One Node · Z-Image

The name isn't marketing, it's a promise: drop One Node · Z-Image onto the canvas and you get a self-contained Z-Image Turbo studio in a single box. Type a prompt, pick a resolution, hit generate. No loader nodes, no KSampler, no wires to trip over. If your entire relationship with ComfyUI is "I want a picture and I don't want to babysit a graph," this is aimed squarely at you.

Z-Image Turbo is the model that's worth this treatment. Alibaba's 6B distilled model made near-Flux quality run on 12GB cards at ~30 seconds per image, and it's become the community's pick for fast local photorealism. Its whole pitch is that it's small and simple - which is exactly why this pack's critic (the one skeptical comment on its r/StableDiffusion launch, +19) has a point: Turbo doesn't need this much hand-holding. Treat the node as a convenience, not a capability upgrade. You're trading the graph for a clean UI, and you can't drop a ControlNet or an upscaler chain inside it.

How it actually works

Here's the mildly sneaky part: the ZImageOneNode class itself does nothing. It declares no inputs and no outputs - just a hidden unique_id, OUTPUT_NODE = True, and a noop function that returns {}. All the magic lives in the JavaScript panel attached to the node.

On generate, that panel assembles the standard Z-Image stack server-side - UNETLoader → ModelSamplingAuraFlow → CLIPTextEncode (positive and negative) → KSampler → VAEDecode → SaveImage - and posts it straight to ComfyUI's /queue endpoint. Your node is a remote control, not a pipeline. It also ships custom /z_image/* routes for the gallery, favorites, model listing, and even reading trigger words out of LoRA safetensors headers, which is how it auto-appends them to your prompt.

The controls that matter

Everything lives in the node's own panel, and the sensible defaults are already set for Turbo: res_multistep / simple, 8 steps, CFG 1 - right in line with community consensus. What you'll actually touch:

• Prompt and a resolution preset - 1024×1024, 1920×1088, 1088×1920, or custom.
• Model / Text Encoder / VAE dropdowns, which just list whatever's in your diffusion_models, text_encoders, and vae folders.
• Z-I2I mode - load an image, drag the denoise strength, regenerate.
• Up to 3 LoRAs with strength sliders, plus auto-inserted trigger words.
• An Advanced toggle exposing steps, CFG, and sampler for when the defaults aren't cutting it.

Nice touch: pick a file with "base" in the name and it auto-flips defaults to 20 steps / CFG 5 for Z-Image Base, which the KB says is the right instinct even if the sampler research there is still settling.

Install

cd ComfyUI/custom_nodes/
git clone https://github.com/Adeliox/ComfyUI-OneNode-Z-Image.git

Restart ComfyUI, then right-click → Add Node → ZImageOneNode. (ComfyUI Manager works too - search "OneNode Z-Image".) There's no requirements.txt: zero extra Python dependencies, which is rare and welcome. The real install cost is models, all from zerointensity/z-image-turbo on HuggingFace:

• z_image_turbo_bf16.safetensors → models/diffusion_models/ (~12GB)
• qwen_3_4b.safetensors → models/text_encoders/ (~8GB)
• ae.safetensors → models/vae/

That's a chunky download, and on disk it's a BF16 model, so the VRAM math still applies: comfortable on 16GB, workable on 12GB, tight below that. This pack targets the standard safetensors setup - if you're a 6GB GGUF user, stick with a graph workflow.

Where people get burned

Two real gotchas. First, the served workflow hardcodes the ModelSamplingAuraFlow shift at 3, while the single most-repeated community Turbo tweak is shift 7. You can't change it from the UI, so if you want that extra pop you're back in the graph. Second, there's no inpainting or ControlNet here - the original FLUX.2 [klein] node this was adapted from had more modes, and people noticed. For those, Z-Image's own Fun ControlNet or LanPaint is the better tool. The node also ignores your custom_nodes/ComfyUI-GGUF models, since it calls the stock UNETLoader.

Honest bottom line: this is a great starter node for pure text-to-image and img2img on Turbo, and the Base auto-detect is genuinely thoughtful. The moment you want control, LoRA stacking math, or an upscaler, graduate to a real workflow - Z-Image is easy enough that the escape hatch is small.