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.
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 in both English and Chinese text prompts.
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.
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.
Capable of generating 204-frame videos (roughly 6-7 seconds at 30 fps) with realistic textures and motion. v 4mp4
It uses bilingual encoders, allowing for strong performance in both English and Chinese text prompts. 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. According to Neurohive
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.