sd-webui-colab VS cog

(Added Aug. 28, 2022) Colab notebook Stable Diffusion WebUi - Altryne by altryne. GitHub repo. txt2img. img2img. inpainting. Gradio user interface. Uses sd-webui repo.

you can try the colab version of hlky repo: https://github.com/altryne/sd-webui-colab, easier to setup or a one cell colab version: https://github.com/pinilpypinilpy/sd-webui-colab-simplified, everything works in one cell

For those without GPU's / not a powerful enough one. You can start the hlky stable diffusion webui (yes, web ui) in Google Colab with this notebook[0].

It's simple and it works, using colab for processing but actually giving you a URL (ngrok-style) to open the pretty web ui in your browser.

I've been using that on-the-go when not at my PC and it's been working very well for me (after trying numerous other colab-dedicated repos, trying to fix them, and failing).

[0]: https://github.com/altryne/sd-webui-colab

The code is directly adapted from the Colab notebook repo based on the hlky GitHub repo. I really don't have much experience with coding and didn't change much of the code other than the paths specific to Kaggle.

I'm running SD-webui on google colab https://github.com/altryne/sd-webui-colab/ with a colab pro account and its awesome but it does crash with attempting larger images. Is anyone using a google colab pro+ account and able to process larger images?

Based off of https://github.com/altryne/sd-webui-colab and https://github.com/hlky/stable-diffusion

View on GitHub

I'm in the same situation. I found this cog project to dockerise ML https://github.com/replicate/cog : you write just one python class and a yaml file, and it takes care of the "CUDA hell" and deps. It even creates a flask app in front of your model.

That helps keep your system clean, but someone with big $s please rewrite pytorch to golang or rust or even nodejs / typescript.

https://github.com/replicate/cog

Our thinking was just that a bunch of folks will want to fine-tune right away, then deploy the fine-tunes, so trying to make that easy... Or even just deploy the models-as-is on their own infra without dealing with CUDA insanity!

I have used this tool before: https://github.com/replicate/cog/tree/main

Then I discovered Cog: Containers for Machine Learning. Looks like a way more flexible solution to plug in the existing infrastructure: you write your custom code and Cog plugs it in a Docker image with FastAPI, no extra ecosystem complexity added.

Highly recommend using cog https://github.com/replicate/cog

Dokku Maintainer here.

Dokku also supports Dockerfiles, Docker Images, Tarballs (similar to heroku slugs), and Cloud Native Buildpacks. I'm also actively working on AWS Lambda support (both for simple usage without much config as well as SAM-based usage) and investigating Replicate's Cog[1] and Railways Nixpacks[2] functionalities for building apps.

There are quite a few options in the OSS space (as well as Commercial offerings from new startups and popular incumbents). It's an interesting space to be in, and its always fun to see how new offerings innovate on existing solutions.

  [1] https://github.com/replicate/cog