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Description
DiffusionBee is an incredibly user-friendly application that allows you to create AI-generated artwork on your computer utilizing Stable Diffusion technology, and it's completely free to use. This platform combines all the latest Stable Diffusion features into a single, intuitive interface. You can easily produce images from text prompts, generate visuals in various artistic styles, or alter existing pictures using descriptive prompts. Additionally, it enables the creation of new images from a base picture and allows for the addition or removal of elements in designated areas through text commands. You can also expand images outward based on your instructions, select specific regions on the canvas to introduce new objects, and leverage AI to enhance the resolution of your creations automatically. Furthermore, you can utilize external Stable Diffusion models that have been trained on particular styles or subjects through DreamBooth. For more experienced users, advanced options such as negative prompts and diffusion steps are available. Importantly, all processing occurs locally on your machine, ensuring privacy as nothing is uploaded to the cloud. Plus, there is a vibrant Discord community where users can seek assistance and share ideas. This supportive network further enriches the experience of utilizing DiffusionBee.
Description
Recent advancements in the realm of text-to-image synthesis have emerged from diffusion models that have been trained on vast amounts of image-text pairs. To successfully transition this methodology to 3D synthesis, it would necessitate extensive datasets of labeled 3D assets alongside effective architectures for denoising 3D information, both of which are currently lacking. In this study, we address these challenges by leveraging a pre-existing 2D text-to-image diffusion model to achieve text-to-3D synthesis. We propose a novel loss function grounded in probability density distillation that allows a 2D diffusion model to serve as a guiding principle for the optimization of a parametric image generator. By implementing this loss in a DeepDream-inspired approach, we refine a randomly initialized 3D model, specifically a Neural Radiance Field (NeRF), through gradient descent to ensure its 2D renderings from various angles exhibit a minimized loss. Consequently, the 3D representation generated from the specified text can be observed from multiple perspectives, illuminated with various lighting conditions, or seamlessly integrated into diverse 3D settings. This innovative method opens new avenues for the application of 3D modeling in creative and commercial fields.
API Access
Has API
API Access
Has API
Integrations
Stable Diffusion
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
DiffusionBee
Website
diffusionbee.com
Vendor Details
Company Name
DreamFusion
Website
dreamfusion3d.github.io