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What is Wan2.2?

Wan2.2 represents a major upgrade to the Wan collection of open video foundation models by implementing a Mixture-of-Experts (MoE) architecture that differentiates the diffusion denoising process into distinct pathways for high and low noise, which significantly boosts model capacity while keeping inference costs low. This improvement utilizes meticulously labeled aesthetic data that includes factors like lighting, composition, contrast, and color tone, enabling the production of cinematic-style videos with high precision and control. With a training dataset that includes over 65% more images and 83% more videos than its predecessor, Wan2.2 excels in areas such as motion representation, semantic comprehension, and aesthetic versatility. In addition, the release introduces a compact TI2V-5B model that features an advanced VAE and achieves a remarkable compression ratio of 16×16×4, allowing for both text-to-video and image-to-video synthesis at 720p/24 fps on consumer-grade GPUs like the RTX 4090. Prebuilt checkpoints for the T2V-A14B, I2V-A14B, and TI2V-5B models are also provided, making it easy to integrate these advancements into a variety of projects and workflows. This development not only improves video generation capabilities but also establishes a new standard for the performance and quality of open video models within the industry, showcasing the potential for future innovations in video technology.

What is CogVideoX?

CogVideoX is an innovative solution for transforming text into dynamic videos. Before utilizing the model, it is crucial to refer to this guide, which explains how to effectively leverage the GLM-4 model for optimizing prompts. This preliminary step is important as the model yields optimal results with longer prompts, and the construction of a well-defined prompt significantly influences the quality of the generated video. The guide provides both the inference and fine-tuning code for SAT weights, along with tips to improve it within the CogVideoX framework. Ambitious researchers often employ this code to enhance their rapid development and stacking capabilities. In an enchanting scene, a beautifully crafted wooden toy ship, complete with intricate masts and sails, glides smoothly over a soft blue carpet designed to resemble the waves of the ocean. The ship's hull features a rich brown color embellished with tiny, detailed windows. The plush carpet creates a perfect backdrop, evoking the expansive nature of the sea, while an array of toys and children's items scattered about adds to the scene's vibrant and imaginative energy. This whimsical scenario not only demonstrates CogVideoX's capabilities but also underscores the significance of a thoughtfully constructed prompt in crafting captivating visual stories, ultimately enhancing the viewer's experience.

Media

Media

Integrations Supported

AIReel
ComfyUI
Fuser
Lucy Edit AI
SiliconFlow
Wan AI
WaveSpeedAI
graphis

Integrations Supported

AIReel
ComfyUI
Fuser
Lucy Edit AI
SiliconFlow
Wan AI
WaveSpeedAI
graphis

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Version
Free Trial Offered?

Pricing Information

Free
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

wan.video

Company Facts

Organization Name

CogVideoX

Company Website

github.com/zai-org/CogVideo

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