Anil-matcha/Seedance-2-API

What it solves

This project provides a Python wrapper for ByteDance's Seedance AI video generation models (2.0, 2.5, and 2 Mini). It simplifies the process of generating high-fidelity, cinematic AI videos from text prompts and static images, specifically addressing the need for realistic human face generation and identity consistency across frames.

How it works

The SDK acts as an interface to the Seedance API delivered via MuAPI. It allows developers to send requests for various generation modes:

• Text-to-Video (T2V): Converts descriptive prompts into video clips.
• Image-to-Video (I2V): Animates static images with motion control.
• Omni-Reference: Conditions video generation on a combination of image, video, and audio references.
• Character Consistency: Creates a multi-panel character sheet from reference photos, which can then be referenced in prompts using a specific ID to maintain a character's identity across different scenes.
• Seedance 2 Mini: A lightweight, cost-effective version for fast prototyping and high-volume pipelines.

Who it’s for

Developers and creators who want to integrate ByteDance's advanced video generation capabilities into their applications or workflows, particularly those needing professional-grade facial fidelity and character consistency.

Highlights

• Realistic Human Faces: High-fidelity facial expressions and skin textures to avoid the "uncanny valley."
• Character Sheets: Ability to generate consistent characters from 1-3 reference photos.
• Multimodal Inputs: Support for image, video, and audio references in a single request.
• Flexible Output: Support for various aspect ratios (16:9, 9:16, 4:3, 3:4) and quality settings (basic vs. high/1080p).
• MCP Server: Includes a Model Context Protocol server allowing AI assistants like Claude or Cursor to invoke these tools directly.