Slides#
Introduction to AI Image Generators#
Hello everyone! Today, I will talking AI Image Generators. These are advanced software systems that use artificial intelligence to either create images from scratch or modify existing ones. They leverage powerful techniques like deep learning and neural networks to create images in ways we couldn’t imagine a few years ago. These tools are becoming increasingly significant, transforming how we approach tasks in sectors like marketing, entertainment, and design.
Brief History and Evolution of AI Image Generators#
Let’s take a quick look at how AI Image Generators have evolved over time. Back in 2014, we saw the introduction of Generative Adversarial Networks, or GANs. These were groundbreaking but had their challenges.
Who knows about the website thispersondoesnotexist.com?
This is a 2021 website that powered by StyleGAN, a neural network developed by Nvidia in 2018.
These faces are really realistic and looks like real people. And back in 2020s GANs was able to do these thing. But one challenge when using GANs is that they are prone to mode collapse. Once the network (generator) success in guessing the correct human face to fool the training network (discriminator) it likely to produce the same face again and again.
Fast forward to today, we use more advanced techniques such as tools like Stable Diffusion. This technique work by noise guessing mechanism which is much easier, to control and refine, allowing for the generation of highly detailed and diverse images.
The evolution of these models includes:
Diffusion Models: Diffusion models, like DALL-E 2 and Stable Diffusion, have gained attention. These models work by gradually transforming noise into coherent images. They are praised for their ability to generate detailed and diverse images with fewer artifacts.
Transformer-based Models: Inspired by advancements in natural language processing, it uses CLIP (Contrastive Language–Image Pre-training) use transformers to understand and generate images from text prompts. This integration of language and vision has opened new possibilities for creative AI applications.
How to Use AI Image Generators#
Now that we have a bit of background, let’s explore how AI Image Generators can be utilized. There are two main approaches: using pre-built systems and self-hosted solutions. Each has its own set of advantages and disadvantages.
Pre-built Systems (e.g., AWS Titan, GCP Imagen)#
Overview of Pre-built Systems:
Pre-built systems are ready-made solutions provided by cloud service providers like Amazon Web Services (AWS) and Google Cloud Platform (GCP).
Advantages:
- Ease of Use and Quick Deployment: These systems are user-friendly and can be set up quickly without needing extensive technical knowledge.
- High Scalability and Reliability: Since they are cloud-based, they can easily handle large workloads and offer reliable performance.
- Access to Cutting-edge Technology and Regular Updates: Users benefit from the latest advancements in AI as these platforms are frequently updated.
- Integration with Other Cloud Services: They can seamlessly integrate with other tools and services offered by the cloud provider.
Disadvantages:
- Cost Considerations and Potential for High Expenses: While convenient, these services can become expensive, especially with high usage.
- Limited Customization and Control: Users may face restrictions in customizing the system to meet specific needs.
- Dependency on Third-party Providers: Relying on external providers means less control over the infrastructure.
- Data Privacy and Security Concerns: Storing data on third-party servers can raise privacy and security issues.
Self-Hosted Image Generators#
Overview of Self-Hosted Solutions:
Self-hosted systems are those that you set up and manage on your own infrastructure. This approach offers more control and flexibility.
Advantages:
- Full Control Over the System and Customization Options: Users can tailor the system to their specific requirements.
- Potentially Lower Long-term Costs: Once set up, ongoing costs can be lower compared to cloud services.
- Enhanced Data Privacy and Security: With data stored locally, there are fewer concerns about privacy breaches.
- Ability to Optimize and Tailor the System to Specific Needs: Users can tweak the system for optimal performance.
Disadvantages:
- Requires Significant Technical Expertise and Resources: Setting up and maintaining the system can be complex and resource-intensive.
- Maintenance and Updates are the User’s Responsibility: Users need to handle all updates and troubleshooting.
- Scalability Challenges: Scaling up the system can be more challenging compared to cloud solutions.
- Initial Setup Can Be Time-consuming and Complex: The setup process can be lengthy and require significant effort.
Deploying an Image Generator Model Using ComfyUI#
OK because we have cover how to use pre-built system like AWS Titan image generator in the previous sharing section. For this one I will focus on the self-hosted system. Using ComfyUI.
Key Features and Benefits#
In the demo you will see how ComfyUI is known for its ease of use and flexibility, allowing users to tailor the interface to their specific needs. It provides a seamless experience for deploying and managing AI image generation models.
Demo#
- Simple Image generation using Flux Dev model.
- In-painting with more advanced workflow.
- Workflow sharing community