Nano Banana Pro Emoji Sticker Batch Generation Complete Guide: Create 50 Custom Stickers in 5 Minutes, Essential for Community Operations

作者API易 - 免费试用 Claude&OpenAI API

In community operations and brand marketing, operators often face the challenge of "needing a large number of stickers to enliven community atmosphere and enhance brand communication, but manual design by designers is costly, time-consuming, and unable to quickly respond to trending topics."The root cause of this problem is that traditional sticker creation requires professional design skills, with each sticker taking more than 30 minutes to create, and a set of 50 stickers may take weeks to complete.This article will provide an in-depth analysis of Nano Banana Pro emoji sticker batch generation technology implementation principles and provide a proven method for quickly creating 50 custom stickers in 5 minutes.

nano-banana-pro-emoji-sticker-batch-generation-guide-en 图示

Nano Banana Pro Emoji Sticker Generation Technology Principles

Nano Banana Pro (Gemini 3 Pro Image) 在2025年11月发布时,引入了业界领先的Character Consistency和风格化渲染技术。这项技术能够在保持同一角色外观一致的前提下,Batch Generation不同表情和动作的系列图像,彻底解决了传统AI模型在Stickers生成中的"角色漂移"和"风格不统一"问题。

Character Consistency and Style Locking

Nano Banana Pro 基于 Gemini 3 Pro 的Reference Images理解能力,能够精准复刻角色特征并批量应用:

Character Feature Extraction: 模型能够从1-3张Reference Images中提取角色的核心视觉特征,包括面部轮廓、五官比例、发型、肤色、服装风格等,准确率达到95%+

Stylized Rendering Control: 支持多种Stickers风格,包括卡通漫画风(粗线条、鲜艳配色)、3D可爱风(Q版、圆润造型)、像素风(复古游戏风格)、手绘风(水彩、素描质感)等

Expression Action Library: 内置50+种常见表情动作模板,包括喜怒哀乐、社交礼仪、网络流行梗等,可一键套用到自定义角色上

Batch Generation Optimization: By intelligently caching reference features, character consistency is maintained when generating multiple expressions while optimizing generation speed, with an average generation time of only 5-8 seconds per expression

🎯 Technical Recommendation: In actual sticker creation projects, we recommend testing Nano Banana Pro sticker generation functionality through the APIYI apiyi.com platform. The platform provides complete batch generation API interfaces, supports character reference image uploads and expression template selection, helping to quickly verify character consistency effects and generation quality.

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表情语义理解与夸张化表达

Nano Banana Pro 使用情绪识别技术,将抽象的情绪描述转化为具有视觉冲击力的表情:

情绪强度映射:

  • 轻度开心 → 微笑、嘴角上扬、眼睛弯曲
  • 中度开心 → 露齿笑、眉毛上挑、脸颊泛红
  • 强烈开心 → 哈哈大笑、眼泪飞出、手舞足蹈
  • 极度开心 → 笑到变形、夸张肢体语言、特效元素

文化梗理解: 支持中文网络流行语和表情梗,如"我裂开了"(角色碎裂效果)、"emo了"(阴郁氛围)、"冲冲冲"(动作线和火焰特效)、"摸鱼"(懒散姿态+鱼元素)等

肢体语言设计: 自动添加符合表情的肢体动作,如生气时的握拳、委屈时的抹泪、疑惑时的挠头、得意时的双手叉腰等

Visual Enhancement Elements: Intelligently add emoji auxiliary elements such as heart bubbles, angry flames, starry eye effects, sweat drops, question/exclamation marks, etc., to enhance expression effectiveness

💡 Selection Recommendation: For community operations and brand marketing teams that need to quickly respond to trending topics, we recommend prioritizing Nano Banana Pro's sticker batch generation functionality. When calling through the APIYI apiyi.com platform, you can directly input trending keywords and brand characters. The platform supports automatic matching of expression actions and internet memes, enabling complete sticker sets to be generated in 5 minutes.

Method for Batch Generating 50 Expressions in 5 Minutes

第 1 步: Prepare CharacterReference Images和风格定位

核心原则: 提供1-3张清晰的角色Reference Images,明确Stickers的视觉风格和目标受众。

角色Reference Images要求:

  1. 正面清晰图: 展示角色的完整面部特征(必需)
  2. 不同角度图: 侧面或3/4侧面图(可选,提升一致性)
  3. 全身比例图: 展示角色身体比例和服装(可选,用于全身表情)

风格选择矩阵:

风格类型 适用场景 视觉特征 目标受众
卡通漫画风 品牌IP、社群互动 粗线条、鲜艳配色、夸张变形 年轻用户、泛娱乐
3D可爱风 儿童产品、萌系品牌 Q版圆润、立体质感、软萌配色 女性用户、低龄群体
像素复古风 游戏社区、科技品牌 8bit风格、复古色板、简化造型 游戏玩家、程序员
手绘水彩风 文艺品牌、个人IP 水彩晕染、柔和笔触、清新色调 文艺青年、高知群体
简约扁平风 企业品牌、工具产品 纯色块、极简线条、克制配色 商务人群、效率工具

实战案例: 为"柴柴日记"IP生成50个Stickers

import google.generativeai as genai
import PIL.Image

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel('gemini-3-pro-image-preview')

# 第1步:Prepare CharacterReference Images
character_name = "柴柴"
character_description = """
角色名称: 柴柴 (Chai Chai)
角色类型: 柴犬拟人化IP
核心特征:
- 物种: 柴犬,橙黄色毛发,白色腹部
- 造型: Q版2头身比例,大头小身体
- 面部: 圆脸,黑豆眼,小鼻子,标志性微笑
- 服装: 红色围巾(冬天)/绿色背心(夏天)
- 气质: 呆萌、治愈、正能量
"""

# 加载Reference Images
character_refs = [
    PIL.Image.open("chaichai_front.png"),    # 正面参考
    PIL.Image.open("chaichai_side.png"),     # 侧面参考
    PIL.Image.open("chaichai_body.png")      # 全身参考
]

# 风格定位
style_guide = """
Stickers风格: 卡通漫画风
- 粗线条黑色描边
- 鲜艳纯色填充(橙、黄、红、绿)
- 夸张可爱的表情变形
- 简单干净的背景(纯色或简单图案)
- 目标平台: 微信/QQ/Line
- 尺寸: 正方形,适合聊天窗口展示
"""

print("Step 1 Complete: 角色参考和风格定位准备完成")
print(f"角色: {character_name}")
print(f"Reference Images数量: {len(character_refs)}")

Reference Images优化技巧:

  • Reference Images背景尽量纯色或简洁,避免干扰
  • 角色占画面60-80%,太小会导致细节丢失
  • 光线均匀,避免强烈阴影影响特征识别
  • 如果是真人照片转Stickers,建议先使用卡通化功能预处理

🚀 Quick Start: We recommend using the APIYI apiyi.com platform's character preset functionality. The platform provides 100+ common IP type presets (small animals, workplace characters, anime characters, etc.), allowing you to directly select similar characters as a starting point. After fine-tuning, you can begin generation without preparing reference images from scratch, entering the generation phase in just 1 minute.

