## 第1集 第3场

### 分镜1：教室全景
- 景别：全景
- 角度：平视
- 场景：高中教室，下午阳光
- 角色：无（空场景）
- 氛围：安静、温暖
- 时长：3秒

### 分镜2：主角入画
- 景别：中景
- 角度：微微仰拍
- 场景：教室门口
- 角色：女主·林小雨
- 动作：推门而入
- 表情：惊讶
- 时长：4秒

### 分镜3：女主特写
- 景别：近景
- 角度：正面
- 场景：教室门口
- 角色：女主·林小雨
- 表情：惊讶转微笑
- 时长：3秒

角色名称：林小雨
性别：女
年龄：17岁
发型：齐肩黑色直发，左侧别一个红色发卡
眼睛：深棕色大眼睛
身材：纤细，身高160cm
服装：白色衬衫 + 深蓝色百褶裙 + 黑色皮鞋
特征：左脸颊有一颗小痣
性格：活泼开朗

场景名称：教室A
类型：高中教室
布局：30人教室，讲台在前方左侧，窗户在右侧
色调：暖黄色调，下午阳光
特征：黑板上有数学公式，窗台有绿植
氛围：明亮、温馨

# 场景批量生成提示词模板
scene_prompts = [
    "anime style, empty high school classroom, afternoon sunlight through windows, warm atmosphere, desks and chairs arranged neatly, blackboard with math formulas, potted plants on windowsill, detailed background, masterpiece",
    "anime style, school hallway, lockers on both sides, afternoon light, clean and bright, perspective view, detailed architecture, masterpiece",
    "anime style, school rooftop, blue sky, chain-link fence, water tower in distance, cherry blossom tree, gentle breeze, detailed background, masterpiece"
]

# Stable Diffusion WebUI API 批量生成示例
import requests
import json

url = "http://127.0.0.1:7860/sdapi/v1/txt2img"

# 基础配置
base_config = {
    "steps": 28,
    "sampler_name": "DPM++ 2M Karras",
    "cfg_scale": 7.5,
    "width": 1024,
    "height": 576,
    "negative_prompt": "EasyNegative, badhandv4, worst quality, low quality, blurry",
    "seed": 12345  # 固定种子确保一致性
}

# 批量生成场景
scenes = [
    {
        "prompt": "anime style, high school classroom, afternoon sunlight, warm atmosphere, empty classroom, detailed background, masterpiece",
        "seed": 12345
    },
    {
        "prompt": "anime style, school hallway, lockers, afternoon light, perspective view, detailed architecture, masterpiece",
        "seed": 12346  # 种子+1，保持风格近似
    },
    {
        "prompt": "anime style, school rooftop, blue sky, fence, cherry blossom tree, detailed background, masterpiece",
        "seed": 12347
    }
]

for i, scene in enumerate(scenes):
    payload = {**base_config, **scene}
    response = requests.post(url, json=payload)
    # 保存结果...

# 为每个分镜生成4个变体
for scene_id in range(1, 11):
    for variant in range(4):
        payload = {
            **base_config,
            "prompt": scene_prompts[scene_id],
            "seed": base_seed + scene_id * 100 + variant,
            "subseed_strength": 0.3  # 控制变体差异程度
        }
        # 生成并保存...

{
  "project": "我的AI漫剧",
  "episode": 1,
  "model": {
    "checkpoint": "counterfeitV30_v30.safetensors",
    "vae": "vae-ft-mse-840000-ema-pruned.safetensors",
    "lora": [
      {"name": "add_detail.safetensors", "weight": 0.8},
      {"name": "anime_style.safetensors", "weight": 0.6}
    ]
  },
  "generation": {
    "sampler": "DPM++ 2M Karras",
    "steps": 28,
    "cfg_scale": 7.5,
    "width": 1024,
    "height": 576,
    "clip_skip": 2
  },
  "negative_prompt": "EasyNegative, badhandv4, worst quality, low quality, blurry, bad anatomy, bad hands, watermark, text",
  "style_keywords": "anime style, detailed, masterpiece, best quality"
}

import requests
import time
import os

SD_API = "http://127.0.0.1:7860/sdapi/v1/txt2img"
OUTPUT_DIR = "./output/episode01"

def generate_image(prompt, negative_prompt, seed, config):
    """调用SD API生成单张图片"""
    payload = {
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "seed": seed,
        "width": config["width"],
        "height": config["height"],
        "steps": config["steps"],
        "sampler_name": config["sampler"],
        "cfg_scale": config["cfg_scale"]
    }
    response = requests.post(SD_API, json=payload)
    if response.status_code == 200:
        result = response.json()
        import base64
        for i, img_data in enumerate(result["images"]):
            img_bytes = base64.b64decode(img_data)
            filename = f"scene_{seed:06d}_{i}.png"
            filepath = os.path.join(OUTPUT_DIR, filename)
            with open(filepath, "wb") as f:
                f.write(img_bytes)
            print(f"已保存: {filename}")
    else:
        print(f"生成失败: {response.status_code}")

