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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
Seedream 4.0 represents a groundbreaking evolution in multimodal AI, seamlessly combining text-to-image generation and text-based image manipulation within a single framework, capable of producing high-resolution visuals up to 4K with remarkable accuracy and speed. This innovative model employs an advanced diffusion transformer and variational autoencoder architecture, enabling it to effectively interpret both written prompts and visual references to generate outputs that are rich in detail and consistency, all while managing intricate elements such as semantics, lighting, and structural integrity adeptly. Additionally, it supports batch generation and multiple references, allowing users to execute precise modifications, whether altering style, background, or specific objects, without compromising the overall scene's quality. Demonstrating unparalleled prompt comprehension, visual appeal, and structural robustness, Seedream 4.0 surpasses its predecessors and competing models in various benchmarks focused on prompt fidelity and visual coherence. This advancement not only enhances creative workflows but also opens new possibilities for artists and designers seeking to push the boundaries of digital art.
API Access
Has API
API Access
Has API
Integrations
Flyne AI
OpenClaw
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
DreamFusion
Website
dreamfusion3d.github.io
Vendor Details
Company Name
ByteDance
Founded
2012
Country
China
Website
seed.bytedance.com/en/seedream4_0