Generate image with txt2img

We're starting a new chapter on image generation, and we're going to make our first PNG.

Diffusion models generate images by starting from noise and step-by-step turning it into what your prompt describes. The QVAC SDK wraps one inference pass into a single call: diffusion({ modelId, prompt }).

The model needs to be loaded with the right modelType. sdcpp-generation is the official constant for the Diffusion engine. Anything else, and diffusion() either rejects the call or runs against the wrong backend.

Result is a DiffusionResult. outputs is a Promise<Uint8Array[]>. Each array entry is one PNG. The default is a single image unless we ask for more.

Standard FLUX.2 setup: a small diffusion model, a prompt encoder, a VAE. The whole pipeline (LLM → tokens → diffusion → latent → VAE → pixels) wires at load time. The combined loadModel would look like below:

const modelId = await loadModel({
  modelSrc: FLUX_2_KLEIN_4B_Q4_0,
  modelType: "sdcpp-generation",
  modelConfig: {
    llmModelSrc: QWEN3_4B_Q4_K_M,
    vaeModelSrc: FLUX_2_KLEIN_4B_VAE,
  },
});

Default is a single image. Same diffusion() shape as every other model in this chapter:

const result = diffusion({ modelId, prompt: "a cat sitting on a sofa" });

outputs[0] is the first image; fs.writeFileSync writes raw PNG bytes with no header needed. Note that the write needs the if (firstImage) guard to avoid a crash if the model fails to return an image:

const outputs = await result.outputs;
const firstImage = outputs[0];
if (!firstImage) throw new Error("No image returned from diffusion");
fs.writeFileSync("../../apps/desktop/output/image-gen/cat.png", firstImage);

console.log(`Generated ${outputs.length} image`);

Open cat.png after running. That's the model's answer to your prompt. The next lesson tunes the size and the step count.

Note: diffusion models are memory-heavy. A 4B-parameter Flux model in FP16 needs roughly 8 GB of VRAM. Make sure your machine has headroom before loading.

Put it to the test

1. Call loadModel with modelType: "sdcpp-generation" and the split-layout modelConfig.
2. Call diffusion({ modelId, prompt }) and await result.outputs.
3. Write outputs[0] to cat.png and console.log the count.