Realistic Lamps Generation with Stable Diffusion XL

SUMMARY

An interior design company approached us to leverage AI technology to save their employees' time. They provided around 5,000 images across approximately 30 different lamp brands, aiming to automate the generation of lamps within various interior settings. The client wanted a system where they could input a prompt with the lamp's name and desired placement, and receive a highly accurate generated image of the lamp in that specific setting.
Given the project's reliance on a large diffusion model, it was clear that a GPU server was necessary to handle the computational load. The client also expressed a desire to host the model, necessitating robust infrastructure to support real-time image generation while managing the costs associated with GPU usage.
Accuracy in image generation was crucial, along with reasonable inference speed to ensure a seamless user experience. Additionally, given the substantial resources required by the GPU server, efficient DevOps services were essential to optimize performance and manage costs effectively, balancing high-quality output with financial practicality.

TECH STACK

Stable Diffusion

AWS

Python

DELIVERY TIMELINE

1 Week

Solution Architecture Design

2 Weeks

Stablle Diffusion XL Fine-Tuning & Customization

2 Weeks

Model Deployment to AWS & Perfomance Monitoring

1 Week

Deployment & Testing

TECH CHALLENGE

Among the technical challenges, ensuring high accuracy in lamp representation was paramount. Even minor discrepancies between the generated images and the actual lamp designs could significantly impact the utility and customer satisfaction. Accuracy was critical as the client needed the generated lamps to closely match their real-world counterparts in detail and style.
Regarding the AWS technical challenges, a critical task was selecting the optimal resources to balance cost-effectiveness with performance. The client demanded an affordable yet efficient solution, leading us to explore various AWS configurations to identify the most cost-efficient GPU instances that could handle the computational demands of our AI models without compromising performance.
Additionally, implementing a serverless solution was integral to the project. This approach allowed us to scale resources dynamically, ensuring that we only paid for the server capacity we actually used. Utilizing AWS’s serverless architectures like Lambda in conjunction with asynchronous SageMaker Endpoint frameworks helped us minimize costs while maintaining the agility to handle varying loads efficiently, aligning with the client’s need for a scalable and economical infrastructure.

SOLUTION

To meet the client's needs for high-quality and customizable image generation, we implemented a combination of advanced AI models. We chose Stable Diffusion XL for its robust capabilities in generating detailed images, supplemented by Lora and Dreambooth for personalized model tuning and enhancing specific attributes of the lamps. Additionally, we utilized CLIP for its ability to generate descriptive text from images automatically, integrating these models to create a seamless workflow that delivers precise and contextually appropriate imagery based on simple text prompts.
For hosting and server management, we opted for Amazon Web Services (AWS), utilizing GPU instances to handle the heavy computational demands of our AI models efficiently. We also implemented the asynchronous SageMaker Endpoint framework, which provided a scalable and flexible solution for managing model deployment. This setup allowed us to offer real-time image generation capabilities while maintaining control over operational costs and ensuring high availability and low latency in image delivery.

PROVIDED SERVICE

Dedicated AI&ML Team

INDUSTRIES

Design & Architecture