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awesome-seedance-2-prompts

awesome-seedance-2-prompts is an open-source prompt engineering library and data asset designed to maximize the potential of Seedance 2.0, ByteDance's next-generation video generation framework. It provides a structured prompt system for fine-grained control over quad-modal inputs, encompassing text, images, audio, and video, and contributes to overcoming the persistent challenges of video generation, such as a lack of directorial control and limitations in maintaining consistency. It's like a large language model (LLM) with a precisely tuned contextual protocol.

awesome-seedance-2-prompts is an open-source prompt engineering library and data asset designed to maximize the potential of Seedance 2.0, ByteDance's next-generation video generation framework. It provides a structured prompt system for finely controlling quad-modal inputs, encompassing text, images, audio, and video, and contributes to overcoming the persistent challenges in video generation: the lack of control over staging and the limitations in maintaining consistency. Similar to how a large language model (LLM) generates high-quality responses through a precisely tuned contextual protocol, this asset acts as a blueprint to help users stage high-resolution videos in multi-modal environments according to their intended scenarios.

Traditional video prompting methods relied on single text inputs, which showed clear limitations in synchronizing camera work, physical laws, and subtle facial expressions. This library integrates temporal encoding techniques and multi-shot design methodologies to structure the flow of the video on a second-by-second basis and perfectly synchronize camera motion control and character consistency. This moves away from the previous random and abstract command listing approach, creating a differentiated value that allows for rendering high-quality results in a single attempt without wasting hardware resources.

In particular, creators and developers who want to build automated video pipelines can use this library to template the time-based shot breakdown structure. When generating animation music video storyboards, they can map the dynamic movement of the camera and the interactions between characters in each sequence to control prompts, suppressing the style bias of the generation model and maintaining a consistent tone and manner. This realizes an innovative virtual workflow that minimizes complex manual adjustments from the planning stage to the video completion stage, significantly reducing the final rendering time and computational costs while delivering sophisticated aesthetic results at the level of a professional film studio.

💻 System Requirements

🧠RAM

NVIDIA GPU 12GB+ 권장 (Wan 2.2 또는 Seedance API 및 로컬 비디오 생성 워크플로우 구동 시)

💾Storage

약 1GB 이내 (리포지토리 텍스트 및 기본 가이드), 로컬 비디오 데이터 캐시용 50GB 이상 권장

Installation

4-1. Quick Start

GitHub 리포지토리 복제 및 프롬프트 라이브러리 로컬 구성

git clone https://github.com/YouMind-OpenLab/awesome-seedance-2-prompts.git cd awesome-seedance-2-prompts

4-2. 상세 설치

필요한 경우 프롬프트 파서 및 종속성 라이브러리 설치

pip install -r requirements.txt

텍스트 또는 JSON 파일로 구성된 프롬프트를 분류하여 RAG 또는 변환 스크립트 실행

python src/parse_prompts.py --input data/prompts.json --style anime

FAQ

What is awesome-seedance-2-prompts?

awesome-seedance-2-prompts is an open-source prompt engineering library and data asset designed to maximize the potential of Seedance 2.0, ByteDance's next-generation video generation framework. It provides a structured prompt system for finely controlling quad-modal inputs, encompassing text, images, audio, and video, and contributes to overcoming the persistent challenges in video generation: the lack of control over staging and the limitations in maintaining consistency. Similar to how a large language model (LLM) generates high-quality responses through a precisely tuned contextual protocol, this asset acts as a blueprint to help users stage high-resolution videos in multi-modal environments according to their intended scenarios. Traditional video prompting methods relied on single text inputs, which showed clear limitations in synchronizing camera work, physical laws, and subtle facial expressions. This library integrates temporal encoding techniques and multi-shot design methodologies to structure the flow of the video on a second-by-second basis and perfectly synchronize camera motion control and character consistency. This moves away from the previous random and abstract command listing approach, creating a differentiated value that allows for rendering high-quality results in a single attempt without wasting hardware resources. In particular, creators and developers who want to build automated video pipelines can use this library to template the time-based shot breakdown structure. When generating animation music video storyboards, they can map the dynamic movement of the camera and the interactions between characters in each sequence to control prompts, suppressing the style bias of the generation model and maintaining a consistent tone and manner. This realizes an innovative virtual workflow that minimizes complex manual adjustments from the planning stage to the video completion stage, significantly reducing the final rendering time and computational costs while delivering sophisticated aesthetic results at the level of a professional film studio.

When should I use awesome-seedance-2-prompts?

awesome-seedance-2-prompts is an open-source prompt engineering library and data asset designed to maximize the potential of Seedance 2.0, ByteDance's next-generation video generation framework. It provides a structured prompt system for fine-grained control over quad-modal inputs, encompassing text, images, audio, and video, and contributes to overcoming the persistent challenges of video generation, such as a lack of directorial control and limitations in maintaining consistency. It's like a large language model (LLM) with a precisely tuned contextual protocol.

📄 Official Docs🐙 GitHub

📝 Update Notes

  1. vvideos7/12/2026

    이번 업데이트를 통해 README 문서 내에 비디오 파일을 자동으로 관리하고 임베딩할 수 있는 기능이 추가되었습니다. 이제 별도의 파일 다운로드 없이도 프롬프트 실행 결과로 생성된 영상을 문서에서 즉시 확인할 수 있습니다. 단백질 구조나 세포 움직임 등 시각적 데이터의 정확한 확인이 중요한 생명공학 연구자분들이 프롬프트의 성능을 더욱 빠르고 직관적으로 검토하는 데 큰 도움이 될 것입니다.

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