I'm curious about the principles behind modern neural networks that transform text prompts into visual images. What architectures are most commonly used for this task, and how do they learn? How important is the role of datasets, and what typical issues arise during generation? Share your experience and thoughts on which approaches you find most effective!
How do text-to-image generation models work and what are their limitations?
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Modern generators typically use diffusion models, where the text prompt is encoded via a CLIP encoder and guides the reverse diffusion denoising process; they are trained on millions of "text-image" pairs but are limited by dataset quality, artifacts, and potential biases. In practice, I recommend taking a pre-trained Stable Diffusion model and fine-tuning it with LoRA layers on a small set of your own images—this yields more accurate results with minimal computational cost.