OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
Image & Video AI

Firered Image Edit

Firered Image Edit is an open-source Python foundation model that generates edited images from source images and written instructions. Its repository provides inference code and lists model versions 1.0 and 1.1; version 1.1 emphasizes portrait consistency, multi-element fusion, stylized text references and makeup effects.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Firered Image Edit — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to build an image-editing workflow around a model and code, especially for prompt-led edits, portraits or multi-image composition.

Who it is for

  • Developers seeking a self-hosted image-editing model and inference code.
  • Creators working on instruction-guided portrait edits, image compositions or text-reference styling.

Documented uses

  • Apply a requested change to an input image using a written prompt.
  • Combine elements from multiple images into a composition.
  • Create portrait retouching or makeup edits and preserve subject identity across edits.
Before you choose

Requirements & limits

The repository lists an Apache-2.0 license and links to model and demo pages on Hugging Face and ModelScope.

  • The primary documented path is repository-based Python inference, rather than a hosted editing application.
  • The model natively supports one to three input images; the Agent module is described for preprocessing larger image sets.
  • Recaptioning requires configuring an LLM provider, while the README says basic Agent region detection and stitching do not require an LLM API.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the documented single-image inference example

Install the listed dependencies and invoke the repository's image-editing example.

Before you start

  • Python environment
  • Repository files, including requirements.txt and inference.py
  • An input image

Steps

  1. Install dependencies with pip install -r requirements.txt.

  2. Run inference.py with an input image, an editing prompt, an output-image path and a seed, following the README example.

Expected outcome

The example is configured to write an edited image to the specified output path.

Guide limitations

  • The example prompt is in Chinese.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Firered Image EditApply a requested change to an input image using a written prompt.Linux, Self-hostedThe repository lists an Apache-2.0 license and links to model and demo pages on Hugging Face and ModelScope.
ControlvideoGenerate video from a text prompt with structural guidance.LinuxThe implementation is available as a public GitHub repository under the MIT license. The README separately links to Hugging Face and Replicate demos.
CatvtonGenerate images showing a garment on a person.Cloud, Self-hostedThe repository documents a local Python setup and a Hugging Face demo. It states that the code, checkpoints and demo are licensed under Creative Commons BY-NC-SA 4.0 for non-commercial reuse with attribution and share-alike terms.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
3 November 2026
Original website title
FireRedTeam/FireRed-Image-Edit · Official homepage title · retained in its source language
How we review sourcesSuggest a correction