合适的GPU后即可进入项目。

AI Studio每天自动赠送 8 小时的GPU算力，显存更大的GPU能够生成尺寸更大的图片哦。

二、点下面"点击运行"小按钮，稍后，再点上面"重启内核"小按钮

到了BML CodeLabe的main.ipynb界面，点击下边的框里左上角的“点击运行”小按钮（或者点进下面的框内用快捷键 Ctrl + Enter）。

提示：下面安装环境的代码，只需要在你第一次进入本项目时运行！

等到显示“加载完毕, 请重启内核”后，请重启内核。

#点左边的"点击运行"小按钮（或者点进下面的框内用快捷键 Ctrl + Enter）,稍后，重启内核! pip install pip ppdiffusers paddlenlp safetensors OmegaConf numpy --upgrade --user

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[?25ldone[?25h Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144573 sha256=ec71ac1020ee1126064ab644b921960493166330ce34f22d92a67c77296c4793Stored in directory: /home/aistudio/.cache/pip/wheels/f0/95/ef/f6e0562ab90f488f118c9b3a257ada03231324672351312f2eSuccessfully built antlr4-python3-runtimeInstalling collected packages: wcwidth, safetensors, antlr4-python3-runtime, regex, pip, OmegaConf, numpy, ftfy, huggingface-hub, typer, paddlenlp, ppdiffusers[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.parl 1.4.1 requires pyzmq==18.1.1, but you have pyzmq 23.2.1 which is incompatible.[0m[31m[0mSuccessfully installed OmegaConf-2.3.0 antlr4-python3-runtime-4.9.3 ftfy-6.1.1 huggingface-hub-0.13.4 numpy-1.21.6 paddlenlp-2.5.2 pip-23.0.1 ppdiffusers-0.14.0 regex-.10.31 safetensors-0.3.0 typer-0.7.0 wcwidth-0.2.6[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip available: [0m[31;49m22.1.2[0m[39;49m -> [0m[32;49m23.0.1[0m[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpython -m pip install --upgrade pip[0m

#如果出现错误请重启内核后再次执行此操作,执行成功后需要重启内核from IPython.display import clear_outputfrom ui.utils import diffusers_auto_updatediffusers_auto_update()clear_output() # 清理很长的内容print('加载完毕, 请重启内核')

加载完毕, 请重启内核

三、一切就绪，开始画

1、文生图的地方

from ui import gui_txt2img # 点左边的"运行一下"图标，等界面出来，点“生成图片”，第一次解压Lora模型和加载网络模型慢点，后面出图就快了display(gui_txt2img.gui) # 生成喜欢的记得点击“收藏图片”，下次启动还会有# 使用ctrl模型功能，选择模型种类，然后请在左侧文件夹上传图片，然后复制完整路径，粘贴到“引导图”，设置ctrl强度即可# 左边文件根目录里的FavoratesLibrary.html和txt2imgLibrary.html里面有图片的生成描述和参数，可以试着换Lora、描述和参数调整

Box(children=(HTML(value='<style>\n@media (max-width:576px) {\n .StableDiffusionUI_txt2img6E29 {\n m…

2、图生图的地方

from ui import gui_img2img # 点左边的"运行一下"图标，等界面出来，点“生成图片”display(gui_img2img.gui) # 生成的图片自动保存到左侧的 data/img2img 的文件夹里# 不要用本界面里的上传图片，在左侧文件夹上传图片，然后复制完整路径，粘贴到 "输入图片"，点"导入"，再去右边点“生成图片”# 生成喜欢的记得点击“收藏图片”，下次启动还会有

Box(children=(HTML(value="<style>\n@media (max-width:576px) {\n .StableDiffusionUI_img2img7CBD {\n m…

3、超分UI使用

from ui import gui_superres # 超分 (图片放大一倍), 在左侧“文件”目录上传图片, 然后修改 "需要超分的图片路径"display(gui_superres.gui) # 生成的图片自动保存到左侧的 outputs/highres 的文件夹里

