Deploy Stable Diffusion as Service - Build your own Stable Diffusion API

1littlecoder
12 Jan 202312:52

TLDRThis tutorial video provides a step-by-step guide on deploying a Stable Diffusion API service, allowing users to integrate Stable Diffusion into their applications without relying on third-party hosting services. The process involves using the diffusers library by Abhishek Thakur, setting up environment variables for model selection and device specification, and running the diffusers API on a chosen port. The video demonstrates how to use Google Colab for hosting and localtunnel for creating an accessible URL. It also showcases using the Swagger UI for API documentation and testing, and concludes with a live example of generating an image from a text prompt using the deployed API, highlighting its potential for various applications.

Takeaways

  • 📚 **API Utilization**: The preferred method to integrate Stable Diffusion into your application is through an API, which can be hosted by a third-party or on your own server.
  • 🛠️ **Self-Hosting Tutorial**: This video provides a tutorial on creating a Stable Diffusion API, also referred to as 'Stable Diffusion as a Service', for those who wish to host the API themselves.
  • 📈 **Library Usage**: The 'diffusers' library from Abhishek Thakur is used, which offers a simple UI for various image generation tasks and is also suitable for API development.
  • 💻 **Google Colab Setup**: If using Google Colab, ensure to select the GPU as the hardware accelerator for the necessary computational power.
  • 📦 **Installation Check**: After installing the diffusers library, verify the installation by using the command line interface (CLI) with the `!diffusers` command.
  • ⚙️ **Environment Variables**: Two crucial environment variables must be set: `X2_IMG_model` to specify the model from Hugging Face Model Hub, and `device` to indicate the computing resource (Cuda, MPS, or CPU).
  • 🌐 **API Deployment**: To deploy the diffusers API, run it on a specified port and use tools like `ngrok` or `local channel` to make it accessible over the internet if not using a personal GPU.
  • 🔗 **API Endpoint Access**: The API can be accessed through a URL, and documentation is available via appending `/docs` to the base URL, leading to the Swagger UI for live testing and documentation.
  • 📈 **Parameter Configuration**: When using the API, parameters such as prompt, negative prompt, scheduler, image dimensions, number of images, guidance scale, number of steps, and seed value need to be specified.
  • 🖼️ **Image Generation**: The API generates images in base64 encoding, which can be decoded and viewed using appropriate tools or services.
  • 🔄 **Cross-Platform Compatibility**: The API can be used across different platforms and applications, including web, Android, and iOS, by making POST requests to the API endpoint with the required parameters.
  • 🌟 **Customization and Cost-Saving**: By deploying your own instance of Stable Diffusion, you can customize it to your needs and potentially save costs compared to using third-party services.

Q & A

  • What is the preferred way to use Stable Diffusion within one's own application?

    -The preferred way to use Stable Diffusion within one's own application is to use it as an API. You would either host the API yourself or use a third-party hosting service to call the API from your application.

  • What is the name of the library used to create a Stable Diffusion API in this tutorial?

    -The library used to create a Stable Diffusion API in this tutorial is called 'diffusers'.

  • What is the command to check if the diffusers library is installed successfully?

    -To check if the diffusers library is installed successfully, you can use the command line interface with the following command: `! diffusers API --help`.

  • What are the two important environment variables that need to be set before invoking the diffusers API?

    -The two important environment variables that need to be set are `X2_IMG_MODEL`, which specifies the model to download from the Hugging Face model Hub, and `device`, which specifies whether to use Cuda, MPS, or CPU.

  • How can you make the diffusers API accessible on the internet if you are using Google Colab?

    -You can use a tunneling service like `ngrok` or `local channel`, which is part of an npm package that comes installed with Google Colab notebooks, to make the diffusers API accessible on the internet.

  • What is the purpose of the diffusers API?

    -The purpose of the diffusers API is to allow users to create a Stable Diffusion service that can be used to generate images from text descriptions or perform other image-related tasks programmatically.

  • How can you access the API documentation for the diffusers API?

