68 lines
3.7 KiB
Markdown
68 lines
3.7 KiB
Markdown
# ComfyUI-ZImageDit
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<img width="559" alt="ComfyUI-zimage-diffusers-wrapper_00002_" src="https://github.com/user-attachments/assets/eac45a4d-ad75-4a0f-9de7-b6bcf51f2292" />
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<img width="559" alt="image" src="https://github.com/user-attachments/assets/bac94951-c82c-46f4-ab14-089875693072" />
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## What is this ?
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- an Alpha repo: unofficial **diffusers** integration of the official **SDNQ pipeline** to run in ComfyUI
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- ...because I wanted to compare quality and be even more vram savy via SDNQ which is not officially supported and experiments with parameters
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## What can I do with this ?
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Check these example LLM "Clones" , credits to the original authors (Civitai) for variety of generes, styles, media.
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<img width="559" height="558" alt="image" src="https://github.com/user-attachments/assets/a523c061-0dab-4cf5-85a4-a527e30fe1e7" />
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<img width="562" height="558" alt="image" src="https://github.com/user-attachments/assets/b79131d2-7794-40d1-b7b4-2f59293fb21f" />
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## Notes:
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- **installation**
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you might have to install some pip packages manually, nothing too difficult
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you need: accelerate, the latest diffusers from source to support z-image pipeline
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- **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)
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- ** diffusers** to install the latest diffusers manually via git to support the pipeline **(from the embedded python folder if using portable comfyui)**:
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`python.exe -m pip install git+https://github.com/huggingface/diffusers.git`
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- for **flash attention (optional) ** find a .whl, if you need you can try these places:
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- seems to be **the best place to find them**:
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- https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/tag/v0.5.4
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- other places
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- 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)
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- prebuilt wheels https://huggingface.co/Kijai/PrecompiledWheels/tree/main
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- prebuilt wheels https://huggingface.co/lldacing/flash-attention-windows-wheel/tree/main
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- about **compile**: does not work, for me.
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- **if startup fails** check requirements for what is needed (quanto is not needed for these nodes, but for the other broken ones)
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- **weights** are downloaded by diffusers on first run for sdnq nodes, in you huggingface default cache folder unless you change it
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- **some option dont work** or I did not finish porting, test.
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- there are **other** files in the other folders but they are experimental, ignore them (you might need quanto even or other installs)
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- internally sampling happens with flowmatching euler
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- only tested on windows (but linux should be even easier)
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- Platform: Windows
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- Python version: 3.12.10 (tags/v3.12.10:0cc8128, Apr 8 2025, 12:21:36) [MSC v.1943 64 bit (AMD64)]
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- pytorch version: 2.8.0+cu128
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- xformers version: 0.0.32.post2
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- Set vram state to: NORMAL_VRAM
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- Device: cuda:0 NVIDIA GeForce RTX 3080 : cudaMallocAsync
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- ComfyUI version: 0.3.75
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- ComfyUI frontend version: 1.33.8
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- Total VRAM 10240 MB, total RAM 32560 MB
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- if you are on linux... you are smart enought to know what to do
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**Enjoy!**
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Enrico aka ErosDiffusion
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ps.: you might have issues installing, but I have no time to support :D
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**additional notes**:
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- this does not use ComfyUI memory management, so use carefully.
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- I have added an option to unload but did not test it not sure it works.
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- 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.
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´´
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