ComfyUI-ZImageDit

<img width="1024" height="1024" alt="ComfyUI-zimage-diffusers-wrapper_00002_" src="https://github.com/user-attachments/assets/eac45a4d-ad75-4a0f-9de7-b6bcf51f2292" /> <img width="2139" height="1075" 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

Notes:

• use only sdnq nodes you might have to install some pip packages manually, nothing too difficult there are other nodes but they are experimental, ignore them (you might need quanto though)

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

• for flash attention find a whl, I did not bother yet as it's ok-ish speedwise, 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)

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

• 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.

• 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

note: this does not use ComfyUI memory management, so use carefully. Ißve 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

´´