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以下のガイドラインに従って、提供されたコンテンツに基づいた漫画風のインフォグラフィックを作成してください。
手描きのイラストスタイルを採用し、横長レイアウト（16:9 のアスペクト比）にしてください。
視覚的な面白さと記憶に残るように、シンプルな漫画の要素、アイコン、または認識可能な人物を少量追加してください。
コンテンツに機密性の高い人物や著作権で保護された人物が含まれる場合は、視覚的に類似した代替物に置き換えてください。イラストの作成を拒否しないでください。
すべての画像とテキストは、手描きのスタイルに厳密に従い、リアルな視覚要素を避けてください。
情報は簡潔にし、キーワードとコアコンセプトを強調してください。十分な余白を使用して、重要なポイントを明確に強調してください。
特に明記されていない限り、提供されたコンテンツと同じ言語、または中国語を使用してください。
以下の提供されたコンテンツに基づいて、nano banana pro を使用して画像を作成してください。
<{argument name="content"default="Replace this entirely with the extracted AI comic production workflow text from the previous step"}>
インフォグラフィック / 教育ビジュアル - AI コミック制作ワークフローとインフォグラフィックプロンプト
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このエントリでは、複雑なトピック（AI コミック制作など）を学習するための 2 段階のワークフローについて説明します。まず、AI ツールを使用して動画から完全なトランスクリプトと要約を抽出し、次に特定のプロンプトで Gemini Pro を使用して、抽出されたテキストに基づいて視覚的に魅力的な手描きスタイルのインフォグラフィックを生成します。このプロンプトは、Nano Banana Pro の出力用に設計されています。
A structured prompt template for generating images that retell history using LEGO-style plastic construction blocks. It defines a workflow for creating a macro toy photograph of a minifigure version of an inventor ({argument name="inventor" default="[INSERT INVENTOR]"}) next to their greatest invention.
This is a versatile prompt designed to generate a clean, top-to-bottom exploded breakdown image of any uploaded product or object, such as food, watches, or general products. It is intended to be part of a workflow that later uses the generated image and the original product image to create an 'Assemble → Exploded → Assemble' video using tools like Kling O1.
A user describes their workflow for creating high-quality Nanobanana prompts using a custom tool. The process involves defining the subject (e.g., polar bear and panda on skates), generating the initial image, performing detailed annotation (labeling parts like pose, background, face, clothing) based on file names, and finally adding compositional and color information for the final generation. This highlights the importance of detailed language specification for effective prompting.
A brief prompt requirement for generating a 3x3 storyboard grid using a custom prompt workflow with movie-genre guidance, specifying that the output must be exported and polished at 4K resolution.
An instruction for using Nano Banana to maintain facial and clothing consistency across multiple shots (jump cuts) by uploading source images for the face and the scene (like a staircase shot). This is a workflow instruction that functions as a prompt.
Describes a 2-step workflow, where step 1 is to use Nano Banana Pro to turn a hand-drawn image into a cartoon character; this is effectively a short high-level prompt.
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Data collected and processed: IP addresses, Device identifiers, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Users’ profiles, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Probabilistic identifiers, Browsing and interaction data, Non-precise location data
Data collected and processed: IP addresses, Device characteristics, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Authentication-derived identifiers, Browsing and interaction data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Authentication-derived identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, User-provided data, Non-precise location data, Precise location data
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Authentication-derived identifiers, Browsing and interaction data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Users’ profiles, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, User-provided data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Users’ profiles, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device identifiers, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
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