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 allows developers to programmatically generate high-fidelity, cinematic videos from text prompts or static images, with a specific focus on maintaining realistic human face consistency and identity across frames.

How it works

The SDK acts as a bridge to the Seedance API delivered via MuAPI. It supports several generation modes:

• Text-to-Video (T2V): Creates clips from descriptive prompts.
• Image-to-Video (I2V): Animates static images using motion control.
• Omni-Reference: Conditions generation on a combination of image, video, and audio references.
• Character Consistency: Generates a 4K character sheet from reference photos, which can then be referenced in prompts via @character:<id> or used as an anchor image for tighter face fidelity.
• Seedance 2 Mini: A lightweight, cost-effective model for fast prototyping and high-volume pipelines.

Who it’s for

Developers and creators who want to integrate professional-grade AI video generation into their applications, specifically those requiring consistent characters, high-resolution (1080p) output, and more permissive content policies than other AI video tools.

Highlights

• Realistic Human Faces: Specialized in high-fidelity facial expressions and skin textures to avoid the "uncanny valley."
• Character Workflow: Ability to create and reuse consistent character identities across different scenes.
• Multimodal Inputs: Supports text, image, and video references in a single request.
• MCP Server Support: Includes a Model Context Protocol server allowing AI agents (like Claude Desktop or Cursor) to invoke video generation tools directly.
• Flexible Output: Supports various aspect ratios (16:9, 9:16, 4:3, 3:4) and quality settings.