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Researchers have developed an improved text-to-image synthesis model called Kandinsky, which utilizes image prior and latent diffusion. The model achieved a FID score of 8.03 on the COCO-30K dataset, making it the state-of-the-art (SOTA) for open-source models. Another paper discusses aligning text-to-image diffusion models with reward backpropagation. Text-to-image generation has seen significant advancements in computer vision through the evolution of generative models. Additionally, a new approach called Latent Consistency Models has achieved SOTA text-to-image generation performance with few-step inference. Google and John Hopkins University researchers have also introduced a faster and more efficient distillation method for text-to-image generation, overcoming diffusion model limitations. These developments in text-to-image synthesis have been shared through various platforms such as GitHub, research papers, and quick reads.
Researchers from Google and John Hopkins University Reveal a Faster and More Efficient Distillation Method for Text-to-Image Generation: Overcoming Diffusion Model Limitations Quick Read: https://t.co/cMReNImUk3 Paper: https://t.co/tIo0ORyTuD… https://t.co/3jaoIveLiP https://t.co/2N61HbymFP
This AI Research Unveils ‘Kandinsky1’: A New Approach in Latent Diffusion Text-to-Image Generation with Outstanding FID Scores on COCO-30K Quick Read: https://t.co/uGVwu2eB3t Paper: https://t.co/FDcxFZ7A2W Github: https://t.co/iZDMRJGMJv If you like our work, you will love our… https://t.co/4bXG60fmJm https://t.co/tOb5QTZZ4t
Latent Consistency Models: Synthesizing High-Resolution Images with Few-step Inference Achieves SotA text-to-image generation performance with few-step inference proj: https://t.co/wxFNPRCixL abs: https://t.co/5WxPY3ZJfJ https://t.co/srMjQyHx2D
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion GitHub: https://t.co/8UkluCQSDH abs: https://t.co/iXhzBdohQ2 FID score of 8.03 on the COCO-30K dataset, SOTA for open-source models. https://t.co/xpwugmGepM
Aligning Text-to-Image Diffusion Models with Reward Backpropagation paper page: https://t.co/3nDMO7GvRO Text-to-image diffusion models have recently emerged at the forefront of image generation, powered by very large-scale unsupervised or weakly supervised text-to-image… https://t.co/jOvbKNGJap https://t.co/rw2XGO502N
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion paper page: https://t.co/GBBsGYdymi Text-to-image generation is a significant domain in modern computer vision and has achieved substantial improvements through the evolution of generative… https://t.co/h4PFLfSYv4 https://t.co/ZZeigtwWWF
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion GitHub: https://t.co/8UkluCQSDH abs: https://t.co/iXhzBdohQ2 FID score of 8.03 on the COCO-30K dataset, SOTA for open-source models. https://t.co/l2zoRKwnlo