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Z-Image Turbo LoRA Inference with Diffusers (AI Toolkit-trained)

API model id: zimage_turbo
URL slug: zimage-turbo

This page documents the reference Diffusers inference pipeline for zimage_turbo (Z-Image Turbo). It is designed for running LoRAs trained with ostris/ai-toolkit while minimizing training preview vs inference mismatch. If you are trying to reproduce AI Toolkit sample previews, treat the code linked below as the source of truth (scheduler wiring, resolution snapping, LoRA application, and conditioning).

Run in the cloud (optional): If you want to reproduce the examples on this page in a pinned runtime without local CUDA/driver setup (and reduce preview‑vs‑inference drift), run it via RunComfy’s Cloud AI Toolkit (Train + Inference). 👉 You can open it here: Cloud AI Toolkit (Train + Inference)

Quick facts

Field	Value
Pipeline	`src/pipelines/zimage_turbo.py`
Base checkpoint	`Tongyi-MAI/Z-Image-Turbo`
Defaults	`sample_steps=8`, `guidance_scale=1.0`, `seed=42`
Resolution snapping	Floors width/height to a multiple of 32
Control image	No
LoRA scale behavior	Dynamic via adapters (`set_adapters`). Scale is set per request via `loras[].network_multiplier`.
Needs AI Toolkit	No

Reference implementation (source of truth)

Pipeline implementation: src/pipelines/zimage_turbo.py
Shared behaviors (snapping, seeding, LoRA base logic): src/pipelines/base.py
Request schema (parameter names): src/schemas/request.py
Model ids (enum): src/schemas/models.py
Pipeline registry (model → class): src/pipelines/__init__.py
Executor (prompt processing + caching): src/tasks/executor.py

Minimal API request

{
  "model": "zimage_turbo",
  "trigger_word": "sks",
  "prompts": [
    {
      "prompt": "[trigger] a photo of a person",
      "width": 1024,
      "height": 1024,
      "seed": 42,
      "sample_steps": 8,
      "guidance_scale": 1.0,
      "neg": ""
    }
  ],
  "loras": [
    {
      "path": "my_lora_job/my_lora.safetensors",
      "network_multiplier": 1.0
    }
  ]
}

Pipeline behavior that matters

Few-step model: defaults (8 steps / CFG 1.0) are part of the intended operating regime.
Requires diffusers.ZImagePipeline (upgrade diffusers if missing).
LoRA is applied as an adapter and can be re-scaled per request.

Preview-matching notes (training preview vs inference mismatch)

Start from 8 steps / CFG 1.0. Large changes in steps/CFG can look like ‘the LoRA isn’t applied’ for distilled few-step models.
If your diffusers build doesn’t include ZImagePipeline, upgrade diffusers to match AI Toolkit.
Width/height are floored to a multiple of 32.

What to compare when debugging mismatch

Confirm the effective width/height after snapping (the server floors to the divisor).
Match sample_steps and the scheduler family (FlowMatch / UniPC / DDPM differences matter).
Match guidance_scale semantics (some pipelines map it to a different internal parameter).
Match loras[].network_multiplier and whether LoRA scale is dynamic vs fused.