一、安裝配置Anaconda
進(jìn)入官網(wǎng)下載安裝包https://www.anaconda.com/并安裝，然后將Anaconda配置到環(huán)境變量中。
打開命令行，依次通過如下命令創(chuàng)建Python運(yùn)行虛擬環(huán)境。
conda env create novelai python==3.10.6
E:\workspace\02_Python\novalai>conda info --envs
# conda environments:
#
base * D:\anaconda3
novelai D:\anaconda3\envs\novelai
conda activate novelai
二、安裝CUDA
筆者的顯卡為NVIDIA，需安裝NVIDIA的開發(fā)者工具進(jìn)入官網(wǎng)https://developer.nvidia.com/，根據(jù)自己計算機(jī)的系統(tǒng)情況，選擇合適的安裝包下載安裝。
打開安裝程序后，依照提示完成安裝。
安裝完成后，在命令窗口輸入如下命令，輸出CUDA版本即安裝成功。
C:\Users\yefuf>nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:41:10_Pacific_Daylight_Time_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0
三、安裝pytorch
進(jìn)入官網(wǎng)https://pytorch.org/，根據(jù)計算機(jī)配置選擇合適的版本進(jìn)行安裝。這里需要注意的是CUDA的平臺選擇，先打開NVIDIA控制面板-幫助-系統(tǒng)信息-組件查看CUDA版本，官網(wǎng)上選擇的計算平臺需要低于計算機(jī)的NVIDIA版本。
配置選擇完成后，官網(wǎng)會生成相應(yīng)的安裝命令。
將安裝命令復(fù)制出，命令窗口執(zhí)行安裝即可。
conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
當(dāng)查到Pytorch官網(wǎng)推薦的CUDA版本跟你的顯卡版本不匹配時，就需要根據(jù)官網(wǎng)的CUDA版本找到對應(yīng)的顯卡驅(qū)動版本并升級顯卡驅(qū)動，對應(yīng)關(guān)系可通過https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html查看
四、安裝git
進(jìn)入git官網(wǎng)https://git-scm.com/，下載安裝即可。
五、搭建stable-diffusion-webui
進(jìn)入項目地址https://github.com/AUTOMATIC1111/stable-diffusion-webui，通過git將項目克隆下來。
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
Cloning into 'stable-diffusion-webui'...
remote: Enumerating objects: 10475, done.
remote: Counting objects: 100% (299/299), done.
remote: Compressing objects: 100% (199/199), done.
remote: Total 10475 (delta 178), reused 199 (delta 100), pack-reused 10176
Receiving objects: 100% (10475/10475), 23.48 MiB | 195.00 KiB/s, done.
Resolving deltas: 100% (7312/7312), done.
克隆下載擴(kuò)展庫。
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients “extensions/aesthetic-gradients”
Cloning into 'extensions/aesthetic-gradients'...
remote: Enumerating objects: 21, done.
remote: Counting objects: 100% (21/21), done.
remote: Compressing objects: 100% (12/12), done.
remote: Total 21 (delta 3), reused 18 (delta 3), pack-reused 0
Receiving objects: 100% (21/21), 1.09 MiB | 1.34 MiB/s, done.
Resolving deltas: 100% (3/3), done.
git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser “extensions/images-browser”
Cloning into 'extensions/images-browser'...
remote: Enumerating objects: 118, done.
remote: Counting objects: 100% (118/118), done.
remote: Compressing objects: 100% (70/70), done.
remote: Total 118 (delta 42), reused 65 (delta 24), pack-reused 0
Receiving objects: 100% (118/118), 33.01 KiB | 476.00 KiB/s, done.
Resolving deltas: 100% (42/42), done.
