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FireRed-Image-Edit - Xiaohongshu's Open-Source General-Purpose Image Editing Model

Author: AI小集 · Original source: AI工具集

What is FireRed-Image-Edit

FireRed-Image-Edit is a general-purpose image editing model open-sourced by Xiaohongshu's Super Intelligence team. Based on a diffusion architecture, it supports text-guided image editing, old photo restoration,virtual try-on, and other multi-functional capabilities. The model supports precise instruction following, high-quality image output, and visual consistency, with outstanding performance in text style preservation, achieving results comparable to closed-source solutions. The model has achieved SOTA results on multiple evaluation sets and is suitable for scenarios such as creative design and e-commerce content creation.

The newly launched FireRed-Image-Edit-1.1 upgrades OOTD element fusion (supporting free combination of 10+ elements and Agent automatic stitching) and portrait ID consistency preservation (the best in the open-source field). It also adds high-fidelity font style reference, dozens of portrait makeup styles, and old photo restoration features, with generation speed optimized to 4.5 seconds/30GB VRAM.

FireRed-Image-Edit

Main Features of FireRed-Image-Edit

  • Text-Guided Image Editing: Users can precisely control modifications to image content through natural language instructions, enabling operations such as object replacement, style adjustment, and background changes.
  • Text Style Preservation: During the editing process, the structure, font, and style of the original text in the image are maintained with high fidelity, ensuring that the output image text is clear and readable.
  • Old Photo Restoration: Supports intelligent restoration of damaged, faded, or low-quality old photos, including denoising, colorization, and clarity enhancement.
  • Virtual Try-On: Supports flexible editing capabilities based on multi-image input, enabling e-commerce application scenarios such as virtual clothing try-on.
  • Visual Consistency Preservation: The model can ensure that the edited image maintains a high degree of consistency with the original image in visual attributes such as lighting, color, and texture, achieving natural transitions.

Technical Principles of FireRed-Image-Edit

  • Diffusion Model Architecture: Built on a Diffusion Model, it generates high-quality images through a gradual denoising process, recovering the target image that conforms to text instructions from random noise.
  • Text Conditional Control: The model uses a text encoder (such as CLIP or T5) to encode natural language instructions into semantic features, performing cross-modal alignment with image features to achieve precise instruction following.
  • Spatial Attention Mechanism: Through optimized attention modules, it precisely locates the image regions that need editing while protecting non-edited regions from changes, achieving localized and refined editing effects.
  • Glyph-Aware Module: A module specifically designed by the model to perceive and preserve text structures, maintaining font styles and stroke features during the editing process to ensure text rendering quality.
  • Multi-Stage Training Strategy: The model is pre-trained on large-scale high-quality editing datasets, combined with reinforcement learning from human feedback to optimize output quality, improving the visual consistency of editing results and user satisfaction.

Project Address of FireRed-Image-Edit

  • GitHub Repository：https://github.com/FireRedTeam/FireRed-Image-Edit
  • HuggingFace Model Library：https://huggingface.co/FireRedTeam/FireRed-Image-Edit-1.0
  • Technical Paper：https://arxiv.org/pdf/2602.13344
  • Online Experience Demo：
    • https://huggingface.co/spaces/FireRedTeam/FireRed-Image-Edit-1.0
    • https://huggingface.co/spaces/FireRedTeam/FireRed-Image-Edit-1.1

Application Scenarios of FireRed-Image-Edit

  • E-Commerce Content Creation: Used for product image beautification, model outfit changes, and background replacement, quickly generating high-quality marketing materials and reducing shooting costs.
  • Advertising Design: Supports rapid iteration of brand visuals, adjusts visual elements according to copy requirements, and accelerates creative implementation.
  • Social Media Operations: Helps creators efficiently retouch images, transfer styles, and add fun elements, improving content production efficiency.
  • Photography Post-Processing: The model can achieve professional-level editing such as portrait retouching, color adjustment, and blemish repair, simplifying workflows.
  • Old Photo Digitization: Used to restore old family photos and historical images, performing colorization, denoising, and clarity enhancement to preserve precious memories.