Detailed Explanation of Transformer
The Transformer is a neural network architecture introduced by Google researchers in 2017 with the paper "Attention Is All You Need," which revolutionized the field of artificial intelligence. Unlike previous architectures (RNN, LSTM), its greatest advantage is the ability to process data in parallel rather than sequentially.
At the core of the Transformer architecture lies the self-attention mechanism. This mechanism simultaneously calculates the relationship of each part of the input with all other parts. It evaluates the contextual relationship of each word in a sentence with all other words, effectively capturing long-range dependencies.
Large language models (LLMs) such as the GPT (Generative Pre-trained Transformer) series, BERT, Claude, and Gemini are built on the transformer architecture. In the visual domain, variants like Vision Transformer (ViT) and DiT (Diffusion Transformer) are used. The FLUX model is a notable example that adopts the DiT approach, using transformer architecture in the diffusion process.
The transformer architecture can be trained with billions of parameters thanks to its scalability, allowing models to learn more sophisticated patterns.
As a practical example, when you tell ChatGPT to "create a painting of a swan lake" using DALL-E 3, the transformer architecture first splits this sentence into tokens, calculates the relationship between each token using the attention mechanism, and then this semantic representation guides the image generation process. In the FLUX model, the DiT (Diffusion Transformer) approach uses the transformer's powerful pattern recognition capability at each denoising step to produce more consistent and higher-quality images.
Tools on tasarim.ai that use transformer architecture include Flux (DiT-based, fast and high-quality generation), DALL-E 3 (prompt understanding with GPT architecture), and Midjourney (aesthetic quality with its own transformer variant). In video generation, Vidu's U-ViT architecture and Sora's transformer-based approach are notable implementations of this technology.
Tip for beginners: To understand the transformer architecture, think about the concept of attention; just like a human, the AI model evaluates how each part of an image or text relates to all others to produce more meaningful results. Understanding this technical concept helps you grasp how the model thinks when writing prompts and enables you to create more effective prompts for better outputs.