How to Build an AI Image Generation App
Last updated
Last updated
Author: Steven Lynn. Dify Technical Writer.
With the rise of image generation, many excellent image generation products have emerged, such as Dall-e, Flux, Stable Diffusion, etc.
In this article, you will learn how to develop an AI image generation app using Dify.
Methods for building an Agent using Dify
Basic concepts of Agent
Fundamentals of prompt engineering
Tool usage
Concepts of large model hallucinations
Click here to go to the Stability API key management page.
If you haven't registered yet, you will be asked to register before entering the API management page.
After entering the management page, click copy to copy the key.
Next, you need to fill in the key in Dify - Tools - Stability by following these steps:
Log in to Dify
Enter Tools
Select Stability
Click Authorize
Fill in the key and save
To optimize interaction, we need an LLM to concretize user instructions, i.e., to write prompts for generating images. Next, we will configure model providers in Dify following these steps.
The Free version of Dify provides 200 free OpenAI message credits.
If the message credits are insufficient, you can customize other model providers by following the steps in the image below:
Click Your Avatar - Settings - Model Provider
If you haven't found a suitable model provider, the groq platform provides free call credits for LLMs like Llama.
Log in to groq API Management Page
Click Create API Key, set a desired name, and copy the API Key.
Back to Dify - Model Providers, select groqcloud, and click Setup.
Paste the API Key and save.
Back to Dify - Studio, select Create from Blank.
In this experiment, we only need to understand the basic usage of Agent.
What is an Agent
An Agent is an AI system that simulates human behavior and capabilities. It interacts with the environment through natural language processing, understands input information, and generates corresponding outputs. The Agent also has "perception" capabilities, can process and analyze various forms of data, and can call and use various external tools and APIs to complete tasks, extending its functional scope. This design allows the Agent to handle complex situations more flexibly and simulate human thinking and behavior patterns to some extent.
Select Agent, fill in the name.
Next, you will enter the Agent orchestration interface as shown below.
Select the LLM. Here we use Llama-3.1-70B provided by groq as an example:
Select Stability in Tools:
Prompts are the soul of the Agent and directly affect the output effect. Generally, the more specific the prompts, the better the output, but overly lengthy prompts can also lead to negative effects.
The engineering of adjusting prompts is called Prompt Engineering.
In this experiment, you don't need to worry about not mastering Prompt Engineering; we will learn it step by step later.
Let's start with the simplest prompts:
Each time the user inputs a command, the Agent will know this system-level instruction, thus understanding that when executing a user's drawing task, it needs to call stability tool.
For example: Draw a girl holding an open book.
Click Generate in the upper right corner of Instructions.
Enter your requirements in the Instructions and click Generate. The generated prompts on the right will show AI-generated prompts.
However, to develop a good understanding of prompts, we should not rely on this feature in the early stages.
Click the publish button in the upper right corner, and after publishing, select Run App to get a web page for an online running Agent.
Copy the URL of this web page to share with other friends.
We can add style instructions in the user's input command, for example: Anime style, draw a girl holding an open book.
But if we want set the default style to anime style, we can add it to the system prompt because we previously learned that the system prompt is known each time the user command is executed and has a higher priority.
In many business scenarios, we need to avoid outputting some unreasonable content, but LLMs are often "dumb" and will follow user instructions without question, even if the output content is wrong. This phenomenon of the model trying hard to answer users by fabricating false content is called model hallucinations. Therefore, we need the model to refuse user requests when necessary.
Additionally, users may also ask some content unrelated to the business, and we also need the Agent to refuse such requests.
We can use markdown format to categorize different prompts, writing the prompts that teach the Agent to refuse unreasonable content under the "Constraints" title. Of course, this format is just for standardization, and you can have your own format.
For example, let's ask: What's for dinner tonight?
In some more formal business scenarios, we can call a sensitive word library to refuse user requests.
Add the keyword "dinner" in Add Feature - Content Moderation. When the user inputs the keyword, the Agent app outputs "Sorry, I don't understand what you're saying."
