butubb
2112c1a870
移植自 mac-agent-os 的 mediacrawler_adapter：不起子进程、不开页面，用浏览器里那份
登录态直接调抖音 Web 接口。产物键名照抄 store/douyin，所以 ingest 那条链路一个字不用改。
**目前能用的（真环境实测，非推断）**：
profile/other : 200, 7075 字节 —— 博主主页指标（粉丝/获赞/作品数/昵称）
aweme/detail : 200, 45425 字节 —— 单条作品详情（含点赞/评论/收藏/分享）
**目前不能用的：作品列表 `aweme/post`。** 两个互相独立的原因：
1. 这个接口被抖音单独升级成了真校验：不带 x-tt-argus 回 403「Uifid Not Found」，
带上 dummy 值回 200 + **空 body**。也就是说「头在不在」骗得过，「真校验」过不了。
同一套头打 profile/other 和 aweme/detail 都是通的 —— 抖音是挑着接口加保护的，
挑中的恰好是「批量拉作品列表」这个最敏感的动作。
2. 改走页面截获也不行：CDP 浏览器打开博主主页会落到「验证码中间页」（当天大量探测的
代价，过几小时要重测）。
所以现在的边界是：**已知作品的指标刷新能做，自动发现新作品做不了**。
**排查中控住变量后得到的两条事实**（都写进注释了）：
· `Accept` / `Accept-Language` / `Referer` 才是主页接口能返回真数据的原因 —— 只有
UA+client hints+Cookie 时是 200 但仅 121 字节的空壳，补上这三个头变 7074 字节。
（我先前猜的 sec-ch-ua 不是关键。）
· 因此 UA 与 client hints 必须**成套地取自同一个浏览器**，所以 BrowserIdentity 一次
从 CDP 取齐 cookie + UA + hints，而不是各自写死。
「200 + 空 body 必须当场报错」也是刻意写死的：放过去它会在下游变成「这个博主没作品」，
把一次失败伪装成一条正常结果 —— 爬虫那条路正是这么栽的，还被翻译成「账号被封」。
测试 +11：cookie 解析、请求头成套性（含 uifid 缺失/回退）、产物键名与 store 对齐、
以及 _get 的三条失败路径（空 body / 403 带网关原话 / 正常返回）。
791 lines
27 KiB
Python
791 lines
27 KiB
Python
"""
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AVE character_portrait — 逐帧参考链定妆照生成（v2.0 重写）
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核心原则:
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⚠️ 前一轮的问题：所有角度共用同一张基准图作 I2I 参考 → 角色漂移 + 方向混乱
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✅ 本方案：逐帧参考链（Sequential Reference Chain）
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图1(正面T2I) → 作为图2参考 → 图2(右半侧I2I) → 作为图3参考 → 图3(右侧面I2I) → 作为图4参考 → 图4(背面I2I)
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每一步只做"小幅姿态变化"，最大限度保持角色一致性
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流程:
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Phase 1: Kling T2I (kling-v3, 高质量) 生成正面全身基准图
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Phase 2: 逐帧 I2I 参考链，小步幅递增
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图1(正面) → 图2(右侧45°半侧面, I2I参考图1)
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图2(右侧45°) → 图3(纯右侧面, I2I参考图2)
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图3(右侧面) → 图4(背面, I2I参考图3)
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Phase 3: 注册到 CharacterRegistry（face_anchor + 各角度图）
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参考强度逐级递增：0.70 → 0.75 → 0.70（近端参考 = 更高一致性）
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用法:
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python character_portrait.py --character "陆晨晖" --desc "..."
