LitVideo: AI Video Generator

LitVideo is the video-generation module of the LitMedia platform, a browser-based studio that aggregates multiple third-party AI video and image models rather than relying on a single proprietary engine. Instead of forcing users to open separate tools for each provider, LitVideo surfaces models such as Seedance 2.0, Kling 3.0, Veo 3.1, Pixverse V6, MiniMax H3, Vidu Q3, and GPT Image 2 inside one interface, letting people pick whichever model suits a given clip or image without leaving the site. The tool is built for social media creators, marketers, and short-form video producers who need a high volume of visual content quickly. Rather than starting from a blank prompt every time, LitVideo organizes work around scenario templates covering dancing clips, product visual-effect shots, two-player interaction videos, fashion editorial looks, film-lens styling, and novelty formats like old-photo colorization or facial photo enhancement. Users browse a gallery of example outputs and can recreate a similar video or image with one click, which lowers the barrier for people who are not comfortable writing detailed generation prompts from scratch. In practice, a user selects a creation mode - text-to-video, image-to-video, straight image generation, or a themed template - chooses an underlying model, supplies a prompt or source image, and lets the platform render the output. Additional post-generation tools cover video extension, lip sync, face swap, AI avatar creation, and converting footage or stills into animated style, so a single project can move from initial generation through several rounds of editing inside the same workspace. Within the broader AI video category, LitVideo's differentiator is breadth of model access and template variety rather than a single best-in-class engine of its own, positioning it closer to a curated aggregator for prosumers and content teams than to a developer-focused or single-model tool. That makes it a reasonable starting point for creators who want to experiment across several state-of-the-art video models without managing separate subscriptions, though it means quality and style will vary depending on which underlying model is selected for a given job.