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ComfyDeploy: How ComfyUI-PixArt_XL works in ComfyUI?

What is ComfyUI-PixArt_XL?

A ComfyUI extension that integrates PixArt-Alpha models directly into ComfyUI with advanced memory management.

How to install it in ComfyDeploy?

Head over to the machine page

  1. Click on the "Create a new machine" button
  2. Select the Edit build steps
  3. Add a new step -> Custom Node
  4. Search for ComfyUI-PixArt_XL and select it
  5. Close the build step dialig and then click on the "Save" button to rebuild the machine

ComfyUI-PixArt_XL

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A ComfyUI extension that integrates PixArt-Alpha models directly into ComfyUI with advanced memory management.

image

Features

  • Use PixArt-Alpha XL models directly in ComfyUI
  • Smart memory management using both GPU and system RAM
  • Automatic fallback to lower resolutions with high-quality upscaling
  • Support for both DPM-Solver and SA-Solver schedulers
  • Full precision processing for maximum stability

Installation

  1. Clone this repository into your ComfyUI custom nodes folder:
cd ComfyUI/custom_nodes
git clone https://github.com/ShmuelRonen/ComfyUI-PixArt_XL.git
  1. Install dependencies:
pip install sentencepiece
  1. Restart ComfyUI

Usage

After installation, you'll have access to two new nodes:

PixArtAlpha ModelLoader

Automatic loads a PixArt-Alpha model from Hugging Face.

Inputs:

  • base_model_path: Path to the model (default: "PixArt-alpha/PixArt-XL-2-1024-MS")

Outputs:

  • model: The loaded model

PixArtAlpha Generation

Generates images using the PixArt-Alpha model.

Inputs:

  • model: The PixArt-Alpha model
  • positive: Positive prompt
  • negative: Negative prompt
  • width, height: Image dimensions
  • steps: Number of sampling steps
  • guidance_scale: How closely to follow the prompt
  • schedule: Sampling scheduler
  • seed: Random seed

Outputs:

  • image: The generated image

Memory Optimization

This implementation includes several advanced techniques to maximize memory efficiency:

  • CPU component offloading
  • Attention slicing
  • VAE optimization
  • Periodic CUDA cache clearing
  • Resolution fallback with quality-preserving upscaling

Acknowledgments