364 lines
10 KiB
Markdown
364 lines
10 KiB
Markdown
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# Phase 0 · 执行手册 · 一步步跑通「一句话→分镜图片」
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## 🎯 目标
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> 在CVM服务器上跑通最小闭环：**输入一句话 → 光湖编排层自动拆剧本+分镜 → 调用免费API生成图片 → 输出一组分镜图片**
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不需要训练模型。不需要租GPU。用现有服务器 + 免费API，先证明编排核心能跑。
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>
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---
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## Step 0 · 登录服务器 · 确认环境
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先SSH登录CVM服务器，确认基础环境：
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```bash
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# 登录服务器（把IP换成你的CVM公网IP）
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ssh root@你的CVM公网IP
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# 确认操作系统
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cat /etc/os-release
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# 确认Python版本（需要3.8+）
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python3 --version
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# 如果没有Python3，安装它：
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# Ubuntu/Debian:
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sudo apt update && sudo apt install -y python3 python3-pip python3-venv
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# CentOS:
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# sudo yum install -y python3 python3-pip
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```
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<aside>
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📋
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**妈妈操作：** 把上面的命令一条条复制粘贴到终端，把每条的输出结果告诉我，我来判断环境是否OK。
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</aside>
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---
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## Step 1 · 创建光湖项目目录
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```bash
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# 创建项目根目录
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mkdir -p /opt/guanghu
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cd /opt/guanghu
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# 创建子目录
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mkdir -p core # 编排核心代码
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mkdir -p output # 生成结果输出
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mkdir -p config # 配置文件
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mkdir -p logs # 日志
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# 创建Python虚拟环境
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python3 -m venv venv
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source venv/bin/activate
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# 安装基础依赖
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pip install requests pillow
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```
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---
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## Step 2 · 注册免费API · 获取密钥
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我们需要两个免费API：
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- **语言模型API**（拆剧本+分镜）→ 用硅基流动(SiliconFlow)的免费DeepSeek
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- **图片生成API**（画分镜图片）→ 用硅基流动的免费FLUX
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### 2.1 注册硅基流动
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1. 打开 [siliconflow.cn](http://siliconflow.cn)
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2. 注册账号（免费）
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3. 进入控制台 → API密钥 → 创建一个密钥
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4. 复制密钥（长得像 `sk-xxxxxxxxxxxxxxxx`）
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### 2.2 写入配置文件
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```bash
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# 把你的API密钥写入配置文件（把sk-xxx换成你的真实密钥）
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cat > /opt/guanghu/config/api_keys.py << 'EOF'
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SILICONFLOW_API_KEY = "sk-你的硅基流动密钥"
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EOF
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```
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<aside>
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💡
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**为什么用硅基流动？**
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- 国内访问快，不用翻墙
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- 免费额度足够开发调试
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- 同时提供DeepSeek（语言）和FLUX（图片）两个模型的API
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- 后面随时可以换成本地部署，编排层不用改
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</aside>
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---
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## Step 3 · 编排核心v0.1 · 光湖的第一个大脑
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这是光湖编排层的第一个版本——最简但完整的编排逻辑：
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```bash
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cat > /opt/guanghu/core/guanghu_brain.py << 'PYEOF'
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#!/usr/bin/env python3
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"""
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光湖编排核心 v0.1 · Phase 0 最小闭环
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一句话 → 拆剧本 → 拆分镜 → 生成图片 → 输出
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"""
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import json
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import os
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import sys
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import requests
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import time
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from pathlib import Path
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# === 配置 ===
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sys.path.insert(0, "/opt/guanghu/config")
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from api_keys import SILICONFLOW_API_KEY
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API_BASE = "https://api.siliconflow.cn/v1"
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LLM_MODEL = "deepseek-ai/DeepSeek-V3" # 免费语言模型
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IMAGE_MODEL = "black-forest-labs/FLUX.1-schnell" # 免费图片模型
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OUTPUT_DIR = Path("/opt/guanghu/output")
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HEADERS = {
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"Authorization": f"Bearer {SILICONFLOW_API_KEY}",
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"Content-Type": "application/json"
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}
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# === 第一层：剧本引擎 ===
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def generate_script(user_input: str) -> dict:
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"""用户一句话 → 完整剧本（标题+场景列表）"""
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print("\n🧠 [编排层] 正在理解你的意图，生成剧本...")
