Seaweed: A 7B-Parameter Video Generation Model from ByteDance

Seaweed, a foundational model for video generation developed by ByteDance, is making waves with its ability to generate high-quality videos from text descriptions. This research effort, detailed in a recent paper, showcases diffusion transformers with approximately 7 billion (7B) parameters, trained using compute equivalent to 1,000 H100 GPUs.

What Changed and Why It Matters

Seaweed represents a significant leap forward in video generation for several reasons:

• Multi-modal Learning: Seaweed is trained on massive amounts of multi-modal data, including videos, images, and text. This diverse training set allows the model to understand and generate content that is contextually rich and visually compelling.
• Flexibility: The model can generate videos in various resolutions, aspect ratios, and durations, making it suitable for a wide range of applications from short films to social media content.
• Lifelike Human Characters: One of Seaweed's standout features is its ability to create lifelike human characters that exhibit a diverse array of actions, gestures, and emotions. This capability opens up new possibilities for virtual production and interactive media.

Key Features and Capabilities

1. Diverse Video Generation

• Landscapes: Seaweed excels at generating intricate and dynamic landscapes, enhancing storytelling with visually stunning environments.
• Human Characters: The model can generate realistic human characters that move naturally and express a wide range of emotions.

2. Enhanced User Controls

• Image Conditioning: Users can provide an image as the first frame to guide the model in generating consistent motion and style throughout the video.
• Frame-to-Frame Control: By conditioning on both the first and last frames, users can create interesting transition videos with greater creative control.

3. Reference-Based Video Generation

• Finetuning for Flexibility: Seaweed can be finetuned to generate videos based on reference images, allowing for flexible input options. Whether it's a human reference image, an object reference image, or a combination of multiple references, the model can synthesize them into dynamic video sequences.

4. Audio-Visual Synchronization

• Human-Centric Generation: Seaweed is adapted to generate content conditioned on audio inputs by Omnihuman. This feature ensures synchronized lip movements and body gestures that align with the tone and timing of the audio, creating a seamless and lifelike interaction.
• Audio Generation: The model can also generate both audio and video together, ensuring that the audio is synced to reflect the action, scene, tone, rhythm, and style of the video. This integration enhances visual storytelling by providing complementary audio.

Demonstrations

Seaweed's capabilities are best demonstrated through its generated content:

• Short Film: A short film created entirely using Seaweed, with only background music and ending titles added manually.
• Image Conditioning Examples: Videos generated from a single image as the first frame, showcasing consistent motion and style.
• Transition Videos: Videos generated by conditioning on both the first and last frames, demonstrating creative control.

Creator Contributions

Several creators have contributed to showcasing Seaweed's capabilities:

• Yuanjing
• Jiahong Huang
• Baiyi Li

Their work highlights the model's versatility and potential for various applications.

Conclusion

Seaweed is a powerful tool for video generation, offering high-quality output with flexible user controls. Its ability to generate lifelike human characters and dynamic landscapes, coupled with audio-visual synchronization, makes it a valuable asset for content creators and virtual production teams.