NVIDIA Synthetic Video Detector
NVIDIA Synthetic Video Detector is an AI-powered microservice designed to determine whether a video is real or AI-generated. It is optimized for content produced by diffusion models and built for media authentication, digital forensics, content verification, broadcast workflows, and media-integrity services. The model analyzes MP4 video input and returns a prediction for each frame on a scale from 0 to 1, where values closer to 0 indicate real footage and values closer to 1 indicate synthetic content. It is designed to remain robust under common video-compression conditions, helping preserve detection reliability when footage has been processed or distributed through typical media pipelines. It uses a Vision Transformer architecture based on an ensemble of DINOv2 and DINOv3 backbones, combining visual representations to distinguish authentic footage from generated video. Input frames are cropped to 504 x 504 pixels and normalized before inference.
Learn more
CogVideoX-3
CogVideoX-3 is a video generation model with new frame generation capabilities that significantly improve image stability and clarity. It delivers superior performance when handling subjects with significant movement, better adheres to instructions, and provides more realistic simulations. It supports image, text, and start-and-end-frame inputs, with video as the output modality, making it useful across text-to-video, image-to-video, and transition-based video workflows. CogVideoX-3 can be used for advertising and marketing by inputting product images or copy to quickly generate dynamic ads in multiple styles, supporting scene transitions and realistic lighting rendering. It also supports short video creation by converting single-frame images or text scripts into smooth, naturally animated short videos, covering both realistic and 3D styles. For tourism promotion, users can upload scenic spot photos and promotional text to generate immersive short videos.
Learn more
LTX-2.5
LTX-2.5 is an open-weights world model for video generation, built as a stronger foundation that teams can run on their own hardware, fine-tune on their data, and deploy on their terms. It improves quality, continuity, control, and efficiency through native multi-shot generation, stronger prompt adherence, and better local performance. Its Diffusion Fidelity Rendering technology allocates rendering compute based on scene complexity to deliver high pixel quality that holds up frame by frame. The model produces cleaner, smoother motion with fewer artifacts and can create connected shots that maintain character, environment, lighting, and voice across cuts. Stronger prompt understanding enables complex creative instructions from shorter prompts, while automatic duration prediction generates the appropriate clip length for the requested action.
Learn more
Seaweed
Seaweed is a foundational AI model for video generation developed by ByteDance. It utilizes a diffusion transformer architecture with approximately 7 billion parameters, trained on a compute equivalent to 1,000 H100 GPUs. Seaweed learns world representations from vast multi-modal data, including video, image, and text, enabling it to create videos of various resolutions, aspect ratios, and durations from text descriptions. It excels at generating lifelike human characters exhibiting diverse actions, gestures, and emotions, as well as a wide variety of landscapes with intricate detail and dynamic composition. Seaweed offers enhanced controls, allowing users to generate videos from images by providing an initial frame to guide consistent motion and style throughout the video. It can also condition on both the first and last frames to create transition videos, and be fine-tuned to generate videos based on reference images.
Learn more
