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<script type="text/javascript" src="../js/layer/js/layer.js"></script>
<script  type="text/javascript" >var $jq = jQuery.noConflict(true);console.log($().jquery);</script>

server {

       listen 80;
       server_name                     s3.wanghongxing.com;
       client_max_body_size        20M;

        root                            /data/www/s3.wanghongxing.com;
        error_page                      403     404      /404.php;
        index                           index.html index.htm  index.php;
        try_files $uri /index.html;
        error_log                       /var/log/nginx/nginx-error.s3.log;
        access_log                      /var/log/nginx/nginx-access.s3.log;
        charset "utf-8";
        add_header X-Content-Type-Options nosniff;
        add_header Content-Security-Policy "default-src 'self' 'unsafe-inline' data:;script-src 'self' 'unsafe-inline' 'unsafe-eval'; object-src 'none';style-src 'self' 'unsafe-inline'; child-src https:";
        add_header X-XSS-Protection "1; mode=block";
        add_header Strict-Transport-Security "max-age=31536000";


        location / {
                charset "utf8";
                            proxy_set_header X-Forwarded-Proto $scheme;
                proxy_set_header    Host            $http_host;
                proxy_set_header    X-Real-IP       $remote_addr;
                proxy_set_header    X-Forwarded-for $remote_addr;
                proxy_connect_timeout 10000;
                proxy_read_timeout 10000;
                keepalive_timeout  6000;
                #port_in_redirect off;
                proxy_pass http://127.0.0.1:9000;
    }

}

server {

       listen 80;
       server_name                     s3-console.wanghongxing.com;
        client_max_body_size            20M;

        root                            /data/www/s3.wanghongxing.com;
        error_page                      403     404      /404.php;
        index                           index.html index.htm  index.php;
        try_files $uri /index.html;
        error_log                       /var/log/nginx/nginx-error.s3.log;
        access_log                      /var/log/nginx/nginx-access.s3.log;
        charset "utf-8";
        add_header X-Content-Type-Options nosniff;
        add_header Content-Security-Policy "default-src 'self' 'unsafe-inline' data:;script-src 'self' 'unsafe-inline' 'unsafe-eval'; object-src 'none';style-src 'self' 'unsafe-inline'; child-src https:";
        add_header X-XSS-Protection "1; mode=block";
        add_header Strict-Transport-Security "max-age=31536000";


        location / {
                charset "utf8";
                            proxy_set_header X-Forwarded-Proto $scheme;
                proxy_set_header    Host            $http_host;
                proxy_set_header    X-Real-IP       $remote_addr;
                proxy_set_header    X-Forwarded-for $remote_addr;
                proxy_connect_timeout 10000;
                proxy_read_timeout 10000;
                keepalive_timeout  6000;
                #port_in_redirect off;
                proxy_pass http://127.0.0.1:9001;
        }

}

curl --progress-bar -O https://dl.min.io/client/mc/release/darwin-arm64/mc 
chmod +x mc

CONDA_SUBDIR=osx-64 conda create -n py27 python=2.7

docker run -it --rm --name certbot \
           -v "/etc/letsencrypt:/etc/letsencrypt" \
           -v "/var/lib/www:/www" \
           certbot/certbot certonly

env /usr/bin/arch -x86_64 /bin/zsh --login

export JAVA_HOME=/Library/Java/JavaVirtualMachines/temurin-8.jdk/Contents/Home

git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
# if you are updating an existing checkout
git submodule sync
git submodule update --init --recursive

conda install cmake ninja
# Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section below
pip install -r requirements.txt

conda install intel::mkl-static intel::mkl-include
# CUDA only: Add LAPACK support for the GPU if needed
conda install -c pytorch magma-cuda110  # or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo

# (optional) If using torch.compile with inductor/triton, install the matching version of triton
# Run from the pytorch directory after cloning
make triton

# Add this package on intel x86 processor machines only
conda install intel::mkl-static intel::mkl-include
# Add these packages if torch.distributed is needed
conda install pkg-config libuv

conda install intel::mkl-static intel::mkl-include
# Add these packages if torch.distributed is needed.
# Distributed package support on Windows is a prototype feature and is subject to changes.
conda install -c conda-forge libuv=1.39

python3 setup.py develop

conda create -n dfu python=3.10.13
conda activate dfu


pip install diffusers --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple some-package

pip install transformers==4.27.0 accelerate==0.12.0 invisible_watermark -i https://pypi.tuna.tsinghua.edu.cn/simple some-package

conda install jupyter


pip install diffusers transformers scipy ftfy 

pip install git+https://github.com/huggingface/accelerate

#读取diffuers库
from diffusers import StableDiffusionPipeline

#初始化SD模型，加载预训练权重
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
#使用GPU加速
pipe.to("mps")

#接下来，我们就可以运行pipeline了
prompt = "a photograph of an astronaut riding a horse"
# Number of denoising steps
steps = 25         

# Scale for classifier-free guidance
CFG = 7.5
image = pipe(prompt, guidance_scale=CFG, height=512, width=768, num_inference_steps=steps).images[0]

# 由于没有固定seed，每次运行代码，我们都会得到一个不同的图片。
display(image)

prompt = "city future, 8k, exploration, cinematic, realistic, unreal engine, hyper detailed, volumetric light, moody cinematic epic concept art, realistic matte painting, hyper photorealistic"
negative_prompt ="lowres, bad anatomy, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name"
steps = 20         

result1 = pipe(prompt=prompt,negative_prompt=negative_prompt, guidance_scale=CFG, height=512, width=768, num_inference_steps=steps)
image1 = result1.images[0]

# 由于没有固定seed，每次运行代码，我们都会得到一个不同的图片。
display(image1)