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What is SeedEdit?

SeedEdit represents a state-of-the-art AI image-editing model developed by the Seed team at ByteDance, enabling users to alter existing images using natural-language instructions while preserving untouched areas. By supplying an input image along with a detailed request for modifications—such as changing styles, eliminating or substituting objects, altering backgrounds, modifying lighting, or updating text—the model produces a final image that integrates these edits smoothly while maintaining the original’s structure, resolution, and identity. Employing a diffusion-based framework, SeedEdit is trained via a meta-information embedding pipeline and a combined loss strategy that blends diffusion and reward losses, striking a careful balance between reconstructing images and regenerating them. This meticulous approach results in exceptional editing precision, detail retention, and adherence to user requests. The most recent version, SeedEdit 3.0, can execute high-resolution edits up to 4K, delivers quick inference times (generally within 10-15 seconds), and supports multiple rounds of sequential editing, making it an essential resource for both creative professionals and hobbyists. Furthermore, its groundbreaking features empower users to realize their artistic ideas with an unprecedented level of ease and adaptability, thereby transforming the landscape of digital image editing.

What is Point-E?

Recent progress in generating 3D objects from text has shown promising results; nonetheless, many of the leading techniques typically require multiple hours on powerful GPUs to produce just one sample, which stands in stark contrast to the more advanced generative image models that can create samples in a matter of seconds or minutes. In this research, we introduce a novel method for 3D object generation that allows for model creation in merely 1-2 minutes using only a single GPU. Our approach begins with generating a synthetic view through a text-to-image diffusion model, and it is followed by constructing a 3D point cloud using a second diffusion model that is conditioned on the image produced. Although our method has not yet reached the highest quality levels of the best existing techniques, it provides a considerably quicker sampling process, thus serving as a valuable alternative for certain applications. Additionally, we make available our pre-trained point cloud diffusion models, as well as the evaluation code and supplementary models, accessible at this provided URL. This endeavor is intended to encourage further research and innovation in the area of rapid 3D object generation, potentially paving the way for more efficient workflows in the industry.

Media

Media

Integrations Supported

GPT-4o
Gemini

Integrations Supported

GPT-4o
Gemini

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

ByteDance

Date Founded

2012

Company Location

China

Company Website

seed.bytedance.com/en/seededit

Company Facts

Organization Name

OpenAI

Date Founded

2015

Company Location

United States

Company Website

openai.com/research/point-e

Categories and Features

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