V 4mp4 【INSTANT ✧】

It uses bilingual encoders, allowing for strong performance in both English and Chinese text prompts.

The model is built on a massive, 30-billion parameter architecture designed for deep understanding of text prompts and visual generation. v 4mp4

Capable of generating 204-frame videos (roughly 6-7 seconds at 30 fps) with realistic textures and motion. It uses bilingual encoders, allowing for strong performance

According to Neurohive, deploying or training this model requires substantial resources: Operating System: Linux Language & Library: Python 3.10.0+ and PyTorch 2.3-cu121 Dependencies: CUDA Toolkit and FFmpeg. According to Neurohive, deploying or training this model

Built on a Diffusion Transformer (DiT) architecture with 48 layers, each containing 48 attention heads, Step-Video-T2V employs 3D Rotary Position Embedding (3D RoPE) to maintain consistency across varying video lengths and resolutions.

It uses a specialized VAE for video generation, achieving 16x16 spatial and 8x temporal compression. This allows for high-quality video reconstruction while accelerating training and inference.