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prompt = f"""你是一个专业的短剧编剧。用户给你一句话，你需要生成一个4-6个场景的短剧剧本。
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用户输入：{user_input}
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请严格按以下JSON格式输出（不要输出其他内容）：
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{{
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"title": "短剧标题",
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"style": "画面风格描述（如：2.5D偏写实韩系精致风格）",
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"scenes": [
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"scene_id": 1,
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"description": "这个场景发生了什么（一句话）",
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"image_prompt": "详细的英文画面描述，用于AI绘图。包含人物外观、表情、动作、场景、光线、构图。风格统一为2.5D semi-realistic Korean drama style。",
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"dialogue": "这个场景的台词（中文）"
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]
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}}"""
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resp = requests.post(f"{API_BASE}/chat/completions", headers=HEADERS, json={
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"model": LLM_MODEL,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.7,
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"max_tokens": 2000
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})
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result = resp.json()["choices"][0]["message"]["content"]
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# 提取JSON（处理可能的markdown代码块包裹）
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if "```json" in result:
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result = result.split("```json")[1].split("```")[0]
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elif "```" in result:
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result = result.split("```")[1].split("```")[0]
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script = json.loads(result.strip())
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print(f" ✅ 剧本生成完成：《{script['title']}》共 {len(script['scenes'])} 个场景")
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return script
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# === 第二层：图片生成引擎 ===
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def generate_image(prompt: str, scene_id: int, output_dir: Path) -> str:
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"""根据画面描述生成图片"""
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print(f" 🎨 [图片引擎] 正在生成场景 {scene_id} 的画面...")
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resp = requests.post(f"{API_BASE}/images/generations", headers=HEADERS, json={
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"model": IMAGE_MODEL,
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"prompt": prompt,
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"image_size": "1024x576", # 16:9 横版
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"num_inference_steps": 4 # FLUX.1-schnell 用4步就够
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})
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data = resp.json()
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image_url = data["images"][0]["url"]
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# 下载图片
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img_data = requests.get(image_url).content
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img_path = output_dir / f"scene_{scene_id:02d}.png"
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img_path.write_bytes(img_data)
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print(f" ✅ 场景 {scene_id} 图片已保存: {img_path}")
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return str(img_path)
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# === 第三层：编排主控 ===
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def run_pipeline(user_input: str):
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"""光湖编排主流程：一句话 → 分镜图片"""
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print("="*60)
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print("🌊 光湖编排核心 v0.1 · Phase 0")
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print(f"📝 用户输入：{user_input}")
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print("="*60)
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# 创建本次输出目录
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timestamp = time.strftime("%Y%m%d_%H%M%S")
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run_dir = OUTPUT_DIR / f"run_{timestamp}"
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run_dir.mkdir(parents=True, exist_ok=True)
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# Step 1: 生成剧本
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script = generate_script(user_input)
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# 保存剧本
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script_path = run_dir / "script.json"
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script_path.write_text(json.dumps(script, ensure_ascii=False, indent=2))
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print(f"\n📄 剧本已保存: {script_path}")
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# 打印剧本
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print(f"\n📖 《{script['title']}》")
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print(f" 风格：{script.get('style', '2.5D韩系精致风格')}")
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for scene in script["scenes"]:
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print(f" 场景{scene['scene_id']}: {scene['description']}")
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if scene.get("dialogue"):
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print(f" 💬 {scene['dialogue']}")
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# Step 2: 逐场景生成图片
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print(f"\n🎬 开始生成 {len(script['scenes'])} 个场景的图片...\n")
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image_paths = []
