ComfyUI-ZImageDit

ComfyUI-ZImageDit

<img width="559" alt="ComfyUI-zimage-diffusers-wrapper_00002_" src="https://github.com/user-attachments/assets/eac45a4d-ad75-4a0f-9de7-b6bcf51f2292" /> <img width="559" alt="image" src="https://github.com/user-attachments/assets/bac94951-c82c-46f4-ab14-089875693072" />

What is this ?

• an Alpha repo: unofficial diffusers integration of the official SDNQ pipeline to run in ComfyUI
• ...because I wanted to compare quality and be even more vram savy via SDNQ which is not officially supported and experiments with parameters

What can I do with this ?

Check these example LLM "Clones" , credits to the original authors (Civitai) for variety of generes, styles, media.

<img width="559" height="558" alt="image" src="https://github.com/user-attachments/assets/a523c061-0dab-4cf5-85a4-a527e30fe1e7" /> <img width="562" height="558" alt="image" src="https://github.com/user-attachments/assets/b79131d2-7794-40d1-b7b4-2f59293fb21f" />

Notes:

• installation you might have to install some pip packages manually, nothing too difficult you need: accelerate, the latest diffusers from source to support z-image pipeline

• install_sdnq.bat might help on windows because it looks like their toml file has an issue with double licensing (open inside the bat and change paths)

• ** diffusers** to install the latest diffusers manually via git to support the pipeline (from the embedded python folder if using portable comfyui): python.exe -m pip install git+https://github.com/huggingface/diffusers.git

• for **flash attention (optional) ** find a .whl, if you need you can try these places:

  • seems to be the best place to find them:
  • https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/tag/v0.5.4
  • other places
    • prebuilt wheels https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/tag/v0.4.10 (i ended up using one package from here, it gives a nice speed boost, sage attention makes it slower, not sure why)
    • prebuilt wheels https://huggingface.co/Kijai/PrecompiledWheels/tree/main
    • prebuilt wheels https://huggingface.co/lldacing/flash-attention-windows-wheel/tree/main

• about compile: does not work, for me.

• if startup fails check requirements for what is needed (quanto is not needed for these nodes, but for the other broken ones)

• weights are downloaded by diffusers on first run for sdnq nodes, in you huggingface default cache folder unless you change it

• some option dont work or I did not finish porting, test.

• there are other files in the other folders but they are experimental, ignore them (you might need quanto even or other installs)

• internally sampling happens with flowmatching euler

• only tested on windows (but linux should be even easier)

  • Platform: Windows
  • Python version: 3.12.10 (tags/v3.12.10:0cc8128, Apr 8 2025, 12:21:36) [MSC v.1943 64 bit (AMD64)]
  • pytorch version: 2.8.0+cu128
  • xformers version: 0.0.32.post2
  • Set vram state to: NORMAL_VRAM
  • Device: cuda:0 NVIDIA GeForce RTX 3080 : cudaMallocAsync
  • ComfyUI version: 0.3.75
  • ComfyUI frontend version: 1.33.8
  • Total VRAM 10240 MB, total RAM 32560 MB

• if you are on linux... you are smart enought to know what to do

Enjoy! Enrico aka ErosDiffusion

ps.: you might have issues installing, but I have no time to support :D

additional notes:

• this does not use ComfyUI memory management, so use carefully.
• I have added an option to unload but did not test it not sure it works.
• the memory footprint is around 7gb vram more or less, you can safely run up to 2048x2048 i can run lmstudio with qwen4 3b in parallel and between ram and vram and this, and never get oom.

´´