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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

Screenshots View All

Screenshots View All

Integrations

APIFree
Eromify
Oxen.ai
Piooy

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

Product Features

Product Features

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