FireRed-Image-Edit - Xiaohongshu's open-source general-purpose image editing model

What is FireRed-Image-Edit?

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

Main functions of FireRed-Image-Edit

• Text-guided image editingUsers can precisely control the modification of image content through natural language commands, and perform operations such as replacing objects, adjusting styles, and changing backgrounds.
• Text style preservedDuring the editing process, the original text structure, font, and style in the image are maintained with high fidelity, ensuring that the text in the output image is clear and readable.
• Restoration of old photosIt supports intelligent restoration of damaged, faded, or low-quality old photos, including noise reduction, colorization, and sharpness enhancement.
• Virtual try-onIt supports flexible editing capabilities based on multiple image inputs, enabling e-commerce applications such as virtual try-on for clothing.
• visual consistencyThe model ensures that the edited image maintains a high degree of consistency with the original image in terms of visual attributes such as lighting, color, and texture, achieving a natural transition.

The technical principles of FireRed-Image-Edit

• Diffusion Model ArchitectureBased on the diffusion model, it generates high-quality images through a stepwise denoising process, recovering the target image that conforms to the text instructions from random noise.
• Text conditional controlThe model uses a text encoder (such as CLIP or T5) to encode natural language instructions into semantic features, and performs cross-modal alignment with image features to achieve accurate instruction following.
• Spatial attention mechanismBy using an optimized attention module, the image region that needs to be edited is precisely located, while the non-editable region is kept unchanged, thus achieving localized and refined editing effects.
• Character shape perception moduleThe model has a specially designed module for perceiving and preserving text structure, maintaining font style and stroke characteristics during editing, and ensuring text rendering quality.
• Multi-stage training strategyThe model is pre-trained on a large-scale, high-quality editing dataset and reinforced with human feedback to optimize output quality, thereby improving the visual consistency and user satisfaction of the editing results.

FireRed-Image-Edit project address

• GitHub repository: https://github.com/FireRedTeam/FireRed-Image-Edit
• Technical Papers: https://github.com/FireRedTeam/FireRed-Image-Edit/blob/main/assets/FireRed_Image_Edit_1_0_Technical_Report.pdf
• Experience the demo onlinehttps://huggingface.co/spaces/FireRedTeam/FireRed-Image-Edit-1.0

Application scenarios of FireRed-Image-Edit

• E-commerce content creationUsed for product image enhancement, model outfit changes, and background replacement, quickly generating high-quality marketing materials and reducing shooting costs.
• Advertising designSupports rapid iteration of brand visuals, adjusts visual elements according to copywriting needs, and accelerates the implementation of creative ideas.
• Social media operationsIt helps creators efficiently edit images, transfer styles, add fun elements, and improve content production efficiency.
• Post-processing of photographyThe model enables professional-level editing such as portrait retouching, color adjustment, and blemish repair, simplifying the workflow.
• Digitizing old photosUsed for restoring old family photos and historical images, colorizing, denoising, and enhancing clarity to preserve precious memories.