Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.
🌀 Abstract: This innovative framework addresses the limitations of current image generation models in handling intricate text prompts and ensuring reliability through verification and self-correction mechanisms. Coordinated by a multimodal large language model (MLLM) agent, GenArtist integrates a diverse library of tools, enabling seamless task decomposition, step-by-step execution, and systematic self-correction. With its tree-structured planning and advanced use of position-related inputs, GenArtist achieves state-of-the-art performance, outperforming models like SDXL and DALL-E 3. This session will delve into the system’s architecture and its groundbreaking potential for advancing image generation and editing tasks.
Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.
🌀 Abstract: This innovative framework addresses the limitations of current image generation models in handling intricate text prompts and ensuring reliability through verification and self-correction mechanisms. Coordinated by a multimodal large language model (MLLM) agent, GenArtist integrates a diverse library of tools, enabling seamless task decomposition, step-by-step execution, and systematic self-correction. With its tree-structured planning and advanced use of position-related inputs, GenArtist achieves state-of-the-art performance, outperforming models like SDXL and DALL-E 3. This session will delve into the system’s architecture and its groundbreaking potential for advancing image generation and editing tasks.
Telegram hopes to raise $1bn with a convertible bond private placement
The super secure UAE-based Telegram messenger service, developed by Russian-born software icon Pavel Durov, is looking to raise $1bn through a bond placement to a limited number of investors from Russia, Europe, Asia and the Middle East, the Kommersant daily reported citing unnamed sources on February 18, 2021.The issue reportedly comprises exchange bonds that could be converted into equity in the messaging service that is currently 100% owned by Durov and his brother Nikolai.Kommersant reports that the price of the conversion would be at a 10% discount to a potential IPO should it happen within five years.The minimum bond placement is said to be set at $50mn, but could be lowered to $10mn. Five-year bonds could carry an annual coupon of 7-8%.
Look for Channels Online
You guessed it – the internet is your friend. A good place to start looking for Telegram channels is Reddit. This is one of the biggest sites on the internet, with millions of communities, including those from Telegram.Then, you can search one of the many dedicated websites for Telegram channel searching. One of them is telegram-group.com. This website has many categories and a really simple user interface. Another great site is telegram channels.me. It has even more channels than the previous one, and an even better user experience.These are just some of the many available websites. You can look them up online if you’re not satisfied with these two. All of these sites list only public channels. If you want to join a private channel, you’ll have to ask one of its members to invite you.
