
The need for AI-generated images has been growing rapidly in recent years. These images are not only used for artistic purposes, but also for practical applications in various industries. For example, in the fashion industry, AI-generated images can be used to create virtual models for showcasing clothing. In the automotive industry, AI-generated images can be used for designing and testing new car models. And the best part? You can now run your own AI image generation machine on Koyeb GPUs.

The [FLUX.1 \[dev\] model](https://huggingface.co/black-forest-labs/FLUX.1-dev) (by BlackForestLabs) is an advanced AI image generation model that produces outstanding output quality. [It is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions](https://huggingface.co/black-forest-labs/FLUX.1-dev). It features competitive prompt following, and is trained using guidance distillation. Additionally, the generated outputs can be utilized for personal, scientific, and non-commercial purposes as outlined in the [FLUX.1 \[dev\] Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).

In this tutorial, we will learn how to set up a Streamlit application, integrate the FLUX model for real-time image generation, and deploy the application using Docker and Koyeb, ensuring a scalable image generation service.

You can deploy the FLUX application as built in this tutorial using the [Deploy to Koyeb](https://www.koyeb.com/docs/build-and-deploy/deploy-to-koyeb-button) button below:

[![Deploy to Koyeb](https://www.koyeb.com/static/images/deploy/button.svg)](https://app.koyeb.com/deploy?name=flux-pytorch&type=git&repository=github.com/koyeb/example-flux-1-pytorch&branch=main&builder=dockerfile&instance_type=gpu-nvidia-rtx-4000-sff-ada)

## Requirements

To successfully follow this tutorial, you will need the following:

- [Git](https://git-scm.com/) installed
- [Python 3.6+](https://www.python.org/downloads/release/python-360/) or later
- A [Hugging Face](https://huggingface.co/) account
- A [Koyeb](https://app.koyeb.com/) account

## Understanding of the components

### Text-To-Image Generation and FLUX Model

The process of text-to-image generation involves the model interpreting input text and translating it into visual representations. This process consists of several steps: first, the model encodes the textual input into a latent space, capturing the semantic meaning of the words. Next, it employs a generative process to sample from this latent space, producing images that align with the described concepts. The FLUX model, developed by BlackForestLabs, is a state-of-the-art 12 billion parameter rectified flow transformer trained on extensive datasets containing pairs of images and their corresponding textual descriptions. This training enables the model to learn the intricate relationships between language and visual content. Consequently, users can input detailed prompts, and the FLUX model generates images that reflect those prompts with accuracy (and creativity).

### Streamlit

Streamlit is an open-source Python library designed to create interactive data applications, often referred to as dashboards. It empowers developers to build and share data apps simply and intuitively, eliminating the need for extensive web development expertise.

Streamlit apps are created as Python scripts, which are then executed within the Streamlit environment. The library offers a set of functions that can be utilized to add interactive elements to the app such as upload file button.

## Steps

1. [Set up the environment](#set-up-the-environment): Start by setting up your project directory, installing necessary dependencies, and configuring environment variables.
2. [Set up Streamlit](#set-up-streamlit): Next, install Streamlit and create the initial user interface for your application.
3. [Generate AI Images with FLUX Model](#generate-ai-images-with-flux-model): Using the FLUX model to generate AI images based on the user prompt and configuration.
4. [Dockerize the Streamlit application](#dockerize-the-streamlit-application): Create a Dockerfile to containerize your application for consistent deployment.
5. [Deploy to Koyeb](#deploy-to-koyeb): Finally, deploy your application on the Koyeb platform.

## Set up the environment

Let's start by creating a new Streamlit project. To keep your Python dependencies organized you should create a virtual environment.

First, create and navigate into a local directory:

```bash
# Create and move to the new directory
mkdir example-koyeb-flux-images
cd example-koyeb-flux-images
```

Afterwards, create and activate a new virtual environment:

```bash
# Create a virtual environment
python -m venv venv

# Active the virtual environment (Windows)
.\venv\Scripts\activate.bat

# Active the virtual environment (Linux)
source ./venv/bin/activate
```

Now, create a `requirements.txt` file with the following dependencies:

```txt
streamlit
watchdog
diffusers
torch
torchvision
einops
huggingface_hub[hf_transfer]
safetensors
sentencepiece
transformers
tokenizers
protobuf
requests
invisible-watermark
accelerate
peft
```

In terms of dependencies, we have included Streamlit for building a web app in Python, Hugging Face for real-time local use of the FLUX model, and watchdog to monitor file system events. The `accelerate` package is used to streamline the training and inference processes of machine learning models, making it easier to manage distributed training and optimize performance. The `invisible-watermark` package is utilized to embed invisible watermarks in generated content, ensuring uniqueness of the outputs produced by the model.