第 2 步: 定义50种表情动作List

核心原则: 覆盖日常社交沟通的高频场景,确保Stickers实用性和传播力。

50种表情分类体系:

情绪类(15个):

  • 😊 开心类: 微笑、大笑、坏笑、偷笑、得意
  • 😭 难过类: 哭泣、委屈、郁闷、emo、心碎
  • 😡 愤怒类: 生气、暴怒、不满、鄙视、无语
  • 😱 惊讶类: 震惊、害怕、惊吓
  • 🤔 疑惑类: 疑问、思考

动作类(15个):

  • 👋 社交礼仪: 打招呼、再见、点赞、比心、鼓掌
  • 🙏 请求表达: 拜托、感谢、道歉、祈祷
  • 🎉 庆祝类: 庆祝、撒花、干杯
  • 💤 日常状态: 睡觉、吃饭、喝水
  • 💪 鼓励类: 加油、冲鸭

梗表达类(10个):

  • 网络流行: 我裂开了、摸鱼、打工人、内卷、躺平
  • 社交场景: 尬聊、社恐、装死、溜了溜了、别骂了

氛围类(10个):

  • 节日节气: 新年快乐、生日快乐、圣诞、情人节
  • 时间问候: 早安、晚安、午安、周末愉快、节日快乐、周一综合症

代码实现:

# 第2步:定义50种表情List
expressions_library = {
    "情绪类": {
        "开心系": [
            {"name": "微笑", "description": "嘴角上扬,眼睛眯成月牙,双手抱在胸前", "intensity": "轻度"},
            {"name": "大笑", "description": "张大嘴巴哈哈笑,眼泪飞出,捂肚子", "intensity": "强烈"},
            {"name": "坏笑", "description": "嘴角斜勾,眼神狡黠,单手摸下巴", "intensity": "中度"},
            {"name": "偷笑", "description": "用手捂嘴偷笑,眼神往旁边瞟,脸颊泛红", "intensity": "轻度"},
            {"name": "得意", "description": "双手叉腰,挺胸抬头,自豪的笑容", "intensity": "中度"}
        ],
        "难过系": [
            {"name": "哭泣", "description": "眼泪如瀑布流下,嘴巴张成O形,双手抹泪", "intensity": "强烈"},
            {"name": "委屈", "description": "眼眶含泪,嘟嘴,低头,身体缩成一团", "intensity": "中度"},
            {"name": "郁闷", "description": "眉头紧皱,眼神空洞,脸颊有阴影,叹气泡泡", "intensity": "中度"},
            {"name": "emo", "description": "全身变灰黑色,头顶乌云,周围飘散碎片", "intensity": "强烈"},
            {"name": "心碎", "description": "胸口出现碎裂的心形,眼神黯淡,身体裂纹", "intensity": "强烈"}
        ],
        "愤怒系": [
            {"name": "生气", "description": "眉毛竖起,嘴巴向下,头顶冒烟,握紧拳头", "intensity": "中度"},
            {"name": "暴怒", "description": "面部涨红,青筋暴起,头顶火焰,大吼状", "intensity": "强烈"},
            {"name": "不满", "description": "翻白眼,撇嘴,双手抱臂,转身背对", "intensity": "轻度"},
            {"name": "鄙视", "description": "斜眼看,嘴角下撇,鼻孔朝天,单手挥手势", "intensity": "中度"}
        ],
        "惊讶系": [
            {"name": "震惊", "description": "眼睛瞪大如铜铃,嘴巴张成O形,头发竖起", "intensity": "强烈"},
            {"name": "害怕", "description": "瑟瑟发抖,冷汗直流,身体缩成一团,脸色发白", "intensity": "强烈"}
        ],
        "疑惑系": [
            {"name": "疑问", "description": "歪头,眉毛一高一低,头顶问号,手指托下巴", "intensity": "轻度"},
            {"name": "思考", "description": "托腮沉思,眉头微皱,眼神专注,头顶灯泡", "intensity": "轻度"}
        ]
    },
    "动作类": {
        "社交礼仪": [
            {"name": "打招呼", "description": "单手高举挥手,笑容满面,身体前倾"},
            {"name": "再见", "description": "挥手告别,转身离开姿势,依依不舍表情"},
            {"name": "点赞", "description": "竖起大拇指,自信笑容,眼睛发光"},
            {"name": "比心", "description": "双手在头顶比心形,甜美笑容,周围爱心泡泡"},
            {"name": "鼓掌", "description": "双手拍掌,开心表情,音符和掌声效果"}
        ],
        "请求表达": [
            {"name": "拜托", "description": "双手合十祈求,眼睛闪亮,脸颊泛红,可怜表情"},
            {"name": "感谢", "description": "双手合十鞠躬,感激笑容,周围感谢文字"},
            {"name": "道歉", "description": "低头鞠躬90度,愧疚表情,冷汗,对不起文字"},
            {"name": "祈祷", "description": "闭眼双手合十,虔诚表情,周围圣光效果"}
        ],
        "庆祝类": [
            {"name": "庆祝", "description": "双手举起欢呼,蹦跳姿势,五彩纸屑飘落"},
            {"name": "撒花", "description": "双手抛洒鲜花,开心表情,花瓣飞舞"},
            {"name": "干杯", "description": "单手举杯,笑容满面,酒杯碰撞特效"}
        ],
        "日常状态": [
            {"name": "睡觉", "description": "闭眼侧躺,呼噜气泡,周围Z字符,安详表情"},
            {"name": "吃饭", "description": "双手捧碗,大口吃饭,脸颊鼓起,满足表情"},
            {"name": "喝水", "description": "双手捧杯,仰头喝水,满足表情,水花效果"}
        ],
        "鼓励类": [
            {"name": "加油", "description": "握拳挥臂,斗志昂扬,眼神坚定,周围火焰"},
            {"name": "冲鸭", "description": "奔跑姿势,速度线,小鸭子元素,冲字特效"}
        ]
    },
    "梗表达类": {
        "网络流行": [
            {"name": "我裂开了", "description": "角色身体出现裂纹,碎片飘散,夸张崩溃表情"},
            {"name": "摸鱼", "description": "懒散姿态,手里抱着鱼,偷偷摸摸的表情"},
            {"name": "打工人", "description": "疲惫表情,黑眼圈,工牌,搬砖姿势"},
            {"name": "内卷", "description": "疯狂学习/工作姿势,周围书本和文件,头晕目眩"},
            {"name": "躺平", "description": "平躺放弃姿势,眼神空洞,周围杂草生长"}
        ],
        "社交场景": [
            {"name": "尬聊", "description": "尴尬笑容,冷汗直流,周围空气凝固效果"},
            {"name": "社恐", "description": "缩成一团,瑟瑟发抖,躲在角落,虚弱表情"},
            {"name": "装死", "description": "倒地不动,眼睛画X,灵魂飘出"},
            {"name": "溜了溜了", "description": "快速逃跑姿势,冲刺速度线,烟尘效果"}
        ]
    },
    "氛围类": {
        "节日": [
            {"name": "新年快乐", "description": "穿红色新衣,放鞭炮动作,福字和红包元素"},
            {"name": "生日快乐", "description": "头戴生日帽,蛋糕和蜡烛,开心庆祝"},
            {"name": "圣诞快乐", "description": "圣诞帽,圣诞树,礼物盒,雪花飘落"},
            {"name": "情人节", "description": "手捧玫瑰,害羞表情,爱心满屏"}
        ],
        "时间问候": [
            {"name": "早安", "description": "伸懒腰,睡眼朦胧,太阳升起,清晨氛围"},
            {"name": "晚安", "description": "穿睡衣,困倦表情,月亮星星,宁静氛围"},
            {"name": "午安", "description": "打盹姿势,阳光温暖,午后慵懒氛围"},
            {"name": "周末愉快", "description": "放松开心,度假装扮,沙滩/咖啡元素"},
            {"name": "周一综合症", "description": "疲惫不堪,黑眼圈,拖着身体,崩溃表情"}
        ]
    }
}

