{"id":64713,"date":"2026-01-23T23:56:22","date_gmt":"2026-01-23T15:56:22","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/64713.html"},"modified":"2026-01-23T23:56:22","modified_gmt":"2026-01-23T15:56:22","slug":"%e6%9c%ac%e5%9c%b0%e7%94%b5%e8%84%91%e8%bf%9e%e6%9c%8d%e5%8a%a1%e5%99%a8%ef%bc%8cssh%e9%9a%a7%e9%81%93%e6%89%93%e9%80%9aai%e7%bb%98%e5%9b%be%e6%9c%80%e5%90%8e%e4%b8%80%e5%85%ac%e9%87%8c","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/64713.html","title":{"rendered":"\u672c\u5730\u7535\u8111\u8fde\u670d\u52a1\u5668\uff0cSSH\u96a7\u9053\u6253\u901aAI\u7ed8\u56fe\u6700\u540e\u4e00\u516c\u91cc"},"content":{"rendered":"

\u672c\u5730\u7535\u8111\u8fde\u670d\u52a1\u5668，SSH\u96a7\u9053\u6253\u901aAI\u7ed8\u56fe\u6700\u540e\u4e00\u516c\u91cc<\/h2>\n

1. \u5f15\u8a00：\u8fdc\u7a0b\u90e8\u7f72\u4e0e\u672c\u5730\u8bbf\u95ee\u7684\u534f\u540c\u6311\u6218<\/h3>\n

\u5728\u5f53\u524dAI\u56fe\u50cf\u751f\u6210\u6280\u672f\u5feb\u901f\u53d1\u5c55\u7684\u80cc\u666f\u4e0b，\u8d8a\u6765\u8d8a\u591a\u5f00\u53d1\u8005\u548c\u521b\u4f5c\u8005\u9009\u62e9\u5728\u9ad8\u6027\u80fd\u8fdc\u7a0b\u670d\u52a1\u5668\u4e0a\u90e8\u7f72\u6a21\u578b\u670d\u52a1\u3002\u7136\u800c，\u53d7\u9650\u4e8e\u663e\u5b58\u3001\u7b97\u529b\u6216\u73af\u5883\u914d\u7f6e，\u672c\u5730\u8bbe\u5907\u5f80\u5f80\u96be\u4ee5\u76f4\u63a5\u8fd0\u884c\u5982Flux.1\u8fd9\u7c7b\u5927\u578b\u6269\u6563\u6a21\u578b\u3002\u800c\u5c06\u6a21\u578b\u90e8\u7f72\u5728\u4e91\u7aef\u540e，\u53c8\u9762\u4e34\u5b89\u5168\u7ec4\u9650\u5236\u3001\u7aef\u53e3\u4e0d\u53ef\u66b4\u9732\u7b49\u7f51\u7edc\u95ee\u9898，\u5bfc\u81f4Web\u754c\u9762\u65e0\u6cd5\u76f4\u63a5\u8bbf\u95ee\u3002<\/p>\n

\u672c\u6587\u56f4\u7ed5\u300c\u9ea6\u6a58\u8d85\u7136 – Flux \u79bb\u7ebf\u56fe\u50cf\u751f\u6210\u63a7\u5236\u53f0\u300d\u8fd9\u4e00\u57fa\u4e8e DiffSynth-Studio \u6784\u5efa\u7684\u56fe\u50cf\u751f\u6210\u7cfb\u7edf，\u6df1\u5165\u8bb2\u89e3\u5982\u4f55\u901a\u8fc7 SSH \u96a7\u9053\u6280\u672f，\u5b9e\u73b0\u672c\u5730\u7535\u8111\u65e0\u7f1d\u8fde\u63a5\u8fdc\u7a0bAI\u7ed8\u56fe\u670d\u52a1，\u771f\u6b63\u6253\u901a\u4ece\u5f00\u53d1\u5230\u4ea4\u4e92\u7684\u201c\u6700\u540e\u4e00\u516c\u91cc\u201d\u3002<\/p>\n

