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Capable of generating 204-frame videos (roughly 6-7 seconds at 30 fps) with realistic textures and motion.

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. v 4mp4

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. Capable of generating 204-frame videos (roughly 6-7 seconds

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. each containing 48 attention heads