Aesthetic Diffusion Model: Revolutionizing Interior Design with AI

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Feb 16 2024

In a paper published in the journal Scientific Reports, researchers introduced a novel method utilizing artificial intelligence (AI) for interior design. This approach, called the aesthetic diffusion model, swiftly generated visually appealing designs based on text descriptions, allowing for style and function specifications. By bypassing traditional design processes, it enhanced efficiency and aesthetic appeal. The method demonstrated efficacy in providing designers with creative solutions, enabling quick modifications, and fostering adaptability across various design domains. Moreover, it streamlined the interior design workflow.

Related Work

Previous studies noted the challenges of low efficiency and aesthetic appeal in interior design. Though capable of generating images from text, traditional diffusion models overlooked aesthetics and needed to rely on more annotated data. It led to dissatisfaction with designs needing to meet aesthetic standards. Inadequate annotations hindered the model's understanding of decoration styles and spatial functions. Designers needed more flexibility to customize designs. A comprehensive framework integrating aesthetics and spatial functions could have improved AI-driven interior design solutions.

Enhanced Aesthetic Diffusion Model

In recent years, significant advancements have been made in text-to-image generation using diffusion models, with models like DallE 2, Midjourney, Stable Diffusion, and Dreambooth emerging as prominent image-generation models. While these models have shown remarkable performance in various scenarios, there remains room for improvement, particularly in generating aesthetically pleasing interior designs with specified decoration styles. This performance gap is especially pertinent in the field of interior design.

This research introduces an enhanced aesthetic diffusion model tailored for generating batches of aesthetically pleasing interior designs to address these limitations. Central to this approach is creating a proprietary dataset called the aesthetic decoration style and spatial function interior dataset (ADSSFID-49). This dataset incorporates information on aesthetic scores, decoration styles, and spatial functionality in interior design, providing a comprehensive foundation for model training.

Moreover, researchers devised a novel composite loss function that integrates aesthetic scores, decoration styles, and spatial functionality as critical components. This function guides the model training process, aiming to produce interior designs that align with predetermined aesthetic criteria, specified decoration styles, and spatial functionalities. By fine-tuning the model with this dataset and loss function, researchers can generate aesthetically pleasing interior designs in bulk, offering a practical solution for interior designers.

The methodology follows a systematic four-stage process. Initially, the dataset is established by gathering high-quality interior design images and annotating them with aesthetic scores, decoration styles, and spatial functionalities. Subsequently, researchers introduce a new composite loss function that builds upon the conventional loss function of the traditional diffusion model. This function incorporates aesthetic scores, decoration styles, spatial functionality, and prior knowledge to guide model training towards desired design outcomes.

During the fine-tuning phase, the improved diffusion model is trained using the ADSSFID-49 dataset, continuously minimizing the loss to acquire knowledge of aesthetic scores, decoration styles, and spatial functionalities. This refined model, the aesthetic interior design diffusion model (AIDDM), enables designers to generate and modify designs efficiently by simply inputting textual descriptions of desired decoration styles and spatial functionalities.

This method offers significant advantages over traditional design approaches, streamlining the design process and enhancing efficiency. By eliminating cumbersome workflow steps and allowing for the rapid generation of multiple designs, the AIDDM accelerates the design decision-making process, ultimately revolutionizing the interior design workflow.

Experimentation Details Summary

In experimentation, researchers trained the diffusion model on a high-performance computer with a Windows 10 operating system, 64GB of random access memory (RAM), and an NVIDIA 3090 graphics card with 24 GB of memory. The training software, PyTorch, processed each image through 100 iterations, conducting preprocessing that entailed proportional resizing to a maximum resolution of 512 pixels on the longer side. Researchers utilized data augmentation, including horizontal flipping, while transformers (Xformers) and half-precision floating point (FP16) were for accelerated computations, and the total training time for fine-tuning the diffusion model amounted to 20 hours.

To facilitate the research objectives, researchers created the ADSSFID-49 dataset to generate numerous aesthetically pleasing interior designs with specified decoration styles. This dataset, meticulously curated by expert interior designers, consisted of over 20,000 high-quality images from reputable websites. Researchers manually annotated these images for decoration styles and spatial functionalities, with aesthetic scores assigned using a state-of-the-art aesthetic scoring model. The resulting dataset formed a comprehensive foundation for model training.

In evaluating the effectiveness of the diffusion model and comparing it with other prominent models, researchers enlisted professional designers to provide subjective assessments based on eight evaluation metrics tailored for interior design. The method demonstrated superiority across various criteria, including aesthetic appeal, decoration style consistency, spatial functionality, realism, and usability through visual comparisons and quantitative evaluations. These findings highlight the practical value of the approach in enhancing the efficiency of interior design workflows and decision-making processes.

Conclusion

To sum up, the proposed aesthetic diffusion model streamlines interior design by allowing designers to input text descriptions, generating visually pleasing designs efficiently. Researchers addressed data limitations by creating an annotated dataset and devising a composite loss function for training the model. Experimental results demonstrate improved design efficiency. However, challenges remain, including the need for comprehensive quantitative evaluation metrics and mitigating cultural bias in understanding decoration styles. Researchers can enhance the model by increasing training data and refining image resolutions to achieve more detailed designs.