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API调用:
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POST /api/characters/generate-portrait
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{"character_name": "陆晨晖", "description": "...", "force": false}
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"""
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import sys
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import os
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import json
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import time
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import base64
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import hashlib
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from pathlib import Path
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path = os.path.dirname(os.path.abspath(__file__))
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sys.path.insert(0, path)
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from lib.config import load_config
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from lib.logger import get_logger
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logger = get_logger('character_portrait')
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environ = os.environ.get
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home = os.path.expanduser('~')
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# ── 缓存目录 ──
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CACHE_DIR = environ(
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'AVE_CACHE_DIR',
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os.path.join(home, 'workbuddy-agent-os/agent-local/tools/ave/cache/character_portrait'),
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)
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os.makedirs(CACHE_DIR, exist_ok=True)
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# ── Kling API ──
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KLING_BASE = 'https://api-beijing.klingai.com'
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BASELINE_MODEL = 'kling-v3'
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ANGLE_MODEL = 'kling-v1'
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DEFAULT_SEED = 42
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# Phase 2 参考链强度（逐级递增保持一致性）
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REF_STRENGTH_CHAIN = (0.70, 0.75, 0.70)
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# Phase 1: 基准图提示词模板
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BASELINE_PROMPT_TEMPLATE = (
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'一张超写实全身人像定妆照，必须正面朝向镜头，如证件照构图。'
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'双脚自然分开与肩同宽，双手自然垂于身体两侧，面部正对相机，双耳左右对称可见。'
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'严禁身体转向侧面或半侧面，不得有角度偏移。'
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'{desc}'
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'干净的浅灰色无缝影棚背景，左右双柔光箱对称布光，面部无阴影，'
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'超高清8k，真实皮肤纹理，织物细节，发丝清晰可见，专业人像摄影质感。'
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)
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# Phase 2: 逐帧参考链定义
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# 每帧: key(存储key), label(显示名), ref_source(参考图来源), strength(I2I强度), prompt(生成提示词)
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ANGLE_CHAIN = [
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{
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'key': 'right_three_quarter',
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'label': '右侧45°半侧面',
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'ref_source': 'baseline',
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'strength': 0.70,
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'prompt': (
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'与参考图完全相同的人物。全身镜头，四分之三右侧面，身体转向右侧约45度。'
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'面部朝向镜头右前方，展现右侧面部特征。站姿放松自然。'
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'保持完全一致的着装、配饰、发型和面部特征。'
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'浅灰色影棚背景，超写实，专业人像布光，真实皮肤纹理，高清细节。'
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),
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},
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{
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'key': 'right_side',
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'label': '纯右侧面',
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'ref_source': 'right_three_quarter',
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'strength': 0.75,
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'prompt': (
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'与参考图完全相同的人物。全身镜头，纯右侧面视角，人物面向右侧90度。'
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'镜头从人物正右侧拍摄，展现鼻梁、嘴唇和下颌线的侧面轮廓。'
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'着装与参考图完全一致，发型侧面层次清晰可见。'
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'浅灰色影棚背景，超写实，轮廓清晰，高清，侧光勾勒面部轮廓。'
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),
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},
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{
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'key': 'back',
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'label': '背面',
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'ref_source': 'right_side',
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'strength': 0.70,
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'prompt': (
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'与参考图完全相同的人物。全身镜头，人物背对镜头，从正后方拍摄。'
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'展现后脑勺的发型层次感，肩背和腰线的服装贴合线条。'
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'从背面可见服装背面的褶皱纹理和鞋子的后跟细节。'
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'浅灰色影棚背景，超写实，真实皮肤纹理，高清8k。'
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),
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},
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]
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# ══════════════════════════════════════════════════════
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# Kling 辅助工具
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# ══════════════════════════════════════════════════════
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def _get_kling_credentials() -> dict:
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"""从 AVE 配置加载 Kling API 凭据。
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Returns:
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{"access_key": str, "secret_key": str}
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Raises:
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ValueError: 缺少配置
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"""
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cfg = load_config()
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kling_cfg = cfg.get('kling', {})
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ak = kling_cfg.get('access_key', '')
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sk = kling_cfg.get('secret_key', '')
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if not ak or not sk:
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raise ValueError('缺少 Kling API 配置 (kling.access_key / kling.secret_key)')
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return {'access_key': ak, 'secret_key': sk}
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def _jwt_token(ak: str, sk: str) -> str:
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"""生成 Kling JWT 认证 token。
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Args:
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ak: Access Key
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sk: Secret Key
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Returns:
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JWT token 字符串
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"""
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try:
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import jwt as pyjwt
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except ImportError:
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raise ImportError('需要 PyJWT 库: pip install pyjwt')
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now = int(time.time())
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return pyjwt.encode(
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{'iss': ak, 'exp': now + 1800, 'nbf': now},
