Seaweed-7B – A video generation model launched by ByteDance

What is Seaweed-7B?

Seaweed-7B is a video generation model introduced by the ByteDance team, featuring approximately 7 billion parameters. Seaweed-7B demonstrates robust video generation capabilities. The model supports generating high-quality video content from text descriptions, images, or audio, accommodating various resolutions and durations. It is widely applicable in scenarios such as video creation, animation generation, and real-time interaction. Seaweed-7B is designed with cost-efficiency in mind, leveraging optimized training strategies and architectures to enable a medium-scale model to rival the performance of larger models while reducing computational costs.

The main functions of Seaweed-7B

• Text-to-Video: Generate video content that matches the text description, supporting complex actions and scenes.
• Image-to-Video: Use an image as the first frame to generate a video with a consistent style, or specify the first and last frames to create a transition video.
• Audio-Driven Video Generation: Generate video content that matches the audio input, ensuring lip movements and actions are synchronized with the audio.
• Long-Take Generation: Support the generation of single-take videos up to 20 seconds long, or extend this capability to up to one minute using advanced techniques.
• Coherent Storytelling: Generate multi-shot long videos while maintaining coherence between scenes and shots.
• Real-Time Generation: Support real-time video generation at 1280×720 resolution and 24fps.
• High Resolution and Super-Resolution: Support video generation at up to 1280×720 resolution, with further upscaling to 2K QHD resolution.
• Camera Control and World Exploration: Enable precise camera control using defined trajectories and provide interactive world exploration features.
• Physical Consistency Enhancement: Enhance the physical consistency and 3D effects of generated videos through post-training on computer-generated synthetic videos.

The Technical Principles of Seaweed-7B

• Variational Autoencoder (VAE): Compresses video data into a low-dimensional latent space and reconstructs the original video from the latent space. Based on a causal 3D convolutional architecture, it supports unified encoding of images and videos, avoiding boundary flickering issues. High-resolution video reconstruction quality is improved through mixed-resolution training (e.g., 256×256, 512×512, etc.).
• Diffusion Transformer (DiT): Generates video content in the latent space of a VAE by progressively denoising to produce high-quality videos. It employs a hybrid flow structure that combines full attention and window attention mechanisms to enhance training efficiency and generation quality. Multi-modal Rotational Position Embedding (MM-RoPE) is utilized to strengthen the fusion of positional information between text and video.
• Multi-stage training strategy: Progressively transition from low-resolution images to high-resolution videos to optimize GPU resource allocation. This includes a pre-training phase (image-only, image + video) and a post-training phase (supervised fine-tuning, human feedback reinforcement learning).
• Optimization Technologies: The Multi-Level Activation Checkpointing (MLAC) reduces GPU memory usage and computational overhead. Fusion of CUDA kernels optimizes I/O operations, improving training and inference efficiency. Diffusion distillation technology reduces the number of function evaluations (NFE) required for generation, accelerating the inference process.
• Data Processing: Clean the data using high-quality video data through methods such as temporal segmentation, spatial cropping, and quality filtering. Enhance the diversity and physical consistency of the training data with synthetic video data. Generate detailed video captions to improve the model’s text understanding ability.

Project address of Seaweed-7B

• Project official website: https://seaweed.video/
• Technical paper: https://seaweed.video/seaweed.pdf

Application scenarios of Seaweed-7B

• Content Creation: Generate high-quality videos from text or images, suitable for advertising, movies, short videos, etc., supporting various styles and scenarios.
• Real-time Interaction: Support real-time video generation for use in virtual reality (VR) and augmented reality (AR), providing an immersive experience.
• Multimedia Entertainment: Generate matching videos based on audio, suitable for music videos and audiobooks.
• Education and Training: Create educational videos and simulation training scenarios for use in scientific experiments, historical reenactments, military training, etc.
• Advertising and Marketing: Produce personalized advertisements and brand promotional videos to enhance appeal and conversion rates.