\u8be5\u955c\u50cf\u96c6\u6210\u4e86 majicflus_v1 \u6a21\u578b，\u5e76\u91c7\u7528 float8 \u91cf\u5316\u6280\u672f，\u663e\u8457\u964d\u4f4e\u663e\u5b58\u5360\u7528，\u4f7f\u5f978GB\u751a\u81f3\u66f4\u4f4e\u663e\u5b58\u7684GPU\u4e5f\u80fd\u6d41\u7545\u8fd0\u884c\u9ad8\u8d28\u91cf\u56fe\u50cf\u751f\u6210\u4efb\u52a1\u3002\u914d\u5408Gradio\u6784\u5efa\u7684\u7b80\u6d01Web\u754c\u9762，\u7528\u6237\u53ef\u901a\u8fc7\u6d4f\u89c8\u5668\u8f7b\u677e\u8f93\u5165\u63d0\u793a\u8bcd\u3001\u8c03\u6574\u79cd\u5b50（seed）\u4e0e\u6b65\u6570（steps），\u5b8c\u6210\u521b\u4f5c\u3002<\/p>\n

\u4f46\u5173\u952e\u95ee\u9898\u662f：\u670d\u52a1\u8fd0\u884c\u5728\u8fdc\u7a0b\u670d\u52a1\u5668\u4e0a，\u5982\u4f55\u5b89\u5168\u3001\u7a33\u5b9a\u5730\u5728\u672c\u5730\u64cd\u4f5c？<\/p>\n

\u7b54\u6848\u5c31\u662f\u2014\u2014SSH\u672c\u5730\u7aef\u53e3\u8f6c\u53d1（Local Port Forwarding）\u3002<\/p>\n

\n

2. \u6280\u672f\u65b9\u6848\u9009\u578b：\u4e3a\u4f55\u9009\u62e9SSH\u96a7\u9053？<\/h3>\n

2.1 \u5e38\u89c1\u8fdc\u7a0b\u8bbf\u95ee\u65b9\u5f0f\u5bf9\u6bd4<\/h4>\n\n\u65b9\u6848\u5b89\u5168\u6027\u6613\u7528\u6027\u6210\u672c\u9002\u7528\u573a\u666f<\/tr>\n\n\n\n\n\n
\u76f4\u63a5\u5f00\u653e\u516c\u7f51IP+\u7aef\u53e3<\/td>\n\u274c \u4f4e（\u6613\u88ab\u626b\u63cf\u653b\u51fb）<\/td>\n\u2705 \u9ad8<\/td>\n\u2705 \u65e0\u989d\u5916\u5f00\u9500<\/td>\n\u6d4b\u8bd5\u73af\u5883\u4e34\u65f6\u4f7f\u7528<\/td>\n<\/tr>\n
Nginx\u53cd\u5411\u4ee3\u7406 + HTTPS<\/td>\n\u2705 \u9ad8<\/td>\n\u26a0\ufe0f \u4e2d（\u9700\u914d\u7f6e\u8bc1\u4e66）<\/td>\n\u2705 \u514d\u8d39（Let's Encrypt）<\/td>\n\u751f\u4ea7\u7ea7Web\u670d\u52a1<\/td>\n<\/tr>\n
Cloudflare Tunnel<\/td>\n\u2705 \u9ad8<\/td>\n\u2705 \u9ad8<\/td>\n\u2705 \u514d\u8d39<\/td>\n\u5916\u90e8\u5171\u4eab\u6f14\u793a<\/td>\n<\/tr>\n
SSH\u672c\u5730\u7aef\u53e3\u8f6c\u53d1<\/td>\n\u2705 \u9ad8（\u52a0\u5bc6\u901a\u9053）<\/td>\n\u2705 \u9ad8<\/td>\n\u2705 \u96f6\u6210\u672c<\/td>\n\u4e2a\u4eba\u5f00\u53d1\u8c03\u8bd5\u9996\u9009<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u5bf9\u4e8e\u5927\u591a\u6570AI\u7814\u7a76\u8005\u6216\u72ec\u7acb\u5f00\u53d1\u8005\u800c\u8a00，SSH\u96a7\u9053\u662f\u6700\u4f18\u89e3：\u65e0\u9700\u4fee\u6539\u9632\u706b\u5899\u89c4\u5219\u3001\u4e0d\u66b4\u9732\u670d\u52a1\u7aef\u53e3\u3001\u5229\u7528\u5df2\u6709SSH\u51ed\u8bc1\u5373\u53ef\u5efa\u7acb\u52a0\u5bc6\u8fde\u63a5\u3002<\/p>\n

2.2 SSH\u96a7\u9053\u5de5\u4f5c\u539f\u7406\u7b80\u6790<\/h4>\n

SSH\u96a7\u9053\u7684\u6838\u5fc3\u673a\u5236\u662f\u7aef\u53e3\u6620\u5c04：<\/p>\n

ssh -L [\u672c\u5730\u7aef\u53e3]:[\u76ee\u6807\u4e3b\u673a]:[\u76ee\u6807\u7aef\u53e3] [\u7528\u6237]@[SSH\u670d\u52a1\u5668]<\/p>\n