Now, you can install the dependencies with the following command:

```bash
pip install -r requirements.txt
```

Now, let's move on to creatng a new Streamlit project.

## Set up Streamlit

In this step, you will set up the Streamlit UI that will define the visual layout of the page, and the ability for users to load the FLUX model to start generating images with AI. All the logic for the project will reside in this file, so you can start by creating an `app.py` file with the following code:

```python
# File: app.py

import os, torch, streamlit
from diffusers import FluxPipeline
from huggingface_hub import login, snapshot_download

# Log in to Hugging Face using the provided token from environment variables
login(token=os.getenv('HF_TOKEN'))

# Set the title of the Streamlit application
streamlit.title("AI Image Generation with FLUX.1-dev")

# Create a text input field for the user to enter a prompt for image generation
prompt = streamlit.text_input("Enter your prompt:", "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k")

# Create four columns for user inputs
col1, col2, col3, col4 = streamlit.columns(4)

# Column 1: Input for image width
with col1:
    num_width = streamlit.number_input("Width:", min_value=1, value=256)

# Column 2: Input for image height
with col2:
    num_height = streamlit.number_input("Height:", min_value=1, value=256)

# Column 3: Input for the number of images to generate
with col3:
    num_images = streamlit.number_input("Images:", min_value=1, value=3)

# Column 4: Input for the number of inference steps
with col4:
    num_inference_steps = streamlit.number_input("Steps:", min_value=1, value=3)

# Button to load the model from Hugging Face
if streamlit.button("Load Model"):
    snapshot_download(repo_id="black-forest-labs/FLUX.1-dev", cache_dir="./FLUX_1_dev")
```

The code above does the following:

- Begins with importing the Streamlit module.
- Logs into Hugging Face using a token stored in environment variables, allowing access to the model repository.
- Defines text values for each step and page's title and description
- Creates four columns to organize user inputs for image dimensions, the number of images, and inference steps. Each column contains a number input field with specified minimum values and default values.
- Creates a `Load Model` button to load the FLUX model from Hugging Face. When clicked, it downloads the model snapshot to a specified cache directory using `snapshot_download` function.

With this, you have setup a UI that is able to load the FLUX model per user request. Now, let's move on to generating AI images based on the user prompt and settings.

## Generate AI Images with FLUX Model

In this step, you will invoke FLUX model to generate AI images based on the user prompt. Per the default values set in the input, the model will be generate 3 images for each prompt by user. Make the following additions in the `app.py` file:

```python
# File: app.py

# Existing code

# Button to generate images using the FLUX model
if streamlit.button("Generate Image"):
    # Load the FLUX model with specified parameters
    pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, cache_dir="./FLUX_1_dev")
    
    # Generate images based on the user input parameters
    images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, height=num_height, width=num_width, num_images_per_prompt=num_images).images
    
    # Create three columns to display the generated images
    cols = streamlit.columns(3)
    
    # Loop through the generated images and display them in the columns
    for i, image in enumerate(images):
        cols[i % 3].image(image, caption=prompt)
```

The code addition above does the following:

- Creates a button titled `Generate Image`. When clicked, it will instantiate a Flux pipeline for text-to-image generation.
- Invokes the pipeline with the user configured parameters to generate the desired height and width, number of inference steps to be taken, and number of images to be generated.
- Creates three columns to organize the generated images.

Now, you can run the Streamlit application with:

```bash
streamlit run ./app.py --server.port 8000
```

The application would now be ready on [http://localhost:8000](http://localhost:8000). Test the application by generating AI images by altering the default input values and your own custom prompt.

Now, let's dockerize the application to ensure consistency between multiple deployments.

