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What is Wan2.5?

Wan2.5-Preview represents a major evolution in multimodal AI, introducing an architecture built from the ground up for deep alignment and unified media generation. The system is trained jointly on text, audio, and visual data, giving it an advanced understanding of cross-modal relationships and allowing it to follow complex instructions with far greater accuracy. Reinforcement learning from human feedback shapes its preferences, producing more natural compositions, richer visual detail, and refined video motion. Its video generation engine supports 1080p output at 10 seconds with consistent structure, cinematic dynamics, and fully synchronized audio—capable of blending voices, environmental sounds, and background music. Users can supply text, images, or audio references to guide the model, enabling highly controllable and imaginative outputs. In image generation, Wan2.5 excels at delivering photorealistic results, diverse artistic styles, intricate typography, and precision-built diagrams or charts. The editing system supports instruction-based modifications such as fusing multiple concepts, transforming object materials, recoloring products, and adjusting detailed textures. Pixel-level control allows for surgical refinements normally reserved for expert human editors. Its multimodal fusion capabilities make it suitable for design, filmmaking, advertising, data visualization, and interactive media. Overall, Wan2.5-Preview sets a new benchmark for AI systems that generate, edit, and synchronize media across all major modalities.

What is Seaweed?

Seaweed, an innovative AI video generation model developed by ByteDance, utilizes a diffusion transformer architecture with approximately 7 billion parameters and has been trained using computational resources equivalent to 1,000 H100 GPUs. This sophisticated system is engineered to understand world representations by leveraging vast multi-modal datasets that include video, image, and text inputs, enabling it to produce videos in various resolutions, aspect ratios, and lengths solely from textual descriptions. One of Seaweed's remarkable features is its proficiency in creating lifelike human characters capable of performing a wide range of actions, gestures, and emotions, alongside intricately detailed landscapes characterized by dynamic compositions. Additionally, the model offers users advanced control features, allowing them to generate videos that begin with initial images to ensure consistency in motion and aesthetic throughout the clips. It can also condition on both the opening and closing frames to create seamless transition videos and has the flexibility to be fine-tuned for content generation based on specific reference images, thus enhancing its effectiveness and versatility in the realm of video production. Consequently, Seaweed exemplifies a groundbreaking advancement at the convergence of artificial intelligence and creative video creation, making it a powerful tool for various artistic applications. This evolution not only showcases technological prowess but also opens new avenues for creators seeking to explore the boundaries of visual storytelling.

Media

No images available

Media

Integrations Supported

AIReel
Inspix AI
Prism
Wan AI
ZenCreator

Integrations Supported

API Availability

API Availability

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

wan.video

Company Facts

Organization Name

ByteDance

Date Founded

2012

Company Location

China

Company Website

seaweed.video/

Categories and Features

AI Models

Not specified

AI Video Generators

Not specified

AI Video Models

Not specified

Categories and Features

AI Models

Not specified

AI Video Generators

Not specified

AI Video Models

Not specified

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