克隆完成后，extensions目錄會多如下文件夾：
下載模型庫https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies，并將下載的.ckpt
放到models/Stable-diffusion文件夾中。模型很大，推薦使用下載器。
安裝項目所需的Python依賴庫。
pip install -r requirements.txt
安裝完成之后，運(yùn)行如下命令，順利的話，當(dāng)程序加載完成模型之后，會自動打開http://127.0.0.1:7860/顯示平臺主頁。
python launch.py --autolaunch
進(jìn)入平臺的設(shè)置頁面，選擇語言為中文，重啟程序之后，即可看到頁面顯示為中文。
在界面中輸入作畫內(nèi)容的正向提示詞（畫想要什么特征）和反向提示詞（畫不想要什么特征），點(diǎn)擊生成即可開始自動作畫。
如上述的提示詞作出的畫如圖（由于隨機(jī)種子不同，生成的畫會有差異）。
六、如何設(shè)置提示詞
這里建議使用元素法典https://docs.qq.com/doc/DWHl3am5Zb05QbGVs，上面有前人整理好的提示詞及效果，以供參考。
七、可能遇到的問題
1、GitHub訪問不了或訪問慢
一般為DNS解析問題，需要修改本地host文件，增加配置內(nèi)容，繞過域名解析，達(dá)到加速訪問的目的。
訪問https://www.ipaddress.com/，分別輸入github.com和github.global.ssl.fastly.net，獲取域名對應(yīng)的IP地址。
打開系統(tǒng)的Host文件，將IP和域名的對應(yīng)關(guān)系配置到Host文件中。
配置文件內(nèi)容如下：
140.82.114.4 github.com
199.232.5.194 github.global.ssl.fastly.net
執(zhí)行命令ipconfig /flushdns刷新DNS即可。
2、pip安裝依賴庫慢或常下載失敗
pip安裝依賴庫時默認(rèn)選擇國外的源，安裝速度會非常慢，可以考慮切換為國內(nèi)源，常用的國內(nèi)源如下：
阿里云 https://mirrors.aliyun.com/pypi/simple/
中國科技大學(xué) https://pypi.mirrors.ustc.edu.cn/simple/
豆瓣(douban) https://pypi.douban.com/simple/
清華大學(xué) https://pypi.tuna.tsinghua.edu.cn/simple/
中國科學(xué)技術(shù)大學(xué) https://pypi.mirrors.ustc.edu.cn/simple/
在安裝依賴庫時，可使用pip install -i 源 空格 安裝包名稱進(jìn)行源的選擇，如pip install -i https://mirrors.aliyun.com/pypi/simple numpy。
也可以通過增加配置文件，使安裝依賴庫時默認(rèn)選擇國內(nèi)的源，在用戶目錄下增加pip.ini文件。
在文件中寫入如下內(nèi)容。
[global]
timeout = 60000
index-url = https://pypi.tuna.tsinghua.edu.cn/simple
[install]
use-mirrors = true
mirrors = https://pypi.tuna.tsinghua.edu.cn
3、安裝CLIP時提示Connection was aborted, errno 10053
出錯時的錯誤打印如下：
(novelai) E:\workspace\02_Python\novalai\stable-diffusion-webui>python launch.py
Python 3.10.6 | packaged by conda-forge | (main, Oct 24 2022, 16:02:16) [MSC v.1916 64 bit (AMD64)]
Commit hash: b8f2dfed3c0085f1df359b9dc5b3841ddc2196f0
Installing clip
Traceback (most recent call last):
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\launch.py", line 251, in <module>
prepare_enviroment()
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\launch.py", line 178, in prepare_enviroment
run_pip(f"install {clip_package}", "clip")
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\launch.py", line 63, in run_pip
return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\launch.py", line 34, in run
raise RuntimeError(message)
RuntimeError: Couldn't install clip.
