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Midjourney is a generative artificial intelligence program and service created and hosted by the San Francisco–based independent research lab Midjourney, Inc. Midjourney generates images from natural language descriptions, called prompts, similar to OpenAI's DALL-E and Stability AI's Stable Diffusion.
Text-to-image model. An image conditioned on the prompt "an astronaut riding a horse, by Hiroshige ", generated by Stable Diffusion, a large-scale text-to-image model 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.
Prompt engineering is enabled by in-context learning, defined as a model's ability to temporarily learn from prompts. The ability for in-context learning is an emergent ability [ 14] of large language models. In-context learning itself is an emergent property of model scale, meaning breaks [ 15] in downstream scaling laws occur such that its ...
2. Define your target audience. The more context you can provide in your prompt, the better—and this includes who might read ChatGPT's responses. For example, if you use ChatGPT to outline a ...
DALL·E, DALL·E 2, and DALL·E 3 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. DALL·E 3 was released natively ...
Stable Diffusion. Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing artificial intelligence boom .
This is a list of notable fashion designers sorted by nationality. It includes designers of haute couture and ready-to-wear. For haute couture only, see the list of grands couturiers. For footwear designers, see the list of footwear designers.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.