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The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [3]Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images.
DALL-E, DALL-E 2, and DALL-E 3 (stylised DALL·E, and pronounced DOLL-E) are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The first version of DALL-E was announced in January 2021. In the following year, its successor DALL-E 2 was released.
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For instance, DALL-E is a deep neural network trained on 650 million pairs of images and texts across the internet that can create artworks based on text entered by the user. [247] In the field of music, transformers are used to create original music for commercials and documentaries through companies such as AIVA and Jukedeck . [ 248 ]
DALL-E 2 is a 3.5-billion cascaded diffusion model that generates images from text by "inverting the CLIP image encoder", the technique which they termed "unCLIP". The unCLIP method contains 4 models: a CLIP image encoder, a CLIP text encoder, an image decoder, and a "prior" model (which can be a diffusion model, or an autoregressive model).
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A view of the fort of Marburg (Germany) and the saliency Map of the image using color, intensity and orientation.. In computer vision, a saliency map is an image that highlights either the region on which people's eyes focus first or the most relevant regions for machine learning models. [1]
OpenAI began collaborating with Broadcom in 2024 to design a custom AI chip capable of both training and inference targeted for mass production in 2026 and to be manufactured by TSMC in 3 nm node. This initiative is intended to reduce OpenAI's dependence on Nvidia GPUs, which are costly and face high demand in the market.