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Description
Gemini Diffusion represents our cutting-edge research initiative aimed at redefining the concept of diffusion in the realm of language and text generation. Today, large language models serve as the backbone of generative AI technology. By employing a diffusion technique, we are pioneering a new type of language model that enhances user control, fosters creativity, and accelerates the text generation process. Unlike traditional models that predict text in a straightforward manner, diffusion models take a unique approach by generating outputs through a gradual refinement of noise. This iterative process enables them to quickly converge on solutions and make real-time corrections during generation. As a result, they demonstrate superior capabilities in tasks such as editing, particularly in mathematics and coding scenarios. Furthermore, by generating entire blocks of tokens simultaneously, they provide more coherent responses to user prompts compared to autoregressive models. Remarkably, the performance of Gemini Diffusion on external benchmarks rivals that of much larger models, while also delivering enhanced speed, making it a noteworthy advancement in the field. This innovation not only streamlines the generation process but also opens new avenues for creative expression in language-based tasks.
Description
Recent advancements in text-based 3D object generation have yielded encouraging outcomes; however, leading methods generally need several GPU hours to create a single sample, which is a stark contrast to the latest generative image models capable of producing samples within seconds or minutes. In this study, we present a different approach to generating 3D objects that enables the creation of models in just 1-2 minutes using a single GPU. Our technique initiates by generating a synthetic view through a text-to-image diffusion model, followed by the development of a 3D point cloud using a second diffusion model that relies on the generated image for conditioning. Although our approach does not yet match the top-tier quality of existing methods, it offers a significantly faster sampling process, making it a valuable alternative for specific applications. Furthermore, we provide access to our pre-trained point cloud diffusion models, along with the evaluation code and additional models, available at this https URL. This contribution aims to facilitate further exploration and development in the realm of efficient 3D object generation.
API Access
Has API
API Access
Has API
Integrations
Gemini
Gemini Enterprise
WeatherNext
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Google DeepMind
Founded
2010
Country
United Kingdom
Website
deepmind.google/models/gemini-diffusion/
Vendor Details
Company Name
OpenAI
Founded
2015
Country
United States
Website
openai.com/research/point-e