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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.
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.
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’re teaching AI to understand and simulate the physical world in motion, with the goal of training models that help people solve problems that require real-world interaction. Introducing Sora, our text-to-video model.
Explore resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's developer platform.
We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples.
When evaluated on the SST-2 NLP dataset rendered as images, a linear classifer on CLIP’s representation matches a CBoW model with direct access to the text. CLIP is also competitive at detecting hateful memes without needing ground truth text.
Experiment with DALL·E, an AI system by OpenAI.
Customize a model’s existing knowledge and behavior for a specific task using text and images via supervised fine-tuning. Plus, use our Model Distillation tools to fine-tune smaller models on the outputs of more capable models.
The new voice capability is powered by a new text-to-speech model, capable of generating human-like audio from just text and a few seconds of sample speech. We collaborated with professional voice actors to create each of the voices.