# 展平为50个表情List
all_expressions = []
for category, subcategories in expressions_library.items():
    for subcategory, expressions in subcategories.items():
        for expr in expressions:
            all_expressions.append({
                "category": category,
                "subcategory": subcategory,
                **expr
            })

print(f"Step 2 Complete: 已定义 {len(all_expressions)} 种表情")
for i, expr in enumerate(all_expressions[:5], 1):
    print(f"{i}. {expr['name']} ({expr['category']} - {expr['subcategory']})")
print("...")

Expression List Customization Tips:

  • Adjust expression proportions based on target audience (add more internet memes for young users, more etiquette for business users)
  • Analyze high-frequency phrases in target communities to customize exclusive meme expressions
  • Reserve 5-10 slots for subsequent trending additions
  • Regularly update and iterate, eliminating low-usage expressions

💰 Cost Optimization: For small teams and individual creators with limited budgets, consider calling the Nano Banana Pro API through the APIYI apiyi.com platform. The platform provides a sticker template library containing 50 categorized common expression templates that can be directly applied without manually writing expression descriptions, saving 80% of preparation time and suitable for rapid market testing.

Step 3: Batch Generate Sticker Images

核心原则: 利用对话式生成Maintain Character Consistency,Batch Generation50个表情的PNG图像。

Batch Generation策略:

import time
from pathlib import Path

# 第3步:Batch GenerationStickers
output_dir = Path("chaichai_sticker_pack")
output_dir.mkdir(exist_ok=True)

# 基础提示词模板
base_prompt_template = """
Create a cute sticker/emoji of the character "{character_name}":

CHARACTER REFERENCE (CRITICAL - MUST MAINTAIN):
{character_description}
Use the provided reference images to ensure exact character consistency.

STICKER SPECIFICATIONS:
- Style: {style}
- Emotion/Action: {emotion_name}
- Description: {emotion_description}
- Exaggeration level: {intensity}

TECHNICAL REQUIREMENTS:
- Format: Sticker/emoji suitable for messaging apps
- Background: Transparent OR simple solid color
- Composition: Character centered, occupying 70-80% of frame
- Expression: Exaggerated and clear for small size display
- Resolution: 512x512 (suitable for WeChat/LINE/Telegram)

OUTPUT REQUIREMENTS:
- Character appearance MUST match reference images EXACTLY
- Expression should be instantly recognizable
- Clean lines and bold colors for clarity
- Appropriate visual effects (hearts, stars, sweat drops, etc.)
"""

# 初始化对话会话(Maintain Character Consistency)
chat_session = model.start_chat()

# 首次生成(建立角色基准)
first_expr = all_expressions[0]
first_prompt = base_prompt_template.format(
    character_name=character_name,
    character_description=character_description,
    style=style_guide,
    emotion_name=first_expr['name'],
    emotion_description=first_expr['description'],
    intensity=first_expr.get('intensity', '中度')
)

print(f"正在生成第1个表情: {first_expr['name']}...")

response = chat_session.send_message(
    [first_prompt] + character_refs
)

first_image = response.images[0]
first_image.save(output_dir / f"001_{first_expr['name']}.png")
print(f"✓ 完成: 001_{first_expr['name']}.png")

# Batch Generation剩余49个表情
for i, expr in enumerate(all_expressions[1:], start=2):
    # 简化后续提示词(利用对话历史保持一致性)
    follow_up_prompt = f"""
Great! Now create the next sticker with the SAME character:

Emotion/Action: {expr['name']}
Description: {expr['description']}
Intensity: {expr.get('intensity', '中度')}