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sk,
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algorithm='HS256',
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)
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def _download(url: str, path: str):
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"""下载文件。
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Args:
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url: 下载 URL
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path: 保存路径
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"""
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import httpx
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os.makedirs(os.path.dirname(path), exist_ok=True)
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with httpx.stream('GET', url, timeout=600) as r:
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r.raise_for_status()
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with open(path, 'wb') as f:
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for chunk in r.iter_bytes(8192):
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f.write(chunk)
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# ══════════════════════════════════════════════════════
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# Kling T2I / I2I 核心
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# ══════════════════════════════════════════════════════
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def _t2i(
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image_path: str,
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prompt: str,
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seed: int = 0,
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model: str = 'kling-v3',
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force: bool = False,
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) -> dict:
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"""Kling 文生图 (T2I)。
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参数:
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model: 默认 BASELINE_MODEL (kling-v3)，可覆盖
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force: 强制重新生成，跳过缓存
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返回: {"path": str, "seed": int}
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"""
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# 检查缓存
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if not force and os.path.exists(image_path):
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seed_file = f'{image_path}.seed'
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used_seed = seed
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if os.path.exists(seed_file):
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used_seed = int(open(seed_file).read().strip())
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logger.info(f'T2I 缓存命中: {image_path}')
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return {'path': image_path, 'seed': used_seed}
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from lib.config import load_config
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import httpx
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import jwt
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# 鉴权
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creds = _get_kling_credentials()
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ak = creds['access_key']
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sk = creds['secret_key']
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token = _jwt_token(ak, sk)
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headers = {'Authorization': f'Bearer {token}', 'Content-Type': 'application/json'}
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# 请求
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model_name = model or BASELINE_MODEL
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payload = {
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'model': model_name,
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'prompt': prompt,
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'aspect_ratio': '9:16',
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'n': 1,
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}
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if seed:
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payload['seed'] = seed
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# 提交
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for attempt in range(3):
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try:
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resp = httpx.post(
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f'{KLING_BASE}/v1/images/generations',
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headers=headers,
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json=payload,
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timeout=30,
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)
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if resp.status_code == 429:
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wait = 15 * (attempt + 1)
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logger.warning(f' 429 限流，等待 {wait}s...')
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time.sleep(wait)
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continue
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resp.raise_for_status()
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data = resp.json()
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task_id = data.get('data', {}).get('task_id', '')
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if not task_id:
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raise RuntimeError(f'Kling T2I 提交失败: {data}')
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break
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except Exception as e:
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if attempt == 2:
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raise
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time.sleep(5)
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else:
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raise RuntimeError('Kling T2I 提交失败（重试耗尽）')
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# 轮询
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for i in range(40):
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time.sleep(5)
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try:
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q = httpx.get(
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f'{KLING_BASE}/v1/images/generations/{task_id}',
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headers=headers,
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timeout=30,
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)
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q.raise_for_status()
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status_data = q.json()['data']
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task_status = status_data.get('task_status', '')
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if task_status == 'succeed':
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images = status_data.get('task_result', {}).get('images', [])
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if not images:
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raise RuntimeError('Kling T2I 成功但无图像')
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img_url = images[0].get('url', '')
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returned_seed = images[0].get('seed', seed)
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# 下载
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logger.info(f' T2I 完成，下载中...')
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_download(img_url, image_path)
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# 写种子文件
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with open(f'{image_path}.seed', 'w') as f:
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f.write(str(returned_seed))
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logger.info(f' ✅ 基准图: {image_path} [seed={returned_seed}]')
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return {'path': image_path, 'seed': returned_seed}
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elif task_status == 'failed':
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reason = status_data.get('fail_reason', '?')
