# Image generation models · Modalities

<!-- https://learn-inference.com/chapters/modalities/image-models -->

Here the analogy to LLMs finally breaks. Diffusion has no autoregressive loop, no KV cache, and no memory-bandwidth wall. It is compute-bound from the first step to the last, and its latency is step count times step cost, predictable before the request starts, which no LLM request ever is.

### 6.5.1 Image generation kernel optimization

With no cache to manage and no batching subtleties to exploit, optimization means making each denoiser pass cheaper. `torch.compile` and kernel fusion from [Chapter 4](https://learn-inference.com/chapters/software/cuda) do most of the work, and quantization helps because low-precision tensor cores are faster.

Note that quantization pays differently here. For LLM decode its main benefit is halved memory traffic; for diffusion, which is not bandwidth-bound, the win is the faster compute path.

### 6.5.2 One weird trick for faster image generation

Every diffusion step actually runs the denoiser twice: once guided by the prompt and once without, combined according to the guidance parameter. So a 50-step generation is 100 passes.

But the composition is decided early. After the first several steps the model has committed: it is not going to change its mind on step 40 and start drawing a dog when it has been drawing a cat. The remaining steps fill in detail, and detail does not need the prompt re-asserted.

> Figure 6.9. Turning guidance off partway through. Each step normally runs the denoiser twice, guided and unguided. Composition is settled early, so the guided pass stops paying for itself: drop it for the last 20 of 50 steps and you save a fifth of the work at the same step count.

Turning guidance off for the last stretch saves passes without reducing step count. Skip it for the final 20 steps of a 50-step run and you do 80 passes instead of 100, with quality generally holding. It is a hack, but it removes a fifth of the generation time without reducing step count.