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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.
We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language. Illustration: Justin Jay Wang. DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions, using a dataset of text–image pairs.
Modern text-to-image systems have a tendency to ignore words or descriptions, forcing users to learn prompt engineering. DALL·E 3 represents a leap forward in our ability to generate images that exactly adhere to the text you provide.
Generative models. Illustration: Ludwig Pettersson. This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning.
In our work, we first show that better generative models achieve stronger classification performance. Then, through optimizing GPT-2 for generative capabilities, we achieve top-level classification performance in many settings, providing further evidence for analysis by synthesis.
Explore resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's developer platform.
Introducing Sora, our text-to-video model. Sora can generate videos up to a minute long while maintaining visual quality and adherence to the user’s prompt. Today, Sora is becoming available to red teamers to assess critical areas for harms or risks.
Experiment with DALL·E, an AI system by OpenAI.
The ImageNet dataset, one of the largest efforts in this space, required over 25,000 workers to annotate 14 million images for 22,000 object categories. In contrast, CLIP learns from text–image pairs that are already publicly available on the internet.
Glow models can generate realistic-looking high-resolution images, and can do so efficiently. Our model takes about 130ms to generate a 256 x 256 sample on a NVIDIA 1080 Ti GPU. Like previous work, we found that sampling from a reduced-temperature model often results in higher-quality samples.