Command: "D:\anaconda3\envs\novelai\python.exe" -m pip install git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1 --prefer-binary
Error code: 1
stdout: Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
Cloning https://github.com/openai/CLIP.git (to revision d50d76daa670286dd6cacf3bcd80b5e4823fc8e1) to c:\users\yefuf\appdata\local\temp\pip-req-build-f8w7kbzg
stderr: Running command git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git 'C:\Users\yefuf\AppData\Local\Temp\pip-req-build-f8w7kbzg'
fatal: unable to access 'https://github.com/openai/CLIP.git/': OpenSSL SSL_read: Connection was aborted, errno 10053
error: subprocess-exited-with-error
git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git 'C:\Users\yefuf\AppData\Local\Temp\pip-req-build-f8w7kbzg' did not run successfully.
exit code: 128
See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git 'C:\Users\yefuf\AppData\Local\Temp\pip-req-build-f8w7kbzg' did not run successfully.
exit code: 128
See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
通過訪CLIP項目GitHub主頁，發(fā)現(xiàn)該項目可以通過如下命令進(jìn)行安裝解決。
pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git
4、項目啟動中提示Connection was reset in connection to github.com
出錯時的錯誤打印如下：
(novelai) E:\workspace\02_Python\novalai\stable-diffusion-webui>python launch.py
Python 3.10.6 | packaged by conda-forge | (main, Oct 24 2022, 16:02:16) [MSC v.1916 64 bit (AMD64)]
Commit hash: b8f2dfed3c0085f1df359b9dc5b3841ddc2196f0
Cloning Stable Diffusion into repositories\stable-diffusion...
Cloning Taming Transformers into repositories\taming-transformers...
Traceback (most recent call last):
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\launch.py", line 251, in <module>
prepare_enviroment()
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\launch.py", line 201, in prepare_enviroment
git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\launch.py", line 85, in git_clone
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\launch.py", line 34, in run
raise RuntimeError(message)
RuntimeError: Couldn't clone Taming Transformers.
Command: "git" clone "https://github.com/CompVis/taming-transformers.git" "repositories\taming-transformers"
Error code: 128
stdout: <empty>
stderr: Cloning into 'repositories\taming-transformers'...
fatal: unable to access 'https://github.com/CompVis/taming-transformers.git/': OpenSSL SSL_connect: Connection was reset in connection to github.com:443
在命令窗口中輸入如下命令，然后重新運(yùn)行程序，但實際操作下來，仍有較大概率在克隆項目的過程中失敗。
git config --global http.postBuffer 524288000
git config --global http.sslVerify "false"
查看lauch.py中的代碼可以發(fā)現(xiàn)，程序在啟動時有對依賴項目進(jìn)行檢查，如項目不存在，則克隆下來。
def prepare_enviroment():
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@d91a2963bf87c6a770d74894667e9ffa9f6de7ff")
xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/CompVis/stable-diffusion.git")
taming_transformers_repo = os.environ.get('TAMING_REANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
codeformer_repo = os.environ.get('CODEFORMET_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
因此，我們打開git bash重新執(zhí)行上述兩條git命令，預(yù)先將項目克隆下來。
git clone https://github.com/CompVis/taming-transformers.git "repositories\taming-transformers"
git clone https://github.com/crowsonkb/k-diffusion.git "repositories\k-diffusion"
git clone https://github.com/sczhou/CodeFormer.git "repositories\CodeFormer"
git clone https://github.com/salesforce/BLIP.git "repositories\BLIP"
克隆完成之后如圖：
5、項目啟動中提示CUDA out of memory
出錯時的錯誤打印如下：
(novelai) E:\workspace\02_Python\novalai\stable-diffusion-webui>python launch.py
Python 3.10.6 | packaged by conda-forge | (main, Oct 24 2022, 16:02:16) [MSC v.1916 64 bit (AMD64)]
Commit hash: b8f2dfed3c0085f1df359b9dc5b3841ddc2196f0
Fetching updates for BLIP...
Checking out commit for BLIP with hash: 48211a1594f1321b00f14c9f7a5b4813144b2fb9...
Installing requirements for CodeFormer
Installing requirements for Web UI
Launching Web UI with arguments:
Moving sd-v1-4.ckpt from E:\workspace\02_Python\novalai\stable-diffusion-webui\models to E:\workspace\02_Python\novalai\stable-diffusion-webui\models\Stable-diffusion.