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raise RuntimeError(f'T2I 生成失败: {reason}')
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except Exception as e:
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if '轮询' in str(e):
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logger.warning(f' {e}')
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break
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logger.warning(f' 轮询异常: {e}')
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time.sleep(5)
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raise TimeoutError('T2I 生成超时（200s）')
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def _i2i(
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output_path: str,
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ref_image: str,
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prompt: str,
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seed: int = 0,
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strength: float = 0.65,
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model: str = 'kling-v1',
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force: bool = False,
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) -> dict:
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"""Kling 图生图 (I2I)，基于参考图生成。
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参数:
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strength: 参考强度，默认 0.65
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model: 模型，默认 ANGLE_MODEL (kling-v1)
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force: 强制重新生成
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返回: {"path": str, "seed": int}
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"""
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# 检查缓存
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if not force and os.path.exists(output_path):
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seed_file = f'{output_path}.seed'
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used_seed = seed
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if os.path.exists(seed_file):
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used_seed = int(open(seed_file).read().strip())
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logger.info(f'I2I 缓存命中: {output_path}')
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return {'path': output_path, 'seed': used_seed}
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import httpx
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import jwt
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# 鉴权
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creds = _get_kling_credentials()
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ak = creds['access_key']
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sk = creds['secret_key']
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token = _jwt_token(ak, sk)
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headers = {'Authorization': f'Bearer {token}', 'Content-Type': 'application/json'}
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# 读取参考图
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if not os.path.exists(ref_image):
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raise FileNotFoundError(f'I2I 参考图不存在: {ref_image}')
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with open(ref_image, 'rb') as f:
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img_b64 = base64.b64encode(f.read()).decode('utf-8')
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model_name = model or ANGLE_MODEL
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ref_strength = strength or 0.7
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payload = {
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'model': model_name,
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'prompt': prompt,
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'image': img_b64,
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'strength': ref_strength,
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'aspect_ratio': '9:16',
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'n': 1,
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}
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if seed:
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payload['seed'] = seed
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# 提交
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for attempt in range(3):
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try:
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resp = httpx.post(
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f'{KLING_BASE}/v1/images/generations',
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headers=headers,
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json=payload,
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timeout=30,
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)
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if resp.status_code == 429:
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wait = 15 * (attempt + 1)
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logger.warning(f' 429 限流，等待 {wait}s...')
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time.sleep(wait)
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continue
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resp.raise_for_status()
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data = resp.json()
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task_id = data.get('data', {}).get('task_id', '')
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if not task_id:
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raise RuntimeError(f'Kling I2I 提交失败: {data}')
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break
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except Exception as e:
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if attempt == 2:
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raise
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time.sleep(5)
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else:
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raise RuntimeError('Kling I2I 提交失败（重试耗尽）')
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# 轮询
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for i in range(40):
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time.sleep(5)
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try:
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q = httpx.get(
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f'{KLING_BASE}/v1/images/generations/{task_id}',
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headers=headers,
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timeout=30,
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)
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q.raise_for_status()
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status_data = q.json()['data']
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task_status = status_data.get('task_status', '')
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if task_status == 'succeed':
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images = status_data.get('task_result', {}).get('images', [])
|
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if not images:
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raise RuntimeError('Kling I2I 成功但无图像')
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img_url = images[0].get('url', '')
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returned_seed = images[0].get('seed', seed)
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# 下载
|
||
logger.info(f' I2I 完成，下载中...')
|
||
_download(img_url, output_path)
|
||
|
||
# 写种子文件
|
||
with open(f'{output_path}.seed', 'w') as f:
|
||
f.write(str(returned_seed))
|
||
|
||
logger.info(f' ✅ 角度图: {output_path} [seed={returned_seed}]')
|
||
return {'path': output_path, 'seed': returned_seed}
|
||
|
||
elif task_status == 'failed':
|
||
reason = status_data.get('fail_reason', '?')
