AI Image 2 Video

AI Image 2 Video is a generative video tool that generates a GPT Image 2 first frame, then animate it with Seedance 2.0 or Kling 3. AI Image 2 Video combines GPT Image 2 image creation with Seedance 2.0 and Kling 3 video generation.

AI Image 2 Video is a browser-based workspace that pairs image generation with video generation. The idea is to build a clean source frame first, using GPT Image 2, and only then hand it to a video model such as Seedance 2.0 or Kling 3. Fixing composition, lighting, text and subject identity while the picture is still a still is cheaper and easier than repairing artifacts after motion has been added. The video generator offers several creation modes: text to video, image to video and multi reference. Users write a prompt of up to 2,500 characters, with helper actions to translate, improve or polish it. Settings cover aspect ratio, resolution, a duration slider from 4 to 15 seconds, and an optional audio track that costs extra credits. Seedance 2.0 is marked as available to paid users only, and generating requires signing in and holding credits. A separate GPT Image 2 page handles prompt-to-image work, reference edits, photo restoration and square, vertical or widescreen formats. The site leans heavily on templates. A prompt library covers ecommerce product shots, packaging references, short-form social covers, poster-based ad storyboards, UI mockups and character sheets, each with a use-template or copy-prompt option. That makes it a practical fit for marketers, ecommerce sellers, social content creators and small teams who need short clips for ads, UGC-style posts, storyboards or product demos without a video production setup. Pricing is credit based, with monthly and yearly subscription plans plus one-time credit packs that stay valid for 12 months. Compared with single-purpose image animators, the differentiator is the combined image-then-video pipeline in one account. Compared with using Kling or Seedance directly, it adds a prompt library and source-frame tooling, at the cost of an extra layer between the user and the underlying models.