Amazing Z-Image Workflow

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Model description

A Z-Image-Turbo workflow, which I developed while experimenting with the model, it extends ComfyUI's base workflow functionality with additional features. It includes three versions that utilize different checkpoints to support various GPU VRAM capacities.

Features

  • Eight configurable image styles for testing and experimentation.

  • Versions in both .safetensors and .gguf formats to support a range of GPUs.

  • Custom sigma values adjusted to my preference
    (subjectively better prompt adherence).

  • Generated images are stored within the "ZImage" folder, organized by date.

  • Includes a trick to enable CivitAI automatic prompt detection.

Workflow Overview

The zip file contains three workflow files, each optimized for different GPU VRAM capacities:

  1. amazing_zimage-GGUF.json : Recommended for GPUs with 12GB or less VRAM.

  2. amazing_zimage-GGUFSMALL.json : For GPUs with less than 8GB VRAM.

  3. amazing_zimage-SAFETENSORS.json: Based directly on the ComfyUI example.

1. amazing_zimage-GGUF.json

Works smoothly with 12GB of VRAM or less, it may handle around 8GB as well.

2. amazing_zimage-GGUFSMALL.json

Optimized for GPUs with limited VRAM (less than 8GB),
though prompt accuracy might be affected.

3. amazing_zimage-SAFETENSORS.json

Based directly on the official ComfyUI example,
suitable for GPUs with around 12GB of VRAM or more.

Required Custom Nodes

The workflows require the following custom nodes:
(which can be installed via ComfyUI-Manager or downloaded from their repositories)

License

This project is licensed under the Unlicense license.

More info: https://github.com/martin-rizzo/AmazingZImageWorkflow

Images made by this model

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