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
NVIDIA GPU 12GB+ recommended (to drive Wan 2.2 or Seedance API and local video generation workflows)
Within approximately 1GB (repository text and basic guides), 50GB or more recommended for local video data cache
⚡ Installation
4-1. Quick Start
Clone GitHub repository and configure local prompt librarygit clone https://github.com/YouMind-OpenLab/awesome-seedance-2-prompts.git cd awesome-seedance-2-prompts
4-2. Detailed Installation
Install prompt parser and dependency libraries if necessarypip install -r requirements.txt
Classify prompts composed of text or JSON files to run RAG or conversion scriptspython 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.
📝 Update Notes
- 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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