|
||
raise RuntimeError(f'I2I 生成失败: {reason}')
|
||
|
||
except Exception as e:
|
||
if '轮询' in str(e):
|
||
logger.warning(f' {e}')
|
||
break
|
||
logger.warning(f' 轮询异常: {e}')
|
||
time.sleep(5)
|
||
|
||
raise TimeoutError('I2I 生成超时（200s）')
|
||
|
||
|
||
# ══════════════════════════════════════════════════════
|
||
# 主流程
|
||
# ══════════════════════════════════════════════════════
|
||
|
||
def generate_baseline(
|
||
description: str,
|
||
char_name: str,
|
||
force: bool = False,
|
||
seed: int = 0,
|
||
ref_image: str = '',
|
||
) -> dict:
|
||
"""Phase 1: 生成正面全身基准图。
|
||
|
||
策略（v2.2）:
|
||
1. 优先用真实照片 ref_image 做 I2I（真实照片→影棚风格，强度 0.85 保留面部）
|
||
2. 无参考照时回退 T2I (kling-v3)
|
||
|
||
参数:
|
||
ref_image: 真实参考照路径。有则走 I2I，无则走 T2I
|
||
|
||
返回:
|
||
{"baseline": "path/to/baseline.png", "seed": int, "method": "i2i|t2i"}
|
||
"""
|
||
output_dir = os.path.join(CACHE_DIR, char_name)
|
||
Path(output_dir).mkdir(parents=True, exist_ok=True)
|
||
|
||
baseline_path = os.path.join(output_dir, 'baseline.png')
|
||
seed_file = f'{baseline_path}.seed'
|
||
|
||
# 缓存检查
|
||
if not force and os.path.exists(baseline_path):
|
||
cached_seed = seed
|
||
if os.path.exists(seed_file):
|
||
cached_seed = int(open(seed_file).read().strip())
|
||
logger.info(f'基准图缓存命中: {baseline_path}')
|
||
return {'baseline': baseline_path, 'seed': cached_seed, 'method': 'cache'}
|
||
|
||
if ref_image and os.path.exists(ref_image):
|
||
# 策略 1: 真实照片 I2I → 影棚风格基准图
|
||
logger.info(f'=== Phase 1: 参考照 I2I 生成基准图 ({char_name}) ===')
|
||
logger.info(f' 参考照: {ref_image}')
|
||
|
||
prompt = (
|
||
'与参考照片完全相同的人物。全身镜头，正面站立直视镜头，如证件照构图。'
|
||
'浅灰色无缝影棚背景，左右双柔光箱对称布光，面部无阴影。'
|
||
'超高清8k，真实皮肤纹理，织物细节。'
|
||
)
|
||
|
||
result = _i2i(
|
||
baseline_path,
|
||
ref_image,
|
||
prompt,
|
||
seed=seed,
|
||
strength=0.85,
|
||
model=ANGLE_MODEL,
|
||
force=force,
|
||
)
|
||
return {
|
||
'baseline': result['path'],
|
||
'seed': result['seed'],
|
||
'method': 'i2i',
|
||
}
|
||
else:
|
||
# 策略 2: 纯 T2I
|
||
logger.info(f'=== Phase 1: T2I 生成基准图 ({char_name}) [kling-v3] ===')
|
||
prompt = BASELINE_PROMPT_TEMPLATE.format(desc=description)
|
||
|
||
result = _t2i(baseline_path, prompt, seed=seed, model=BASELINE_MODEL, force=force)
|
||
return {
|
||
'baseline': result['path'],
|
||
'seed': result['seed'],
|
||
'method': 't2i',
|
||
}
|
||
|
||
|
||
def generate_sequential_chain(
|
||
baseline_path: str,
|
||
char_name: str,
|
||
force: bool = False,
|
||
seed: int = 0,
|
||
) -> dict:
|
||
"""Phase 2: 逐帧参考链生成多角度定妆照。
|
||
|
||
v2.0 核心变更（对比 v1.0）:
|
||
❌ v1.0 问题: 所有角度都用同一张 baseline 作 I2I 参考
|
||
→ 从正面直接跳到90°侧面，姿态变化太大，角色容易漂移
|
||
✅ v2.0 方案: 逐帧参考链
|
||
图1(正面) → 图2(右侧45°) → 图3(纯右侧面) → 图4(背面)