## Dockerize the Streamlit application

Dockerizing deployments helps by creating a consistent and reproducible environment, ensuring that the application runs the same way on any system. It simplifies dependencies management and enhances scalability, making deployments more efficient and reliable. To dockerize, create a `Dockerfile` at the root of your project with the following code:

```Dockerfile
FROM python:3.12 AS runner

WORKDIR /app

COPY app.py requirements.txt .

RUN pip install -r requirements.txt --root-user-action=ignore
RUN pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121 --root-user-action=ignore # [!code ++]

RUN apt-get update 
RUN apt-get install -y libsm6 libxext6 git git-lfs # [!code ++]
RUN rm -rf /var/lib/apt/lists/*

EXPOSE 8000 # [!code ++]

ENV HF_HUB_ENABLE_HF_TRANSFER=1

ARG HF_TOKEN # [!code ++]
CMD ["streamlit", "run", "./app.py", "--server.port", "8000"] # [!code ++]
```

Apart from the usual Dockerfile to deploy Python applications, following tweaks/additions have been made in this code:

- `pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121 --root-user-action=ignore` to install PyTorch with CUDA support for GPU acceleration.
- `RUN apt-get update && apt-get install -y libsm6 libxext6 git git-lfs && rm -rf /var/lib/apt/lists/*` is used to install `git-lfs` and `git`, and then clean up package lists to reduce image size.
- `EXPOSE 8000` is used to specify the port on which the Streamlit application will run.
- `CMD ["streamlit", "run", "./app.py", "--server.port", "8000"]` is used to define the command to start the Streamlit app on port 8000.

With all configured, let's move on to deploy the application to Koyeb.

## Deploy to Koyeb

Now that you have the application running locally you can also deploy it on Koyeb and make it available on the internet.

Create a [new repository on your GitHub account](https://github.com/new) so that you can push your code.

You can download a [standard `.gitignore` file](https://raw.githubusercontent.com/github/gitignore/main/Python.gitignore) for Python from GitHub to exclude certain directories and files from being pushed to the repository:

```bash
curl -L https://raw.githubusercontent.com/github/gitignore/main/Python.gitignore -o .gitignore
```

Run the following commands in your terminal to commit and push your code to the repository:

```bash
git init
git add .
git commit -m "first commit"
git branch -M main
git remote add origin [Your GitHub repository URL]
git push -u origin main
```

You should now have all your local code in your remote repository. Now it is time to deploy the application.

Within the [Koyeb control panel](https://app.koyeb.com/), while on the Overview tab, initiate the app creation and deployment process by clicking Create Web Service.

1. Select **GitHub** as the deployment method.
2. Select your repository from the menu. Alternatively, deploy from the [example repository associated with this tutorial](https://github.com/koyeb/example-flux-1-pytorch) by entering `https://github.com/koyeb/example-flux-1-pytorch` in the public repository field.
4. In the Instance selection, select a GPU Instance.
5. Set your HuggingFace access token in the `HF_TOKEN` environment variable.
6. Finally, click the **Deploy** button.

Once the application is deployed, you can visit the Koyeb service URL (ending in `.koyeb.app`) to access the Streamlit application.

## Conclusion

In this tutorial, you built an AI Image Generation application using FLUX dev model with Streamlit framework. During the process, you learned how to invoke the Flux pipeline in python to generate AI images on the go, customized with user fed prompt and settings, and use the Streamlit framework to quickly prototype the user interface.

Given that the application was deployed using the Git deployment option, subsequent code push to the deployed branch will automatically initiate a new build for your application. Changes to your application will become live once the deployment is successful. In the event of a failed deployment, Koyeb retains the last operational production deployment, ensuring the uninterrupted operation of your application.

Perform real-time inference on high-performance infrastructure with zero-configuration.

Real-Time Inference with No-Ops

Learn how to use the FLUX model to generate images based on prompts in real-time.

Use FLUX, PyTorch, and Streamlit to Build an AI Image Generation App


Recently, AI image creation has seen significant progress. Many new tools are available to make the creative process easier with flexible, powerful functionality. One of these tools, ComfyUI, has grown to prominence due to its versatile, ease of use, and high-quality image generation.

Whether you're an artist wanting to try new things or a developer wanting to use AI for visual content, ComfyUI is a powerful tool that can help solve your image-based needs. In this guide, we'll explain what ComfyUI is, how it works, and how to set it up on a Koyeb GPU.

We'll also show you how to install ComfyUI Manager to add custom modules and give a step-by-step process for making high-quality images with Flux. By the end of this guide, you'll know everything you need to get the most out of ComfyUI and create amazing visuals.

