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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 DeepSeek-V2?

DeepSeek-V2 represents an advanced Mixture-of-Experts (MoE) language model created by DeepSeek-AI, recognized for its economical training and superior inference efficiency. This model features a staggering 236 billion parameters, engaging only 21 billion for each token, and can manage a context length stretching up to 128K tokens. It employs sophisticated architectures like Multi-head Latent Attention (MLA) to enhance inference by reducing the Key-Value (KV) cache and utilizes DeepSeekMoE for cost-effective training through sparse computations. When compared to its earlier version, DeepSeek 67B, this model exhibits substantial advancements, boasting a 42.5% decrease in training costs, a 93.3% reduction in KV cache size, and a remarkable 5.76-fold increase in generation speed. With training based on an extensive dataset of 8.1 trillion tokens, DeepSeek-V2 showcases outstanding proficiency in language understanding, programming, and reasoning tasks, thereby establishing itself as a premier open-source model in the current landscape. Its groundbreaking methodology not only enhances performance but also sets unprecedented standards in the realm of artificial intelligence, inspiring future innovations in the field.

Media

Media

Integrations Supported

SiliconFlow
AIReel
ComfyUI
Fuser
Lucy Edit AI
Wan AI
WaveSpeedAI
graphis

Integrations Supported

SiliconFlow
AIReel
ComfyUI
Fuser
Lucy Edit AI
Wan AI
WaveSpeedAI
graphis

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

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

DeepSeek

Date Founded

2023

Company Location

China

Company Website

deepseek.com

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