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

What is Seaweed-7B?

Seaweed-7B is a video generation model developed by ByteDance, boasting 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, supporting various resolutions and durations, and is widely used in video creation, animation generation, real-time interaction, and other scenarios. Seaweed-7B's design prioritizes cost-effectiveness, leveraging optimized training strategies and architecture to achieve performance comparable to large-scale models while reducing computational costs.

Main functions of Seaweed-7B

• Text to VideoGenerates video content that matches the text description, supporting complex actions and scenes.
• Image to videoUse an image as the first frame to generate a video with the same style, or specify the first and last frames to generate a transition video.
• Audio-driven video generationGenerate matching video content based on audio input, ensuring that lip movements and actions are synchronized with the audio.
• Long shot generationIt supports generating single-lens videos up to 20 seconds long, or videos up to one minute long based on extended technology.
• Coherent storytellingGenerate long videos with multiple shots, maintaining continuity between scenes and shots.
• Real-time generationSupports real-time video generation at 1280×720 resolution and 24fps.
• High resolution and super resolutionSupports generating videos with resolutions up to 1280×720, based on further upsampling to 2K QHD resolution.
• Camera control and world explorationIt supports precise camera control using defined trajectories, providing interactive world exploration capabilities.
• Enhanced physical consistencyPost-training based on computer-generated synthetic videos enhances the physical consistency and 3D effects of video generation.

Technical principles of Seaweed-7B

• Variational Autoencoder (VAE)This method compresses video data into a low-dimensional latent space and reconstructs the original video from that space. Based on a causal 3D convolutional architecture, it supports unified encoding of images and videos, avoiding edge flickering issues. Training at mixed resolutions (such as 256×256, 512×512, etc.) improves the reconstruction quality of high-resolution videos.
• Diffusion converter (DiT)The system generates video content within the latent space of VAEs, progressively denoising to produce high-quality videos. A hybrid stream architecture, combining full attention and window attention mechanisms, is used to improve training efficiency and generation quality. Multimodal Rotation-Local Encoding (MM-RoPE) is employed to enhance the fusion of positional information between text and video.
• Multi-stage training strategyThe process starts with low-resolution images and gradually transitions to high-resolution videos, optimizing GPU resource allocation. It includes a pre-training phase (images only, images + video) and a post-training phase (supervised fine-tuning, human feedback reinforcement learning).
• Optimization technologyMulti-level Activation Checkpointing (MLAC) reduces GPU memory footprint and computational overhead. Integration with CUDA kernels optimizes I/O operations, improving training and inference efficiency. Diffusion distillation reduces the number of function evaluations (NFEs) required for generation, accelerating the inference process.
• Data processingHigh-quality video data is used for data cleaning based on methods such as time segmentation, spatial cropping, and quality filtering. Synthetic video data is used to enhance the diversity and physical consistency of the training data. Detailed video subtitles are generated to enhance the model's text understanding capabilities.

Seaweed-7B project address

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

Application scenarios of Seaweed-7B

• Content creationGenerates high-quality videos from text or images, suitable for advertisements, movies, short videos, etc., and supports multiple styles and scenarios.
• Real-time interactionIt supports real-time video generation for use in virtual reality (VR) and augmented reality (AR), providing an immersive experience.
• Multimedia EntertainmentGenerates matching videos based on audio, suitable for music videos and audiobooks.
• Education and TrainingGenerate educational videos and simulated training scenarios for use in scientific experiments, historical reenactments, military training, etc.
• Advertising and MarketingGenerate personalized ads and brand promotional videos to increase appeal and conversion rates.