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Description
Fooocus is a user-friendly, open-source image generation tool that operates offline, built on Gradio and utilizing Stable Diffusion XL (SDXL) technology. It is crafted for ease of use, allowing users to concentrate on crafting prompts while the software manages the intricate details. Additionally, Fooocus features an offline prompt enhancement engine based on GPT-2 and incorporates sampling upgrades, which guarantee high-quality results for both concise and extensive prompts. The software also boasts functionalities such as inpainting, outpainting, upscaling, and image prompting, employing its proprietary algorithms to deliver better performance than conventional SDXL techniques. Users can choose from various presets, including anime and realistic styles, while also benefiting from an intuitive interface that supports advanced customization options. The installation process is quick and straightforward, requiring only a few clicks, and Fooocus is compatible with systems featuring a minimum of 4GB NVIDIA GPU memory. Currently, Fooocus is in a phase of limited long-term support, primarily concentrating on addressing bugs, and there are no immediate intentions to transition to newer model architectures, which may affect long-term enhancements. This combination of features makes Fooocus a compelling choice for those interested in image generation.
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
NVIDIA DRIVE
Stable Diffusion XL (SDXL)
Pricing Details
Free
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
lllyasviel
Country
China
Website
github.com/lllyasviel/Fooocus
Vendor Details
Company Name
OpenAI
Founded
2015
Country
United States
Website
openai.com/research/point-e