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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.
This paper takes that approach to a next level by using image-to-image GANs applied to G-buffers from the game engine to generate temporally consistent photorealistic GTA V frames. View Paper Skip-Convolutions for Efficient Video Processing
Behind The Scenes: Understanding video object segmentation (VOS)
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 multimodal AI system that can generate novel videos with text, images or video clips.
In this blog post, I will briefly describe some of the technical challenges we faced while building Green Screen, and provide insight into the process of developing a real-time, interactive machine learning tool for the web.
A series of conversations with prominent AI researchers and artists on their perspectives on creative AI.