    -You can access the API documentation by appending `/docs` to the base URL of the API. This will take you to the Swagger UI, where you can see the documentation and try out the API live.

  • What kind of request body is required to make a call to the text-to-image endpoint of the diffusers API?

    -The request body for the text-to-image endpoint should include a prompt, negative prompt, scheduler, image height and width, number of images, guidance scale, number of steps, and a seed value.

  • What is the format of the image returned by the diffusers API after a successful text-to-image conversion?

    -The image returned by the diffusers API is in a base64 encoded string format, which can be decoded to view the actual image.

  • How long does it typically take for the diffusers API to generate an image on Google Colab?

    -It typically takes about 20 to 30 seconds on Google Colab for the diffusers API to generate an image, depending on the complexity of the request and the resources available.

  • Can the diffusers API be used to create custom models or fine-tune existing ones?

    -Yes, the diffusers API can be used to create custom models or fine-tune existing ones available on the Hugging Face model Hub, as long as they are compatible with diffusers.

  • What is the benefit of deploying your own Stable Diffusion instance instead of using a third-party service?

    -Deploying your own Stable Diffusion instance allows you to save costs and have more control over the service. It can also be integrated directly into your application for a seamless user experience.

Outlines

00:00

🚀 Hosting a Stable Diffusion API for Custom Applications

The paragraph introduces the concept of using table diffusion within custom applications by creating a stable diffusion API. It discusses the option of either using a third-party hosting service or self-hosting the API on your own server. The video tutorial aims to teach viewers the easiest way to create a self-hosted stable diffusion API, which the presenter refers to as 'stable diffusion as a service API.' The process involves using the 'diffusers' library by Abhishek Thakur, which is demonstrated through Google Colab with a GPU for accelerated processing. The installation of the diffusers library and setting up of environment variables are covered, including specifying the model from the Hugging Face model Hub and the device to be used (CUDA, MPS, or CPU). The paragraph concludes with the initial steps to run the diffusers API and the use of tools like ngrok or local channel for port tunneling.

05:01

🌐 Deploying and Accessing the Stable Diffusion API

This paragraph explains the steps to deploy the diffusers API locally on Google Colab and then make it accessible over the internet using local channel for port tunneling. It details the process of running the API, including setting the port and tunneling that port to create an internet-accessible link. The paragraph also covers the retrieval of the text-to-image model and ensuring that the API is functioning correctly before making API calls. The use of Swagger UI for API documentation and live testing is introduced, highlighting the request body requirements such as prompt, negative prompt, scheduler, image dimensions, number of images, guidance scale, number of steps, and seed value. The process of making an API call is demonstrated, including specifying a prompt and receiving a base64 encoded image in response.

10:01

🖼️ Decoding Base64 Image and Using the API Externally

The final paragraph demonstrates how to decode a base64 encoded image and use the deployed stable diffusion API externally. It shows how to copy the base64 string, remove unnecessary characters, and use an online tool to convert it back into an image. The paragraph also provides a practical example of using the API with an external tool, Hopscotch, to make a POST request with a different prompt and receive an image in response. The response time and the factors affecting it, such as the number of images and steps, are discussed. The paragraph concludes with a reminder that the Google Colab notebook used in the demonstration is a proof of concept and would not be accessible once the session ends, but the viewer is encouraged to deploy their own instance of the stable diffusion API for long-term use.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion refers to a type of machine learning model that is used for generating images from textual descriptions. It is a prominent example of generative AI and is known for its ability to create high-quality images. In the context of the video, it is the core technology around which the API service is being built, allowing users to integrate text-to-image generation capabilities into their own applications.

💡API

API stands for Application Programming Interface, which is a set of rules and protocols that allows different software applications to communicate and interact with each other. In the video, the presenter is teaching viewers how to create an API for the Stable Diffusion model, enabling its use within other applications by making API calls.

💡diffusers Library

The diffusers library is a Python library developed by Abhishek Thakur that simplifies the use of Stable Diffusion models. It provides a user interface for tasks such as text-to-image generation and image-to-image editing. The library is central to the video's tutorial on deploying a Stable Diffusion API, as it is used to facilitate the creation and management of the API service.

