Z-Image

Z-Image is a 6B-parameter text-to-image family from Tongyi-MAI that understands both English and Chinese prompts. It encodes prompts with a Qwen3 4B text encoder and decodes with the same 16-channel VAE as FLUX.1. InvokeAI supports two variants:

• Z-Image Turbo is distilled for fast, low-step generation with CFG disabled (CFG Scale 1.0). This is the checkpoint the starter models install.
• Z-Image Base is the undistilled foundation model. It needs more steps, and supports CFG and negative prompts.

A Diffusers pipeline’s variant is detected on install. Single-file, GGUF and SDNQ files are registered as Turbo. If you install a Base checkpoint in one of those formats, change its Variant to Z-Image Base in the Model Manager so the right defaults and schedulers apply.

Z-Image works in the Generate tab, on the Canvas (text-to-image, image-to-image, inpainting and outpainting) and in the workflow editor.

License

Z-Image Turbo, Z-Image (Base) and their Qwen3 text encoder are released under Apache 2.0, which allows commercial use. None of the downloads is gated.

This is a summary, not legal advice. Read the full license on the model’s HuggingFace page before using a model commercially.

Hardware

See the System Requirements table. In short: a Q4_K GGUF or NVFP4 transformer runs in about 8 GB of VRAM, while the Q8 and full-precision (BF16) builds want 16 GB or more. The full Diffusers pipeline is roughly a 33 GB download.

For more headroom you can enable FP8 Storage on a full-precision Z-Image model, use an SDNQ build, or turn on Low-VRAM mode.

Installing

The easiest path is the Z-Image Turbo bundle in the Model Manager. It installs the Q4_K GGUF transformer, the quantized Qwen3 encoder, the FLUX VAE and both Z-Image ControlNets.

Z-Image needs three components:

Component	Diffusers / SDNQ pipeline	GGUF / single-file install
Transformer	bundled	the .gguf / .safetensors file
Text encoder (Qwen3 4B)	bundled	installed separately
VAE (FLUX.1)	bundled	installed separately (any FLUX VAE)

The starter models:

Starter	Format	Size	Also installs
Z-Image Turbo	Diffusers pipeline	~33 GB	nothing, everything is bundled
Z-Image Turbo (quantized)	GGUF Q4_K	~4 GB	Qwen3 encoder (GGUF Q6_K, ~3.3 GB) + FLUX VAE
Z-Image Turbo (Q8)	GGUF Q8_0	~6.6 GB	Qwen3 encoder (GGUF Q6_K) + FLUX VAE
Z-Image Turbo (NVFP4)	Comfy-Org nvfp4 single file	~4.5 GB	Qwen3 encoder (FP4 mixed, ~3.5 GB) + FLUX VAE
Z-Image Turbo (SDNQ uint4 + SVD)	SDNQ Diffusers pipeline	~5 GB	nothing, everything is bundled

A full-precision Z-Image Qwen3 Text Encoder (~8 GB) is also available as a separate starter.

The NVFP4 build keeps its quantized layers packed in memory and decodes them on the fly, so it needs about a third of the full-precision transformer’s memory.

Choosing components

When the selected Z-Image model is not a self-contained pipeline, the Components section next to the model shows three slots:

• Component source: a Diffusers (or SDNQ) Z-Image pipeline to borrow the encoder and VAE from.
• Qwen3 Encoder: a standalone Qwen3 4B encoder.
• VAE: Z-Image decodes with the FLUX VAE, so FLUX-base VAEs are listed here.

You need either a component source or both a Qwen3 encoder and a VAE before you can generate.

Generation settings

Selecting a Z-Image model applies these defaults:

Variant	Steps	CFG Scale	Scheduler	Size
Z-Image Turbo	9	1.0	Euler	1024×1024
Z-Image Base	50	4.0	Euler	1024×1024

• CFG Scale: 1.0 turns CFG off, which is what Turbo is trained for. Values below 1.0 are not accepted.
• Negative prompt: only used when CFG Scale is above 1.0. With Turbo at 1.0 it has no effect.
• Schedulers: Euler (the default and recommended), Heun (2nd order) (better quality, about twice as slow) and LCM. LCM works with Turbo only and is not offered for Z-Image Base.
• Resolution: width and height must be multiples of 16.
• Prompt weighting: Compel-style prompt syntax does not apply to Z-Image. Write prompts in plain language.

In the workflow editor, Denoise - Z-Image also has a Shift input that overrides the timestep shift. Leave it blank to have it calculated from the image size, which is the recommended setting.

ControlNet

Two Z-Image ControlNets are available as starters (both are part of the bundle):

• Z-Image ControlNet Union handles Canny, HED, Depth, Pose and MLSD control images.
• Z-Image ControlNet Tile is useful for upscaling and adding detail.

On the Canvas, add a Control Layer while a Z-Image model is selected. The layer uses a Z-Image ControlNet with a default weight of 0.75. The weight ranges from 0 to 2, and 0.65 to 0.80 is the recommended range. Only one Z-Image control layer can be used per generation.

In the workflow editor, the same thing is the Z-Image ControlNet node feeding the Control input of Denoise - Z-Image. That node also needs the VAE input connected, which it uses to encode the control image.

Regional prompting

Z-Image supports Regional Guidance layers on the Canvas with positive prompts. Regional negative prompts, auto-negative and regional reference images are not supported for Z-Image.

In the workflow editor, Prompt - Z-Image takes an optional mask, and Denoise - Z-Image accepts either one conditioning or a collection of them.

LoRA

Z-Image LoRAs are supported in Kohya, PEFT/Diffusers and LyCORIS formats. They are applied to both the transformer and the Qwen3 encoder. In the workflow editor, use Apply LoRA - Z-Image or Apply LoRA Collection - Z-Image.

Seed Variance Enhancer

Z-Image Turbo tends to produce similar images across different seeds. The Seed Variance Enhancer - Z-Image node, available in the workflow editor only, adds reproducible, seed-based noise to the prompt conditioning to increase variety:

• Strength: 0 is off, 0.1 (the default) is subtle, 0.5 is strong.
• Randomize Percent: the share of embedding values that receive noise (default 50).

Connect it between Prompt - Z-Image and the denoise node’s positive conditioning.

PiD decoding

Z-Image has no PiD decoder of its own and uses the FLUX PiD decoders, since it shares FLUX.1’s VAE. See PiD Decode.