![]() ![]() Scaling up the model has become the easiest and the standard practice to improve the state-of-the-arts. Prismer was designed to follow this similar direction and explore how to better utilise pre-trained models when building a new model, particularly with multi-task and multi-modal capabilities. This similar concept has also been shown to be effective in the widely popular ControlNet (but in a slightly different perspective), which provided conditional multi-modal control for the Stable Diffusion model, only requiring some lightweight fine-tuning. The concept of "model ensembling" is very appealing, for which I was personally particularly inspired by the success of Socratic Models, which demonstrated that a wide range of multi-modal tasks could be achieved in a zero-shot manner by simply connecting them together using "language as the universal control interface". ![]() This idea eventually led to the development of the Prismer project as it is. This still allowed us to utilise a diverse pool of pre-trained models to provide useful domain knowledge, but without the need to heavily modify the network architecture of each model. However, we quickly realised that the complexity of this research was too much for an internship project, because we have to accommodate the design of each model to make them compatible with each other, and also need to design a training scheme that can fine-tune all these models simultaneously.Īs a result, we shifted our focus to a more specific task: multi-modal reasoning. As with many of my past research projects, we initially set out with an overly ambitious goal, of designing a multi-modal generative framework >.< This is to create a system that could perform "any-directional" multi-modal generation tasks, such as image-to-text, text-to-image, depth-to-image, image-to-depth, and even (depth+segmentation)-to-image, where the model to solve each task would be initialised with a separately pre-trained domain expert. This game works perfectly in modern browsers and requires no installation.ĭevast.io has been played by thousands of gamers who rated it 4.7 / 5 with 9374 votes.Īdventure Games, io Games, Multiplayer Games, Survival Games a try.Prismer is my internship project with NVIDIA Machine Learning Research Team. It only takes one Devast io game to get hooked for many hours, see for how long you can survive!ĭevast.io is one of the best Adventure Game you can play on Kevin Games. Press C to access the Crafting and Skill menusĭeveloped by LapaMauve and released in July, 2018, this title is fully playable in modern browsers.Use W, A, S, D or the Arrow keys to walk around.Exciting crafting options, enhancements and fortifications.Hunger, temperature, safety and other needs to be taken care of.Realistic lighting conditions during ever changing day and night periods.Real-time survival on multiplayer servers.Gather resources, craft weapons and construct shelter where you can store your food and find safety after a hard day’s work. Devast.io is a survival IO game set in a dystopian future where the apocalypse took away millions of lives and the handful of people left are fighting for their lives in the wild. ![]()
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