|
||
每张图都用上一张图作 I2I 参考，只做小幅姿态变化
|
||
|
||
返回:
|
||
{
|
||
"right_three_quarter": "path/to/angle_right_three_quarter.png",
|
||
"right_side": "path/to/angle_right_side.png",
|
||
"back": "path/to/angle_back.png",
|
||
"chain": [步骤详情],
|
||
"seed": int
|
||
}
|
||
"""
|
||
output_dir = os.path.join(CACHE_DIR, char_name, 'angles')
|
||
Path(output_dir).mkdir(parents=True, exist_ok=True)
|
||
|
||
results = {}
|
||
chain_log = []
|
||
final_seed = seed
|
||
|
||
# 构建参考映射: 每个步骤的参考图来源
|
||
ref_map = {'baseline': baseline_path}
|
||
for step in ANGLE_CHAIN:
|
||
ref_map[step['key']] = os.path.join(output_dir, f'angle_{step["key"]}.png')
|
||
|
||
for i, step in enumerate(ANGLE_CHAIN):
|
||
output_path = ref_map[step['key']]
|
||
ref_image = ref_map[step['ref_source']]
|
||
step_strength = step['strength']
|
||
step_prompt = step['prompt']
|
||
|
||
logger.info(f'==================================================')
|
||
logger.info(f'Phase 2 Step {i+1}/{len(ANGLE_CHAIN)}: {step["label"]}')
|
||
logger.info(f' 参考图: {step["ref_source"]} → {step["key"]}')
|
||
logger.info(f' 强度: {step_strength}')
|
||
|
||
try:
|
||
result = _i2i(
|
||
output_path,
|
||
ref_image,
|
||
step_prompt,
|
||
seed=seed,
|
||
strength=step_strength,
|
||
model=ANGLE_MODEL,
|
||
force=force,
|
||
)
|
||
results[step['key']] = result['path']
|
||
final_seed = result.get('seed', seed)
|
||
logger.info(f' ✅ {step["label"]} [seed={final_seed}]')
|
||
|
||
chain_log.append({
|
||
'step': i + 1,
|
||
'label': step['label'],
|
||
'ref_source': step['ref_source'],
|
||
'strength': step_strength,
|
||
'output': result['path'],
|
||
'seed': final_seed,
|
||
})
|
||
except Exception as e:
|
||
logger.warning(f' ❌ {step["label"]} 生成失败: {e}')
|
||
chain_log.append({
|
||
'step': i + 1,
|
||
'label': step['label'],
|
||
'ref_source': step['ref_source'],
|
||
'strength': step_strength,
|
||
'output': None,
|
||
'error': str(e),
|
||
})
|
||
|
||
# 写 chain meta
|
||
meta_path = os.path.join(output_dir, '_chain_meta.json')
|
||
meta = {
|
||
'character': char_name,
|
||
'method': 'sequential_reference_chain_v2',
|
||
'baseline': baseline_path,
|
||
'seed': final_seed,
|
||
'chain': chain_log,
|
||
}
|
||
Path(meta_path).write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding='utf-8')
|
||
|
||
return {
|
||
'right_three_quarter': results.get('right_three_quarter', ''),
|
||
'right_side': results.get('right_side', ''),
|
||
'back': results.get('back', ''),
|
||
'chain': chain_log,
|
||
'seed': final_seed,
|
||
}
|
||
|
||
|
||
def register_portraits(
|
||
char_name: str,
|
||
baseline_path: str,
|
||
angles: dict,
|
||
) -> bool:
|
||
"""Phase 3: 将定妆照注册到 CharacterRegistry。