IMPORTANT:
- Keep the character appearance EXACTLY the same as previous stickers
- Only change the facial expression and body pose
- Maintain the same art style and quality
"""

    print(f"正在生成第{i}个表情: {expr['name']}...")

    try:
        response = chat_session.send_message(follow_up_prompt)
        image = response.images[0]
        filename = f"{i:03d}_{expr['name']}.png"
        image.save(output_dir / filename)
        print(f"✓ 完成: {filename}")

        # 控制生成速度,避免API限流
        time.sleep(2)

    except Exception as e:
        print(f"✗ 失败: {expr['name']} - {str(e)}")
        continue

    # 每生成10个暂停一下,检查质量
    if i % 10 == 0:
        print(f"\n--- 已完成 {i}/50 Stickers表情,暂停检查质量 ---\n")
        time.sleep(5)

print(f"\nStep 3 Complete: Batch Generation完成!")
print(f"成功生成: {len(list(output_dir.glob('*.png')))} 个表情")
print(f"输出目录: {output_dir.absolute()}")

Batch Generation优化技巧:

分批次生成策略:

# 将50个表情分为5批,每批10个
batch_size = 10
total_batches = (len(all_expressions) + batch_size - 1) // batch_size

for batch_idx in range(total_batches):
    start_idx = batch_idx * batch_size
    end_idx = min(start_idx + batch_size, len(all_expressions))
    batch_expressions = all_expressions[start_idx:end_idx]

    print(f"\n=== 开始第 {batch_idx + 1}/{total_batches} 批次 ===")
    print(f"表情范围: {start_idx + 1}-{end_idx}")

    # 每批次重新启动对话会话(避免上下文过长)
    if batch_idx > 0:
        chat_session = model.start_chat()
        # 重新发送Reference Images建立基准
        chat_session.send_message([base_prompt_template.format(...)] + character_refs)

    # 生成本批次表情
    for expr in batch_expressions:
        # ... 生成代码 ...
        pass

    print(f"=== 第 {batch_idx + 1} 批次完成 ===\n")

质量检查与重新生成:

def check_sticker_quality(image_path, character_refs):
    """
    检查生成的Stickers质量
    """
    from PIL import Image
    import imagehash

    img = Image.open(image_path)

    # 1. 分辨率检查
    width, height = img.size
    resolution_ok = (width >= 512 and height >= 512)

    # 2. 透明度检查(是否有alpha通道)
    has_transparency = img.mode in ('RGBA', 'LA') or (img.mode == 'P' and 'transparency' in img.info)

    # 3. 角色相似度检查(使用感知哈希)
    ref_hash = imagehash.average_hash(character_refs[0])
    sticker_hash = imagehash.average_hash(img)
    similarity = 1 - (ref_hash - sticker_hash) / 64.0

    consistency_ok = similarity > 0.7  # 相似度阈值

    result = {
        "resolution_ok": resolution_ok,
        "has_transparency": has_transparency,
        "character_consistency": similarity,
        "pass": resolution_ok and consistency_ok
    }

    return result

# 批量质检
failed_stickers = []
for sticker_path in output_dir.glob("*.png"):
    result = check_sticker_quality(sticker_path, character_refs)
    if not result["pass"]:
        failed_stickers.append({
            "file": sticker_path.name,
            "reason": "低一致性" if not result["character_consistency"] > 0.7 else "低分辨率"
        })
        print(f"⚠️  {sticker_path.name}: 质量不达标,需要重新生成")

# 重新生成不合格的表情
if failed_stickers:
    print(f"\n需要重新生成 {len(failed_stickers)} 个表情")
    for item in failed_stickers:
        # ... 重新生成代码 ...
        pass

🎯 Quality Assurance Recommendation: The APIYI apiyi.com platform provides automatic sticker quality detection functionality, including character consistency analysis, expression clarity assessment, size specification checks, etc. The system automatically flags unqualified expressions and provides optimization suggestions, ensuring all 50 expressions in the set are 100% compliant with publishing standards without manual individual checks.

第 4 步: Add TextLabels和后期优化

核心原则: 为表情添加简洁有力的文字Labels,增强表达效果和传播力。

文字Labels策略:

from PIL import Image, ImageDraw, ImageFont

# 第4步:为表情Add TextLabels
def add_text_to_sticker(image_path, text, output_path, font_path="simhei.ttf"):
    """
    为StickersAdd TextLabels
    """
    img = Image.open(image_path)
    width, height = img.size

    # 创建绘图对象
    draw = ImageDraw.Draw(img)

    # 字体设置(根据Stickers大小自适应)
    font_size = int(height * 0.15)  # 字体大小为图片高度的15%
    try:
        font = ImageFont.truetype(font_path, font_size)
    except:
        font = ImageFont.load_default()

    # 计算文字位置(居中底部)
    bbox = draw.textbbox((0, 0), text, font=font)
    text_width = bbox[2] - bbox[0]
    text_height = bbox[3] - bbox[1]

    x = (width - text_width) / 2
    y = height - text_height - int(height * 0.08)  # 距离底部8%

    # 添加描边效果(增强可读性)
    outline_width = max(2, int(font_size * 0.08))
    for adj_x in range(-outline_width, outline_width + 1):
        for adj_y in range(-outline_width, outline_width + 1):
            draw.text((x + adj_x, y + adj_y), text, font=font, fill='black')

    # 绘制主文字(白色)
    draw.text((x, y), text, font=font, fill='white')

    # 保存
    img.save(output_path)

# 为每个表情添加对应文字
text_labeled_dir = output_dir / "with_text"
text_labeled_dir.mkdir(exist_ok=True)

for i, expr in enumerate(all_expressions, 1):
    input_path = output_dir / f"{i:03d}_{expr['name']}.png"
    output_path = text_labeled_dir / f"{i:03d}_{expr['name']}_文字版.png"

    if input_path.exists():
        add_text_to_sticker(
            input_path,
            expr['name'],  # 使用表情名称作为文字
            output_path
        )
        print(f"✓ Add Text: {expr['name']}")

print(f"\nStep 4 Complete: 文字Labels完成!")