\u5176\u4e2d：<\/p>\n

    \n
  • -L \u8868\u793a\u672c\u5730\u76d1\u542c（Local Forward）<\/li>\n
  • 6006:127.0.0.1:6006 \u8868\u793a：\u5c06\u672c\u57306006\u7aef\u53e3\u7684\u6570\u636e，\u901a\u8fc7SSH\u52a0\u5bc6\u4f20\u8f93\u81f3\u670d\u52a1\u5668，\u5e76\u8f6c\u53d1\u7ed9\u670d\u52a1\u5668\u4e0a\u7684 127.0.0.1:6006<\/li>\n
  • \u56e0\u4e3aAI\u670d\u52a1\u5728\u670d\u52a1\u5668\u672c\u5730\u542f\u52a8（server_name="0.0.0.0"），\u6240\u4ee5\u53ef\u901a\u8fc7\u56de\u73af\u5730\u5740\u8bbf\u95ee<\/li>\n<\/ul>\n

    🔐 \u5b89\u5168\u4f18\u52bf：\u6574\u4e2a\u901a\u4fe1\u8fc7\u7a0b\u7ecf\u8fc7SSH\u52a0\u5bc6，\u5373\u4f7f\u7f51\u7edc\u88ab\u76d1\u542c\u4e5f\u65e0\u6cd5\u83b7\u53d6\u56fe\u50cf\u8bf7\u6c42\u5185\u5bb9\u6216\u751f\u6210\u7ed3\u679c\u3002<\/p>\n

    \n

    3. \u5b9e\u8df5\u90e8\u7f72\u5168\u6d41\u7a0b<\/h3>\n

    3.1 \u73af\u5883\u51c6\u5907\u4e0e\u4f9d\u8d56\u5b89\u88c5<\/h4>\n

    \u786e\u4fdd\u8fdc\u7a0b\u670d\u52a1\u5668\u5df2\u5177\u5907\u4ee5\u4e0b\u57fa\u7840\u73af\u5883：<\/p>\n

      \n
    • Python >= 3.10<\/li>\n
    • CUDA\u9a71\u52a8\u6b63\u5e38（nvidia-smi \u53ef\u8bc6\u522bGPU）<\/li>\n
    • PyTorch\u652f\u6301CUDA（\u63a8\u8350 torch==2.3.0+cu118）<\/li>\n<\/ul>\n

      \u5b89\u88c5\u6838\u5fc3\u5e93：<\/p>\n

      pip install diffsynth -U
      \npip install gradio modelscope torch<\/p>\n

      \u9a8c\u8bc1GPU\u53ef\u7528\u6027：<\/p>\n

      import torch
      \nprint(torch.cuda.is_available()) # \u5e94\u8f93\u51fa True<\/p>\n

      \n

      3.2 \u7f16\u5199\u5e76\u542f\u52a8Web\u670d\u52a1\u811a\u672c<\/h4>\n

      \u521b\u5efa web_app.py \u6587\u4ef6，\u5185\u5bb9\u5982\u4e0b：<\/p>\n

      import torch
      \nimport gradio as gr
      \nfrom modelscope import snapshot_download
      \nfrom diffsynth import ModelManager, FluxImagePipeline<\/p>\n

      def init_models():
      \n # \u6a21\u578b\u5df2\u9884\u7f6e\u5728\u955c\u50cf\u4e2d，\u4ec5\u6ce8\u518c\u8def\u5f84
      \n snapshot_download(model_id="MAILAND\/majicflus_v1", allow_file_pattern="majicflus_v134.safetensors", cache_dir="models")
      \n snapshot_download(model_id="black-forest-labs\/FLUX.1-dev", allow_file_pattern=["ae.safetensors", "text_encoder\/model.safetensors", "text_encoder_2\/*"], cache_dir="models")<\/p>\n

      model_manager = ModelManager(torch_dtype=torch.bfloat16)<\/p>\n

      # \u4f7f\u7528 float8 \u52a0\u8f7d DiT \u4e3b\u5e72，\u8282\u7701\u7ea6 40% \u663e\u5b58
      \n model_manager.load_models(
      \n ["models\/MAILAND\/majicflus_v1\/majicflus_v134.safetensors"],
      \n torch_dtype=torch.float8_e4m3fn, device="cpu"
      \n )<\/p>\n