|
||
|
||
参数:
|
||
char_name: 角色名
|
||
baseline_path: 基准图路径
|
||
angles: {"right_three_quarter": ..., "right_side": ..., "back": ...}
|
||
|
||
返回: 是否成功
|
||
"""
|
||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||
from character_registry import CharacterRegistry
|
||
|
||
try:
|
||
registry_path = os.path.join(
|
||
os.path.dirname(os.path.abspath(__file__)),
|
||
'character_registry',
|
||
'registry.yaml',
|
||
)
|
||
registry = CharacterRegistry()
|
||
|
||
# 注册 face_anchor
|
||
ref_images = {'face_anchor': [baseline_path], 'body': []}
|
||
|
||
# 注册 body 各角度
|
||
for key in ('right_three_quarter', 'right_side', 'back'):
|
||
if key in angles and angles[key]:
|
||
ref_images['body'].append({'path': angles[key], 'option': key, 'type': 'body'})
|
||
|
||
registry.update_reference_images(char_name, ref_images)
|
||
logger.info(f'✅ 已注册定妆照到 CharacterRegistry: {char_name}')
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.warning(f'注册到 CharacterRegistry 失败: {e}')
|
||
return False
|
||
|
||
|
||
def generate_and_register_portraits(
|
||
character_name: str,
|
||
description: str,
|
||
force: bool = False,
|
||
seed: int = 0,
|
||
ref_image: str = '',
|
||
) -> dict:
|
||
"""全流程: 基准图 → 多角度 → 注册。
|
||
|
||
参数:
|
||
character_name: 角色名（与 registry.yaml 一致）
|
||
description: 角色描述（自然语言）
|
||
force: 强制重新生成
|
||
seed: 固定种子
|
||
ref_image: 真实参考照路径。有则 I2I 生成基准图，无则 T2I
|
||
|
||
返回:
|
||
{
|
||
"character": "角色名",
|
||
"baseline": "path/to/baseline.png",
|
||
"angles": {"right_three_quarter": "...", "right_side": "...", "back": "..."},
|
||
"registry_updated": True/False,
|
||
"seed": 42,
|
||
}
|
||
"""
|
||
result = {
|
||
'character': character_name,
|
||
'baseline': '',
|
||
'angles': {},
|
||
'registry_updated': False,
|
||
'seed': seed,
|
||
}
|
||
|
||
logger.info(f'==================================================')
|
||
logger.info(f'🎯 定妆照全流程: {character_name}')
|
||
|
||
# Phase 1: 基准图
|
||
try:
|
||
logger.info('📷 Phase 1/2: 生成基准图（正面全身定妆照）...')
|
||
baseline_result = generate_baseline(
|
||
description=description,
|
||
char_name=character_name,
|
||
force=force,
|
||
seed=seed,
|
||
ref_image=ref_image,
|
||
)
|
||
baseline_path = baseline_result.get('baseline', '')
|
||
used_seed = baseline_result.get('seed', seed)
|
||
result['baseline'] = baseline_path
|
||
result['seed'] = used_seed
|
||
logger.info(f' 基准图: {baseline_path}')
|
||
logger.info(f' seed: {used_seed}')
|
||
except Exception as e:
|
||
logger.error(f' ❌ Phase 1 失败: {e}')
|
||
result['error'] = f'Phase 1 (基准图) 失败: {str(e)}'
|
||
return result
|
||
|
||
# Phase 2: 逐帧参考链
|
||
try:
|
||
logger.info('📸 Phase 2/2: 逐帧参考链生成多角度定妆照...')
