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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
MOSS-Image represents a versatile family of open-weight models designed for transforming natural-language prompts and structured layout specifications into images. Both its Base and fine-tuned SFT checkpoints utilize a unified inference interface and accommodate prompts with a maximum length of 4,096 tokens. Users have the flexibility to either articulate a scene in written form or supply JSON layout data that dictates the placement of objects and text within the image. The project offers comprehensive resources, including Python and Gradio workflows, a Diffusers pipeline, model weights, and detailed guidance for local deployment and fine-tuning processes. This model is capable of producing a variety of visual content, such as illustrations, photorealistic images, posters, and other designed compositions that can integrate specific text elements. Additionally, developers have the option to leverage supported image-generation runtimes or a hosted API to enhance their projects. Aimed at developers, researchers, and creative individuals, the software facilitates the generation and modification of images with precise layout control, making it a valuable tool in various creative endeavors. As users explore MOSS-Image, they can unlock new possibilities in visual storytelling and design innovation.
API Access
Has API
No
API Access
Has API
Yes
Integrations
ComfyUI
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
DreamFusion
Website
dreamfusion3d.github.io
Vendor Details
Company Name
OpenMOSS
Country
China
Website
openmoss.ai/MOSS-Image/