Search results
Results from the WOW.Com Content Network
The Stable Diffusion model supports the ability to generate new images from scratch through the use of a text prompt describing elements to be included or omitted from the output. [8] Existing images can be re-drawn by the model to incorporate new elements described by a text prompt (a process known as "guided image synthesis" [ 49 ] ) through ...
According to a test performed by Ars Technica, the outputs generated by Flux.1 Dev and Flux.1 Pro are comparable with DALL-E 3 in terms of prompt fidelity, with the photorealism closely matched Midjourney 6 and generated human hands with more consistency over previous models such as Stable Diffusion XL. [32]
Latent diffusion model was published in December 2021 and became the basis for the later Stable Diffusion (August 2022). [65] In 2022, Midjourney [66] was released, followed by Google Brain's Imagen and Parti, which were announced in May 2022, Microsoft's NUWA-Infinity, [67] [2] and the source-available Stable Diffusion, which was released in ...
In August 2022, the company co-released an improved version of their Latent Diffusion Model called Stable Diffusion together with the CompVis Group at Ludwig Maximilian University of Munich and a compute donation by Stability AI. [14] [15] On December 21, 2022, Runway raised US$50 million [16] in a Series C round.
Stable Diffusion (2022-08), released by Stability AI, consists of a denoising latent diffusion model (860 million parameters), a VAE, and a text encoder. The denoising network is a U-Net, with cross-attention blocks to allow for conditional image generation.
As with other text-to-image models, Aurora generates images from natural language descriptions, called prompts. [64] The capacity to generate images using Flux was added in August 2024, with The Verge reporting that the kinds of prompts that would be "immediately blocked" on other services seemed to be permitted by Grok. Their journalist was ...
Text-to-image models, such as Stable Diffusion, Midjourney and others, while impressive in their ability to generate images from text descriptions, often produce inaccurate or unexpected results. One notable issue is the generation of historically inaccurate images.
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.