Diffusion models generate images (and increasingly video and audio) by learning to reverse a process of adding noise. Training: take a clean image and progressively add random noise until it's pure noise — teach the model to predict and remove that noise at each step. Inference: start from pure noise and iteratively denoise, guided by a text or image prompt.
The big names are Stable Diffusion (open-source), Midjourney (API/product), DALL-E 3 (OpenAI), Ideogram, and Flux. They've largely displaced the previous generation of GANs because they produce more diverse, controllable, and higher-quality outputs.
For production use cases — product imagery, marketing creative, UI mockups, document layout — diffusion models have become a genuine workflow tool, not just a novelty. The main integration path is via APIs (OpenAI Images, Stability AI, Replicate) rather than self-hosting, unless you have GPU budget and need fine-tuned styles.
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