What is Seaweed-7B
Seaweed-7B is launched by the ByteDance teamvideo generationmodel, with approximately 7 billion parameters. Seaweed-7B possesses powerful video generation capabilities. The model supports generating high-quality video content from text descriptions, images, or audio, supports multiple resolutions and durations, and is widely used in scenarios such as video creation, animation generation, and real-time interaction. Seaweed-7B is designed with a focus on cost-effectiveness, based on optimized training strategies and architecture, allowing a medium-scale model to match large models in performance and reducing computational costs.
Main features of Seaweed-7B
- Text-to-video: Generates video content matching the text description, supporting complex actions and scenes.
- Image-to-video: Uses an image as the first frame to generate a video consistent with its style, or specifies the first and last frames to generate a transition video.
- Audio-driven video generation: Generates video content matching the audio input, ensuring that lip movements and actions are synchronized with the audio.
- Long shot generation: Supports generating single-shot videos up to 20 seconds, or videos up to one minute based on extension techniques.
- Coherent storytelling: Generates multi-shot long videos, maintaining coherence between scenes and shots.
- Real-time generation: Supports real-time video generation at 1280×720 resolution and 24fps.
- High resolution and super-resolution: Supports generating videos up to 1280×720 resolution, based on further upsampling to 2K QHD resolution.
- Camera control and world exploration: Supports precise camera control with defined trajectories, providing interactive world exploration functionality.
- Enhanced physical consistency: Based on post-training with computer-generated synthetic videos, enhances the physical consistency and 3D effects of video generation.
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. Based on mixed-resolution training (such as 256×256, 512×512, etc.) to improve the reconstruction quality of high-resolution videos.
- Diffusion Transformer (DiT): Generates video content in the VAE latent space, gradually denoising to generate high-quality videos. Uses a hybrid flow structure, combining full attention and window attention mechanisms, to improve training efficiency and generation quality. Uses multimodal rotary position embedding (MM-RoPE) to enhance the fusion of positional information between text and video.
- Multi-stage training strategy: Gradually transitions from low-resolution images to high-resolution videos, optimizing GPU resource allocation. Includes a pre-training stage (images only, images + video) and a post-training stage (supervised fine-tuning, reinforcement learning from human feedback).
- Optimization techniques: Multi-level activation checkpointing (MLAC) reduces GPU memory usage and computational overhead. Fused CUDA kernels optimize 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: Use high-quality video data and perform data cleaning based on methods such as temporal segmentation, spatial cropping, and quality filtering. Use synthetic video data to enhance the diversity and physical consistency of the training data. Generate detailed video captions to enhance the model's text understanding capability.
Seaweed-7B Project Address
- 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, film, short videos, etc., supporting multiple styles and scenarios.
- Real-Time Interaction: Support real-time video generation, used in virtual reality (VR) and augmented reality (AR), providing immersive experiences.
- Multimedia Entertainment: Generate matching videos from audio, suitable for music videos and audiobooks.
- Education and Training: Generate educational videos and simulated training scenarios, used in scientific experiments, historical reenactments, military training, etc.
- Advertising and Marketing: Generate personalized advertisements and brand promotional videos to improve appeal and conversion rates.