LatentDiffusion: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
Downloading: 100%|██████████████████████████████████████████████████████████████████| 939k/939k [00:00<00:00, 1.26MB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████| 512k/512k [00:01<00:00, 344kB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████████| 389/389 [00:00<?, ?B/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████████| 905/905 [00:00<?, ?B/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████| 4.41k/4.41k [00:00<?, ?B/s]
Downloading: 100%|████████████████████████████████████████████████████████████████| 1.59G/1.59G [03:56<00:00, 7.23MB/s]
Loading weights [7460a6fa] from E:\workspace\02_Python\novalai\stable-diffusion-webui\models\Stable-diffusion\sd-v1-4.ckpt
Global Step: 470000
Traceback (most recent call last):
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\launch.py", line 252, in <module>
start()
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\launch.py", line 247, in start
webui.webui()
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\webui.py", line 148, in webui
initialize()
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\webui.py", line 83, in initialize
modules.sd_models.load_model()
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\modules\sd_models.py", line 252, in load_model
sd_model.to(shared.device)
File "D:\anaconda3\envs\novelai\lib\site-packages\pytorch_lightning\core\mixins\device_dtype_mixin.py", line 113, in to
return super().to(*args, **kwargs)
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 987, in to
return self._apply(convert)
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 639, in _apply
module._apply(fn)
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 639, in _apply
module._apply(fn)
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 639, in _apply
module._apply(fn)
[Previous line repeated 2 more times]
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 662, in _apply
param_applied = fn(param)
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 985, in convert
return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 2.00 GiB total capacity; 1.68 GiB already allocated; 0 bytes free; 1.72 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
根據(jù)提示，先嘗試用如下命令改變pytorch配置，仍舊報錯!
set PYTORCH_CUDA_ALLOC_CONF=garbage_collection_threshold:0.6,max_split_size_mb:128
嘗試增加代碼with torch.no_grad()，使內(nèi)存就不會分配參數(shù)梯度的空間，仍舊報錯！
由于提示內(nèi)存溢出，先通過控制面板->所有控制面板項->管理工具->系統(tǒng)信息，查看顯卡內(nèi)存大小。
官方推薦的顯卡內(nèi)存大小為4GB以上，而筆者的顯卡內(nèi)存只有2GB，顯然GPU不符合要求。查看項目的命令選項，發(fā)現(xiàn)項目支持CPU計算--use-cpu。
(novelai) E:\workspace\02_Python\novalai\stable-diffusion-webui>python launch.py -h
Python 3.10.6 | packaged by conda-forge | (main, Oct 24 2022, 16:02:16) [MSC v.1916 64 bit (AMD64)]
Commit hash: b8f2dfed3c0085f1df359b9dc5b3841ddc2196f0
Installing requirements for Web UI
Launching Web UI with arguments: -h
usage: launch.py [-h] [--config CONFIG] [--ckpt CKPT] [--ckpt-dir CKPT_DIR] [--gfpgan-dir GFPGAN_DIR]
[--gfpgan-model GFPGAN_MODEL] [--no-half] [--no-half-vae] [--no-progressbar-hiding]
[--max-batch-count MAX_BATCH_COUNT] [--embeddings-dir EMBEDDINGS_DIR]
[--hypernetwork-dir HYPERNETWORK_DIR] [--localizations-dir LOCALIZATIONS_DIR] [--allow-code]
[--medvram] [--lowvram] [--lowram] [--always-batch-cond-uncond] [--unload-gfpgan]
[--precision {full,autocast}] [--share] [--ngrok NGROK] [--ngrok-region NGROK_REGION]
[--enable-insecure-extension-access] [--codeformer-models-path CODEFORMER_MODELS_PATH]
[--gfpgan-models-path GFPGAN_MODELS_PATH] [--esrgan-models-path ESRGAN_MODELS_PATH]
[--bsrgan-models-path BSRGAN_MODELS_PATH] [--realesrgan-models-path REALESRGAN_MODELS_PATH]
[--scunet-models-path SCUNET_MODELS_PATH] [--swinir-models-path SWINIR_MODELS_PATH]
[--ldsr-models-path LDSR_MODELS_PATH] [--clip-models-path CLIP_MODELS_PATH] [--xformers]
[--force-enable-xformers] [--deepdanbooru] [--opt-split-attention] [--opt-split-attention-invokeai]
[--opt-split-attention-v1] [--disable-opt-split-attention]
[--use-cpu {all,sd,interrogate,gfpgan,swinir,esrgan,scunet,codeformer} [{all,sd,interrogate,gfpgan,swinir,esrgan,scunet,codeformer} ...]]
