Run SD Inference

Key Insight

Stable Diffusion 1.5 turned text-to-image generation into something you can run on a single consumer GPU, and the diffusers library wraps the whole pipeline — text encoder, U-Net, and VAE decoder — behind a few lines of code. This project builds intuition for the three knobs that matter most at inference: the classifier-free guidance (CFG) scale (how hard the model is pushed toward your prompt), the sampler (the ODE/SDE solver that takes each denoising step, e.g. DDIM or DPM-Solver++), and the number of steps (more steps = slower but usually cleaner). Sweeping each one while holding the others fixed exposes the trade-offs — high CFG sharpens prompt adherence but oversaturates, while a higher-order sampler reaches good quality in far fewer steps — all without touching the model weights.