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DALL·E 2 can create original, realistic images and art from a text description. It can combine concepts, attributes, and styles. In January 2021, OpenAI introduced DALL·E. One year later, our newest system, DALL·E 2, generates more realistic and accurate images with 4x greater resolution.
DALL·E is a simple decoder-only transformer that receives both the text and the image as a single stream of 1280 tokens—256 for the text and 1024 for the image—and models all of them autoregressively.
DALL·E 3 is designed to decline requests that ask for an image in the style of a living artist. Creators can now also opt their images out from training of our future image generation models.
Notably, we achieved our results by directly applying the GPT-2 language model to image generation. Our results suggest that due to its simplicity and generality, a sequence transformer given sufficient compute might ultimately be an effective way to learn excellent features in many domains.
Explore resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's developer platform.
Experiment with DALL·E, an AI system by OpenAI.
We show that scaling a simple pre-training task is sufficient to achieve competitive zero-shot performance on a great variety of image classification datasets. Our method uses an abundantly available source of supervision: the text paired with images found across the internet.
Sora is a diffusion model, which generates a video by starting off with one that looks like static noise and gradually transforms it by removing the noise over many steps. Sora is capable of generating entire videos all at once or extending generated videos to make them longer.
Explore resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's developer platform.
GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, image, and video and generates any combination of text, audio, and image outputs.