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

1littlecoder
12 Jan 202312:52

TLDRThe video tutorial outlines the process of deploying a Stable Diffusion API service for applications. It emphasizes using the 'diffusers' library by Abhishek Thakur, which simplifies tasks like text-to-image conversion and image inpainting. The guide covers setting up the API using Google Colab, installing the library, and setting environment variables for model and device specifications. It also demonstrates how to run the API locally and tunnel it to the internet using tools like ngrok or local channel for accessibility. The tutorial includes a step-by-step process to make API calls, including setting prompts and parameters for image generation, and decoding the base64 encoded image response. It concludes by showing how the API can be used in external applications, offering a cost-effective alternative to third-party services for those with access to a GPU or cloud computing resources.

Takeaways

  • 📚 **API Usage**: To integrate Stable Diffusion into your application, use it as an API service.
  • 🛠️ **Self-Hosting**: If you prefer not to use third-party hosting, you can host the API on your own server.
  • 🎥 **Tutorial Focus**: The video teaches how to create a Stable Diffusion API, calling it 'Stable Diffusion as a Service API'.
  • 📚 **Library Utilization**: The tutorial uses the 'diffusers' library by Abhishek Thakur for creating the API.
  • 💻 **Google Colab Setup**: For development, use a Google Colab notebook with a GPU for hardware acceleration.
  • 📦 **Installation**: Install the diffusers library using `pip install diffusers` and verify with the command line.
  • ⚙️ **Environment Variables**: Set `X2_IMG_model` for model selection and `device` for specifying the hardware (CPU, GPU, etc.).
  • 🌐 **API Deployment**: Run the diffusers API on a specified port and use tools like `ngrok` or `local channel` for internet access.
  • 📈 **API Interface**: The API uses FastAPI and provides a Swagger UI for documentation and live testing.
  • 📝 **Parameter Settings**: When making API calls, specify parameters like prompt, negative prompt, scheduler, image dimensions, etc.
  • 🖼️ **Image Output**: The API response returns a base64 encoded image, which can be decoded and viewed.
  • 🔄 **Cross-Platform Usage**: The API can be accessed and used across various platforms and applications.

Q & A

  • What is the preferred way to use Stable Diffusion within your own application?

    -The preferred way is to use it as an API, where you host the Stable Diffusion API service and call it from your application.

  • What library is recommended for creating a Stable Diffusion API?

    -The library recommended for creating a Stable Diffusion API is called 'diffusers', developed by Abhishek Thakur.

  • How can you check if the diffusers library is successfully installed?

    -You can check the successful installation of the diffusers library by running '!diffusers -h' in the command line, which should provide you with help information including the arguments you can pass.

  • What are the two important environment variables you need to set before invoking the API?

    -The two important environment variables you need to set are 'X2_IMG_MODEL' to specify the model to download from Hugging Face Model Hub and 'DEVICE' to specify the hardware to use (Cuda, PS, or CPU).

  • How can you make your API accessible on the internet if you don't have your own GPU?

    -You can use a service like localtunnel (local channel in Google Collab) to tunnel your local API server to an internet-accessible URL.

  • What is the role of the 'X2_IMG_MODEL' environment variable?

    -The 'X2_IMG_MODEL' environment variable is used to specify the model to download from Hugging Face Model Hub when the API is invoked.

  • What does the 'DEVICE' environment variable control?

    -The 'DEVICE' environment variable controls the hardware accelerator to be used, such as Cuda for NVIDIA GPUs, PS for Apple Silicon chips, or just CPU for central processing units.

  • How can you access the Swagger UI for your API?

    -You can access the Swagger UI for your API by appending '/docs' to the URL of your API endpoint.

  • What kind of request body and parameters are needed to make a text-to-image API call?

    -To make a text-to-image API call, you need to send a request body that includes a prompt, negative prompt, scheduler, image height, image weight, number of images, guidance scale, number of steps, and seed value.

  • How long does it typically take to get a response from the API?

    -It typically takes about 20 to 30 seconds on Google Colab to get a response from the API, depending on the mission and the parameters set.

  • What is the format of the image returned by the API?

    -The image returned by the API is in the format of a base 64 encoded string, which needs to be decoded to view the actual image.

Outlines

00:00

🚀 Hosting a Stable Diffusion API Service

This paragraph explains how to use table diffusion within your own application by creating a stable diffusion API service. The tutorial covers the process of hosting the API on your own server without relying on third-party services. It introduces the diffusers library by Abhishek Thakur, which simplifies tasks like text-to-image and image-to-image painting. The steps include setting up a Google Colab notebook with a GPU, installing the diffusers library, and setting necessary environment variables for the model and device. The paragraph also discusses how to run the diffusers API locally and tunnel it to the internet using tools like ngrok or local channel for accessibility.