后期优化选项:

# 批量调整尺寸(适配不同平台)
platform_sizes = {
    "微信": (240, 240),
    "QQ": (200, 200),
    "Telegram": (512, 512),
    "Line": (370, 320),
    "Discord": (128, 128)
}

for platform, size in platform_sizes.items():
    platform_dir = output_dir / platform
    platform_dir.mkdir(exist_ok=True)

    for sticker_path in text_labeled_dir.glob("*.png"):
        img = Image.open(sticker_path)
        img_resized = img.resize(size, Image.Resampling.LANCZOS)
        img_resized.save(platform_dir / sticker_path.name)

    print(f"✓ 已生成 {platform} 平台尺寸 ({size[0]}x{size[1]})")

# 批量添加水印(版权保护)
def add_watermark(image_path, watermark_text, output_path):
    """添加半透明水印"""
    img = Image.open(image_path).convert("RGBA")

    # 创建水印层
    watermark = Image.new("RGBA", img.size, (0, 0, 0, 0))
    draw = ImageDraw.Draw(watermark)

    # 水印文字(小字号,低透明度)
    font_size = int(img.height * 0.08)
    font = ImageFont.load_default()

    # 绘制水印(右下角)
    draw.text(
        (img.width - 100, img.height - 30),
        watermark_text,
        font=font,
        fill=(255, 255, 255, 80)  # 白色,30%透明度
    )

    # 合并
    watermarked = Image.alpha_composite(img, watermark)
    watermarked.save(output_path)

# 为所有表情添加水印
watermark_text = "@柴柴日记"
for sticker_path in text_labeled_dir.glob("*.png"):
    output_path = output_dir / "watermarked" / sticker_path.name
    output_path.parent.mkdir(exist_ok=True)
    add_watermark(sticker_path, watermark_text, output_path)

print("✓ 水印添加完成")

文字Labels最佳实践:

  • 文字简短有力,2-4个字最佳,最多不超过6个字
  • 使用粗体字体,确保小尺寸下清晰可读
  • 添加黑色描边或阴影,在任何背景下都清晰
  • 文字位置统一(全部底部或全部顶部),保持视觉一致性
  • 可选择不Add Text,制作纯图版和文字版两套

第 5 步: Package & Publish和Distribution

核心原则: 整理文件,制作预览图,上传到目标平台进行发布。

打包整理:

import zipfile
import shutil

# 第5步:打包和发布准备
release_dir = Path("chaichai_sticker_pack_release")
release_dir.mkdir(exist_ok=True)

# 创建发布包结构
folders = {
    "纯图版": text_labeled_dir.parent,
    "文字版": text_labeled_dir,
    "微信版": output_dir / "微信",
    "QQ版": output_dir / "QQ",
    "Telegram版": output_dir / "Telegram"
}

for folder_name, folder_path in folders.items():
    if folder_path.exists():
        target = release_dir / folder_name
        if target.exists():
            shutil.rmtree(target)
        shutil.copytree(folder_path, target)

# 生成预览图(九宫格)
def create_preview_grid(sticker_dir, output_path, grid_size=(3, 3)):
    """创建Stickers预览九宫格"""
    sticker_files = sorted(list(sticker_dir.glob("*.png")))[:grid_size[0] * grid_size[1]]

    if not sticker_files:
        return

    # 加载第一张确定单个尺寸
    first_img = Image.open(sticker_files[0])
    cell_size = first_img.size[0]

    # 创建大画布
    grid_width = cell_size * grid_size[0]
    grid_height = cell_size * grid_size[1]
    grid_image = Image.new('RGBA', (grid_width, grid_height), (255, 255, 255, 0))

    # 粘贴各个表情
    for idx, sticker_file in enumerate(sticker_files):
        img = Image.open(sticker_file)
        row = idx // grid_size[0]
        col = idx % grid_size[0]
        x = col * cell_size
        y = row * cell_size
        grid_image.paste(img, (x, y), img if img.mode == 'RGBA' else None)

    grid_image.save(output_path)

# 生成各版本预览图
create_preview_grid(
    text_labeled_dir,
    release_dir / "预览图_九宫格.png",
    grid_size=(3, 3)
)

# 创建压缩包
zip_path = Path(f"柴柴Stickers_{datetime.now().strftime('%Y%m%d')}.zip")
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
    for root, dirs, files in os.walk(release_dir):
        for file in files:
            file_path = Path(root) / file
            arcname = file_path.relative_to(release_dir)
            zipf.write(file_path, arcname)

print(f"\nStep 5 Complete: 发布包已准备完成!")
print(f"发布目录: {release_dir.absolute()}")
print(f"压缩包: {zip_path.absolute()}")
print(f"压缩包大小: {zip_path.stat().st_size / 1024 / 1024:.2f} MB")

平台上传指南:

微信表情开放平台:

  1. 访问「表情开放平台」sticker.weixin.qq.com
  2. 注册设计师账号,填写个人/企业信息
  3. 点击"上传表情",选择"静态表情"
  4. 批量上传PNG文件(建议240×240尺寸)
  5. 填写Stickers名称、简介、标签
  6. 提交审核(通常3-7个工作日)

QQ表情商店:

  1. 访问「QQ表情开放平台」e.qq.com
  2. 注册成为表情设计师
  3. 上传Stickers(200×200或240×240)
  4. 设置免费/付费(付费可分成)
  5. 等待审核通过

Telegram Stickers:

  1. 与 @Stickers 机器人对话
  2. 发送 /newpack 创建新Stickers
  3. 逐个上传PNG文件(512×512,背景透明)
  4. 为每个表情设置emoji对应
  5. 发送 /publish 完成发布
  6. 获得分享链接 t.me/addstickers/your_pack_name

Line Stickers:

  1. 访问 Line Creators Market creator.line.me
  2. 注册创作者账号
  3. 上传Stickers(370×320主图 + 96×74缩略图)
  4. 填写包名、说明、价格
  5. 提交审核(通常1-2周)

💡 Publishing Recommendation: Through the APIYI apiyi.com platform's one-click publishing feature, you can simultaneously submit stickers to multiple platforms such as WeChat, QQ, Telegram, etc., automatically adapting to each platform's size requirements and format specifications. The platform also provides review progress tracking and data analysis to help optimize sticker dissemination effectiveness.

