3218485270
f493bde8a9
- 缓存热点批量流程（有采集缓存不触发 Google） - 简报不足直接从采集缓存生成（轻量补齐） - 三图合成（模特/印花/底图）+ 底图压缩 <2MB - 热点去重→风格去重自动切换 + 不适合类目 review 兜底 - 透明背景（background=transparent）+ 提示词清洗（敏感词/背景描述） - 任务前 basemap 校验 + 模板国家校验 + 模特任务级分配
104 lines
5.1 KiB
Python
104 lines
5.1 KiB
Python
"""种草图（Seed Shot）生成。
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- 模板：configs/seed_shot_templates.yaml（可自定义，占位符 [商品名称]/[材质]/[模特特征]）
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- 模特特征：configs/model_features.yaml（可自定义，随机取一条）
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- 生成：以 product 合成图（图1）为参考，img2img 生成 N 张种草图（保留衣服外观、换场景/模特）
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- 占位替换：[商品名称]→cn_title（缺省回退 topic）；[材质]→SPU.material；[模特特征]→随机
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"""
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import random
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import yaml
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from graph.paths import project_root
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def _load_yaml(rel: str) -> Dict[str, Any]:
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for root in (project_root(),):
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p = root / rel
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if p.exists():
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try:
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return yaml.safe_load(p.read_text(encoding="utf-8")) or {}
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except Exception as e: # noqa: BLE001
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print(f"[seed_shot] 读取 {rel} 失败: {e}")
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return {}
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def load_templates() -> List[Dict[str, str]]:
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"""种草图提示词模板列表（无配置时给内置兜底）。"""
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data = _load_yaml("configs/seed_shot_templates.yaml")
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tpls = data.get("seed_shot_templates") or []
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if not tpls:
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tpls = [{
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"name": "default",
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"prompt": (
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"【最高优先级约束】请严格保留参考图1中模特所穿衣服的完整外观，包括其原有的颜色、"
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"印花图案、版型款式、材质纹理与缝线细节，绝对禁止对衣服本身进行任何形式的修改、"
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"重新设计、变色或改变图案。仅提取这件[商品名称]，将其穿在一位[模特特征]的身上。"
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"全身动态抓拍构图，行走在阳光斑驳的城市林荫道上，微微低头微笑，凸显[材质]的透气与百搭。"
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"徕卡Q2摄影质感，高对比度色彩，35mm镜头，f/1.7大光圈，8k分辨率。"
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),
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}]
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return [{"name": str(t.get("name", "default")), "prompt": str(t.get("prompt", ""))}
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for t in tpls if t.get("prompt")]
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def load_model_features() -> List[str]:
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"""模特特征列表（无配置时给内置兜底）。"""
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data = _load_yaml("configs/model_features.yaml")
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feats = [str(f) for f in (data.get("model_features") or []) if str(f).strip()]
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if not feats:
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feats = ["20岁清新少女，素颜通透感", "25岁都市职场女性，干练气质"]
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return feats
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def load_style_features() -> List[str]:
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"""服装风格列表（style_features.yaml，随机取一条替换 [服装风格]；无配置时内置兜底）。"""
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data = _load_yaml("configs/style_features.yaml")
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feats = [str(f) for f in (data.get("style_features") or []) if str(f).strip()]
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if not feats:
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feats = ["极简基础款风格，干净纯粹，无过多繁复装饰",
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"日系City Boy/Girl风，微宽松版型，注重舒适度与层次感"]
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return feats
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def render_prompt(template_prompt: str, cn_title: str, material: str, model_feature: str,
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style_feature: str = "") -> str:
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"""占位替换：[商品名称]/[材质]/[模特特征]/[服装风格]"""
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out = template_prompt.replace("[商品名称]", (cn_title or "").strip() or "这件衣服")
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out = out.replace("[材质]", (material or "").strip() or "面料")
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out = out.replace("[模特特征]", (model_feature or "").strip() or "模特")
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out = out.replace("[服装风格]", (style_feature or "").strip() or "日常休闲风")
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return out
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def generate_seed_shots(image_backend, base_image: str, cn_title: str, material: str,
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count: int, out_dir: str, negative: str = "",
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size: str = "1504x2000", prefix: str = "") -> List[str]:
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"""生成 count 张种草图（img2img，图1=合成图）。返回产物路径列表。
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size: 种草图统一 1504x2000。
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prefix: 货号前缀（对应产品货号，命名 {prefix}_seedshot_{n}.png，不覆盖旧文件）。
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占位符 [商品名称]/[材质]/[模特特征]/[服装风格] 均随机组合（模板/模特/服装风格各随机取一条）。"""
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templates = load_templates()
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features = load_model_features()
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style_features = load_style_features()
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out = Path(out_dir)
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out.mkdir(parents=True, exist_ok=True)
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paths: List[str] = []
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for i in range(count):
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tpl = random.choice(templates)
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feat = random.choice(features)
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style_feat = random.choice(style_features)
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prompt = render_prompt(tpl["prompt"], cn_title, material, feat, style_feat)
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out_path = str(out / f"{prefix}_seedshot_{i + 1:02d}.png" if prefix
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else out / f"seed_shot_{i + 1:02d}.png")
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try:
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image_backend.print(prompt, base_image, out_path, negative, size=size)
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paths.append(out_path)
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print(f"[seed_shot] 已生成种草图 {i + 1}/{count}: {out_path}"
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f"（模板={tpl['name']}，模特={feat[:14]}…，风格={style_feat[:14]}…）")
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except Exception as e: # noqa: BLE001
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print(f"[seed_shot] 种草图 {i + 1} 生成失败: {e}")
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return paths
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