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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 DreamFusion?

Recent progress in text-to-image synthesis has been driven by diffusion models trained on vast collections of image-text pairs. To effectively adapt this approach for 3D synthesis, there is a critical need for large datasets of labeled 3D assets and efficient architectures capable of denoising 3D information, both of which are currently insufficient. This research aims to tackle these obstacles by utilizing an established 2D text-to-image diffusion model to facilitate text-to-3D synthesis. We introduce a groundbreaking loss function based on probability density distillation, enabling a 2D diffusion model to guide the optimization of a parametric image generator effectively. By applying this loss within a DeepDream-inspired framework, we enhance a randomly initialized 3D model, specifically a Neural Radiance Field (NeRF), through gradient descent, ensuring its 2D renderings from various angles demonstrate reduced loss. As a result, the generated 3D representation can be viewed from multiple viewpoints, illuminated under different lighting conditions, or integrated seamlessly into a variety of 3D environments. This innovative approach not only addresses existing limitations but also paves the way for the broader application of 3D modeling in both creative and commercial sectors, potentially transforming industries reliant on visual content.

Media

Media

Integrations Supported

GPT-4o
Gemini

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

ByteDance

Date Founded

2012

Company Location

China

Company Website

seed.bytedance.com/en/seededit

Company Facts

Organization Name

DreamFusion

Company Website

dreamfusion3d.github.io

Categories and Features

AI Photo Editors

Not specified

Categories and Features

AI Tools

Not specified

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