05:01

🖼️ Using the Stable Diffusion API for Image Generation

The second paragraph delves into the operational aspect of the diffusers API, focusing on how to use it for generating images. It details the process of invoking the API using a specific port and accessing it through a tunneled internet link. The paragraph demonstrates how to use the Swagger UI for live testing and understanding the request body parameters required for tasks like text-to-image, including the prompt, negative prompt, scheduler, image dimensions, and other settings. It also touches on the response format, which is initially in base64 encoding, and how to decode it to view the generated image. The paragraph concludes with a mention of the possibility to perform other image manipulations like image-to-image translation and in-painting using different models.

10:01

🌐 Deploying and Accessing the Stable Diffusion API Externally

The final paragraph showcases the versatility of the deployed stable diffusion API, highlighting its use beyond Google Colab and on any external machine. It guides on how to make API calls using a tool like Hopscotch, a platform for testing API requests. The paragraph illustrates the process of making a POST request with a custom prompt and parameters to generate an image. It emphasizes the importance of parameters like the number of images, steps, and guidance scale in determining the request's processing time. The successful response is indicated by a 200 status code, and the resulting image is decoded from base64 to be viewed. The paragraph concludes by encouraging viewers to explore the diffusers repository and seek further assistance if needed.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion refers to a type of machine learning model used for generating images from textual descriptions. It is a prominent theme in the video as the tutorial focuses on deploying this model as a service through an API. The video demonstrates how to create an API for Stable Diffusion 2.1, which can be utilized for various image generation tasks.

💡API

API stands for Application Programming Interface, which is a set of rules and protocols that allows different software applications to communicate with each other. In the context of the video, the API is being created to enable applications to access the Stable Diffusion model for image generation services.

💡Diffusers Library

The Diffusers Library is a Python library developed by Abhishek Thakur that simplifies the use of various diffusion models, including Stable Diffusion, for tasks like text-to-image conversion. It is highlighted in the video as a crucial tool for setting up the Stable Diffusion API.

💡Google Colab

Google Colab is a cloud-based platform provided by Google that allows users to write and execute Python code in a simplified environment. It is mentioned in the video as a platform where one can write and test the Stable Diffusion API, especially when a local GPU is not available.

💡GPU

GPU stands for Graphics Processing Unit, which is a specialized electronic circuit designed to handle the complex graphical calculations required for video game and 3D rendering. In the context of the video, a GPU is necessary for the computationally intensive tasks of training and running the Stable Diffusion model.

💡ngrok

ngrok is a tool that creates a secure tunnel from a public URL to a localhost running on a personal computer. It is mentioned as a method to tunnel the API hosted on Google Colab to make it accessible over the internet.

💡Local Channel

Local Channel is a service that, like ngrok, allows for the tunneling of local servers to the internet without the need for additional setup. It is part of the npm package and is used in the video to demonstrate how to make the Google Colab-hosted API accessible externally.

💡Environment Variables

Environment variables are a set of dynamic values that can affect the way running processes behave on a system. In the video, they are used to specify the model to be downloaded from the Hugging Face model Hub and to define the device type for running the API.

💡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. In the context of the video, it is the source from which the Stable Diffusion model is downloaded when the API is invoked.

💡Fast API

Fast API is a modern, fast web framework for building APIs with Python based on standard Python type hints. It is mentioned as the underlying framework supporting the Stable Diffusion API's server process, enabling the creation of the Swagger UI for documentation and live testing.

💡Swagger UI

Swagger UI is a web-based tool that allows users to visualize and interact with the API's documentation. It is used in the video to demonstrate how developers can understand the request body, parameters, and try out the API live.

💡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 images are returned as Base64-encoded strings, which the user then decodes to view the actual image.

Highlights

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

This tutorial explains how to create a Stable Diffusion API without using third-party hosting services.

The video is named 'Stable Diffusion as a Service API', offering a self-hosted solution for the API.

The diffusers library by Abhishek Thakur is used, which simplifies tasks like text-to-image and image-to-image painting.

To start, open a Google Colab notebook and select GPU as the hardware accelerator.

Install the diffusers library using pip, ensuring all dependencies are included.

Verify successful installation by using the diffusers command line interface.

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

The model specified will be downloaded from the Hugging Face model Hub when the API is invoked.

You can use any Hugging Face model compatible with diffusers from the model Hub, including custom or fine-tuned models.

Running the diffusers API requires specifying the port and, if necessary, using a tunneling service like ngrok or local channel.

The API can be accessed and tested using the Swagger UI, which provides live interaction and documentation.

Parameters for the API include prompt, negative prompt, scheduler, image height, weight, number of images, guidance scale, number of steps, and seed value.

The API call results in a base64 encoded image, which can be decoded and viewed.

The process works not only on Google Colab but also on any external machine, making it versatile for various applications.

The diffusers API can be deployed on Google Colab as a proof of concept, and with a personal GPU or cloud service, it can be used more permanently.

The tutorial provides a cost-effective way to deploy Stable Diffusion and offers the ability to make API calls for custom applications.

The video concludes with a demonstration of how to use the API with a different prompt and a successful image generation.