AI-Powered Image Generation for Interior, Exterior, and Landscape Design with Stable Diffusion

Introduction

The field of image generation has witnessed remarkable advancements in recent years, with deep learning models like Stable Diffusion emerging as powerful tools. This case study explores the application of Stable Diffusion for generating interior, exterior, and landscape images from a single input image.

Problem Statement

Traditional methods of image generation, such as manual drawing or 3D modeling, are time-consuming and often require specialized skills. There is a growing demand for efficient and automated tools that can generate high-quality images based on user-provided input images and prompt. In some cases furniture of interior, structure of exterior and visible area of landscape image’s changes its position in the generated image. Again, in some cases users don’t want a specific object in the generated image. And Generating best quality image is another major issue. 

The Solution: AI Powered Floor Plan Reading

Stable Diffusion, a image-to-image generative model, offers a promising solution for generating diverse and realistic images. By leveraging the power of deep learning, Stable Diffusion can create new images based on a given input image and a text prompt. For controlling the Stable Diffusion model we used ControlNet architecture. To keep aligning/positioning of the objects we used MLSD model of stable diffusion.

Steps involved image-to-image generation:

• Image Preparation:
• Prepared a high-quality input image that serves as a base for the generated images.
• We Considered some factors such as image resolution. If a user provides a low resolution image the generated image will be a high resolution. image, Moreover users can define what kind of resolution image they want.
• The lighting conditions of the input image is a big factor for Generated image. If an object is clear in the input image, maybe the model can’t identify the object.
• Text Prompt Engineering:

• Craft a descriptive text prompt that accurately conveys the desired style, theme, or specific elements for the generated image.
• The prompt can include keywords, phrases, or even entire sentences to guide the generation process.
• There is a negative prompt for excluding objects in the generated image.
• Models Overview:

• For the interior we used a pretrained model “interiordesignsuperm_v2.safetensors” that was trained with a huge number of interior images dataset.
• “Architecturerealmix_v11.safetensors” model is used for exterior image generation. This model is trained with exterior image dataset.
• Finally, for landscape image generation the “landscapesupermix_v21.safetensors” model is trained with a landscape dataset.
• Stable Diffusion Generation:

• Feed the input image and text prompt into the Stable Diffusion MLSD model and generate a line-segment image for interior. For exterior, feed the input image and corresponding text prompt into the Stable Diffusion MLSD model and generate line-segment image. In the same way feed landscape input image and prompt into Stable Diffusion MLSD model for generating landscape-line-segment image.
• Generated line-segment image and user prompt is feeded into Stable Diffusion model with ControlNet for generating final output image for example interior, exterior and landscape.
• The Stable Diffusion model will generate multiple image variations based on the provided inputs, capturing the essence of the original image while incorporating the desired modifications. There is a parameter for generating the total number of images as the user wants.
• Image Refinement:

• Evaluated the generated images and select the most suitable ones based on quality, relevance, and adherence to the desired style/prompt.
• If necessary, refine the generated images using prompt and negative prompt to make further adjustments or enhancements.

Benefits of AI-Powered Architect Image Generation

The benefits of AI-powered image-to-image generation for interior, exterior, and landscape design are numerous:

• Efficiency: Stable Diffusion can generate high-quality images in a fraction of the time required by traditional methods.
• Creativity: The model can create unique and imaginative image variations that would be difficult or impossible to achieve manually.
• Customization: The ability to control the generation process through text prompts allows for customization and personalization of the generated images. Users can drop a specific product using a negative prompt from the generated image.
• Time Save: Stable Diffusion model can generate image very quickly within minutes. That is not possible by manual process or human hand.
• Versatility: Stable Diffusion can be applied to a wide range of applications, including interior design, architecture, and creative arts.

The Challenges and Limitations

While AI-powered image-to-image generation offers significant benefits, there are also some challenges and limitations to consider:

• Quality Control: While Stable Diffusion can generate impressive results, there may be variations in image quality and consistency for example within generated 5 images maybe 1 image quality is low but it is rare.
• Copyright Issues: Using generated images for commercial purposes may raise copyright concerns, especially if they closely resemble existing copyrighted works. That’s a sensitive issue/moment for some cases.
• Bias: AI models may inadvertently perpetuate biases present in the training data, leading to biased or discriminatory outputs.
• Time Complexity: Sometimes Stable Diffusion models take 4 to 5 minutes to generate a very high quality pixel image.
• Ethical Considerations: The use of AI-generated images raises ethical questions regarding authenticity, originality, and potential biases in some country or in a specific region or for specific findings of people and their culture.

Technologies Used

There are so many technologies used for completing this project, some are below:

• Stable Diffusion Pretrained Model: A image-to-image generative model based on deep learning.
• Deep Learning Framework: A framework such as PyTorch for implementing and training the Stable Diffusion model is used.
• Image Processing Libraries: Libraries like OpenCV, PIL for image manipulation and analysis, and Transformer, ControlNet are the most used libraries.
• CUDA-enabled GPU: For efficient processing of AI models use T4 GPU for image generation for interior, exterior, landscape model.

Conclusion

AI-powered image generation using tools like the Stable Diffusion model represents a transformative approach to design in the architectural and creative industries. By allowing designers to quickly produce and iterate on high-quality images for interiors, exteriors, and landscapes, AI tools save time and costs while increasing accessibility. Despite some limitations, such as hardware requirements and the need for precise prompts, the benefits of speed, flexibility, and cost-effectiveness position AI as a game-changer in the world of design visualization.