easydiffusion VS automatic

Stable diffusion because it's free and has tons of customization, EasyDiffusion is the simplest to install. But you should download custom models from Civit.ai because the default one is bad.

A simple 1-click way to create beautiful artwork on your computer using AI. No dependencies or technical knowledge required. https://easydiffusion.github.io/

Easy Diffusion

Easydiffusion is what I use. I jumped in way past when it was just a CLU. https://easydiffusion.github.io/

install EasyDiffusion (https://github.com/easydiffusion/easydiffusion/)

Easy Diffusion (previously cmdr2 UI) can run SDXL in 768x768 in about 7 GB of VRAM. And SDXL 512x512 in about 5 GB of VRAM.

Regular SD can run in less than 2 GB of VRAM with Easy Diffusion.

1. Installation (no dependencies, python etc): https://github.com/easydiffusion/easydiffusion#installation

Easiest 1-click way to install and use Stable Diffusion on your computer."

https://github.com/easydiffusion/easydiffusion

And while Whisper is OpenAI, it is trivial to use locally and extremely usefull

https://github.com/chidiwilliams/buzz

I would install automatic1111. If you can't try easy diffusion first. Once you get advance you can install comfyUI and controlnet addon.

> it won't ever be a viable option

For production workloads, I generally agree. It's an unsupported hack with a questionable future, I wouldn't do anything money-making with it.

However, for tinkering and consumer workloads, it already works pretty well. Enough of cuDNN and cuBLAS work to run PyTorch and in turn, Stable Diffusion with https://github.com/lshqqytiger/ZLUDA - there's even a fairly user-friendly setup process already in https://github.com/vladmandic/automatic .

I was able to get a personal non-ML related project working on my AMD card in just a few minutes, which saved me a lot of development time before I then deployed the production workload on NV hardware (this is probably why AMD pulled the plug on the project - it's almost more of a boost to NV than anything else, AMD really need people to be writing code on ROCm to deploy on AMD datacenter hardware).

While I currently use SD.Next[1], I have tested ComfyUI locally with my AMD card. The UI can be daunting, but you learn quite a great deal about how a Stable Diffusion pipeline works. In addition some innovations and advances find their way into ComfyUI first.

[1] https://github.com/vladmandic/automatic

I used SD.NEXT ( https://github.com/vladmandic/automatic ) and https://civitai.com/models/82098/add-more-details-detail-enhancer-tweaker-lora and epicphotogasm_lastUnicorn

I found this thread on GitHub talking about how this was fixed in the latest version with an optional setting. I tried enabling it, as they mentioned, but it just resulted in an immediate CUDA out of memory error when starting generation. So it seems I'm actually needing the shared memory, which I assume is my issue.

As always, do check out our more detailed changelog, give us a quick install from our Repo, and stop by our Discord Server for any questions or help you may need.

SD.Next

Today I finally wanted to give SDXL a chance, so I set everythin up according to Vladmandic's Wiki https://github.com/vladmandic/automatic/wiki/SD-XL

I'm trying to get SDXL working on Vlad's SDNext, but I keep getting the error in the title when trying to run basic operations. I'm not sure what's going on, I followed his guide for it to a T.

We used an inference container based on SDNext, along with a custom worker written in Typescript that implemented the job processing pipeline. The worker used HTTP to communicate with both the SDNext container and with our batch framework.