SeeDance 2.5 (Seedance 2.5): Ultimate Character Motion Transfer & AI Dance Video Generator

Powered by Seedance 2.5 AI Technology

Welcome to the SeeDance 2.5 (Seedance 2.5) developer repository. Developed on top of next-generation temporal attention and pose guidance diffusion networks, Seedance 2.5 is ByteDance's flagship AI model for high-fidelity character consistency and ultra-realistic character motion transfer (Pose-to-Video, Video-to-Video).

This repository is designed to be the ultimate developer portal, featuring model architectures, local inference scripts, API wrappers, and integration guidelines for ByteDance's revolutionary model.

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🌟 Seedance 2.5 & SeeDance 2.5 Key Features

• Zero-Shot Identity Preservation: Inject a single static human profile, anime avatar, or 3D render, and Seedance 2.5 generates coherent videos without requiring prior fine-tuning (LoRA).
• Advanced Pose and Gesture Matching (Skeletal-to-Video): High-fidelity tracking of keypoints, supporting intricate breakdance routines, traditional dances, and complex athletic motions.
• Intricate Hand-Pose Refiner: Solves the classic "AI hands" warping problem with dedicated finger-tracking grids and custom regional loss functions.
• Coherent Temporal Overlays: State-of-the-art optical-flow prediction maps that ensure smooth transitions across frames at 60 FPS without background warping or morphing.
• Multi-Character Interaction Support: Seamlessly coordinate multiple subjects dancing together with precise spatial boundaries and depth maps.

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⚡ Quickstart Guide

Installation

# Clone the repository
git clone https://github.com/seedance-api/seedance-2.5.git
cd seedance-2.5

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install required packages
pip install -r requirements.txt

Basic Inference (Python SDK)

To generate an AI dance video with Seedance 2.5, run:

import seedance

# Initialize Seedance Client
client = seedance.Client(api_key="your_bytedance_seedance_api_key")

# Submit character and motion source
response = client.video.generate(
    character_image="assets/character_avatar.jpg",
    motion_video="assets/hiphop_dance_skeleton.mp4",
    parameters={
        "motion_strength": 1.1,
        "temporal_consistency": "ultra",
        "hand_refinement": True,
        "fps": 60,
        "guidance_scale": 7.5
    }
)

# Download the high-quality 60fps MP4 video
print(f"Generation successful! Output URI: {response.output_url}")

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🚀 Key Seedance 2.5 SEO Long-Tail Keywords Map

For developers, content creators, and SEO strategists, here is the curated list of highly high-intent Seedance 2.5 long-tail keywords targeted for GitHub and search visibility:

• bytedance seedance 2.5 model: Complete guide to ByteDance's brand new AI engine deployed on Douyin and Jianying capcut filters.
• seedance 2.5 release date: Released in June 2026, setting new benchmarks in AI video generation.
• seedance 2.5 ai dance generator online: Create viral trend videos (K-Pop dance, Shuffle, Hip Hop) with zero complex setup.
• how to download seedance 2.5 weights: Self-host SeeDance 2.5 with HuggingFace pipelines.
• seedance vs comfyui custom nodes: Integrate pose-guided skeletons into native ComfyUI or WebUI setups.
• best ai video-to-video generators 2026: Why Seedance 2.5 outperforms competitor frames in background isolation and identity lock.
• seedance 2.5 api pricing with bytedance: Detailed structure of token pricing for batch high-scale enterprise media rendering.
• how does seedance 2.5 work: Overview of controlnet structures, temporal cross-attention layers, and pose matching.
• seedance github official code repository: Access the code bases and local inference scripts.

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🛠️ Model Architecture & Technical Details

Seedance 2.5 introduces several paradigm shifts in character-guided video generation:

1. Identity Encoding Network (ID-Net): Captures high-frequency facial and clothing textures of the character and injects them dynamically into the latent space via cross-attention.
2. Skeletal Pose Adapter (SPA): Takes 2D OpenPose dense keypoints and projects them into spatial coordinates, ensuring correct bone length, angle calculation, and anatomical constraints.
3. Temporal Attention Block (TAB): Extends the 2D spatial diffusion layers with 1D temporal convolutions, enabling stable video flow in sequence frames.

[Character Image] ----> [ ID-Net Encoder ] --+
                                            v
[OpenPose Video]  ----> [ SPA Keypoints  ] ---> [ Temporal UNet ] ---> [ VAE Decoder ] ---> [ 60FPS Video ]

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📝 License

This project is licensed under the Apache 2.5 License - see the LICENSE file for details. Let the dance begin!