You can consult the [project repository](https://github.com/koyeb/example-comfyui) as work through this guide. You can deploy ComfyUI as configured in this tutorial using the [Deploy to Koyeb](https://www.koyeb.com/docs/build-and-deploy/deploy-to-koyeb-button) button below:

[![Deploy to Koyeb](https://www.koyeb.com/static/images/deploy/button.svg)](https://app.koyeb.com/deploy?name=example-comfyui&type=git&repository=koyeb%2Fexample-comfyui&branch=main&builder=dockerfile&instance_type=gpu-nvidia-rtx-4000-sff-ada&env%5B%5D=&ports=8501%3Bhttp%3B%2F)

## What is ComfyUI?

[ComfyUI](https://github.com/comfyanonymous/ComfyUI) is a powerful, easy-to-use tool for creating images with AI. Users can design workflows, adjust settings, and see results immediately.

### Key features

- **User-friendly interface:** ComfyUI is designed to be simple so users of all skill levels can easily navigate and use its features.
- **Modular workflow design:** Users can create custom workflows by connecting different components, enabling flexibility in the image creation process.
- **Real-time feedback:** ComfyUI provides instant visual feedback so users can see the effects of their adjustments and fine-tune their workflows as needed.
- **Integration with AI models:** ComfyUI supports a variety of pre-trained AI models, as well as custom models, allowing users to generate images that meet their specific needs.

### Use cases

ComfyUI is great for many different uses, such as:

- **Artistic creation:** Artists can use ComfyUI to explore new creative ideas, make unique visuals, and try out different styles and techniques.
- **Graphic design:** Graphic designers can use ComfyUI to quickly create design elements, make mockups, and streamline their work process.
- **Content creation:** Marketers, bloggers, and content creators can use ComfyUI to make eye-catching visuals for their content, making it more engaging and appealing.
- **Research and development:** AI researchers and developers can use ComfyUI to test and visualize the performance of different models, experiment with new algorithms, and push the limits of AI-driven image generation.

## Basics of a ComfyUI workflow

At its core, a ComfyUI workflow is a series of connected modules, each doing a specific job in the image creation process. You can arrange these modules in different ways to get different results, giving you the flexibility to customize your workflows to fit your needs.

### Key components of the workflow

Let's see the example of the default ComfyUI workflow to understand better the components:

![ComfyUI default workflow](/static/images/tutorials/comfyui-flux/comfyui_default_flow.png)

The components of the workflow can be grouped in the following categories:

- **Input modules:** This is the starting point of any workflow. The input module lets you set the initial settings like image size, model choice, and input data (such as sketches, text prompts, or existing images). In the example above, for instance, the `Load Checkpoint` and `CLIP Text Encode` components are input modules.
- **Processing modules:** These parts handle various aspects of the image creation process, like applying filters, adjusting colors, refining details, and more. You can stack or arrange processing modules in parallel to create complex effects. In the example above, the `KSampler` component is a processing module.
- **Output module:** These are the final stages of the workflow, where the generated image is created and saved. You can specify the output format, resolution, and other details. In the example above, the `Save Image` component is an output module.
- **Control modules:** These parts give you additional control over the workflow, allowing you to tweak settings, adjust weights, and fine-tune the process. Control modules are essential for getting the desired results and ensuring high-quality outputs. In the example above, the `Empty Latent Image` component is a control module.

## Install ComfyUI on Koyeb GPUs

If you want to use the power of cloud computing for your image generation tasks, installing ComfyUI on a Koyeb GPU is a great choice. Koyeb offers powerful GPU instances that can handle the demanding requirements of AI-driven image generation, making sure your processing is fast and efficient.

### Prerequisites

Before starting the installation process, make sure you have the following:

- A **Koyeb account:** [Sign up](https://app.koyeb.com/auth/signup) for a Koyeb account if you don't already have one.

### Create a Dockerfile

We'll start by preparing a Dockerfile so we can make sure we have all of the dependencies installed, especially for GPU support. Create a `Dockerfile` with the following contents:

```dockerfile copy
# Use the official Python base image
FROM python:3.12

# Clone the repository
RUN git clone https://github.com/comfyanonymous/ComfyUI.git

# Set the working directory
WORKDIR /ComfyUI

# Update pip, install GPU dependencies, and install Comfy dependencies
RUN pip install --upgrade pip && pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121 && pip install -r requirements.txt

# Set the entry point for the container
CMD python3 main.py --listen 0.0.0.0 --port ${PORT:-8188}
```

This Dockerfile sets up a container image for running ComfyUI. Here's what it does step-by-step:

