Transforming Interior Design: AIGC’s Text‑to‑Image, Lora, and IP‑Adapter Techniques

Background and Motivation

As artificial intelligence continues to improve its creative capabilities, AI‑generated content (AIGC) has become a powerful tool for the design industry. Traditional interior design relies heavily on designers' experience and clients' imagination, often taking months and incurring high costs to produce a satisfactory plan. AIGC enables computers to learn and imitate various artistic and design styles, generating high‑quality interior renderings in seconds.

Data Collection and Model Training

The team gathered a large collection of interior space images covering mainstream Chinese market styles such as modern, Chinese‑style, Nordic, light‑luxury, and new Chinese designs. Using these high‑quality image‑text pairs, they trained a large‑scale text‑to‑image model capable of responding to detailed prompts and producing refined visual results.

Core AIGC Techniques

Text‑to‑Image (Diffusion) Models

Users provide a textual description (prompt); the model encodes the prompt and performs multiple diffusion steps to generate an image, finally decoding it into a visual output. Common implementations include Stable Diffusion and DALL‑E.

Lora Fine‑Tuning

Lora provides a low‑cost method to adapt large models for specific styles. By training on a small set of images, Lora learns shared features such as color, texture, and layout, allowing the model to generate images that reflect those characteristics. This is especially useful when textual descriptions alone cannot capture a desired style.

IP‑Adapter Style Transfer

IP‑Adapter enhances a pre‑trained diffusion model by adding an extra cross‑attention layer that processes image features. It accepts both text and an example image as prompts, decoupling their attention so the generated image faithfully incorporates the visual style of the reference without interference. Unlike Lora, IP‑Adapter does not require per‑style model training, reducing resource and time costs.

Application Cases

Consumer‑Facing Interior Design (极有家‑真能造)

Users upload a photo of their home (finished or bare). The system extracts spatial structure, preserves it with ControlNet, and then applies Lora or IP‑Adapter to generate multiple style concepts within seconds, helping shoppers explore décor ideas and make faster purchasing decisions.

AI‑Powered Product Photography for Furniture Sellers

Merchants provide a white‑background product image. The system segments the product, then uses AIGC to generate realistic background scenes in various styles, creating attractive catalog images that boost conversion while saving time and cost.

Conclusion

AIGC has become a transformative force in interior design, offering personalized, intelligent solutions that dramatically accelerate the design process and lower costs. By combining a rich interior image dataset with advanced Lora and IP‑Adapter techniques, the team built a robust AIGC model capable of generating diverse style concepts quickly. For consumers, this expands design options and simplifies inspiration gathering; for merchants, AI‑driven scene generation enhances product presentation and market competitiveness. Ongoing improvements in AIGC will further broaden its impact across design domains.