Agentic-AIGC provides a comprehensive cookbook for developing agent-based AI-generated content, focusing on complex video creation workflows. It addresses the limitations of single-model AIGC by enabling intelligent agents to orchestrate multiple AI tools, facilitating seamless multi-stage production from concept to final output. The project targets engineers and researchers aiming to build autonomous video creation systems.

How It Works

This project leverages intelligent agent architectures to tackle the intricate demands of professional video production, which traditional AIGC struggles with due to a lack of orchestration capabilities. Agentic-AIGC employs specialized agents that coordinate diverse AI models for tasks like scriptwriting, scene generation, character animation, audio synthesis, and editing. This multi-tool coordination ensures crucial elements like multi-modal alignment, narrative coherence, and visual continuity across complex, temporal sequences, leading to professional-quality, automated video generation.

Quick Start & Requirements

• Installation: Clone the repository, create a Python 3.10 Conda environment, install system dependencies (pynini, ffmpeg), and Python packages (pip install -r requirements.txt).
• Models: Requires downloading specific models (e.g., CosyVoice, MiniCPM, Whisper, ImageBind) via git lfs and huggingface-cli, tailored to desired video features.
• Configuration: LLM API keys and base URLs must be configured in Agentic-AIGC/environment/config/config.yml.
• Hardware: Minimum 8GB GPU memory is recommended; supports Linux and Windows.
• Resources: Links to Demos Documentation and Bilibili Homepage are provided for usage examples.

Highlighted Details

• Features six distinct, ready-to-use video production recipes, including beat-synced edits, novel-to-screen adaptation, news summarization, meme generation, AI music covers, and cross-cultural content adaptation.
• Enables the creation of autonomous agents capable of making independent creative decisions and managing complex production pipelines.
• Facilitates cross-modal applications, seamlessly integrating text, audio, and visual elements while maintaining narrative and visual consistency.

Maintenance & Community

The project acknowledges contributions from the open-source community and various AI service providers. No specific community channels (e.g., Discord, Slack) or direct contributor information are detailed in the provided README.

Licensing & Compatibility

No explicit licensing information is present in the provided README text.

Limitations & Caveats

All content used in demonstrations is strictly for research and non-commercial purposes. The project relies on sourced audio and visual assets from the internet, with a disclaimer regarding intellectual property rights. Users must download specific models based on their needs, potentially leading to significant storage requirements.