Stable Diffusion

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I'm excited to release ENFUGUE into beta with v0.2 - full SDXL support, mixed 1.5/XL Diffusion/Inpainting/Refining Pipelines, and full MacOS support with a portable installation. Free and open-source. (github.com)

submitted 1 year ago* (last edited 1 year ago) by benjamin@lemmy.dbzer0.com to c/stable_diffusion@lemmy.dbzer0.com

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Hello everyone!

My name's Benjamin, I'm the developer of ENFUGUE, a self-hosted Stable Diffusion Web UI that's built around an intuitive canvas interface, while still trying to deliver the power and deep customization of the popular tab-and-slider web UI's.

I'm taking it out of Alpha and into Beta with the v0.2 release, which brings SDXL support while still maintaining most of the feature set of 1.5 by allowing you to configure multiple checkpoints for various diffusion plans. It also has a ton of changes since 0.1 as suggested by other users, like the the ability to point ENFUGUE to the directories of other Web UI installations to share models and other files.

This is not monetized software in any way; I simply built the tool I wanted to use, and wanted to share. Thanks you taking a look!

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[–] benjamin@lemmy.dbzer0.com 1 points 1 year ago (6 children)

You're the best, thanks so much for trying it and getting it working!

I don't think it's ever not worth chasing improved performance, so I'm definitely going to continue looking for optimizations. While cannibalizing the code for Comfy and A1111, I saw a lot (and I mean a lot) of shortcuts being made over the official Stability code release that improves performance in specific situations. I'm going to try and see how I can leverage some of those shortcuts into options for the user to tune to their hardware.

This latest release has attracted some more developer attention (and also some inquiries from hosting providers about offering Enfugue in the cloud!) I'm hoping that some of the authors of those improvements find their way to the Enfugue repository and perhaps are inspired to contribute.

With that being said, TensorRT will definitely knock your socks off in terms of speed if you haven't used it before, if you've got the hardware for it. I'd be happy to troubleshoot whatever went wrong with your Windows install - there should be up to three enfugue-engine.log files in your ~/.cache/ directory that will have more information about what went wrong, if you'd like to share them here (or we can start a GitHub thread if you have that.)

Thank you again for all your help!

[–] Stampela@startrek.website 1 points 1 year ago (5 children)

Now knowing where to look, I did some fixing by myself! Main issue is that I had CUDA 10 and 12, no 11. Then after going insane about that tiny difference... I landed on something I lack the knowledge to decipher: "PyInstallerImportError: Failed to load dynlib/dll 'C:\Program Files\NVIDIA GPU Computing Toolkit\TensorRT-8.6.1.6\lib\nvinfer_plugin.dll'. Most likely this dynlib/dll was not found when the application was frozen."

All I can say is that the file is there.

[–] benjamin@lemmy.enfugue.ai 2 points 1 year ago (4 children)

Hey! I am able to reproduce that error by using the CUDA 12 version of TensorRT.

PyInstallerImportError: Failed to load dynlib/dll 'C:\\TensorRT-8.6.1.6\\lib\\nvinfer_plugin.dll'. Most likely this dynlib/dll was not found when the application was frozen.

Please make sure you downloaded the top file here, not the bottom.

I was able to modify my PATH and point to the right TensorRT, then restart the server, and it worked for me (no machine restart needed.)

Please let me know if that works for you :)

[–] Stampela@startrek.website 1 points 1 year ago

... I'll check later, but I do remember grabbing the "right one" as I had version 12, so this might very well be it.

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