Luma AI Releases Ray2 Flash Video Model With Faster Generation Time

Luma AI, a Palo Alto-based Artificial Intelligence (AI) firm, released a new video generation AI model last week. Dubbed Ray2 Flash, the model is an distilled version of the recently launched Ray2 Frontier Video Generation Model. The company said the flash model is both faster and cheaper than the big language model (LLM), maintaining realistic video generation capabilities. Ray2 is a flash video generation model multimodal and supports both text and images as input. It can be accessed through the dream machine platform which was introduced last year.

Luma AI introduced Ray2 Flash AI model

One in Post On X (East was known as Twitter), Luma AI’s official handle announced the launch of the new video model. The Ray2 Flash AI model is currently available within the dream machine, and the application programming interface is expected to arrive soon.

The post claimed that the distilled video generation model is three times faster and three times cheaper than the Frontier Ray2. This rate is also available without limitations, which means that users can generate more videos with the same number of credits. The Ray2 flash is available only to the paid customers of the dream machine.

In particular, users get credits once on free tier. After finishing these, users must buy a subscription. The stage membership begins at $ 6.99 (about Rs 609 609) per month, which provides 3,200 monthly credits.

The Ray2 flash model is based on the Frontier Ray2 model, which was released in January. At the time of launch, the AI ​​firm stated that large -scale video models can generate realistic videos with natural and coherent motion. The model also has a high accelerated adherence and it can stay close to the user input.

Last year, the company introduced the dream machine platform. The platform offers both image and video generation abilities and users can make output in a wide range of styles including cinematic, animation, realistic and more. The Gadgets 360 tried the platform and found that the speedy adherence to the stage is sub-buffer. It struggles with many characters or when the signal is very complex.