      # Text Encoder \u548c VAE \u4fdd\u6301 bfloat16 \u7cbe\u5ea6
      \n model_manager.load_models(
      \n [
      \n "models\/black-forest-labs\/FLUX.1-dev\/text_encoder\/model.safetensors",
      \n "models\/black-forest-labs\/FLUX.1-dev\/text_encoder_2",
      \n "models\/black-forest-labs\/FLUX.1-dev\/ae.safetensors",
      \n ],
      \n torch_dtype=torch.bfloat16, device="cpu"
      \n )<\/p>\n

      pipe = FluxImagePipeline.from_model_manager(model_manager, device="cuda")
      \n pipe.enable_cpu_offload() # \u52a8\u6001\u5378\u8f7d\u975e\u6d3b\u8dc3\u6a21\u5757\u81f3CPU
      \n pipe.dit.quantize() # \u542f\u7528\u5185\u90e8\u91cf\u5316\u903b\u8f91
      \n return pipe<\/p>\n

      pipe = init_models()<\/p>\n

      def generate_fn(prompt, seed, steps):
      \n if seed == -1:
      \n import random
      \n seed = random.randint(0, 99999999)
      \n image = pipe(prompt=prompt, seed=seed, num_inference_steps=int(steps))
      \n return image<\/p>\n

      with gr.Blocks(title="Flux WebUI") as demo:
      \n gr.Markdown("# 🎨 Flux \u79bb\u7ebf\u56fe\u50cf\u751f\u6210\u63a7\u5236\u53f0")
      \n with gr.Row():
      \n with gr.Column(scale=1):
      \n prompt_input = gr.Textbox(label="\u63d0\u793a\u8bcd (Prompt)", placeholder="\u8f93\u5165\u63cf\u8ff0\u8bcd…", lines=5)
      \n with gr.Row():
      \n seed_input = gr.Number(label="\u968f\u673a\u79cd\u5b50 (Seed)", value=0, precision=0)
      \n steps_input = gr.Slider(label="\u6b65\u6570 (Steps)", minimum=1, maximum=50, value=20, step=1)
      \n btn = gr.Button("\u5f00\u59cb\u751f\u6210\u56fe\u50cf", variant="primary")
      \n with gr.Column(scale=1):
      \n output_image = gr.Image(label="\u751f\u6210\u7ed3\u679c")<\/p>\n

      btn.click(fn=generate_fn, inputs=[prompt_input, seed_input, steps_input], outputs=output_image)<\/p>\n

      if __name__ == "__main__":
      \n demo.launch(server_name="0.0.0.0", server_port=6006)<\/p>\n

      \u5173\u952e\u6280\u672f\u70b9\u8bf4\u660e<\/h5>\n\n\u7ec4\u4ef6\u4f5c\u7528<\/tr>\n\n\n\n\n
      torch.float8_e4m3fn<\/td>\nFloat8\u91cf\u5316\u683c\u5f0f，\u5927\u5e45\u51cf\u5c11DiT\u4e3b\u5e72\u5185\u5b58\u5360\u7528<\/td>\n<\/tr>\n
      enable_cpu_offload()<\/td>\n\u81ea\u52a8\u5c06\u975e\u8ba1\u7b97\u6a21\u5757\u79fb\u81f3CPU，\u9002\u914d\u4f4e\u663e\u5b58\u8bbe\u5907<\/td>\n<\/tr>\n
      quantize()<\/td>\n\u6fc0\u6d3b\u6a21\u578b\u5185\u90e8\u91cf\u5316\u63a8\u7406\u6d41\u7a0b，\u63d0\u5347\u541e\u5410\u6548\u7387<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u542f\u52a8\u670d\u52a1：<\/p>\n

      python web_app.py<\/p>\n

      \u6b64\u65f6\u670d\u52a1\u5c06\u5728\u670d\u52a1\u5668\u7684 0.0.0.0:6006 \u76d1\u542c\u8bf7\u6c42\u3002<\/p>\n

      \n

      3.3 \u914d\u7f6eSSH\u96a7\u9053\u5b9e\u73b0\u672c\u5730\u8bbf\u95ee<\/h4>\n

      \u5728\u672c\u5730\u7535\u8111\u7ec8\u7aef\u6267\u884c\u4ee5\u4e0b\u547d\u4ee4：<\/p>\n

      ssh -L 6006:127.0.0.1:6006 -p [SSH\u7aef\u53e3\u53f7] root@[\u670d\u52a1\u5668IP]<\/p>\n

