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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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Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->visualdl->paddlenlp) (1.1.0)
Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->visualdl->paddlenlp) (0.10.0)
Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->visualdl->paddlenlp) (2.8.2)
Requirement already satisfied: sniffio>=1.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from anyio<5,>=3.4.0->starlette<0.27.0,>=0.26.1->fastapi->paddlenlp) (1.3.0)
Requirement already satisfied: setuptools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->visualdl->paddlenlp) (56.2.0)
Building wheels for collected packages: antlr4-python3-runtime
  Building wheel for antlr4-python3-runtime (setup.py) ... [?25ldone
[?25h  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144573 sha256=ec71ac1020ee1126064ab644b921960493166330ce34f22d92a67c77296c4793
  Stored in directory: /home/aistudio/.cache/pip/wheels/f0/95/ef/f6e0562ab90f488f118c9b3a257ada03231324672351312f2e
Successfully built antlr4-python3-runtime
Installing 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-2022.10.31 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_output
from ui.utils import diffusers_auto_update
diffusers_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

  • 重要提示：有喜欢的图片记得点 [收藏图片] 不然重启后图片会消失
  • 收藏图片：会生成[FavoratesLibrary.html]图书馆文件
  • 生成图片：会临时保存到左侧的 data 文件夹里的 txt2img 文件夹里
  • 生成图片：会生成[txt2imgLibrary.html]图书馆文件
  • lora 文件在 data/lora目录中，描述中用 <lora:{文件名(不带扩展名)}:{权重}> 来使用
  • 画二次元：模型名称改为 import/anything_v4 即可
  • 换回写实：模型名称改为 import/chilloutmix 即可
  • 例如 lora文件为"ABC.safetensors" 则提示写为<lora:ABC:1> ,支持同时使用多个 lora,注意文件名中不可以包含 “<>:”
  • 网络模型文件在data/models/import目录中
  • 支持 4096x4096 大图，32g 建议不要超过1600x1600 可能回爆炸
  • 图像放大已经修复,可以用
  • 支持控制网络（ControlNet），支持预处理支持使用普通图片即可完成引导，另外控制网络需要更多的显存，引导图请填写完整图片路径
  • 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
  • 增加高清修复强度控制，影响高清修复添加噪声强度和修复步数
  • 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
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

  • 重要提示：有喜欢的图片记得点 [收藏图片] 不然重启后图片会消失
  • 收藏图片：会生成[FavoratesLibrary.html]图书馆文件
  • 生成图片：会临时保存到左侧的 data 文件夹里的 img2img 文件夹里
  • 生成图片：会生成[img2imgLibrary.html]图书馆文件
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）

  • 让模型认识新的物体或学习到新的风格
  • 除了直接调用外，PaddleNLP还提供好用的二次开发能力
  • 可以参照下方或PaddleNLP仓库的教程，只需要几张图片，就可以定制属于自己的文图生成模型。

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

  • 注意:训练图片文件夹中需要参与训练的每个图片必须有 一个同名的txt文件,里面是相关的提示词,否则运行会报错.
from ui import gui_text_to_image_lora #训练lora
display(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 https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers/examples/dreambooth 配置下面的选项

from ui import gui_dreambooth #训练dreambooth
display(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 os
import tarfile
from pathlib import Path
os.environ['PPNLP_HOME']='./data'
print(os.path.basename(
        os.path.dirname('./data/models/import/anything_v4')))#import
print(os.path.basename('./data/models/import/anything_v4'))#anything_v4



def 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')

此文章为搬运
原项目链接

# 计算机视觉
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