Eugene Yan's article offers a grounded introduction to text-to-image generation, starting with the mechanics of diffusion models. It explains how images are progressively noised during training and then denoised during generation, and it positions this process as the backbone of modern text-to-image systems.
From there, the piece turns to text conditioning, describing how text embeddings are injected into the diffusion process so that generated images reflect the prompt. It also covers guidance, which adjusts the model's output to follow the text more strongly, and discusses latent-space approaches that make generation more efficient.
The article grounds these ideas in hands-on experiments with DDPM, showing how the theoretical pieces come together in practice. Because there is only one source, there are no conflicting claims to reconcile; the piece reads as a coherent survey rather than a debate.