      \u793a\u4f8b：<\/p>\n

      ssh -L 6006:127.0.0.1:6006 -p 22 root@47.98.123.45<\/p>\n

      \u4fdd\u6301\u6b64\u7ec8\u7aef\u7a97\u53e3\u5f00\u542f（\u5173\u95ed\u5373\u65ad\u5f00\u8fde\u63a5），\u7136\u540e\u6253\u5f00\u672c\u5730\u6d4f\u89c8\u5668\u8bbf\u95ee：<\/p>\n

      👉 http:\/\/127.0.0.1:6006<\/p>\n

      \u4f60\u5c06\u770b\u5230\u5b8c\u6574\u7684WebUI\u754c\u9762，\u53ef\u81ea\u7531\u8f93\u5165\u63d0\u793a\u8bcd\u5e76\u751f\u6210\u56fe\u50cf\u3002<\/p>\n

      💡 \u63d0\u793a：\u82e5\u4f7f\u7528Windows\u7cfb\u7edf，\u53ef\u901a\u8fc7 PowerShell \u6216 WSL \u6267\u884c\u4e0a\u8ff0\u547d\u4ee4；Mac\/Linux \u7528\u6237\u76f4\u63a5\u4f7f\u7528\u5185\u7f6e\u7ec8\u7aef\u5373\u53ef\u3002<\/p>\n

      \n

      4. \u6d4b\u8bd5\u9a8c\u8bc1\u4e0e\u53c2\u6570\u8c03\u4f18\u5efa\u8bae<\/h3>\n

      4.1 \u63a8\u8350\u6d4b\u8bd5\u63d0\u793a\u8bcd<\/h4>\n

      \u5c1d\u8bd5\u4ee5\u4e0b\u9ad8\u8bed\u4e49\u5bc6\u5ea6\u63d0\u793a\u8bcd\u9a8c\u8bc1\u751f\u6210\u8d28\u91cf：<\/p>\n

      \u8d5b\u535a\u670b\u514b\u98ce\u683c\u7684\u672a\u6765\u57ce\u5e02\u8857\u9053，\u96e8\u591c，\u84dd\u8272\u548c\u7c89\u8272\u7684\u9713\u8679\u706f\u5149\u53cd\u5c04\u5728\u6e7f\u6f09\u6f09\u7684\u5730\u9762\u4e0a，\u5934\u9876\u6709\u98de\u884c\u6c7d\u8f66，\u9ad8\u79d1\u6280\u6c1b\u56f4，\u7ec6\u8282\u4e30\u5bcc，\u7535\u5f71\u611f\u5bbd\u5e45\u753b\u9762\u3002<\/p>\n

      4.2 \u63a8\u8350\u53c2\u6570\u7ec4\u5408<\/h4>\n\n\u53c2\u6570\u5efa\u8bae\u503c\u8bf4\u660e<\/tr>\n\n\n\n\n
      Seed<\/td>\n0 \u6216 -1（\u968f\u673a）<\/td>\n\u63a7\u5236\u521d\u59cb\u566a\u58f0\u5f62\u6001<\/td>\n<\/tr>\n
      Steps<\/td>\n20 ~ 30<\/td>\n\u6b65\u6570\u8fc7\u4f4e\u5f71\u54cd\u7ec6\u8282，\u8fc7\u9ad8\u6536\u76ca\u9012\u51cf<\/td>\n<\/tr>\n
      Prompt<\/td>\n\u4f7f\u7528\u5177\u4f53\u5f62\u5bb9\u8bcd\u589e\u5f3a\u63a7\u5236\u529b<\/td>\n\u5982\u201c\u900f\u660e\u8231\u4f53\u201d\u3001\u201c\u51b7\u8272\u8c03\u5149\u5f71\u201d\u7b49<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u2705 \u6210\u529f\u6807\u5fd7：\u56fe\u50cf\u7ed3\u6784\u6e05\u6670\u3001\u8272\u5f69\u534f\u8c03\u3001\u7b26\u5408\u63d0\u793a\u8bed\u4e49\u3002<\/p>\n

      \n

      5. \u968f\u673a\u79cd\u5b50（Seed）\u7684\u590d\u73b0\u673a\u5236\u4e0e\u5de5\u7a0b\u5b9e\u8df5<\/h3>\n

      5.1 Seed\u7684\u672c\u8d28\u4f5c\u7528<\/h4>\n

      \u5728\u6269\u6563\u6a21\u578b\u4e2d，\u56fe\u50cf\u751f\u6210\u59cb\u4e8e\u4e00\u6bb5\u9ad8\u65af\u566a\u58f0\u3002\u968f\u673a\u79cd\u5b50\u51b3\u5b9a\u4e86\u8fd9\u6bb5\u566a\u58f0\u7684\u521d\u59cb\u5316\u72b6\u6001\u3002\u53ea\u8981\u4ee5\u4e0b\u6761\u4ef6\u4e0d\u53d8：<\/p>\n

