diffusion( )
Generates images using a loaded diffusion model.
function diffusion(params: DiffusionClientParams): {
progressStream: AsyncGenerator<DiffusionProgressTick>;
outputs: Promise<Uint8Array[]>;
stats: Promise<DiffusionStats | undefined>;
};
| Field | Type | Required? | Description |
|---|
| modelId | string | ✓ | The identifier of the loaded diffusion model |
| prompt | string | ✓ | Text prompt describing the image to generate |
| negative_prompt | string | ✗ | Text describing what to avoid in the generated image |
| width | number | ✗ | Image width in pixels (must be a multiple of 8) |
| height | number | ✗ | Image height in pixels (must be a multiple of 8) |
| steps | number | ✗ | Number of diffusion steps |
| cfg_scale | number | ✗ | Classifier-free guidance scale for SD 1.x / 2.x / XL / SD3 models (typical range 1–20, default 7) |
| guidance | number | ✗ | Distilled guidance for FLUX models (typical range 1–10, default 3.5) |
| sampling_method | SamplingMethod | ✗ | Sampling algorithm |
| scheduler | Scheduler | ✗ | Noise scheduler |
| seed | number | ✗ | Random seed for reproducibility |
| batch_count | number | ✗ | Number of images to generate |
| vae_tiling | boolean | ✗ | Enable VAE tiling for large images on limited VRAM |
| cache_preset | string | ✗ | Cache preset identifier |
"euler" | "euler_a" | "heun" | "dpm2" | "dpm++2m" | "dpm++2mv2" | "dpm++2s_a" | "lcm" | "ipndm" | "ipndm_v" | "ddim_trailing" | "tcd" | "res_multistep" | "res_2s"
"discrete" | "karras" | "exponential" | "ays" | "gits" | "sgm_uniform" | "simple" | "lcm" | "smoothstep" | "kl_optimal" | "bong_tangent"
object — Object with the following fields:
| Field | Type | Description |
|---|
| progressStream | AsyncGenerator<DiffusionProgressTick> | Stream of generation progress ticks |
| outputs | Promise<Uint8Array[]> | Generated image buffers (resolves when generation completes) |
| stats | Promise<DiffusionStats | undefined> | Performance statistics |
| Field | Type | Description |
|---|
| step | number | Current diffusion step |
| totalSteps | number | Total number of steps |
| elapsedMs | number | Elapsed time in milliseconds |
| Field | Type | Description |
|---|
| modelLoadMs | number | undefined | Model loading time in milliseconds |
| generationMs | number | undefined | Single generation time in milliseconds |
| totalGenerationMs | number | undefined | Total generation time in milliseconds |
| totalWallMs | number | undefined | Total wall-clock time in milliseconds |
| totalSteps | number | undefined | Total diffusion steps performed |
| totalGenerations | number | undefined | Number of generations completed |
| totalImages | number | undefined | Number of images produced |
| totalPixels | number | undefined | Total pixels generated |
| width | number | undefined | Output image width |
| height | number | undefined | Output image height |
| seed | number | undefined | Seed used for generation |
import fs from "fs";
// Basic usage
const { outputs, stats } = diffusion({ modelId, prompt: "a cat" });
const buffers = await outputs;
fs.writeFileSync("output.png", buffers[0]);
// With progress tracking
const { progressStream, outputs: images } = diffusion({
modelId,
prompt: "a cat sitting on a windowsill",
width: 512,
height: 512,
steps: 20,
cfg_scale: 7,
});
for await (const { step, totalSteps } of progressStream) {
console.log(`${step}/${totalSteps}`);
}
const imageBuffers = await images;
