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stable-video-diffusion-img2vid-fp16 is a generative image-to-video model developed by Stability AI that takes in a still image as input and generates a short video clip from it. This model is similar to lcm-video2video, which is a fast video-to-video model with a latent consistency, and animelike2d, though the latter's description is not provided. It is also related to stable-video-diffusion and stable-video-diffusion-img2vid, which are other image-to-video diffusion models. Model inputs and outputs The stable-video-diffusion-img2vid-fp16 model takes in a single still image as input and generates a short video clip of 14 frames at a resolution of 576x1024. The model was trained on a large dataset to learn how to convert a static image into a dynamic video sequence. Inputs Image**: A single input image at a resolution of 576x1024 pixels. Outputs Video**: A generated video clip of 14 frames at a resolution of 576x1024 pixels. Capabilities The stable-video-diffusion-img2vid-fp16 model is capable of generating short video sequences from static input images. The generated videos can capture motion, camera pans, and other dynamic elements, though they may not always achieve perfect photorealism. The model is intended for research purposes and can be used to explore generative models, study their limitations and biases, and generate artistic content. What can I use it for? The stable-video-diffusion-img2vid-fp16 model is intended for research purposes only. Possible applications include: Researching generative models and their capabilities Studying the limitations and biases of generative models Generating artistic content and using it in design or other creative processes Developing educational or creative tools that leverage the model's capabilities The model should not be used to generate factual or true representations of people or events, as it was not trained for that purpose. Any use of the model must comply with Stability AI's Acceptable Use Policy. Things to try With the stable-video-diffusion-img2vid-fp16 model, you can experiment with generating video sequences from a variety of input images. Try using different types of images, such as landscapes, portraits, or abstract art, to see how the model handles different subject matter. Explore the model's limitations by trying to generate videos with complex elements like faces, text, or fast-moving objects. Observe how the model's outputs evolve over the course of the video sequence and analyze the consistency and quality of the generated frames.

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Updated 5/27/2024