- First, it starts with a base Python image, specifically version 3.12.
- Next, it downloads the ComfyUI repository from GitHub. This repository contains all the code and resources needed to run ComfyUI.
- It sets the working directory to where ComfyUI was downloaded, so any subsequent commands are executed in the correct location.
- Next, it updates the `pip` package manager to the latest version to ensure compatibility with new packages. Chained to this `RUN` instruction are additional `pip` commands that install the GPU dependencies including PyTorch, and a command that installs ComfyUI's dependencies from the included `requirements.txt` file.
- Finally, it sets the default command to run the ComfyUI application when the container starts. This command uses **`0.0.0.0`** as the IP address, which means the application will be accessible from any network interface. It listens on the port defined by the `PORT` environment variable, using ComfyUI's default port 8188 as a fallback if the environment variable is unset.

### Create the repository

The final step is to create a [new repository on GitHub](https://github.com/new) to store the project files.

Once you're ready, run the following commands in your terminal to commit and push your code to the repository:

```bash copy
echo "# ComfyUI-Flux" >> README.md
git init
git add .
git commit -m "First Commit"
git branch -M main
git remote add origin [Your GitHub repository URL]
git push -u origin main
```

You should now have all your local code in your remote repository. Now it is time to deploy the Dockerfile.

### Deploy to Koyeb

In the [Koyeb control panel](https://app.koyeb.com/), while on the **Overview** tab, initiate the app creation and deployment process by clicking **Create App**.

On the App deployment page:

1. Select **GitHub** as your deployment method.
2. Choose the repository where your code resides. For example, `ComfyUI-Flux`.
3. Select the GPU you wish to use, for example, `RTX-4000-SFF-ADA`.
4. In the **Builder** section, choose **Dockerfile**.
5. In the **Service name** section, choose an appropriate name.
6. Finally, click **Deploy**.

After a few minutes, your application should be available at the indicated URL.

## What is ComfyUI Manager?

While ComfyUI comes with a set of pre-built modules, its real power comes from its extensibility through custom modules.

[ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager) is a plugin that lets users manage and install custom modules directly within the ComfyUI interface. It provides an easy-to-use interface for browsing available modules, installing them with a single click, and integrating them into your workflows.

## Install ComfyUI Manager

To install ComfyUI Manager, the simplest way is to modify your Dockerfile to include the necessary commands to install it.

Update your `Dockerfile` to add an additional `git clone` command as follows:

```dockerfile copy
# Use the official Python base image
FROM python:3.12

# Clone the repository
RUN git clone https://github.com/comfyanonymous/ComfyUI.git

# Set the working directory
WORKDIR /ComfyUI

# Update pip, install GPU dependencies, and install Comfy dependencies
RUN pip install --upgrade pip && pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121 && pip install -r requirements.txt

# Clone ComfyUI-Manager
RUN git clone https://github.com/ltdrdata/ComfyUI-Manager.git /ComfyUI/custom_nodes/ComfyUI-Manager

# Set the entry point for the container
CMD python3 main.py --listen 0.0.0.0 --port ${PORT:-8188}
```

This Dockerfile sets up a container for running ComfyUI and installs also ComfyUI Manager. The only difference from the previous Dockerfile is that it clones the ComfyUI manager repository into ComfyUI's `custom_nodes` directory.

Once you're done, commit the changes to your repository and push them to GitHub:

```bash copy
git add .
git commit -m "Add ComfyUI Manager"
git push origin main
```

Your Koyeb application will automatically create a new deployment based on your updated Dockerfile.

## Using custom modules

With ComfyUI Manager installed, you can now easily integrate custom modules into your workflows.

Whether you're looking to add new processing techniques, experiment with different models, or create entirely new workflows, the possibilities are endless.

We will use ComfyUI Manager to install Flux in the next section to generate high-quality images.

## Workflow for high-quality image generation with Flux

One of the great features of ComfyUI is that it can work with advanced AI models like Flux, which are specifically made for creating high-quality images. In this section, we'll guide you through a detailed process for generating amazing images using ComfyUI and Flux.

### What is Flux?

[Flux](https://github.com/black-forest-labs/flux) is a powerful AI model made for creating high-quality images. It's one of the latest deep learning models that use large datasets, complex structures, and advanced algorithms to generate images that look realistic and are artistically impressive.

