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ComfyDeploy: How Tiled Diffusion & VAE for ComfyUI works in ComfyUI?
What is Tiled Diffusion & VAE for ComfyUI?
The extension enables large image drawing & upscaling with limited VRAM via the following techniques: 1.Two SOTA diffusion tiling algorithms: [a/Mixture of Diffusers](https://github.com/albarji/mixture-of-diffusers) and [a/MultiDiffusion](https://github.com/omerbt/MultiDiffusion) 2.pkuliyi2015's Tiled VAE algorithm.
How to install it in ComfyDeploy?
Head over to the machine page
- Click on the "Create a new machine" button
- Select the
Edit
build steps - Add a new step -> Custom Node
- Search for
Tiled Diffusion & VAE for ComfyUI
and select it - Close the build step dialig and then click on the "Save" button to rebuild the machine
Tiled Diffusion & VAE for ComfyUI
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 <a href="https://arxiv.org/abs/2302.08113"><img width="32" alt="MultiDiffusion Paper" src="https://github.com/shiimizu/ComfyUI-TiledDiffusion/assets/54494639/b753b7f6-f9c0-405d-bace-792b9bbce5d5"></a>
- Mixture of Diffusers <a href="https://arxiv.org/abs/2302.02412"><img width="32" alt="Mixture of Diffusers Paper" src="https://github.com/shiimizu/ComfyUI-TiledDiffusion/assets/54494639/b753b7f6-f9c0-405d-bace-792b9bbce5d5"></a>
- pkuliyi2015 & Kahsolt's Tiled VAE algorithm
- ~~pkuliyi2015 & Kahsolt's TIled Noise Inversion method~~
[!NOTE]
Sizes/dimensions are in pixels and then converted to latent-space sizes.
Features
- ✅ 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
Tiled Diffusion
<div align="center"> <img width="500" alt="Tiled_Diffusion" src="https://github.com/shiimizu/ComfyUI-TiledDiffusion/assets/54494639/7cb897a3-a645-426f-8742-d6ba5cf04b64"> </div>[!TIP]
- Set
tile_overlap
to 0 anddenoise
to 1 to see the tile seams and then adjust the options to your needs.- Increase
tile_batch_size
to increase speed (if your machine can handle it).- Use the colorfix node if your colors look off.
Options
| Name | Description |
|-------------------|--------------------------------------------------------------|
| method
| Tiling strategy. |
| tile_width
| Tile's width |
| tile_height
| Tile's height |
| tile_overlap
| Tile's overlap |
| tile_batch_size
| The number of tiles to process in a batch |
How can I specify the tiles' arrangement?
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
SpotDiffusion
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.
Tiled VAE
<div align="center"> <img width="900" alt="Tiled_VAE" src="https://github.com/shiimizu/ComfyUI-TiledDiffusion/assets/54494639/b5850e03-2cac-49ce-b1fe-a67906bf4c9d"> </div> <br>The recommended tile sizes are given upon the creation of the node based on the available VRAM.
[!NOTE]
Enablingfast
for the decoder may produce images with slightly higher contrast and brightness.
Options
| Name | Description |
|-------------|----------------------------------------------------------------------------------------------------------------------------------------------|
| tile_size
| <blockquote>The image is split into tiles, which are then padded with 11/32 pixels' in the decoder/encoder.</blockquote> |
| fast
| <blockquote><p>When Fast Mode is disabled:</p> <ol> <li>The original VAE forward is decomposed into a task queue and a task worker, which starts to process each tile.</li> <li>When GroupNorm is needed, it suspends, stores current GroupNorm mean and var, send everything to RAM, and turns to the next tile.</li> <li>After all GroupNorm means and vars are summarized, it applies group norm to tiles and continues. </li> <li>A zigzag execution order is used to reduce unnecessary data transfer.</li> </ol> <p>When Fast Mode is enabled:</p> <ol> <li>The original input is downsampled and passed to a separate task queue.</li> <li>Its group norm parameters are recorded and used by all tiles' task queues.</li> <li>Each tile is separately processed without any RAM-VRAM data transfer.</li> </ol> <p>After all tiles are processed, tiles are written to a result buffer and returned.</p></blockquote> |
| color_fix
| <blockquote>Only estimate GroupNorm before downsampling, i.e., run in a semi-fast mode.</blockquote><p>Only for the encoder. Can restore colors if tiles are too small.</p> |
Workflows
The following images can be loaded in ComfyUI.
<div align="center"> <img alt="ComfyUI_07501_" src="https://github.com/shiimizu/ComfyUI-TiledDiffusion/assets/54494639/c3713cfb-e083-4df4-a310-9467827ee666"> <p>Simple upscale.</p> </div> <br> <div align="center"> <img alt="ComfyUI_07503_" src="https://github.com/shiimizu/ComfyUI-TiledDiffusion/assets/54494639/b681b617-4bb1-49e5-b85a-ef5a0f6e4830"> <p>4x upscale. 3 passes.</p> </div>License
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.
Citation
@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}
}