        \n
      • \u6a21\u578b\u6743\u91cd<\/li>\n
      • \u63d0\u793a\u8bcd（prompt）<\/li>\n
      • \u63a8\u7406\u6b65\u6570（steps）<\/li>\n
      • \u8c03\u5ea6\u7b97\u6cd5（scheduler）<\/li>\n<\/ul>\n

        \u90a3\u4e48\u76f8\u540c\u7684 seed \u5fc5\u7136\u751f\u6210\u5b8c\u5168\u4e00\u81f4\u7684\u56fe\u50cf\u3002<\/p>\n

        \u7c7b\u6bd4\u7406\u89e3：seed \u5c31\u50cf\u201c\u5730\u56fe\u79cd\u5b50\u201d，\u8f93\u5165\u76f8\u540c\u6570\u5b57，\u6c38\u8fdc\u751f\u6210\u540c\u4e00\u5f20\u4e16\u754c\u5730\u56fe\u3002<\/p>\n

        5.2 \u5b9e\u9645\u5e94\u7528\u7b56\u7565：\u63a2\u7d22 \u2192 \u9501\u5b9a \u2192 \u5fae\u8c03<\/h4>\n

        \u9636\u6bb5\u4e00：\u63a2\u7d22\u591a\u6837\u6027（seed = -1）<\/h5>\n

        \u521d\u671f\u8bbe\u7f6e seed \u4e3a -1，\u8ba9\u7cfb\u7edf\u81ea\u52a8\u968f\u673a\u91c7\u6837，\u5feb\u901f\u6d4f\u89c8\u6a21\u578b\u521b\u9020\u529b\u8fb9\u754c\u3002<\/p>\n

        if seed == -1:
        \n seed = random.randint(0, 99999999)<\/p>\n

        \u9636\u6bb5\u4e8c：\u9501\u5b9a\u7406\u60f3\u6784\u56fe<\/h5>\n

        \u4e00\u65e6\u53d1\u73b0\u67d0\u5f20\u56fe\u50cf\u63a5\u8fd1\u9884\u671f\u6548\u679c，\u7acb\u5373\u8bb0\u5f55\u5176 seed \u503c，\u4f8b\u5982 seed=739201\u3002<\/p>\n

        \u9636\u6bb5\u4e09：\u56fa\u5b9aseed\u5fae\u8c03prompt<\/h5>\n

        \u4fdd\u6301 seed \u4e0d\u53d8，\u4ec5\u4fee\u6539\u63d0\u793a\u8bcd\u4e2d\u7684\u5173\u952e\u8bcd，\u89c2\u5bdf\u53d8\u5316\u8d8b\u52bf：<\/p>\n

          \n
        • "\u98de\u884c\u6c7d\u8f66" \u2192 "\u60ac\u6d6e\u5217\u8f66\u8f68\u9053"<\/li>\n
        • "\u591c\u665a" \u2192 "\u9ec4\u660f"<\/li>\n<\/ul>\n

          \u2705 \u4f18\u52bf：\u6392\u9664\u566a\u58f0\u5e72\u6270，\u7cbe\u51c6\u8bc4\u4f30prompt\u6539\u52a8\u5bf9\u8f93\u51fa\u7684\u5f71\u54cd\u3002<\/p>\n

          \n

          5.3 \u6784\u5efa\u201c\u7406\u60f3\u56fe\u50cf\u201d\u7ba1\u7406\u6d41\u7a0b<\/h4>\n

          \u5efa\u8bae\u5efa\u7acb\u6807\u51c6\u5316\u5de5\u4f5c\u6d41\u4ee5\u6700\u5927\u5316seed\u4ef7\u503c：<\/p>\n

          1. \u521b\u5efa\u79cd\u5b50\u6570\u636e\u5e93（CSV\u683c\u5f0f）<\/h5>\n

          prompt,seed,steps,model_version,notes,image_path
          \n"\u8d5b\u535a\u670b\u514b\u57ce\u5e02",739201,20,majicflus_v1,"\u5149\u5f71\u51fa\u8272",.\/outputs\/cyber_city_739201.png
          \n"\u4e1c\u65b9\u4ed9\u4fa0\u5c71\u6c34",982103,25,majicflus_v1,"\u4e91\u96fe\u5c42\u6b21\u597d",.\/outputs\/mountain_fog_982103.png<\/p>\n