nano-banana-pro-emoji-sticker-batch-generation-guide-en 图示

Nano Banana Pro Stickers应用场景

场景 1: 社群运营与活跃度提升

社群运营者需要专属Stickers增强成员归属感和活跃度。

应用价值:

  • 提升社群辨识度:专属Stickers成为社群文化符号
  • 增强用户粘性:成员使用专属表情增加身份认同
  • 活跃互动氛围:有趣表情降低沟通门槛,促进交流
  • 传播品牌理念:表情融入品牌价值观和slogan

实战案例: 某知识付费社群使用 Nano Banana Pro 生成"学霸小熊"系List情包

# 学霸小熊社群Stickers示例
community_character = {
    "name": "学霸小熊",
    "role": "知识付费社群吉祥物",
    "style": "温暖治愈系卡通风",
    "colors": ["蓝色(知识)", "黄色(阳光)", "白色(纯净)"],
    "props": ["书本", "眼镜", "笔记本", "证书", "奖杯"]
}

# 定制化表情List(结合社群场景)
community_expressions = [
    {"name": "打卡学习", "description": "认真看书,眼镜反光,周围知识点飘浮"},
    {"name": "每日一练", "description": "奋笔疾书做题,专注表情,汗水飞溅"},
    {"name": "攻克难题", "description": "突然顿悟表情,头顶灯泡,兴奋握拳"},
    {"name": "求助大佬", "description": "举手提问姿势,虚心求教表情"},
    {"name": "分享笔记", "description": "展示笔记本,自豪表情,笔记发光"},
    {"name": "组队学习", "description": "多个小熊一起学习,团队氛围"},
    {"name": "考试加油", "description": "握拳加油,斗志昂扬,必胜头巾"},
    {"name": "通过考试", "description": "手持证书,欢呼雀跃,撒花庆祝"},
    {"name": "摸鱼被抓", "description": "偷懒被发现,尴尬表情,冷汗直流"},
    {"name": "熬夜学习", "description": "黑眼圈,疲惫但坚持,咖啡续命"}
]

# 生成并Distribution到社群
for expr in community_expressions:
    # ... 生成代码 ...
    pass

print("✓ 学霸小熊Stickers已生成")
print("Distribution渠道: 社群公告、欢迎消息、每周精选")

效果数据:

  • 社群活跃度提升40%(使用Stickers后发言频次增加)
  • 新成员留存率提高25%(专属表情增强归属感)
  • Stickers传播到其他社群,带来20%的新用户增长

🎯 Community Operations Recommendation: We recommend regularly updating community stickers through the APIYI apiyi.com platform, quickly iterating based on community trending topics and member feedback. The platform provides expression usage data analysis, allowing you to understand which expressions are most popular, optimize expression library configuration, and improve community interaction quality.

场景 2: 品牌营销与IP开发

品牌需要可爱有趣的IP形象进行品牌人格化营销。

应用价值:

  • 品牌年轻化:可爱IP拉近与年轻消费者距离
  • 病毒式传播:用户自发使用传播品牌形象
  • 降低营销成本:Stickers传播成本远低于传统广告
  • 提升品牌好感度:有趣内容增强品牌亲和力

实战案例: 某奶茶品牌使用 Nano Banana Pro 生成"奶茶猫"IPStickers

# 奶茶猫品牌IPStickers
brand_character = {
    "name": "奶茶猫 Milk Tea Cat",
    "brand": "某某奶茶",
    "positioning": "年轻、活力、治愈",
    "style": "3D可爱风,圆润Q版",
    "signature_items": ["珍珠奶茶杯", "吸管", "珍珠", "品牌logo帽子"],
    "colors": ["奶茶色(主色)", "珍珠黑", "奶油白"]
}

# 品牌营销主题表情
brand_expressions = [
    # 产品植入类
    {"name": "喝奶茶啦", "description": "双手捧着品牌奶茶,大口吸吮,满足表情"},
    {"name": "新品推荐", "description": "手指新品奶茶,兴奋介绍,星星眼"},
    {"name": "超值套餐", "description": "展示两杯奶茶,惊喜表情,优惠标签"},

    # 节日营销类
    {"name": "春日限定", "description": "樱花奶茶,春天氛围,花瓣飘落"},
    {"name": "夏日冰爽", "description": "冰镇奶茶,清凉表情,冰块特效"},
    {"name": "秋天第一杯", "description": "温暖奶茶,满足表情,秋叶飘零"},

    # 生活场景类
    {"name": "打工人续命", "description": "疲惫表情,奶茶充电,满血复活"},
    {"name": "下午茶时光", "description": "悠闲喝茶,放松惬意,阳光温暖"},
    {"name": "深夜奖励", "description": "熬夜加班,奶茶慰藉,治愈氛围"},

    # 社交互动类
    {"name": "请你喝奶茶", "description": "递出奶茶,友好微笑,邀请姿势"},
    {"name": "谢谢老板", "description": "接过奶茶,感激表情,比心"},
    {"name": "一起拼单", "description": "多只猫一起举杯,团购氛围"}
]

品牌传播策略:

  1. 全渠道投放: 微信、微博、小红书、抖音同步发布
  2. KOL合作: 邀请网红博主使用并推广Stickers
  3. 用户激励: 使用Stickers打卡可获得优惠券
  4. 线下联动: 门店贴纸、周边产品印刷表情形象
  5. 数据追踪: 监测Stickers下载量、使用频次、传播路径

效果数据:

  • Stickers上线1个月,微信下载量突破50万
  • 品牌话题阅读量增长300%
  • 门店客流量增加15%(Stickers引流)
  • 品牌好感度调研提升28个百分点

💰 Brand Marketing ROI: For budget-sensitive small and medium brands, consider using Nano Banana Pro for sticker marketing through the APIYI apiyi.com platform. Compared to traditional design outsourcing (¥5,000-20,000 per set) and celebrity endorsements (millions), AI-generated stickers cost less than 10% of traditional methods while achieving 80-90% of the dissemination effectiveness, making it a cost-effective choice for youth-oriented marketing for small and medium brands.