def batch_generate(prompt_list, config, base_seed=10000):
    """批量生成"""
    os.makedirs(OUTPUT_DIR, exist_ok=True)
    for i, prompt in enumerate(prompt_list):
        print(f"正在生成第 {i+1}/{len(prompt_list)} 张...")
        generate_image(
            prompt=prompt,
            negative_prompt=config["negative_prompt"],
            seed=base_seed + i,
            config=config
        )
        time.sleep(1)  # 避免请求过快
    print(f"批量生成完成！共 {len(prompt_list)} 张")

# 使用示例
config = {
    "width": 1024,
    "height": 576,
    "steps": 28,
    "sampler": "DPM++ 2M Karras",
    "cfg_scale": 7.5,
    "negative_prompt": "EasyNegative, badhandv4, worst quality, low quality"
}

prompts = [
    "anime style, high school classroom, afternoon sunlight...",
    "anime style, school hallway, lockers, afternoon light...",
    # ... 更多提示词
]

batch_generate(prompts, config)

# 方法1：使用Permutation功能（V7支持）
/imagine prompt: anime style, {classroom|hallway|rooftop}, afternoon sunlight, detailed, masterpiece

# 方法2：使用脚本自动发送命令
# 可以使用Discord.js或PyCord编写自动化脚本

# 通过第三方API服务（如GoAPI）批量调用
POST https://api.goapi.ai/mj/v2/fetch
{
  "taskId": "task_id_from_submit",
  "botType": "MID_JOURNEY"
}

import requests
import json
import base64
import os

# 即梦AI API配置
JIMENG_API_KEY = "your_api_key"
JIMENG_API_URL = "https://jimeng.api.bytedance.com/v1/image/generation"

def generate_jimeng(prompt, negative_prompt=None, style="anime"):
    """调用即梦AI API生成图片"""
    headers = {
        "Authorization": f"Bearer {JIMENG_API_KEY}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": "seedream-5.0",
        "prompt": prompt,
        "negative_prompt": negative_prompt or "",
        "style": style,
        "width": 1024,
        "height": 576,
        "num_images": 1
    }
    response = requests.post(JIMENG_API_URL, headers=headers, json=payload)
    result = response.json()
    if result.get("code") == 0:
        # 保存图片
        image_data = base64.b64decode(result["data"]["images"][0])
        return image_data
    else:
        print(f"生成失败: {result.get('msg')}")
        return None

import dashscope
from dashscope import ImageSynthesis

dashscope.api_key = "your_api_key"

def batch_generate_wanxiang(prompt_list, style="<anime_style>"):
    """通义万相批量生成"""
    results = []
    for prompt in prompt_list:
        response = ImageSynthesis.call(
            model="wanx-v2",
            prompt=prompt,
            negative_prompt="低质量, 模糊, 水印",
            n=1,
            size="1024*576"
        )
        if response.status_code == 200:
            results.append(response.output)
        else:
            print(f"生成失败: {response.message}")
    return results

项目名_集数_场景号_分镜号_变体号.扩展名

示例：
mycomic_ep01_s03_f02_v01.png
mycomic_ep01_s03_f02_v02.png
mycomic_ep01_s03_f03_v01.png

project/
├── scripts/              # 分镜脚本
│   ├── ep01.md
│   └── ep02.md
├── characters/           # 角色设定
│   ├── linxiaoyu.json
│   └── zhangwei.json
├── prompts/              # 提示词库
│   ├── scenes.txt
│   ├── characters.txt
│   └── config.json
├── output/               # 生成结果
│   ├── ep01/
│   │   ├── raw/          # 原始生成
│   │   ├── selected/     # 筛选后
│   │   └── final/        # 最终版
│   └── ep02/
├── assets/               # 素材库
│   ├── backgrounds/
│   ├── characters/
│   └── effects/
└── config/               # 配置文件
    ├── project.json
    └── presets/

1. 整理分镜脚本
    ↓
2. 创建角色/场景设定卡
    ↓
3. 编写提示词模板
    ↓
4. 配置项目参数
    ↓
5. 第一轮：批量生成空场景（10-20张）
    ↓
6. 筛选场景，修复问题
    ↓
7. 第二轮：批量生成角色立绘（每个角色5-10张）
    ↓
8. 筛选角色立绘
    ↓
9. 第三轮：批量生成完整分镜（角色+场景）
    ↓
10. 质量筛选与修复
    ↓
11. 最终归档