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There are several architectures that have been used to create Text-to-Video models. Similar to Text-to-Image models, these models can be trained using Recurrent Neural Networks (RNNs) such as long short-term memory (LSTM) networks, which has been used for Pixel Transformation Models and Stochastic Video Generation Models, which aid in consistency and realism respectively. [31]
Re-captioning is used to augment training data, by using a video-to-text model to create detailed captions on videos. [ 7 ] OpenAI trained the model using publicly available videos as well as copyrighted videos licensed for the purpose, but did not reveal the number or the exact source of the videos. [ 5 ]
Dream Machine is a text-to-video model created by Luma Labs and launched in June 2024. It generates video output based on user prompts or still images. Dream Machine has been noted for its ability to realistically capture motion, while some critics have remarked upon the lack of transparency about its training data.
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs were founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.
Since the 20th century, artists have used AI to create art, some of which has been exhibited in museums and won awards. [1] During the AI boom of the early 2020s, text-to-image models such as Midjourney, DALL-E, Stable Diffusion, and FLUX.1 became widely available to the public, allowing non-artists to quickly generate imagery with little effort.
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