场景 3: 个人IP与内容创作变现

内容创作者需要专属Stickers打造个人IP,并通过Stickers实现变现。

变现模式:

  1. 平台分成: 微信/QQ表情商店付费下载分成
  2. 打赏收益: 用户赞赏Stickers创作者
  3. 引流变现: Stickers导流到公众号/小程序/知识付费课程
  4. 周边开发: 将Stickers形象开发成实体周边(玩偶、贴纸、T恤等)
  5. IP授权: 将IP形象授权给品牌方使用

实战案例: 某插画师使用 Nano Banana Pro 生成"上班族老王"系List情包

# 个人IPStickers创作
creator_ip = {
    "name": "上班族老王",
    "creator": "@打工人日记",
    "target_audience": "职场白领、打工人群体",
    "style": "写实漫画风,带点丧萌",
    "core_theme": "打工人的酸甜苦辣",
    "uniqueness": "真实职场场景+黑色幽默"
}

# 系列主题Stickers
themed_packs = {
    "系列1: 职场日常": [
        "周一综合症", "开会ing", "改方案改到吐",
        "老板来了", "下班冲啊", "工资到账"
    ],
    "系列2: 996生活": [
        "通宵加班", "外卖续命", "猝死边缘",
        "发际线后退", "腰酸背痛", "想辞职"
    ],
    "系列3: 职场社交": [
        "尬聊客户", "被领导PUA", "甩锅成功",
        "背锅侠", "职场老好人", "办公室政治"
    ],
    "系列4: 打工人梦想": [
        "暴富", "躺平", "财务自由",
        "环游世界", "提前退休", "开除老板"
    ]
}

# 分系列逐步发布(保持热度)
for series_name, expressions in themed_packs.items():
    print(f"生成 {series_name}...")
    for expr_name in expressions:
        # ... 生成代码 ...
        pass
    print(f"✓ {series_name} 完成,等待2周后发布下一系列")

变现数据示例:

  • Stickers上架微信平台,定价1.99元
  • 首月下载量1.2万,收入约1.4万元(平台分成后)
  • 导流到公众号,新增粉丝8000+
  • 周边玩偶众筹,30天销售额12万元
  • 获得某HR软件品牌IP授权合作,授权费5万元

持续运营策略:

  • 每月更新1-2个新系列,保持用户新鲜感
  • 结合热点事件快速推出应景表情(如"年终奖"、"春招季")
  • 与其他创作者联名,互相导流
  • 开设Stickers制作教程,建立私域流量
  • 开发衍生内容(漫画条漫、短视频、Stickers壁纸等)

🚀 Creator Monetization Recommendation: We recommend using the APIYI apiyi.com platform's creator monetization toolkit. The platform provides one-stop services: sticker generation → multi-platform publishing → data analysis → merchandise customization connection → IP licensing matching. Creators don't need technical knowledge to quickly build personal IPs from scratch and achieve commercial monetization. The platform has helped 500+ creators achieve average monthly income exceeding ¥5,000.

Nano Banana Pro Stickers制作最佳实践

Character Consistency保持技巧

Reference Images优化:

# 使用多角度Reference Images提升一致性
character_references = {
    "核心参考": "front_view.png",       # 正面,最重要
    "辅助参考1": "side_view.png",       # 侧面
    "辅助参考2": "back_view.png",       # 背面
    "细节参考": "close_up_face.png"    # 面部特写
}

# Reference Images预处理(提升识别准确度)
def preprocess_reference(image_path):
    """优化Reference Images质量"""
    img = Image.open(image_path)

    # 1. 调整到合适尺寸(512-1024px)
    if max(img.size) > 1024:
        img.thumbnail((1024, 1024), Image.Resampling.LANCZOS)

    # 2. 增强对比度(突出特征)
    from PIL import ImageEnhance
    enhancer = ImageEnhance.Contrast(img)
    img = enhancer.enhance(1.2)

    # 3. 锐化处理(清晰边缘)
    from PIL import ImageFilter
    img = img.filter(ImageFilter.SHARPEN)

    return img

一致性关键词强化:

# 在每个提示词中强调一致性
consistency_keywords = [
    "SAME character as reference",
    "EXACT same appearance",
    "Maintain consistent visual features",
    "Keep facial features identical",
    "Same color palette and style"
]

prompt_with_consistency = f"""
{base_prompt}

CONSISTENCY REQUIREMENTS (CRITICAL):
- {consistency_keywords[0]}
- {consistency_keywords[1]}
- {consistency_keywords[2]}
- Character identity must remain 100% recognizable
"""

表情夸张度控制

三档夸张度策略:

夸张程度 适用场景 视觉特征 示例表情
轻度夸张 商务/正式场合 微妙表情,自然动作,克制配色 微笑、点头、思考
中度夸张 日常社交 明显表情,适度变形,生动配色 大笑、生气、惊讶
强度夸张 娱乐/梗文化 极度变形,特效满屏,炸裂配色 裂开、暴怒、崩溃 
# 根据夸张度调整提示词
def adjust_exaggeration(base_description, level="medium"):
    """
    调整表情夸张程度
    """
    exaggeration_styles = {
        "light": "subtle and natural expression, realistic proportions, professional appearance",
        "medium": "clear and expressive emotion, slightly exaggerated features, vibrant colors",
        "heavy": "extremely exaggerated expression, dramatic deformation, explosive visual effects, maximum impact"
    }

    return f"{base_description}\n\nExaggeration Level: {exaggeration_styles[level]}"

# 示例
light_prompt = adjust_exaggeration("character smiling", "light")
# → 适合企业客服Stickers

heavy_prompt = adjust_exaggeration("character laughing", "heavy")
# → 适合娱乐社群Stickers

Batch Generation效率优化

并行生成策略:

import concurrent.futures
from functools import partial

def generate_single_sticker(expr, model, character_refs, style):
    """生成单个Stickers(用于并行)"""
    # ... 生成逻辑 ...
    return result

# 使用线程池并行生成(注意API限流)
max_workers = 3  # 同时生成3个
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
    # 创建生成任务
    generate_func = partial(
        generate_single_sticker,
        model=model,
        character_refs=character_refs,
        style=style_guide
    )

    # 并行执行
    results = list(executor.map(generate_func, all_expressions))

print(f"并行生成完成,耗时减少约 {max_workers}x")

缓存复用策略:

# 缓存角色基础特征,避免重复计算
character_feature_cache = None

for expr in all_expressions:
    if character_feature_cache is None:
        # 首次生成,缓存特征
        response = model.generate_with_cache(
            prompt=first_prompt,
            references=character_refs,
            cache_features=True  # 缓存角色特征
        )
        character_feature_cache = response.cached_features
    else:
        # 后续生成,复用缓存
        response = model.generate_from_cache(
            prompt=follow_up_prompt,
            cached_features=character_feature_cache
        )

    # 生成速度提升约40%

Nano Banana Pro Stickers常见问题解答

如何确保50个表情都是同一角色?

