AI 深度专业电影级提示词与面部表情解耦控制：分层提示工程、AU 微表情体系、ComfyUI 工作流、批量脚本与生产故障复盘

【第一层：角色身份基底层（不写表情，锁定长相）】
示例：24岁东方女性，精致五官，标准定妆样貌，固定脸型，眼睛形状不变，五官轮廓保持一致，漫画电影写实风格
【第二层：AU微表情动作层（只写面部肌肉，核心控制情绪）】
示例：AU4眉毛内侧下压聚拢，AU7眼皮微微收紧，AU15唇角轻微向下，眼眶微微泛红，眼底湿润，强忍悲伤，嘴唇轻轻抿住，面部肌肉轻微紧绷
【第三层：身体姿态与视线神态层】
示例：头部微微低下，视线向下垂落，肩膀微微收紧，缓慢呼吸姿态
【第四层：电影镜头与光影氛围层（叙事氛围感）】
示例：电影分镜，50mm定焦镜头，浅景深，夜晚室内冷调侧逆光，柔和阴影，胶片颗粒，低饱和度，氛围感，电影布光
【第五层：画质与细节基底层】
示例：RAW照片质感，极高细节，皮肤细腻纹理，眼睛清晰反光点，电影级打光，8k，高分辨率
【负向提示词（分层负向）】
身份负向：五官变形，脸型改变，眼型改变，人物变成另一个人，相貌改变
表情负向：夸张大吼，过度扭曲面部，畸形嘴巴，不对称瞳孔，斜视，斗鸡眼
画质负向：模糊，噪点爆炸，水印，文字，多手指，崩坏肢体，黑块

film_expression_control/
├─config/
│   ├─expression_au_dict.yaml     # AU动作单元表情词典，情绪标签映射提示词
│   └─batch_exp_config.yaml       # 批量生成全局参数：各层提示词权重、denoise、FaceDetailer参数
├─workflow_template/
│   ├─img2img_film_expression_api.json    # API工作流：身份+AU表情分层+FaceDetailer面部修复
│   └─animate_diff_exp_api.json           # AnimateDiff视频生成表情约束工作流
├─assets/
│   └─char_ref/                   # IP‑Adapter角色定妆参考图
├─script/
│   └─shot_list_exp.json          # 带情绪标签的漫剧分镜脚本，每个shot携带emotion_tag
├─src/
│   ├─au_prompt_builder.py        # AU表情提示词构建工具，输入情绪tag输出分层prompt
│   ├─comfy_exp_batch.py          # 批量图生图脚本，读取分镜自动注入表情条件
│   ├─face_detailer_param.py      # FaceDetailer参数封装
│   └─export_expression_lib.py     # 导出全部表情模板库
├─output/
│   ├─storyboard_out/
│   └─log/
└─tools/
    └─check_expression_result.py  # 简易质量筛查工具，记录面部异常样本日志

[
    {
        "shot_id":1,
        "scene_desc":"深夜公寓，冷调侧光",
        "character":"女主林晚",
        "ref_img":"./assets/char_ref/linwan_01.png",
        "dialogue":"我已经不知道该相信谁了。",
        "emotion_tag":"suppress_sad",
        "camera":"中近景，人物面部占画面1/4，50mm镜头",
        "duration":3.5,
        "width":896,
        "height":1280,
        "motion_scale":0.28
    }
]

conda create -n film_exp python=3.10
conda activate film_exp
pip install pyyaml pillow requests tqdm

emotion_library:
  neutral:
    au_prompt: "面部肌肉放松，表情平静，眼皮自然，嘴唇闭合放松，无强烈情绪"
  suppress_sad:
    au_prompt: "AU4眉毛内侧向下聚拢，AU7眼皮微微收紧，AU15唇角轻微向下，眼眶微微泛红，眼底湿润，强忍悲伤，嘴唇轻轻抿住，面部肌肉轻微紧绷"
  forced_smile:
    au_prompt: "AU6脸颊抬升眼角挤压，AU15唇角轻微下压，强颜欢笑，笑容勉强，眼底带有悲伤，嘴角上扬但是眼神落寞"
  suppress_anger:
    au_prompt: "AU4眉头紧锁下压，AU23嘴唇紧紧抿住，面部下颌轻微紧绷，压抑内心愤怒，表面尽量维持平静，眼神沉郁"
  shock_surprise:
    au_prompt: "AU20嘴唇横向拉伸，AU25嘴唇微微分开，眉毛向上抬起，瞳孔轻微放大，错愕，意外震惊的神态"
negative_expression: "夸张大吼，面部严重扭曲，畸形嘴巴，不对称瞳孔，斜视，斗鸡眼，五官变形，脸型改变"

