Introducing RoomAI, a powerful AI-enabled interior design tool tailored for interior designers, homeowners, and anyone longing to reinvent their living spaces. Harnessing our advanced AI technology, RoomAI can generate thousands of unique and innovative design proposals with a single click, based on your room structure and layout, to ignite your design inspiration.

Key Features of RoomAI:

One-Stop Design Solution: Supports a wide array of room types, including living rooms, dining rooms, kitchens, bedrooms, bathrooms, dressing rooms, and children's rooms.
Diverse Styles: Choose from a range of distinct design styles, including Industrial, Modern, Minimalist, Coastal, Country, and Scandinavian.
Color Schemes by Renowned Designers: Each design style comes with five or more color schemes recommended by renowned designers, making your design even more professional.
Customizable Design: Supports customized designs using different renovation materials to create your unique dream home.

Download RoomAI for free right now to start building your dream house and share your design achievements with your friends!

2. How it works

We use ControlNet adapting Stable Diffusion to use M-LSD detected edges in an input image in addition to a text input to generate an output image. The training data is generated using a learning-based deep Hough transform to detect straight lines from Places2 and then use BLIP to generate captions. The Canny model is used as a starting checkpoint and train the model with 150 GPU-hours with Nvidia A100 80G.

ControlNet is a neural network structure which allows control of pretrained large diffusion models to support additional input conditions beyond prompts. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small ( 50k samples). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal device. Alternatively, if powerful computation clusters are available, the model can scale to large amounts of training data (millions to billions of rows). Large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc.