ComfyUI Extension: SKIT_ComfyUI-ZImage-Generate

Authored by suravaya113

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A ComfyUI custom node for generating images with Z-Image through Hugging Face Diffusers with configurable model selection and generation parameters.

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Custom Nodes (1)

README

ComfyUI Z-Image Generate

A single ComfyUI custom node for generating images with Z-Image through Hugging Face Diffusers.

Node

  • Z-Image Generate

Inputs:

  • prompt: text prompt
  • negative_prompt: optional negative prompt
  • model_id: Hugging Face model id, default Tongyi-MAI/Z-Image
  • width, height: output image size
  • steps: diffusion steps
  • guidance_scale: prompt guidance strength
  • seed: deterministic seed
  • dtype: auto, bfloat16, float16, or float32
  • clear_cache: unload the cached pipeline after generation

Output:

  • IMAGE

Install

Clone this repository into your ComfyUI custom nodes folder:

cd ComfyUI/custom_nodes
git clone https://github.com/YOUR_USERNAME/ComfyUI-ZImage-Generate.git

Install dependencies in the same Python environment that runs ComfyUI:

pip install -r ComfyUI-ZImage-Generate/requirements.txt

Then restart ComfyUI.

Model Download

On the first run, Diffusers downloads the model from Hugging Face. Large models can take time and disk space.

The default model id is:

Tongyi-MAI/Z-Image

You can also try another compatible Z-Image model id in the node's model_id field.

Notes

  • CUDA is used automatically when available.
  • auto dtype uses bfloat16 on CUDA and float32 on CPU.
  • This node keeps one pipeline cached for faster repeated generations.
  • Enable clear_cache if you want to release VRAM after a generation.

Repository Structure

ComfyUI-ZImage-Generate/
|-- __init__.py
|-- nodes.py
|-- requirements.txt
|-- README.md
`-- .gitignore

Run ComfyUI workflows without the setup

No installs, no CUDA version roulette, no GPU sitting idle on your bill. Bring a workflow and run it in the browser.

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