          2. \u6dfb\u52a0\u6807\u7b7e\u5206\u7c7b\u7cfb\u7edf<\/h5>\n

          \u4fbf\u4e8e\u540e\u7eed\u68c0\u7d22\u4e0e\u590d\u7528：<\/p>\n

            \n
          • style:cold_tone, style:warm_tone<\/li>\n
          • layout:center_focus, layout:wide_shot<\/li>\n
          • lighting:neon_glow, lighting:sunset<\/li>\n<\/ul>\n
            3. \u6279\u91cf\u91cd\u7ed8\u811a\u672c\u793a\u4f8b<\/h5>\n

            for seed in [739201, 982103]:
            \n image = pipe(prompt=prompt, seed=seed, num_inference_steps=30)
            \n image.save(f"regen_{seed}.png")<\/p>\n

            \u7528\u9014\u5305\u62ec：<\/p>\n

              \n
            • \u9ad8\u6e05\u91cd\u7ed8（\u63d0\u9ad8\u5206\u8fa8\u7387）<\/li>\n
            • \u5c40\u90e8\u91cd\u7ed8（\u66f4\u6362\u80cc\u666f）<\/li>\n
            • \u89d2\u8272\u4e00\u81f4\u6027\u7cfb\u5217\u751f\u6210<\/li>\n<\/ul>\n
              \n

              6. \u6ce8\u610f\u4e8b\u9879\u4e0e\u5e38\u89c1\u95ee\u9898<\/h3>\n

              6.1 Seed\u590d\u73b0\u7684\u8fb9\u754c\u6761\u4ef6<\/h4>\n

              \u5c3d\u7ba1seed\u5177\u6709\u5f3a\u590d\u73b0\u80fd\u529b，\u4f46\u4ecd\u53d7\u4ee5\u4e0b\u56e0\u7d20\u5f71\u54cd：<\/p>\n\n\u9650\u5236\u9879\u5f71\u54cd\u8bf4\u660e<\/tr>\n\n\n\n\n\n
              \u6a21\u578b\u7248\u672c\u53d8\u66f4<\/td>\n\u66f4\u6362 .safetensors \u6587\u4ef6\u540e，\u76f8\u540cseed\u4e0d\u518d\u4fdd\u8bc1\u8f93\u51fa\u4e00\u81f4<\/td>\n<\/tr>\n
              \u8c03\u5ea6\u5668\u66f4\u6362<\/td>\n\u5982\u4ece Euler \u6539\u4e3a DPM++，\u53bb\u566a\u8def\u5f84\u6539\u53d8<\/td>\n<\/tr>\n
              LoRA\u542f\u7528\u72b6\u6001<\/td>\n\u52a8\u6001\u52a0\u8f7dLoRA\u4f1a\u6539\u53d8\u6f5c\u5728\u7a7a\u95f4\u6620\u5c04<\/td>\n<\/tr>\n
              \u786c\u4ef6\u6d6e\u70b9\u8bef\u5dee<\/td>\n\u6781\u5c11\u6570\u60c5\u51b5\u4e0bGPU\u8ba1\u7b97\u5b58\u5728\u5fae\u5c0f\u504f\u5dee<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

              📌 \u6700\u4f73\u5b9e\u8df5：\u4f7f\u7528Docker\u6216conda\u73af\u5883\u5feb\u7167\u56fa\u5316\u4f9d\u8d56\u7248\u672c，\u786e\u4fdd\u957f\u671f\u53ef\u590d\u73b0\u3002<\/p>\n

              6.2 \u5e38\u89c1\u8fde\u63a5\u95ee\u9898\u6392\u67e5<\/h4>\n\n\u95ee\u9898\u73b0\u8c61\u53ef\u80fd\u539f\u56e0\u89e3\u51b3\u65b9\u6cd5<\/tr>\n\n\n\n\n\n
              \u672c\u5730\u65e0\u6cd5\u8bbf\u95ee 127.0.0.1:6006<\/td>\nSSH\u96a7\u9053\u672a\u5efa\u7acb\u6210\u529f<\/td>\n\u68c0\u67e5IP\u3001\u7aef\u53e3\u3001\u7528\u6237\u540d\u662f\u5426\u6b63\u786e<\/td>\n<\/tr>\n
              \u9875\u9762\u52a0\u8f7d\u7a7a\u767d<\/td>\nGradio\u672a\u7ed1\u5b9a 0.0.0.0<\/td>\n\u786e\u4fdd demo.launch(server_name="0.0.0.0")<\/td>\n<\/tr>\n
              GPU\u663e\u5b58\u4e0d\u8db3<\/td>\n\u672a\u542f\u7528\u91cf\u5316\u6216CPU\u5378\u8f7d<\/td>\n\u786e\u8ba4 float8 \u548c enable_cpu_offload() \u5df2\u542f\u7528<\/td>\n<\/tr>\n
              \u6a21\u578b\u52a0\u8f7d\u5931\u8d25<\/td>\n\u8def\u5f84\u9519\u8bef\u6216\u6587\u4ef6\u7f3a\u5931<\/td>\n\u68c0\u67e5 models\/ \u76ee\u5f55\u4e0b\u6a21\u578b\u662f\u5426\u5b58\u5728<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
              \n

