How to make diffusion inference faster

A diffusion model generates by denoising over many steps, and each step is a full forward pass through a large network. So there are two fronts to attack: run it fewer times, and make each run cheaper. Here are the main levers.

1. Fewer steps: better schedulers

The easiest win is the sampler. Higher-order solvers like DPM-Solver reach good quality in 10–20 steps instead of hundreds — no retraining needed. Start here; see diffusion schedulers explained.

2. Far fewer steps: distillation

To go below what a solver can do, the model itself is distilled to generate in a handful of steps — sometimes just one. Consistency models, latent consistency models (LCM) and distilled "turbo" variants trade a little quality for a dramatic drop in step count, opening the door to real-time and on-device use.

3. Cheaper steps: latent space

Running the loop on full-resolution pixels is enormously expensive. Latent diffusion compresses the image into a small latent space, denoises there, and decodes once at the end — cutting the cost of every step and making high-resolution generation practical.

4. Cheaper math: quantization & kernels

Running weights at lower precision (quantization) and using fused, hardware-optimized GPU kernels shrinks the cost of each forward pass without changing the algorithm. These systems-level tricks compound with everything above.

5. More output per run: batching & caching

At the serving layer, batching many requests through the GPU at once raises throughput per dollar, and caching repeated computation avoids redundant work. These do not make a single image faster, but they make a fleet far cheaper.

Putting it together

Real deployments stack these: a distilled model, in latent space, with a good solver, quantized, served in batches. That is the practical craft of diffusion inference — turning an elegant research sampler into something economical enough to ship.

Related reading: What is diffusion inference? · Diffusion schedulers explained