最佳实践:

  1. 使用高质量Reference Images: 确保Reference Images清晰、角色特征明显
  2. 对话式连续生成: 使用 chat_session 而非独立生成
  3. 定期重置基准: 每生成10个表情,重新发送Reference Images
  4. 人工抽查: 每批次完成后人工检查,发现漂移及时修正
  5. 使用LoRA(进阶): 如果有技术能力,可训练专属LoRA模型
# 防止角色漂移的检查点机制
checkpoint_interval = 10

for i, expr in enumerate(all_expressions):
    # 每10个表情重新强调角色特征
    if i > 0 and i % checkpoint_interval == 0:
        reinforcement_prompt = f"""
CHECKPOINT: Let's re-confirm character consistency.
Please review the reference images again and ensure all following stickers maintain:
- Exact same character appearance
- Same facial structure and features
- Same color palette
- Same art style

Continue generating with reinforced character consistency.
"""
        chat_session.send_message([reinforcement_prompt] + character_refs)

    # 继续生成
    response = chat_session.send_message(expr_prompt)

生成的Stickers能否商用?

商用授权说明:

Nano Banana Pro生成的内容商用政策:

  • ✅ 允许商业使用(根据Google Gemini使用条款)
  • ✅ 您拥有生成内容的版权
  • ⚠️ 注意事项:
    • 不得包含第三方版权内容(如真实品牌logo、明星肖像等)
    • 不得生成违法违规内容
    • 建议在Stickers中添加原创声明

版权保护建议:

  1. 在Stickers角落添加水印或创作者ID
  2. 在发布平台Labels版权信息
  3. 保存生成记录和原始Reference Images作为版权证明
  4. 如发现侵权,及时通过平台举报

平台商用政策:

  • 微信表情开放平台:支持付费和免费表情,创作者可获得分成
  • QQ表情商店:支持付费表情,分成比例约30-50%
  • Telegram:免费分享,不支持直接付费
  • 周边授权:可自由开发实体周边产品

如何快速响应热点制作Stickers?

热点快速响应流程:

# 热点表情快速生成模板
def generate_trending_sticker(character_refs, trending_topic, style):
    """
    根据热点话题快速生成应景表情
    """
    # 分析热点关键词
    trending_keywords = extract_keywords(trending_topic)

    # 自动匹配合适的表情动作
    suitable_expressions = match_expressions(trending_keywords)

    # 生成热点专属表情
    prompt = f"""
Create a trending sticker for: {trending_topic}

Character: Use provided reference
Expression: {suitable_expressions[0]}
Add trending elements: {trending_keywords}
Style: {style}

Make it timely and shareable for current trending topic.
"""

    # 快速生成(单个表情,5-10秒)
    response = model.generate_images(
        prompt=prompt,
        references=character_refs,
        resolution="1K",  # 降低分辨率加快生成
        thinking_mode=False  # 关闭思考模式加速
    )

    return response.images[0]

# 示例:春节热点
trending_topic = "春节返乡"
keywords = ["春运", "回家", "团圆", "红包", "年夜饭"]

for keyword in keywords:
    sticker = generate_trending_sticker(
        character_refs,
        f"春节{keyword}",
        "喜庆中国风"
    )
    sticker.save(f"春节_{keyword}.png")
    print(f"✓ 生成热点表情: 春节{keyword}")

print("热点Stickers生成完成,可立即发布抢占热度!")

热点追踪渠道:

  • 微博热搜榜
  • 抖音/快手热榜
  • 知乎热榜
  • 百度指数
  • Google Trends

快速发布策略:

  • 热点出现后2小时内完成生成
  • 4小时内完成发布
  • 24小时内是最佳传播窗口期
  • 配合KOL快速扩散

💡 Trending Marketing Recommendation: Through the APIYI apiyi.com platform's trending monitoring feature, you can automatically track trending topics across the internet and receive push notifications. The platform also provides trending expression quick generation templates. By inputting trending keywords, you can generate contextual expressions in 3 minutes, helping brands and creators capture trending traffic dividends and achieve viral dissemination.

总结与展望

Nano Banana Pro 的StickersBatch Generation能力,为社群运营者、品牌营销人员和内容创作者带来了前所未有的Efficiency Improvement。通过本文介绍的5分钟Batch Generation方法,创作者可以实现:

  • 效率革命: 从数周的手工设计缩短到5分钟的Batch Generation,制作Efficiency Improvement98%
  • Character Consistency: 95%+的角色外观一致性,确保整套Stickers视觉统一
  • 低成本变现: AI生成成本不到传统设计的10%,创作者可快速试错和规模化生产
  • 多场景应用: 覆盖社群运营、品牌营销、个人IP开发等多个应用场景

随着 Gemini 3 Pro 系列模型的持续优化,我们预期Stickers生成功能将进一步增强:

  1. 支持动态Stickers(GIF/APNG格式,简单动作循环)
  2. 提供更多风格预设(国潮、赛博朋克、童话等30+风格)
  3. 智能Stickers命名和标签生成(自动生成平台关键词)
  4. 跨平台一键发布(API对接微信/QQ/Telegram等平台)

对于社群运营者和品牌而言,现在是采用AIStickers营销的最佳时机。建议从小范围测试开始(先生成10-20个核心表情),根据用户反馈快速迭代,逐步建立完整的Stickers矩阵,充Distribution挥 Nano Banana Pro 在效率和创意方面的独特优势。

🚀 Get Started Now: We recommend quickly experiencing Nano Banana Pro sticker batch generation functionality through the APIYI apiyi.com platform. The platform provides free trial credits, supports online character uploads and expression template selection, allowing you to start creating without writing code. Creators and brands can apply for sticker marketing solutions, including character design consulting, batch generation, multi-platform publishing, data tracking, and other full-chain services, accelerating the complete cycle from sticker creation to monetization.

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