4、 定制化训练（高阶）

在下面GIF例子中，我们进行了如下操作，更多信息可进入PaddleNLP examples中查看更多例子。

(1)输入了代表该人物的新单词：<Alice>，新词用<>括起来，主要是为了避免与已有的单词混淆；

(2)指定了与该人物比较接近的单词：girl, 该单词作为先验（已知）知识，可以帮助模型快速理解新单词，从而加快模型训练；

(3)提供了含有下面6张图片的文件夹地址：resources/Alices，在这里我们指定了resources目录下的Alices文件夹。

text_inversion 训练

from ui import gui_train_text_inversion # 训练display(gui_train_text_inversion.gui) # 训练结果会保存到 outputs/textual_inversion 的文件夹# 二次元：需要训练的模型名称改为 import/anything_v4# 写实的：需要训练的模型名称改为 import/chilloutmix

Box(children=(Box(children=(Dropdown(description='训练目标', description_tooltip='训练目标是什么？风格还是实体？', index=1, layou…

LoRA 训练

from ui import gui_text_to_image_lora #训练loradisplay(gui_text_to_image_lora.gui)

Box(children=(Box(children=(Dropdown(description='训练目标', description_tooltip='训练目标是什么？风格还是实体？', index=1, layou…

模型训练完成之后，可以使用新模型预测了！

现在模型已经认识 <Alice> 这个object（人物、物体，everything…）了。

比如输入Prompt：<Alice> at the lake，就可以生成一张站在湖边的 <Alice> 图像了。

from ui import gui_train_text_inversion # 使用训练好的权重进行加载预测display(gui_train_text_inversion.gui) # 生成的图片自动保存到左侧的 outputs/text_inversion_txt2img 的文件夹里# 输入 <Alice> 相关的Prompt试试吧，例如：<Alice> at the lake，看看模型是否学到了<Alice>这个object。记得删除默认给出的negative_prompt反面描述。

下面是Dreambooth训练部分

可以参考 /PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers/examples/dreambooth 配置下面的选项

from ui import gui_dreambooth #训练dreamboothdisplay(gui_dreambooth.gui)

Box(children=(Box(children=(Combobox(value='MoososCap/NOVEL-MODEL', description='需要训练的模型名称', layout=Layout(ali…

现在你已经学会了如何让模型认识新的物体，模型不仅可以学习新物体还可以学习新风格。风格的学习与物体的学习方法与流程一致，我们仅需在UI界面中设置学习“style”，开始训练即可。

欢迎各位贡献自己训练好的模型权重到PaddleNLP，比如"xxx艺术家风格的模型"，让更多开发者了解、使用自己的作品。欢迎加入交流群，了解如何贡献。

import osimport tarfilefrom pathlib import Pathos.environ['PPNLP_HOME']='./data'print(os.path.basename(os.path.dirname('./data/models/import/anything_v4')))#importprint(os.path.basename('./data/models/import/anything_v4'))#anything_v4def decompression_models(src_models_path,dest_models_path):# if os.path.exists(path,os.environ['PPNLP_HOME']+'/models/import') == False:#os.makedirs(path,os.environ['PPNLP_HOME']+'/models/import')# if os.path.exists(path,os.environ['PPNLP_HOME']+'/datasets') == False:#os.makedirs(path,os.environ['PPNLP_HOME']+'/datasets')# if os.path.exists(path,os.environ['PPNLP_HOME']+'/modules') == False:#os.makedirs(path,os.environ['PPNLP_HOME']+'/modules')# if os.path.exists(path,os.environ['PPNLP_HOME']+'/packages') == False:#os.makedirs(path,os.environ['PPNLP_HOME']+'/packages')if os.path.exists(src_models_path) == False:os.makedirs(src_models_path)print('decompression '+src_models_path)with os.scandir(src_models_path) as it:for i in it:if i.is_file() and i.name.endswith('tar.gz') :print(i.name+" -> "+ dest_models_path)model=tarfile.open(i)model.extractall(path=dest_models_path)print("completed")with os.scandir(os.environ['PPNLP_HOME']+'/') as it:for i in it:if i.is_dir() and i.name.startswith('data') :decompression_models(i.path,os.environ['PPNLP_HOME']+'/models/import')

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