|
||
logger.info(' 参考链: 正面 → 右侧45°半侧面 → 纯右侧面 → 背面')
|
||
logger.info(' 每张以上一张为 I2I 参考，强度 0.70→0.75→0.70')
|
||
angles = generate_sequential_chain(
|
||
baseline_path=baseline_path,
|
||
char_name=character_name,
|
||
force=force,
|
||
seed=used_seed,
|
||
)
|
||
result['angles'] = {
|
||
'right_three_quarter': angles.get('right_three_quarter', ''),
|
||
'right_side': angles.get('right_side', ''),
|
||
'back': angles.get('back', ''),
|
||
}
|
||
logger.info(f' 右侧45°半侧面: {result["angles"]["right_three_quarter"][:50] if result["angles"]["right_three_quarter"] else "失败"}')
|
||
logger.info(f' 纯右侧面: {result["angles"]["right_side"][:50] if result["angles"]["right_side"] else "失败"}')
|
||
logger.info(f' 背面: {result["angles"]["back"][:50] if result["angles"]["back"] else "失败"}')
|
||
except Exception as e:
|
||
logger.error(f' ❌ Phase 2 失败: {e}')
|
||
result['warning'] = f'Phase 2 (多角度) 部分失败: {str(e)}'
|
||
|
||
# Phase 3: 注册
|
||
try:
|
||
logger.info('📋 Phase 3/2: 注册到 CharacterRegistry...')
|
||
reg_ok = register_portraits(
|
||
char_name=character_name,
|
||
baseline_path=baseline_path,
|
||
angles=result['angles'],
|
||
)
|
||
result['registry_updated'] = reg_ok
|
||
logger.info(f' {"✅ 注册成功" if reg_ok else "⚠️ 注册失败"}')
|
||
except Exception as e:
|
||
logger.warning(f' 注册异常: {e}')
|
||
result['registry_updated'] = False
|
||
|
||
logger.info(f'✅ 定妆照生成完成: {character_name}')
|
||
return result
|
||
|
||
|
||
# ══════════════════════════════════════════════════════
|
||
# CLI 入口
|
||
# ══════════════════════════════════════════════════════
|
||
|
||
def cli(args: list):
|
||
"""CLI 入口。"""
|
||
import argparse
|
||
|
||
parser = argparse.ArgumentParser(description='两阶段定妆照生成（观鱼方案）')
|
||
parser.add_argument('--character', required=True, help='角色名')
|
||
parser.add_argument('--desc', type=str, default='', help='角色描述（自然语言）')
|
||
parser.add_argument('--force', action='store_true', default=False, help='强制重新生成')
|
||
parser.add_argument('--seed', type=int, default=0, help='固定种子')
|
||
parser.add_argument('--desc-file', type=str, default='', help='从文件读取角色描述')
|
||
parser.add_argument('--ref', type=str, default='', help='真实参考照路径（推荐，代替T2I）')
|
||
|
||
parsed = parser.parse_args(args)
|
||
|
||
desc = parsed.desc
|
||
if parsed.desc_file:
|
||
desc_file = Path(parsed.desc_file)
|
||
if desc_file.exists():
|
||
desc = desc_file.read_text(encoding='utf-8').strip()
|
||
|
||
result = generate_and_register_portraits(
|
||
character_name=parsed.character,
|
||
description=desc,
|
||
force=parsed.force,
|
||
seed=parsed.seed,
|
||
ref_image=parsed.ref,
|
||
)
|
||
|
||
print()
|
||
print('==================================================')
|
||
print('✅ 定妆照生成结果:')
|
||
print(f' 基准图: {result.get("baseline", "失败")}')
|
||
if result.get('angles'):
|
||
for key, label in [('right_three_quarter', '右侧45°半侧面'), ('right_side', '纯右侧面'), ('back', '背面')]:
|
||
print(f' {label}: {result["angles"].get(key, "失败")}')
|
||
print(f' seed: {result.get("seed", "N/A")}')
|
||
print(f' 已注册: {"✅" if result.get("registry_updated") else "❌"}')
|
||
|
||
if result.get('error'):
|
||
print(f' 错误: {result["error"]}')
|
||
if result.get('warning'):
|
||
print(f' 警告: {result["warning"]}')
|
||
|
||
|
||
if __name__ == '__main__':
|
||
import sys
|
||
cli(sys.argv[1:])
|