diffusion

Forward noising is fixed, reverse denoising is learned, and the training loss is a plain noise-prediction regression. The signal is why that beats a GAN's minimax and what the VAE and U-Net each do in latent space. Here is the answer.

GANs defined generative modeling for years, and interviewers want the minimax game, the failure modes (mode collapse, instability), and the precise reason diffusion displaced them for images while GANs still win on speed.

Generative training fails in distinctive ways: GANs collapse to a few outputs, diffusion samples come out noisy or blurry. The symptom tells you which knob to turn. Here is the diagnosis.

A GPU-heavy generative system where sampling steps dominate the bill and one bad image is a headline. The signal is the diffusion serving pipeline, the cost levers on per-image GPU time, and a mandatory two-sided safety layer.

A GPU-heavy generative-audio system with text/genre/lyric conditioning, minutes-long coherence, and copyright constraints. The signal is the generation pipeline plus the async, cost, and safety layer that makes it shippable.

Video generation is image generation plus the brutal constraint of temporal consistency, and it is the most GPU-intensive generative task there is. The signal is diffusion over spacetime plus a serving design that survives multi-minute jobs.