### Key features of Flux

- **High-resolution outputs:** Flux is designed to produce high-resolution images, making it perfect for tasks where detail and clarity are important, like digital art, graphic design, and print media.
- **Artistic flexibility:** One of Flux's standout features is its ability to adapt to different artistic styles. Whether you want photorealism, impressionism, or a more abstract look, Flux can be adjusted to create images that match your desired style.
- **Deep learning architecture:** Flux uses a deep neural network architecture, which includes multiple layers of convolutional, generative, and refinement processes. This complex architecture allows it to understand and replicate intricate patterns, textures, and lighting effects in images.
- **Contextual understanding:** Unlike some earlier models, Flux has a strong ability to understand the context within images. This means it can accurately maintain the coherence of a scene, ensuring that elements in the image are not only visually consistent but also logically related.
- **Versatile input methods:** Flux supports various input methods, including text prompts, sketches, and even other images. This versatility allows users to guide the image generation process more effectively, providing a high degree of creative control.

### Applications of Flux

- **Digital art:** Artists use Flux to create detailed and stylistically diverse pieces of digital art. Its ability to interpret and generate different artistic styles makes it a valuable tool for creators looking to push the boundaries of digital expression.
- **Graphic design:** Graphic designers benefit from Flux's high-resolution outputs and its capability to generate assets that can be directly used in branding, marketing materials, and product designs.
- **Content creation:** For content creators, Flux offers a way to quickly generate unique visuals that can enhance blog posts, social media content, and other digital media.
- **AI research:** Researchers in the field of artificial intelligence use Flux to explore new techniques in image generation, test theories in deep learning, and develop new applications for AI in the creative industries.

## Install Flux on ComfyUI with ComfyUI Manager

To install a Flux model/checkpoint on ComfyUI, you can use the ComfyUI Manager. Open the ComfyUI Manager by clicking **Manager** in the sidebar menu.

This will open the manager window:

![ComfyUI Manager main menu](/static/images/tutorials/comfyui-flux/comfyui_manager_main_menu.png)

From here, click on **Model Manager** and then search for "flux":

![ComfyUI Manager search flux](/static/images/tutorials/comfyui-flux/comfyui_manager_search_flux.png)

For this example, install the "Comfy Org/FLUX.1 [schnell]" checkpoint by click the **install** button.

After a couple of minutes the download and install should complete:

![ComfyUI Manager flux install complete](/static/images/tutorials/comfyui-flux/comfyui_manager_flux_install_complete.png)

Now you can build your workflow to generate a high-quality image. You can use our [sample workflow](https://github.com/koyeb/example-comfyui/blob/main/koyeb-workflow.json) as a starting point. Download the file to your local computer.

To load the workflow into ComfyUI, click the **Load** button in the sidebar menu and select the `koyeb-workflow.json` file you just downloaded. Next, select the Flux checkpoint in the **Load Checkpoint** node and type in your prompt in the **CLIP Text Encode (Prompt)** node.

When you are ready, press **CTRL-Enter** to run the workflow and generate the image:

![ComfyUI run Koyeb workflow](/static/images/tutorials/comfyui-flux/comfyui_run_koyeb_workflow.png)

Here is the prompt included in the Koyeb sample workflow file followed by an example of an image generated from it:

> A serene twilight scene by a calm lake surrounded by tall, evergreen pine trees. The sky is painted with soft shades of pink, orange, and purple as the sun sets in the background. A small wooden dock extends into the water, with a single lantern casting a warm glow. Gentle ripples on the lake's surface reflect the vibrant colors of the sky, while a few fireflies dance around the dock. In the distance, mist rises from the water, adding a mystical quality to the peaceful, nature-filled landscape.

![ComfyUI generated image](/static/images/tutorials/comfyui-flux/comfyui_generated_image.png)

## Conclusion

ComfyUI is a powerful and flexible tool for making images with AI. It has a user-friendly interface, lets you design your own workflows, and can work with advanced models like Flux. Whether you are an artist, designer, or researcher, ComfyUI gives you the tools you need to create amazing visuals easily.

By following the steps in this guide, you learned how to set up ComfyUI on a Koyeb GPU, add custom modules with ComfyUI Manager, and create high-quality images using a detailed workflow. The possibilities are endless, and with ComfyUI, the only limit is your imagination.


Learn how to deploy ComfyUI, an image creation workflow manager, to Koyeb to generate images with Flux, an advanced image generation AI model.

Flux

Use FLUX, PyTorch, and Streamlit to Build an AI Image Generation App

Using ComfyUI, ComfyUI Manager, and Flux to Generate High-Quality Images on Koyeb