[--listen] [--port PORT] [--show-negative-prompt] [--ui-config-file UI_CONFIG_FILE]
[--hide-ui-dir-config] [--freeze-settings] [--ui-settings-file UI_SETTINGS_FILE] [--gradio-debug]
[--gradio-auth GRADIO_AUTH] [--gradio-img2img-tool {color-sketch,editor}] [--opt-channelslast]
[--styles-file STYLES_FILE] [--autolaunch] [--theme THEME] [--use-textbox-seed]
[--disable-console-progressbars] [--enable-console-prompts] [--vae-path VAE_PATH]
[--disable-safe-unpickle] [--api] [--nowebui] [--ui-debug-mode] [--device-id DEVICE_ID]
[--administrator] [--cors-allow-origins CORS_ALLOW_ORIGINS] [--tls-keyfile TLS_KEYFILE]
[--tls-certfile TLS_CERTFILE] [--server-name SERVER_NAME]
options:
-h, --help show this help message and exit
--config CONFIG path to config which constructs model
--ckpt CKPT path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to
the list of checkpoints and loaded
--ckpt-dir CKPT_DIR Path to directory with stable diffusion checkpoints
--gfpgan-dir GFPGAN_DIR
GFPGAN directory
--gfpgan-model GFPGAN_MODEL
GFPGAN model file name
--no-half do not switch the model to 16-bit floats
--no-half-vae do not switch the VAE model to 16-bit floats
--no-progressbar-hiding
do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware
acceleration in browser)
--max-batch-count MAX_BATCH_COUNT
maximum batch count value for the UI
--embeddings-dir EMBEDDINGS_DIR
embeddings directory for textual inversion (default: embeddings)
--hypernetwork-dir HYPERNETWORK_DIR
hypernetwork directory
--localizations-dir LOCALIZATIONS_DIR
localizations directory
--allow-code allow custom script execution from webui
--medvram enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage
--lowvram enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM
usage
--lowram load stable diffusion checkpoint weights to VRAM instead of RAM
--always-batch-cond-uncond
disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram
--unload-gfpgan does not do anything.
--precision {full,autocast}
evaluate at this precision
--share use share=True for gradio and make the UI accessible through their site
--ngrok NGROK ngrok authtoken, alternative to gradio --share
--ngrok-region NGROK_REGION
The region in which ngrok should start.
--enable-insecure-extension-access
enable extensions tab regardless of other options
--codeformer-models-path CODEFORMER_MODELS_PATH
Path to directory with codeformer model file(s).
--gfpgan-models-path GFPGAN_MODELS_PATH
Path to directory with GFPGAN model file(s).
--esrgan-models-path ESRGAN_MODELS_PATH
Path to directory with ESRGAN model file(s).
--bsrgan-models-path BSRGAN_MODELS_PATH
Path to directory with BSRGAN model file(s).
--realesrgan-models-path REALESRGAN_MODELS_PATH
Path to directory with RealESRGAN model file(s).
--scunet-models-path SCUNET_MODELS_PATH
Path to directory with ScuNET model file(s).
--swinir-models-path SWINIR_MODELS_PATH
Path to directory with SwinIR model file(s).
--ldsr-models-path LDSR_MODELS_PATH
Path to directory with LDSR model file(s).
--clip-models-path CLIP_MODELS_PATH
Path to directory with CLIP model file(s).
--xformers enable xformers for cross attention layers
--force-enable-xformers
enable xformers for cross attention layers regardless of whether the checking code thinks you
can run it; do not make bug reports if this fails to work
--deepdanbooru enable deepdanbooru interrogator
--opt-split-attention
force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch
cuda.
