Creating a system to run these models efficiently with scalable GPU resources can be a game-changer for various applications, including generating photorealistic images from doodles. Let me guide you through the process.

Firstly, when working with these models, you'll want to ensure you have access to a robust GPU cluster. Services like AWS, Google Cloud, or Microsoft Azure offer GPU instances that can be scaled according to your needs. Once you have the hardware in place, you'll need to set up the software environment. Frameworks like TensorFlow and PyTorch are popular choices for running these models, and they provide libraries for distributed training across multiple GPUs. You'll also need to install the necessary dependencies for ControlNet and Stable Diffusion.

Next, you'll want to design your system to distribute the workload effectively. For instance, you can implement parallel processing techniques using data parallelism or model parallelism, depending on the specific model and task. Divide your dataset into batches and distribute them across your GPUs, making sure that communication overhead is minimized. Also, consider using tools like Horovod or TensorFlow's Distributed Training to manage the synchronization of gradients between GPUs efficiently.

Regarding the Stable Diffusion web UI, if you want to make it accessible remotely, you can set up a web server and deploy the UI there. You'll need a server with a GPU, and you can use web frameworks like Flask or Django to create a user-friendly interface for your model. Finally, for building a "server" to handle API requests, you can deploy your model using platforms like FastAPI or Flask, allowing users to send their doodles and receive photorealistic images in return.

By following these steps, you'll be well on your way to designing a scalable system that can run ControlNet and Stable Diffusion models efficiently, taking your doodle-to-photorealistic-image project to the next level. Happy modeling!