Z-Image-Turbo ControlNet - ComfyUI & One Click Windows Installer

Alibaba has officially released the ControlNet model for Z-Image Turbo, marking a major leap forward in controlled image synthesis. This custom setup offers an all-in-one one-click installer that automatically installs and configures everything you need.
The workflow currently supports Canny and Depth ControlNet modes. Both can be toggled or combined as needed for flexible composition and edge-guided output. The included Union model provides improved coherence between prompt description and visual output while maintaining high speed and image fidelity.
 
Included in the Installer Package
It automatically sets up:
ComfyUI and the necessary custom nodes
Sage Attention 2
Flash Attention 2
Triton for Windows
PyTorch: 2.8.0+cu128
 
Preloaded Models
z_image_turbo-Q5_K_M.gguf — Hugging Face
Z-Image-Turbo-Fun-Controlnet-Union-2.1.safetensors — Hugging Face
Qwen3-4B-UD-Q5_K_XL.gguf text encoder — Hugging Face
z_image_turbo_vae.safetensors VAE model — Hugging Face
2xLexicaRRDBNet_Sharp.pth upscale model — Hugging Face
 
Performance and Speed
Generates 1024x1024 images in under 1 minute 30 seconds (9 steps) on an RTX 4050 (6 GB VRAM) using the Q5_K_M GGUF model. Performance scales automatically — expect faster generation and higher resolution on GPUs such as the RTX 4090.
 
System Requirements
GPU: Nvidia RTX 30XX, 40XX, or 50XX series (FP16 support required)
VRAM: 4 GB minimum (8+ recommended)
OS: Windows
Storage: 20 GB free space
 
Usage Notes
The workflow supports both Z-Image-Turbo GGUF models and standard Z-Image-Turbo diffusion models.
Use the Fast Groups Bypasser node to enable or disable workflow sections.
Disable unused ControlNet modules (Canny or Depth) before generation to optimize performance.
For best results, craft descriptive text prompts and refine them with assistance from an LLM.