--opt-split-attention-invokeai
force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is
unavailable.
--opt-split-attention-v1
enable older version of split attention optimization that does not consume all the VRAM it can
find
--disable-opt-split-attention
force-disables cross-attention layer optimization
--use-cpu {all,sd,interrogate,gfpgan,swinir,esrgan,scunet,codeformer} [{all,sd,interrogate,gfpgan,swinir,esrgan,scunet,codeformer} ...]
use CPU as torch device for specified modules
--listen launch gradio with 0.0.0.0 as server name, allowing to respond to network requests
--port PORT launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to
7860 if available
--show-negative-prompt
does not do anything
--ui-config-file UI_CONFIG_FILE
filename to use for ui configuration
--hide-ui-dir-config hide directory configuration from webui
--freeze-settings disable editing settings
--ui-settings-file UI_SETTINGS_FILE
filename to use for ui settings
--gradio-debug launch gradio with --debug option
--gradio-auth GRADIO_AUTH
set gradio authentication like "username:password"; or comma-delimit multiple like
"u1:p1,u2:p2,u3:p3"
--gradio-img2img-tool {color-sketch,editor}
gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing
--opt-channelslast change memory type for stable diffusion to channels last
--styles-file STYLES_FILE
filename to use for styles
--autolaunch open the webui URL in the system's default browser upon launch
--theme THEME launches the UI with light or dark theme
--use-textbox-seed use textbox for seeds in UI (no up/down, but possible to input long seeds)
--disable-console-progressbars
do not output progressbars to console
--enable-console-prompts
print prompts to console when generating with txt2img and img2img
--vae-path VAE_PATH Path to Variational Autoencoders model
--disable-safe-unpickle
disable checking pytorch models for malicious code
--api use api=True to launch the api with the webui
--nowebui use api=True to launch the api instead of the webui
--ui-debug-mode Don't load model to quickly launch UI
--device-id DEVICE_ID
Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed
before)
--administrator Administrator rights
--cors-allow-origins CORS_ALLOW_ORIGINS
Allowed CORS origins
--tls-keyfile TLS_KEYFILE
Partially enables TLS, requires --tls-certfile to fully function
--tls-certfile TLS_CERTFILE
Partially enables TLS, requires --tls-keyfile to fully function
--server-name SERVER_NAME
Sets hostname of server
嘗試構(gòu)造如下運(yùn)行參數(shù)，--use-cpu all使所有模塊均使用CPU計算，--lowram --always-batch-cond-uncond使用低內(nèi)存配置選項，程序可以成功運(yùn)行。
(novelai) E:\workspace\02_Python\novalai\stable-diffusion-webui>python launch.py --lowram --always-batch-cond-uncond --use-cpu all
Python 3.10.6 | packaged by conda-forge | (main, Oct 24 2022, 16:02:16) [MSC v.1916 64 bit (AMD64)]
Commit hash: b8f2dfed3c0085f1df359b9dc5b3841ddc2196f0
Installing requirements for Web UI
Launching Web UI with arguments: --lowram --always-batch-cond-uncond --use-cpu all
Warning: caught exception 'Expected a cuda device, but got: cpu', memory monitor disabled
LatentDiffusion: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
Loading weights [7460a6fa] from E:\workspace\02_Python\novalai\stable-diffusion-webui\models\Stable-diffusion\sd-v1-4.ckpt
Global Step: 470000
Applying cross attention optimization (Doggettx).
Model loaded.
Loaded a total of 0 textual inversion embeddings.
Embeddings:
Running on local URL: http://127.0.0.1:7860
To create a public link, set `share=True` in `launch()`.
