Eachlabs | AI Workflows for app builders
seededit-3.0

SeedEdit 3.0 | Image Edit

A text-guided image editing model that keeps the original details but makes specific changes like adjusting lighting, removing objects, or changing the style.

Avg Run Time: 15.000s

Model Slug: seededit-3-0

Category: Image to Image

Input

Enter an URL or choose a file from your computer.

Advanced Controls

Output

Example Result

Preview and download your result.

Preview
Each execution costs $0.0300. With $1 you can run this model about 33 times.

Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

Table of Contents
Overview
Technical Specifications
Key Considerations
Tips & Tricks
Capabilities
What Can I Use It For?
Things to Be Aware Of
Limitations

Overview

Seededit-3.0 is a text-guided image editing model developed as part of the Seedream series by ByteDance. The model is designed to perform precise, instruction-based edits on images while preserving the original details and structure. Its core capability is to interpret natural language prompts to make targeted changes—such as adjusting lighting, removing or adding objects, or altering artistic style—without compromising the integrity of the source image.

The underlying architecture of Seededit-3.0 is based on a diffusion transformer (DiT) framework, paired with a powerful variational autoencoder (VAE) for efficient image representation and reconstruction. This combination allows the model to achieve high-quality edits with strong consistency and fidelity. Seededit-3.0 stands out for its balance between instruction-following accuracy and preservation of original image content, making it suitable for both creative and professional applications where detail retention is critical. Its design also enables efficient training and inference, supporting scalable deployment for a range of image editing tasks.

Technical Specifications

  • Architecture: Diffusion Transformer (DiT) with Variational Autoencoder (VAE)
  • Parameters: Not explicitly stated in available sources
  • Resolution: Supports high-resolution outputs; 2K and 4K generation referenced in successor models
  • Input/Output formats: Accepts standard image formats (e.g., PNG, JPEG) and natural language prompts; outputs edited images in common formats
  • Performance metrics: Evaluated on instruction alignment, consistency, structural integrity, and text-editing performance; outperformed by Seedream 4.0 in recent benchmarks but remains competitive in consistency and preservation

Key Considerations

  • Ensure prompts are clear and specific to achieve the desired edit while maintaining image realism
  • The model excels at preserving original details, but overly complex or ambiguous instructions may reduce output quality
  • For best results, use stepwise editing: apply one change at a time and iterate as needed
  • There is a trade-off between instruction responsiveness and consistency; highly aggressive edits may risk structural fidelity
  • Prompt engineering is crucial—describe the desired change precisely and avoid conflicting instructions

Tips & Tricks

  • Use concise, unambiguous prompts to guide the model toward specific edits (e.g., "remove the lamp from the table" instead of "make the table look cleaner")
  • For style changes, specify both the target style and elements to preserve (e.g., "change the painting to an impressionist style, keep the color palette similar")
  • When adjusting lighting or color, reference both the desired effect and the area to apply it (e.g., "brighten the background, keep the subject unchanged")
  • For object removal or addition, describe the object and its context clearly (e.g., "remove the person standing on the left, fill in the background with grass")
  • Iteratively refine results by making incremental edits and reviewing outputs after each step

Capabilities

  • Performs targeted image edits guided by natural language instructions
  • Maintains high fidelity to original image details and structure
  • Supports a wide range of editing tasks: lighting adjustment, object removal/addition, style transfer, color correction, and more
  • Delivers consistent results across multiple editing steps
  • Adaptable to various creative and professional workflows requiring precise, non-destructive edits

What Can I Use It For?

  • Professional photo retouching where specific elements need to be altered without affecting the overall composition
  • Creative projects such as digital art, where style or content modifications are guided by text prompts
  • Business use cases including product photography enhancement, marketing material customization, and visual content localization
  • Personal projects like restoring old photos, removing unwanted objects, or experimenting with artistic transformations
  • Industry-specific applications such as fashion (changing clothing styles), real estate (modifying room layouts), and e-commerce (background replacement)

Things to Be Aware Of

  • Some experimental features may not handle highly complex or abstract instructions reliably
  • Users have reported occasional inconsistencies when performing multiple, simultaneous edits in a single prompt
  • Performance is generally robust, but resource requirements increase with higher resolution outputs
  • Consistency is a noted strength, with users praising the model’s ability to preserve character and scene integrity across edits
  • Positive feedback highlights the model’s ease of use and natural language understanding
  • Common concerns include occasional difficulty with fine-grained text editing and rare structural artifacts in challenging scenarios

Limitations

  • May struggle with highly complex, multi-step edits in a single prompt, leading to reduced consistency or unintended changes
  • Not optimal for tasks requiring extensive scene reconstruction or generation of entirely new content beyond the scope of the original image
  • Performance and output quality may lag behind newer models (e.g., Seedream 4.0) in certain advanced editing benchmarks