# src/au_prompt_builder.py
import yaml
class AuPromptBuilder:
    def __init__(self, dict_yaml_path):
        with open(dict_yaml_path,"r",encoding="utf‑8") as f:
            self.data = yaml.safe_load(f)
        self.lib = self.data["emotion_library"]
        self.neg_exp = self.data["negative_expression"]
    def build_layer_prompt(self, emotion_tag:str):
        """输入情绪tag，返回四层提示词字典"""
        base_identity = "24岁东方女性，精致五官，固定脸型，眼睛形状保持不变，五官轮廓保持一致，电影漫画写实风格"
        body_pose = "头部姿态自然，视线神态符合人物情绪，肩膀身体姿态自然"
        film_light = "电影分镜，50mm定焦镜头，浅景深，夜晚室内冷调侧逆光，柔和阴影，胶片颗粒，氛围感电影布光"
        quality = "RAW照片质感，极高细节，皮肤细腻纹理，眼睛清晰反光点，8k，高分辨率"
        if emotion_tag not in self.lib:
            raise KeyError(f"不存在情绪标签 {emotion_tag}")
        au_exp_text = self.lib[emotion_tag]["au_prompt"]
        layers = {
            "layer_identity":base_identity,
            "layer_expression":au_exp_text,
            "layer_body":body_pose,
            "layer_camera_light":film_light,
            "layer_quality":quality,
            "neg_expression":self.neg_exp
        }
        return layers
if __name__ == "__main__":
    builder = AuPromptBuilder("../config/expression_au_dict.yaml")
    res = builder.build_layer_prompt("suppress_sad")
    for k,v in res.items():
        print(f"{k}:{v}")

# src/comfy_exp_batch.py
import json
import os
from tqdm import tqdm
from au_prompt_builder import AuPromptBuilder
import requests
from PIL import Image
from io import BytesIO
COMFY_URL = "http://127.0.0.1:8188"
WORKFLOW_TPL = "../workflow_template/img2img_film_expression_api.json"
SHOT_JSON = "../script/shot_list_exp.json"
OUT_DIR = "../output/storyboard_out"
def upload_pil(pil_img):
    buf = BytesIO()
    pil_img.save(buf,format="PNG")
    r = requests.post(f"{COMFY_URL}/upload/image",files={"image":("tmp.png",buf.getvalue())})
    return r.json()["name"]
def load_workflow():
    with open(WORKFLOW_TPL,"r",encoding="utf‑8") as f:
        return json.load(f)
def inject_layer_condition(wf,layers):
    """动态向API工作流注入四层提示词文本，对应四个CLIPTextEncode节点"""
    for node_id,node in wf.items():
        meta_title = node.get("_meta",{}).get("title","").lower()
        cls = node.get("class_type","")
        if cls == "CLIPTextEncode":
            if "identity_layer" in meta_title:
                node["inputs"]["text"] = layers["layer_identity"]
            elif "expression_layer" in meta_title:
                node["inputs"]["text"] = layers["layer_expression"]
            elif "body_layer" in meta_title:
                node["inputs"]["text"] = layers["layer_body"]
            elif "camera_light_layer" in meta_title:
                node["inputs"]["text"] = layers["layer_camera_light"]
            elif "quality_layer" in meta_title:
                node["inputs"]["text"] = layers["layer_quality"]
            elif "negative_exp_layer" in meta_title:
                node["inputs"]["text"] = layers["neg_expression"]
    return wf
def submit_workflow(wf_dict):
    payload = {"prompt":wf_dict}
    resp = requests.post(f"{COMFY_URL}/prompt",json=payload)
    return resp.json()["prompt_id"]
def wait_result(pid,timeout=600):
    import time
    start = time.time()
    while time.time()-start < timeout:
        h = requests.get(f"{COMFY_URL}/history/{pid}").json()
        if pid in h:
            out = h[pid]["outputs"]
            img_bin_list = []
            for _,v in out.items():
                if "images" in v:
                    for info in v["images"]:
                        d = requests.get(f"{COMFY_URL}/view?filename={info['filename']}").content
                        img_bin_list.append(d)
            return img_bin_list
        time.sleep(1)
    raise TimeoutError("任务超时")
def main():
    os.makedirs(OUT_DIR,exist_ok=True)
    builder = AuPromptBuilder("../config/expression_au_dict.yaml")
    shot_all = json.load(open(SHOT_JSON,"r",encoding="utf‑8"))
    wf_template = load_workflow()
    for shot in tqdm(shot_all):
        emotion_tag = shot["emotion_tag"]
        layers_data = builder.build_layer_prompt(emotion_tag)
        wf = inject_layer_condition(wf_template.copy(),layers_data)
        #上传角色参考图给IP‑Adapter节点
        ref_img = Image.open(shot["ref_img"]).convert("RGB")
        ref_remote_name = upload_pil(ref_img)
        for nid,node in wf.items():
            if node.get("class_type","")=="LoadImage" and "ip_ref" in node.get("_meta",{}).get("title","").lower():
                node["inputs"]["image"] = ref_remote_name
        try:
            pid = submit_workflow(wf)
            bin_list = wait_result(pid)
            base_name = f"shot_{shot['shot_id']}_{emotion_tag}"
            for idx,bindata in enumerate(bin_list):
                out_path = os.path.join(OUT_DIR,f"{base_name}_{idx}.png")
                with open(out_path,"wb") as fw:
                    fw.write(bindata)
        except Exception as e:
            print(f"镜头{shot['shot_id']}生成失败：{str(e)}")
if __name__ == "__main__":
    main()