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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.
Description
Z-Image is a family of open-source image generation foundation models created by Alibaba's Tongyi-MAI team, utilizing a Scalable Single-Stream Diffusion Transformer architecture to produce both photorealistic and imaginative images from textual descriptions with only 6 billion parameters, which enhances its efficiency compared to many larger models while maintaining competitive quality and responsiveness to instructions. This model family comprises several variants, including Z-Image-Turbo, a distilled version designed for rapid inference that achieves results with as few as eight function evaluations and sub-second generation times on compatible GPUs; Z-Image, the comprehensive foundation model tailored for high-fidelity creative outputs and fine-tuning processes; Z-Image-Omni-Base, a flexible base checkpoint aimed at fostering community-driven advancements; and Z-Image-Edit, specifically optimized for image-to-image editing tasks while demonstrating strong adherence to instructions. Each variant of Z-Image serves distinct purposes, catering to a wide range of user needs within the realm of image generation.
API Access
Has API
API Access
Has API
Integrations
APIFree
Eromify
Oxen.ai
Piooy
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
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
DreamFusion
Website
dreamfusion3d.github.io
Vendor Details
Company Name
Z-Image
Founded
1999
Country
China
Website
github.com/Tongyi-MAI/Z-Image