At statworx, we are constantly exploring new ideas and possibilities in the field of artificial intelligence. Recent months have been marked by generative models, particularly those developed by OpenAI (e.g., ChatGPT, DALL-E 2) but also open-source projects like Stable Diffusion. ChatGPT is a text-to-text model while DALL-E 2 and Stable Diffusion are text-to-image models, which create impressive images based on a short text description provided by the user. During the evaluation of those research trends, we discovered a great way to use our GPU to allow the statcrew to generate their own digital avatars.
The technology behind text-to-image generator Stable Diffusion
But first, let’s dive into the background of our work. Text-to-image generators, such as DALL-E 2 and Stable Diffusion, are based on diffusion architectures of artificial neural networks. It often takes months to train them on a vast amount of data from the internet, and this is only possible with supercomputers. However, even after training, the OpenAI models still require a supercomputer to generate new images, as their size exceeds the capacity of personal computers. OpenAI has made their models available through interfaces (https://openai.com/product#made-for-developers), but the model weights themselves have not been publicly released.
Stable Diffusion, on the other hand, was developed as a text-to-image generator but has a size that allows it to be executed on a personal computer. The open-source project is a joined collaboration of several research institutes. Its public availability allows researchers and developers to adapt the trained model for their own purposes by fine-tuning. Stable Diffusion is small enough to be executed on a personal computer, but fine-tuning it is significantly faster on a workstation (like ours provided by HP with two NVIDIA RTX8000 GPUs). Although it is significantly smaller than, for example, DALL-E2, the quality of the generated images is still outstanding.
All these models are controlled using prompts, which are text inputs that describe the image to be generated. Those text inputs are named prompts. For artificial intelligence, text is not natively understandable, because all these algorithms are based on mathematical operations which cannot be applied to text directly. Therefore, a common method is to generate what is known as an embedding, which means converting text into mathematical vectors. The understanding of text comes from the training of the translation model from text to embeddings. The high dimensional embedding vectors are generated in a way that the distance of the vectors to each other represents the relationship of the original texts. Similar methods are also used for images, and special models are trained to perform this task.
CLIP: A hybrid model of OpenAI for image-text integration with contrastive learning approach
One such model is CLIP, a hybrid model developed by OpenAI that combines the strengths of image recognition models and language models. The basic principle of CLIP is to embed matching text and image pairs. Those embedding vectors of the texts and images are calculated so that the distance of the vector representations of the matching pairs is minimized. A distinctive feature of CLIP is that it is trained using a contrastive learning approach, in which two different inputs are compared to each other, and the similarity between them is maximized while the similarity of non-matching pairs is minimized in the same run. This allows the model to learn more robust and transferable representations of images and texts, resulting in improved performance across a variety of tasks.
Customize image generation through Textual Inversion
With CLIP as a preprocessing step of the Stable Diffusion pipeline that creates the embeddings of the user prompts, it opens a powerful and efficient opportunity to teach the model new objects or styles. This fine-tuning is called textual inversion. Figure 1 shows this training process. With a minimum of three images of an object or style and a unique text identifier, Stable Diffusion can be steered to generate images of this specific object or style. In the first step a <tag> is chosen that aims to represent the object. In this case the object is Johannes, and a few pictures of Johannes are provided. In each training step one random image is chosen from the pictures. Further, a more explainable prompt like a “rendering of <tag>” is provided and in each training step one of those prompts is randomly chosen. The part <tag> is exchanged by the defined tag (in this case <Johannes>). With textual inversion the model’s dictionary is expanded and after sufficient training steps the new fine-tuned model can be plugged into the Stable Diffusion pipeline. This leads to a new image of Johannes whenever the tag <Johannes> is part of the user prompt. Styles and other objects can be added to the generated image, depending on the prompt.
Figure 1: Fine-tuning CLIP with textual inversion.
This is what it looks like when we create AI-generated avatars of our #statcrew
At statworx, we did so with all interested colleagues which enabled them to set their digital avatars in the most diverse contexts. With the HP workstation at hand, we could use the integrated NVIDIA RTX8000 GPUs and hereby reduce the training time by a factor of 15 compared to a desktop CPU. As you can see from the examples below, the statcrew enjoyed it to generate a bunch of images in the most different situations. The following pictures show a few selected portraits of your most trusted AI consultants. 🙂
Prompts from top left to bottom right:
- <Andreas> looks a lot like christmas, santa claus, snow
- Robot <Paul>
- <Markus> as funko, trending on artstation, concept art, <Markus>, funko, digital art, box (, superman / batman / mario, nintendo, super mario)
- <Johannes> is very thankful, art, 8k, trending on artstation, vinyl·
- <Markus> riding a unicorn, digital art, trending on artstation, unicorn, (<Markus> / oil paiting)·
- <Max> in the new super hero movie, movie poster, 4k, huge explosions in the background, everyone is literally dying expect for him
- a blonde emoji that looks like <Alex>
- harry potter, hermione granger from harry potter, portrait of <Sarah>, concept art, highly detailed
Stable Diffusion together with Textual Inversion represent exciting developments in the field of artificial intelligence. They offer new possibilities for creating unique and personalized avatars but it’s also applicable to styles. By continuing to explore these and other AI models, we can push the boundaries of what is possible and create new and innovative solutions to real-world problems. Stay tuned!
Image Source: Adobe Stock 546181349