Stable Diffusion Backstage for Yile Technology

Reduced time and cost associated with operation by streamlining model and machine management with Stable Diffusion backstage.

Built a Stable Diffusion backstage that allows efficient management of various models and machines, saving time and costs associated with administrative tasks.

About

Yile Technology

Established in 2018, Yile Technology comprises a professional operations team passionate about gaming, focused on development and marketing. With a commitment to innovation and excellence, they develop high-quality game apps such as Bao Ni Fa and G-bao Online.

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Challenge
Solution

Streamlining Operations with Stable Diffusion Backstage Service

To meet the diverse design needs of different departments, Yile needed to deploy various training models across multiple machines. They sought a flexible and unified management solution to save time and control machine usage costs effectively.

Going Cloud customized the Stable Diffusion Backstage service to establish a backend system tailored to the client's needs, providing the following functionalities:

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Key Features
  • Model Management: Centralized management of all models, along with the flexibility to allocate each model to different machines as needed.
  • Diversified Content Creation: Enabling upload of various training data and Extra Networks* function for enhanced image creation capability.
  • Machine Scheduling: Optimizing machine schedule by managing daily power operations and automatically powering off machines during idle times.
  • Cost Monitoring: Visualization of machine usage costs, including alerts for different departmental usage patterns, machine type, and consumption.

*Extra Networks: A Stable Diffusion function used to manage and apply specialized add-on files, enabling customization of images.

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Key Features

Solution Architecture

Result

Optimized Efficiency and Reduced Costs

The introduction of the customized Stable Diffusion Backstage enabled Yile Technology to; 

  • Manage various Stable Diffusion models efficiently, with users from different departments sharing uploaded models in a unified platform.
  • Gain insights on departmental usage patterns and machine utilization for enhanced cost management.
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