How does CLIP Text-to-image generation work?

I haven't been teaching any classes outside of uh well I haven't been teaching any classes that you've seen on YouTube recently. And that's in part because I'm teaching at NYU's ITP this semester. So, that class is a general introduction to machine learning art. It covers a lot of the recordings you'll probably already see on my YouTube channel, so I don't really upload those recordings. Also, for purely privacy reasons for the students there.

This semester, I'm not covering a whole lot of new things, but when I am covering something new, I'll try to record a video that I can post to YouTube for everyone. One of the things I am trying to go a little bit deeper into that I haven't really done a class on or anything is text-to-image. You've probably seen a couple of my tutorials on how to use text-image notebooks. Still, I thought it would be helpful to step back a little and talk a bit about how text-to-image works and why OpenAI's CLIP model is so important these days.

If you've watched any of my intro to machine learning classes, you know I usually like to start with a video or a demo about a model called Attention GAN. Maybe a year and a half ago, the Attention GAN was sort of the most popular or the state of the art for text-to-image. My joke was that it was terrible. Now, I kind of have to show it and be like "haha, that was what we thought state of the art was a year and a half ago, and now we've got this thing called CLIP." I'll talk a bit about how text-to-image works around CLIP in general, and maybe that'll help provide a bit of insight into what you're doing when you're using one of these tools or notebooks.

What is CLIP?

Only in the past year have we really seen this explosion in text-to-image tools, primarily because of a model called OpenAI CLIP. CLIP stands for Contrastive Language-Image Pre-Training, developed by a lab called OpenAI. Essentially, CLIP compares an image to a caption and provides a score on how likely the two are to match. That score is often referred to as "loss." For simplicity, it can be thought of as purely a score where you provide both a caption and an image, and it gives a score indicating how likely these two are to match.

The important part is this model scrapped a huge amount of the internet, particularly image and description pairs, which already existed. This scraped data might include alt tags on HTML or captions from images, such as those on Flickr or Instagram. OpenAI doesn't specify what they scraped, which is critical for understanding some of the hacks and ways people are finding things and some of the downsides. There's a lot of racist imagery trained into CLIP models because of the data scraped from sites like Reddit or 4chan. Despite these issues, the model works well in many other areas, which is why a lot of artists use it.

How Does Text-to-Image Generation Work?

A key way to understand this is to know that we don't use CLIP by itself. When generating images using CLIP, you'll often hear it as "CLIP plus" an image generator, like CLIP plus VQGAN or CLIP plus diffusion models. You provide a caption, and CLIP scores it, but the actual image generation often happens by pairing CLIP with a Generative Adversarial Network (GAN) or diffusion model. This GAN generates the images, and CLIP scores them. The GAN can then improve the image based on the CLIP score.

Various image generators have different qualities and constraints. For example, the guided diffusion model is popular due to its ability to generate photo-realistic images, although it's slower. Before that, VQGAN was very popular. These models generate different images and have different looks, so choosing the right tool for the job is crucial.

Popular CLIP Models

There are many CLIP-based models. They often started with models like Deep Daze, Big Sleep, which was based on BigGAN, and guided diffusion models. Models such as StyleClip (CLIP with a StyleGAN) and Hypertron are popular, each having their unique functionalities and strengths.

These models use the scoring system from CLIP to inform the image generation process, allowing for impressive visual outputs. Sometimes artists use specific tricks in their text prompts to influence the final image, a practice known as prompt engineering.

How Does Image Generation Work?

Here's a quick rundown of the process:

Text Prompt: You start with a text prompt like "a beautiful painting of a dog wearing a dolphin."
Initial Image: The model typically starts from a random image.
Step/Iteration: This is where the magic happens. The model will make tiny adjustments based on the CLIP score, iterating multiple times to refine the image.
Final Image: After many steps, you'll get the generated image that closely matches your text prompt.

The number of iterations or steps is crucial. The more steps, the better the model can refine towards generating a high-quality image.

Prompt Engineering

Over time, people have discovered certain hacks for prompt engineering. Specific keywords and phrases can influence the model significantly. For instance, adding descriptions like "trending on ArtStation" or "I can't believe the detail in the needlework" can influence the generated image's style.

Styles and Artists

People are leveraging CLIP to generate artwork mimicking styles of various known artists. Using specific phrases or artists' names in the text prompts can give your generated artwork a similar style. There are resources and communities dedicated to these text-to-image models, offering various tips and tricks.

Learning and Community

The field of text-to-image generation is incredibly vibrant, with many artists and researchers constantly experimenting and sharing their discoveries. The techniques and tools are frequently updated, making it a robust and welcoming community for anyone interested in this area.

Keywords

Text-to-image
CLIP
OpenAI
GAN
Diffusion Model
Prompt Engineering
StyleGAN
Machine Learning Art

FAQs

1. What is CLIP? CLIP stands for Contrastive Language-Image Pre-Training, a model by OpenAI that compares an image to a caption and provides a score on how likely the two are to match.

2. How does text-to-image generation work? Text-to-image generation typically involves pairing the CLIP model with an image generator, such as a GAN or diffusion model. CLIP scores the generated images against the text prompt, and the image generator iterates to improve the image based on these scores.

3. What is prompt engineering? Prompt engineering is the practice of tweaking the text prompts used in text-to-image models to influence the final generated image. Certain phrases and keywords can significantly impact the generated image's style and detail.

4. How do different image generators impact the final image? Different image generators, like VQGAN and guided diffusion, have unique qualities and constraints. Guided diffusion tends to generate more photo-realistic images but is slower, while VQGAN may produce stylized images faster.

5. What are some common CLIP-based models? Some common CLIP-based models include Deep Daze, Big Sleep, StyleClip, and guided diffusion models. Each has unique functionalities and strengths.

6. Are there any ethical considerations with using CLIP? Yes, since CLIP scrapes large amounts of internet data, it can include biased or inappropriate content. Users should be aware of these ethical implications when generating images.