This summary of the video was created by an AI. It might contain some inaccuracies.
00:00:00 – 00:13:45
The video presented by Billy from Generative Labs is a comprehensive tutorial on creating a stable diffusion API using Runpod's serverless platform. Key points include setting up necessary tools like Docker, Git, custom models, and Postman, and using a serverless worker template from GitHub. Billy explains building and deploying a Docker image, uploading it to Docker Hub, and configuring a serverless endpoint on Runpod. The tutorial also covers secure API key storage and using Postman for API requests, including various job types. Demonstrations include generating images with specific models and modifying prompts. The video concludes with a review of stable diffusion options and encourages viewer interaction.
00:00:00
In this part of the video, Billy from Generative Labs provides a detailed tutorial on creating a stable diffusion API using Runpod serverless. The tutorial covers the setup prerequisites, such as having a Runpod account, Docker, Git, custom models, and Postman installed. Billy walks through the steps including downloading the serverless worker template from GitHub, cloning the repository, and preparing custom models. He explains how to handle model files within the Docker file, modifying paths if necessary, and discusses the Handler method, which defines the entry point for the API. Additionally, Billy describes modifying the function to support various stable diffusion APIs, including text to image, image to image, and more.
00:03:00
In this part of the video, the speaker discusses the process of building and deploying a Docker image. They explain the use of the `docker build` and `docker tag` commands to build the image, followed by the `docker push` command to upload the image to Docker Hub. After successfully pushing the image, they move to the Run Pod platform to create a serverless endpoint. This involves creating a template that references the Docker Hub image and adjusting the container disk size if necessary. The speaker then navigates to the serverless endpoints section to create a new endpoint, specifying the number of workers and the idle timeout settings. Important details include noting the ID of the serverless endpoint and the need for an API key for further steps.
00:06:00
In this part of the video, viewers are guided on how to create and securely store an API key from the settings menu. The tutorial then moves to using Postman, where a configuration file can be imported to simplify the setup process. This setup includes providing a serverless endpoint ID and the API key. Four types of requests are available in the collection: running asynchronous jobs, running asynchronous jobs with a webhook, running synchronous jobs, and checking the status of a currently running job. The video demonstrates running an asynchronous job by setting the API name in the request body, receiving a job ID, and using it to check the job status until completion. The tutorial shows how to visualize the resulting image directly in Postman and explains the alternative of making synchronous requests, which wait until completion before returning the response. The segment concludes by issuing another synchronous request to explore other available models.
00:09:00
In this part of the video, the API name is changed to “get models.” The response payload reveals two available models: “model safe tensor” and “model 2 safe tensor.” The “model 2 safe tensor” string is copied and used in the request body as the value for the “SD model checkpoint” key. After issuing the request, a photorealistic image of a girl at the beach is generated, verifying that the specified model was used by checking the “St model hash” in the response payload. The base 64 encoded image is then copied to make an image-to-image request. The API name is changed to “image to image,” and a new key, “init images,” which takes an array of base 64 encoded strings, is added. The base 64 string is then pasted into this key.
00:12:00
In this part of the video, the presenter changes a prompt slightly by altering the hair color to demonstrate functionality. They also obtain a list of current stable diffusion options by setting the API name to get options. The segment concludes the tutorial, encouraging viewers to like, subscribe, and leave feedback or suggestions for future tutorials.