Check out the SD-WebUI extension for more information.

This extension enables large image drawing & upscaling with limited VRAM via the following techniques:

• Reproduced SOTA Tiled Diffusion methods
  • MultiDiffusion
  • Mixture of Diffusers
• pkuliyi2015 & Kahsolt's Tiled VAE algorithm
• pkuliyi2015 & Kahsolt's TIled Noise Inversion method

• Supported models
  • SD1.x, SD2.x, SDXL, SD3
  • FLUX
• ControlNet support
• StableSR support
• Tiled Noise Inversion
• Tiled VAE
• Regional Prompt Control
• Img2img upscale
• Ultra-Large image generation

If you have the Math Expression node (or something similar), you can use that to pass in the latent that's passed in your KSampler and divide the tile_height/tile_width by the number of rows/columns you want.

C = number of columns you want
R = number of rows you want

pixel width of input image or latent // C = tile_width
pixel height of input image or latent // R = tile_height

Paper

A tiling algorithm that attempts to eliminate seams by randomly shifting the denoise window per timestep. It is mainly used for fast inferences by setting tile_overlap to 0; otherwise, it's better to stick with the other tiling strategies as they produce better outputs.

This additional feature is experimental, in testing, and subject to change.

The recommended tile sizes are given upon the creation of the node based on the available VRAM.

The following images can be loaded in ComfyUI.

Simple upscale.

4x upscale. 3 passes.

Great thanks to all the contributors! 🎉🎉🎉
The implementation of MultiDiffusion, Mixture of Diffusers, and Tiled VAE code is currently under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License since it was borrowed from the wonderful SD-WebUI extension. Anything else GPLv3.

@article{jimenez2023mixtureofdiffusers,
  title={Mixture of Diffusers for scene composition and high resolution image generation},
  author={Álvaro Barbero Jiménez},
  journal={arXiv preprint arXiv:2302.02412},
  year={2023}
}

@article{bar2023multidiffusion,
  title={MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation},
  author={Bar-Tal, Omer and Yariv, Lior and Lipman, Yaron and Dekel, Tali},
  journal={arXiv preprint arXiv:2302.08113},
  year={2023}
}