💡Google Colab

Google Colab is a cloud-based platform provided by Google that allows users to write and execute Python code in a simple interface, with the added benefit of free access to computing resources, including GPUs. In the video, Google Colab is used as a platform to demonstrate how to set up and run the Stable Diffusion API, taking advantage of its GPU capabilities for model training and inference.

💡ngrok

ngrok is a tool that creates a secure tunnel from a public URL to a localhost running on a personal computer. It is often used to expose local development servers to the internet temporarily for testing and sharing purposes. The video mentions ngrok as a potential method to tunnel the API hosted on Google Colab to make it accessible over the internet.

💡local channel

local channel is a tool that, like ngrok, allows for the exposure of local servers to the internet. It is part of an npm package and is used within the context of the video to tunnel the API hosted on Google Colab, providing a URL that can be accessed over the internet without the need for additional services like ngrok.

💡Environment Variables

Environment variables are a set of dynamic values that can affect the way running processes behave on a system. In the video, two important environment variables are mentioned: X2_IMG_MODEL and DEVICE. These are used to specify the model to be downloaded from the Hugging Face model hub and the type of hardware to run the API on, respectively.

💡Hugging Face model Hub

The Hugging Face model Hub is a repository of machine learning models that can be used for various tasks, including natural language processing and computer vision. In the context of the video, it is the source from which the Stable Diffusion model is downloaded when the API is invoked, allowing for the use of custom or fine-tuned models.

💡FastAPI

FastAPI is a modern, fast web framework for building APIs with Python. It is mentioned in the video as the underlying technology supporting the diffusers API, highlighting its role in creating a user-friendly and efficient API for serving Stable Diffusion model functionalities.

💡Swagger UI

Swagger UI is a web-based tool that provides a graphical interface for visualizing and interacting with an API's documentation. It is used in the video to demonstrate how developers can view and understand the API's documentation, try out different endpoints, and see the request bodies and parameters required for making API calls.

💡Base64 Encoding

Base64 encoding is a method of encoding binary data into text format so that it can be easily transferred over text-based systems. In the video, the generated image from the Stable Diffusion API is returned as a Base64 encoded string. The presenter then demonstrates how to decode this string to view the image, showcasing the step from API response to visual output.

Highlights

The preferred way to use Stable Diffusion within your own application is through an API.

You can host the Stable Diffusion API service yourself without relying on third-party hosting services.

The tutorial covers the easiest way to create a Stable Diffusion API, referred to as 'Stable Diffusion as a Service API'.

The diffusers library from Abhishek Thakur simplifies tasks like text-to-image and image-to-image painting.

To use the diffusers library, you need a GPU, which can be accessed via Google Colab if you don't have your own.

Install the diffusers library using pip install diffusers in quiet mode to install all required dependencies.

Set two important environment variables: X2_IMG_model and device, to specify the model and hardware to use.

The API can be invoked after setting up the environment variables and running the diffusers API command.

For text-to-image, image-to-image, or in-painting tasks, additional environment variables may be required as specified in the diffusers GitHub repository.

Running the diffusers API will start the server process and provide a URL for accessing the service.

The API can be accessed and tested using a tool like ngrok or local channel for tunneling if running on Google Colab.

The API uses Swagger UI for documentation and live testing of the request body and parameters.

You can specify parameters such as prompt, negative prompt, scheduler, image height and weight, number of images, guidance scale, number of steps, and seed value for the API call.

The API call can be made from any application, such as a React, Android, or iOS app, using the provided endpoint and request body.

The response from the API is in base64 encoding, which can be decoded to view the generated image.

The tutorial demonstrates how to use the API with a cURL command and an external tool like Hopscotch for testing.

The diffusers API can be deployed on platforms like Google Colab, AWS, or your own GPU for offering Stable Diffusion as a service.

The tutorial provides a proof of concept for deploying Stable Diffusion, which can be scaled up for production use.

The source code and further instructions are available in the linked Google Colab notebook in the video description.