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Our ensemble of diffusion models, called eDiff-I, results in improved text alignment while maintaining the same inference computation cost and preserving high visual quality, outperforming previous large-scale text-to-image diffusion models on the standard benchmark. View Paper.
With Stable Diffusion, we use an existing model to represent the text that’s being imputed into the model. We then use the CLIP model from OpenAI, which learns a representation of images, and text, which are compatible. What this ultimately enables is a similar encoding of images and text that’s useful to navigate.
Our latent diffusion models (LDMs) achieve highly competitive performance on various tasks, including unconditional image generation, inpainting, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.
A series of conversations with prominent AI researchers and artists on their perspectives on creative AI.
Demonstrable passion for AI, shown through continuous self-education via reading papers and hands-on experimentation. Program Highlights Tailored Mentorship: Participants are paired with seasoned researchers, with weekly check-ins to ensure steady progress.