然而，開始作畫時提示RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'錯誤！如果安裝網(wǎng)上的處理方法，將half函數(shù)在工程中替換為float函數(shù)，則會出現(xiàn)device不匹配問題。
Traceback (most recent call last):
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\modules\ui.py", line 185, in f
res = list(func(*args, **kwargs))
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\webui.py", line 57, in f
res = func(*args, **kwargs)
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\modules\txt2img.py", line 48, in txt2img
processed = process_images(p)
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\modules\processing.py", line 423, in process_images
res = process_images_inner(p)
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\modules\processing.py", line 508, in process_images_inner
uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\modules\prompt_parser.py", line 138, in get_learned_conditioning
conds = model.get_learned_conditioning(texts)
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\repositories\stable-diffusion\ldm\models\diffusion\ddpm.py", line 558, in get_learned_conditioning
c = self.cond_stage_model(c)
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\modules\sd_hijack.py", line 338, in forward
z1 = self.process_tokens(tokens, multipliers)
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\extensions\aesthetic-gradients\aesthetic_clip.py", line 202, in __call__
z = self.process_tokens(remade_batch_tokens, multipliers)
File "E:\workspace\02_Python\novalai\stable-diffusion-webui\modules\sd_hijack.py", line 353, in process_tokens
outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "D:\anaconda3\envs\novelai\lib\site-packages\transformers\models\clip\modeling_clip.py", line 722, in forward
return self.text_model(
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "D:\anaconda3\envs\novelai\lib\site-packages\transformers\models\clip\modeling_clip.py", line 643, in forward
encoder_outputs = self.encoder(
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "D:\anaconda3\envs\novelai\lib\site-packages\transformers\models\clip\modeling_clip.py", line 574, in forward
layer_outputs = encoder_layer(
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "D:\anaconda3\envs\novelai\lib\site-packages\transformers\models\clip\modeling_clip.py", line 316, in forward
hidden_states = self.layer_norm1(hidden_states)
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\modules\normalization.py", line 190, in forward
return F.layer_norm(
File "D:\anaconda3\envs\novelai\lib\site-packages\torch\nn\functional.py", line 2515, in layer_norm
return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'
考慮到--use-cpu參數(shù)可以指定模塊，則嘗試使工程中的部分模塊用CPU計算，其余在可用內(nèi)存方位內(nèi)用GPU計算，最終構(gòu)造參數(shù)如下，項目可成功作畫。
然而，此方式作畫效率非常低，一般每張圖片約5-6分鐘。當(dāng)參數(shù)設(shè)置較大時，會達(dá)到數(shù)小時。因此如果有條件可以升級計算機(jī)的顯卡配置，或租賃云服務(wù)器效果會更好。
(novelai) E:\workspace\02_Python\novalai\stable-diffusion-webui>python launch.py --lowram --always-batch-cond-uncond --precision full --no-half --opt-split-attention-v1 --use-cpu sd --autolaunch
Python 3.10.6 | packaged by conda-forge | (main, Oct 24 2022, 16:02:16) [MSC v.1916 64 bit (AMD64)]
Commit hash: b8f2dfed3c0085f1df359b9dc5b3841ddc2196f0
Installing requirements for Web UI
Launching Web UI with arguments: --lowram --always-batch-cond-uncond --precision full --no-half --opt-split-attention-v1 --use-cpu sd
Warning: caught exception 'Expected a cuda device, but got: cpu', memory monitor disabled
LatentDiffusion: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
Loading weights [7460a6fa] from E:\workspace\02_Python\novalai\stable-diffusion-webui\models\Stable-diffusion\sd-v1-4.ckpt
Global Step: 470000
Applying v1 cross attention optimization.
Model loaded.
Loaded a total of 0 textual inversion embeddings.
Embeddings:
Running on local URL: http://127.0.0.1:7860
To create a public link, set `share=True` in `launch()`.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [06:30<00:00, 19.50s/it]
Total progress: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [06:10<00:00, 18.51s/it]
參考文獻(xiàn)：
AI作畫保姆級教程來了！逆天，太強(qiáng)了！文章來源：http://www.zghlxwxcb.cn/news/detail-813017.html
【作者：墨葉扶風(fēng)http://blog.csdn.net/yefufeng】文章來源地址http://www.zghlxwxcb.cn/news/detail-813017.html
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