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Diffusion Models for Images — DDPM, Latent Diffusion, CFG, Stable Training
How denoising diffusion and latent diffusion power modern image gen (DALL·E, Stable Diffusion class systems): forward noise, score matching, DDIM-style fast sampling, classifier-free guidance, and production concerns — VRAM, latency, safety filters, and eval (FID, CLIP score, red-team). Connects the five GenAI planes for *generation-first* (non-LLM) stacks.
Diffusion is a denoising loop, not a one-shot GAN
Denoising diffusion probabilistic models (DDPM) (Ho et al., 2020) learn to reverse a forward process that adds Gaussian noise to an image (or to latent variables) across T time steps. At generation time, the model iteratively denoises from pure noise to a sample: each step is a conditional prediction of noise or the clean signal given the current noised state.
Latent Diffusion Models (LDM) (Rombach et al., 2022) run diffusion in a VAE latent space (lower resolution) and decode with a VAE decoder to pixels — the standard recipe behind Stable Diffusion-class open weights because 512×512 pixel diffusion would be prohibitively expensive at scale.
Interviews care about the sampling budget (how many U-Net forward passes), classifier-free guidance (CFG) for text-to-image adherence vs diversity , and serving VRAM — not only FID numbers .
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