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for scene in script["scenes"]:
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img_path = generate_image(
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prompt=scene["image_prompt"],
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scene_id=scene["scene_id"],
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output_dir=run_dir
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)
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image_paths.append(img_path)
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time.sleep(1) # 避免API限流
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# 完成
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print("\n" + "="*60)
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print("🎉 光湖编排完成！")
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print(f"📁 所有文件保存在: {run_dir}")
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print(f" 📄 剧本: script.json")
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for i, path in enumerate(image_paths, 1):
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print(f" 🖼️ 场景{i}: {Path(path).name}")
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print("="*60)
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return {"script": script, "images": image_paths, "output_dir": str(run_dir)}
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# === 入口 ===
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if __name__ == "__main__":
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if len(sys.argv) > 1:
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user_input = " ".join(sys.argv[1:])
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else:
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user_input = input("\n🌊 光湖 · 说一句话，开始创作：")
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run_pipeline(user_input)
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PYEOF
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chmod +x /opt/guanghu/core/guanghu_brain.py
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```
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<aside>
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🧠
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**这就是光湖的第一个大脑。** 虽然只有100多行代码，但它已经具备了编排层的核心逻辑：
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- 🧠 理解用户意图（调LLM拆剧本）
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- 🎬 拆分镜（LLM自动分场景+生成画面描述）
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- 🎨 调引擎生图（调FLUX生成每个分镜的画面）
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- 📁 组装输出（所有结果保存在一个目录）
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后面所有的升级（加视频、加语音、加质量校验）都是在这个骨架上加东西。骨架不变。
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</aside>
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---
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## Step 4 · 跑起来！
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```bash
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# 确保在虚拟环境里
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cd /opt/guanghu
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source venv/bin/activate
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# 第一次运行！输入一句话试试
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python3 core/guanghu_brain.py "一个穿白裙子的女孩在樱花树下回头微笑"
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```
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如果一切正常，你会看到：
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1. 🧠 编排层生成剧本（4-6个场景）
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2. 🎨 逐个场景生成图片
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3. 📁 所有文件保存在 `/opt/guanghu/output/run_时间戳/` 目录
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```bash
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# 查看生成的文件
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ls -la /opt/guanghu/output/run_*/
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# 查看剧本内容
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cat /opt/guanghu/output/run_*/script.json | python3 -m json.tool
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```
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<aside>
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🎯
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**Phase 0 通过标准：**
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输入一句话 → 编排层自动拆出4-6个分镜 → 每个分镜生成一张图片 → 保存到output目录。
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**看到图片就算成功！** 哪怕图片不够好看也没关系，那是后面优化的事。Phase 0只需要证明：编排核心能跑通。
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</aside>
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---
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## Step 5 · 把图片下载到本地看
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|
|
|||
|
|
```bash
|
|||
|
|
# 在你的本地电脑（不是服务器）打开终端，把图片下载下来
|
|||
|
|
# 把IP和时间戳换成你的
|
|||
|
|
scp -r root@你的CVM公网IP:/opt/guanghu/output/run_* ~/Desktop/光湖输出/
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
或者如果妈妈不方便用scp，可以在服务器上临时开一个HTTP服务看图：
|
|||
|
|
|
|||
|
|
```bash
|
|||
|
|
# 在服务器上（临时用，看完关掉）
|
|||
|
|
cd /opt/guanghu/output
|
|||
|
|
python3 -m http.server 8080
|
|||
|
|
# 然后浏览器打开 http://你的CVM公网IP:8080 就能看到图片了
|
|||
|
|
# 看完按 Ctrl+C 关掉
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
---
|
|||
|
|
|
|||
|
|
## 📋 执行清单
|
|||
|
|
|
|||
|
|
- [ ] Step 0 · 登录服务器，确认Python3可用
|
|||
|
|
- [ ] Step 1 · 创建光湖项目目录 + 虚拟环境
|
|||
|
|
- [ ] Step 2 · 注册硅基流动，获取免费API密钥
|
|||
|
|
- [ ] Step 3 · 创建编排核心脚本
|
|||
|
|
- [ ] Step 4 · 运行！一句话→分镜图片
|
|||
|
|
- [ ] Step 5 · 下载图片到本地看效果
|
|||
|
|
|
|||
|
|
---
|
|||
|
|
|
|||
|
|
## ⚠️ 可能遇到的问题
|
|||
|
|
|
|||
|
|
- **报错：ModuleNotFoundError: No module named 'requests'**
|
|||
|
|
|
|||
|
|
说明依赖没装好。运行：
|
|||
|
|
|
|||
|
|
`pip install requests pillow`
|
|||
|
|
|
|||
|
|
- **报错：API返回401或403**
|
|||
|
|
|
|||
|
|
说明API密钥不对。检查 `/opt/guanghu/config/api_keys.py` 里的密钥是否正确。
|
|||
|
|
|
|||
|
|
- **报错：JSON解析失败**
|
|||
|
|
|
|||
|
|
说明LLM返回的格式不太标准。再跑一次就行，或者告诉我具体报错，我帮你改脚本。
|
|||
|
|
|
|||
|
|
- **图片生成很慢**
|
|||
|
|
|
|||
|
|
正常。FLUX.1-schnell已经是最快的了（4步推理），但通过API还要排队。一张图大概10-30秒。6张图1-3分钟。
|