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.
Step-Video-T2V represents a significant step in the open-source video generation space, focusing on both high-definition quality and temporal coherence, as analyzed by Analytics Vidhya. If you'd like, I can: Find generated by this model Look up benchmark comparisons to Sora or Gen-3 Find installation guides for it Let me know which of these would be most helpful! AI responses may include mistakes. Learn more stepfun-ai/Step-Video-T2V - GitHub v 4mp4
It uses bilingual encoders, allowing for strong performance in both English and Chinese text prompts. Built on a Diffusion Transformer (DiT) architecture with
The model is built on a massive, 30-billion parameter architecture designed for deep understanding of text prompts and visual generation. AI responses may include mistakes
The model incorporates Direct Preference Optimization (DPO), leveraging human feedback to ensure the generated content aligns with human aesthetic and quality expectations. Key Features