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LatestKrea1.0
KreaText → Video

Krea Releases Open-Source Real-Time Video Model

The new 14-billion-parameter model is a distilled, more efficient version of a larger foundation, designed for interactive video generation.

Oct 8, 2025
NotableApache 2.0
Krea Realtime Video

Krea has introduced a new open-source model designed for real-time video generation. Named Krea Realtime Video, the model is available under a permissive Apache 2.0 license, allowing for broad commercial and research use in interactive applications.

The model's key innovation lies in its efficiency. It is a 14-billion-parameter text-to-video generator that was created using knowledge distillation. According to the company's release card on Hugging Face, it was trained on the outputs of a larger, more powerful "teacher" model called Wan2.1-T2V-14B. This technique allows the smaller model to achieve high-quality results with significantly lower computational requirements, enabling its real-time performance.

This release is significant because most state-of-the-art video models are kept behind closed APIs. By providing an open and performant alternative, Krea empowers developers to build novel video tools, creative applications, and new user experiences without being tied to a proprietary service. This could accelerate innovation in the generative video space by lowering the barrier to entry for real-time creation.

Model at a Glance

  • Size: 14 billion parameters
  • License: Apache 2.0
  • Capabilities: Real-time text-to-video and video-to-video
  • Architecture: Distilled from a larger foundation model

Sources

  • krea/krea-realtime-video

    Hugging Face

    Visit
OlderinclusionAI Releases Ming-UniVision MoE Multimodal ModelinclusionAI · Any-to-Any · last yearNewerEPFL Releases SVI for Streaming Image-to-VideoEPFL VITA · Image → Video · last year

Get the model

Hugging Face

Specs

Parameters14B
Size28.6 GB
PrecisionFP16
LicenseAPACHE-2.0
Downloads33.8K
Likes310

Can you run it?

Runs on a laptop — about 15.3 GB at FP8.

  • FP16 (as published)

    32 GB GPU (RTX 5090) · 48 GB Mac

    29.6 GB
  • FP8

    16 GB GPU (RTX 4080 / 5070 Ti) · 24 GB Mac

    15.3 GB
Your machine
  • Covers the published checkpoint; text encoders and VAE loaded alongside may add a few GB (many runtimes offload them to CPU).

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text → Video

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