Tag: text to image

by Synced 2023-07-14 Number of comments0
AI Computer Vision & Graphics Machine Learning & Data Science Research

Shanghai AI Lab, CUHK & Stanford U Extend Personalized Text-to-Image Diffusion Models Into Animation Generators Without Tuning

In a new paper AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning, a research team presents AnimateDiff, a general and practical framework that is able to generate animated images for any personalized text-to-image (T2I) model, without any extra training and model-specified tuning.

by Synced 2023-01-09 Number of comments0
AI Machine Learning & Data Science Research

Google’s Masked Generative Transformers Achieve SOTA Text-To-Image Performance With Improved Efficiency

In the new paper Muse: Text-To-Image Generation via Masked Generative Transformers, a Google Research team introduces Muse, a transformer-based text-to-image synthesis model that leverages masked image modelling to achieve state-of-the-art performance while being significantly faster than diffusion or autoregressive models.

by Synced 2022-06-01 Number of comments0
AI Machine Learning & Data Science Nature Language Tech Research

Google’s Imagen Text-to-Image Diffusion Model With Deep Language Understanding Defeats DALL-E 2

In the new paper Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding, a Google Brain research team presents Imagen, a text-to-image diffusion model that combines deep language understanding and photorealistic image generation capabilities to achieve a new state-of-the-art FID score of 7.27 on the COCO dataset.

by Synced 2022-04-13 Number of comments0
AI Machine Learning & Data Science Research

OpenAI’s unCLIP Text-to-Image System Leverages Contrastive and Diffusion Models to Achieve SOTA Performance

In the new paper Hierarchical Text-Conditional Image Generation with CLIP Latents, an OpenAI research team combines the advantages of contrastive and diffusion models for text-conditional image generation tasks. Their proposed unCLIP model improves image diversity with minimal loss in photorealism and caption similarity, and produces image quality comparable to the state-of-the-art text-to-image system GLIDE.