              7. \u603b\u7ed3<\/h3>\n

              \u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u901a\u8fc7SSH\u96a7\u9053\u6280\u672f，\u5b89\u5168\u9ad8\u6548\u5730\u5c06\u90e8\u7f72\u5728\u8fdc\u7a0b\u670d\u52a1\u5668\u4e0a\u7684\u300c\u9ea6\u6a58\u8d85\u7136 – Flux \u79bb\u7ebf\u56fe\u50cf\u751f\u6210\u63a7\u5236\u53f0\u300d\u670d\u52a1\u6620\u5c04\u81f3\u672c\u5730\u6d4f\u89c8\u5668，\u5b9e\u73b0\u96f6\u5ef6\u8fdf\u4ea4\u4e92\u4f53\u9a8c\u3002<\/p>\n

              \u6211\u4eec\u5b8c\u6210\u4e86\u4ee5\u4e0b\u5173\u952e\u5b9e\u8df5：<\/p>\n

                \n
              • \u2705 \u638c\u63e1\u4e86\u57fa\u4e8e diffsynth \u6846\u67b6\u90e8\u7f72Flux.1\u6a21\u578b\u7684\u5b8c\u6574\u6d41\u7a0b<\/li>\n
              • \u2705 \u5b9e\u73b0\u4e86 float8 \u91cf\u5316\u52a0\u8f7d，\u663e\u8457\u964d\u4f4e\u663e\u5b58\u9700\u6c42<\/li>\n
              • \u2705 \u5229\u7528 SSH -L \u5efa\u7acb\u52a0\u5bc6\u96a7\u9053，\u7ed5\u8fc7\u516c\u7f51\u66b4\u9732\u98ce\u9669<\/li>\n
              • \u2705 \u6df1\u5165\u7406\u89e3\u4e86\u968f\u673a\u79cd\u5b50（seed）\u5728\u56fe\u50cf\u590d\u73b0\u4e2d\u7684\u6838\u5fc3\u4f5c\u7528<\/li>\n
              • \u2705 \u5efa\u7acb\u4e86\u4e00\u5957\u201c\u63a2\u7d22\u2192\u9501\u5b9a\u2192\u5fae\u8c03\u2192\u5f52\u6863\u201d\u7684\u9ad8\u6548\u521b\u4f5c\u6d41\u7a0b<\/li>\n<\/ul>\n

                \u6700\u7ec8\u76ee\u6807\u4e0d\u662f\u4f9d\u8d56\u8fd0\u6c14\u751f\u6210\u201c\u5076\u7136\u7684\u597d\u56fe\u201d，\u800c\u662f\u80fd\u591f\u4e3b\u52a8\u53ec\u5524\u51fa\u201c\u4f60\u5fc3\u4e2d\u8bbe\u60f3\u7684\u90a3\u4e00\u5f20\u201d\u3002\u800c\u8fd9\u4e00\u5207，\u59cb\u4e8e\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u5b57\u2014\u2014seed，\u5e76\u901a\u8fc7SSH\u96a7\u9053，\u8fde\u63a5\u8d77\u672c\u5730\u7075\u611f\u4e0e\u4e91\u7aef\u7b97\u529b\u3002<\/p>\n

                \n

                \u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n

                \u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f？\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a，\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf，\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df，\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

                \u672c\u5730\u7535\u8111\u8fde\u670d\u52a1\u5668，SSH\u96a7\u9053\u6253\u901aAI\u7ed8\u56fe\u6700\u540e\u4e00\u516c\u91cc
                \n1. \u5f15\u8a00：\u8fdc\u7a0b\u90e8\u7f72\u4e0e\u672c\u5730\u8bbf\u95ee\u7684\u534f\u540c\u6311\u6218
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