cog
stable-diffusion
cog | stable-diffusion | |
---|---|---|
20 | 142 | |
7,186 | 2,438 | |
3.2% | - | |
9.4 | 9.8 | |
6 days ago | over 1 year ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cog
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AI Grant Traction in OSS Startups
View on GitHub
- Insanely Fast Whisper: Transcribe 300 minutes of audio in less than 98 seconds
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Talk-Llama
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.
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Llama 2 – Meta AI
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!
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Handling concurrent requests to ML model API
I have used this tool before: https://github.com/replicate/cog/tree/main
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Opinions on Cog: Containers for machine learning
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.
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can someone teach me how to install the new stable diffusion repo?
Highly recommend using cog https://github.com/replicate/cog
- Run Stable Diffusion on Your M1 Mac’s GPU
- replicate/cog: Containers for machine learning
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Why companies move off Heroku (besides the cost)
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
stable-diffusion
- [Stable Diffusion] Aide nécessaire à l'augmentation de la taille du fichier maximum sur l'installation locale
- [Machine Learning] [P] Exécutez une diffusion stable sur le GPU de votre M1 Mac
- Its time!
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Anybody running SD on a Macbook Pro? What are you using and how did you install it?
Yes, you can install it with Python! https://github.com/lstein/stable-diffusion works with macOS, and you can control all the common parameter via their WebUI or CLI :)
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How do I save the arguments for images I create when using the terminal? (Apple M1 Pro)
I'm using lstein fork ("dream") and when I create an image from the terminal, it also writes back to the terminal like this:
- I Resurrected “Ugly Sonic” with Stable Diffusion Textual Inversion
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AI Seamless Texture Generator Built-In to Blender
> Whenever I ask for something like ‘seamless tiling xxxxxx’ it kinda sorta gets the idea, but the resulting texture doesn’t quite tile right.
Getting seamless tiling requires more than just have "seamless tiling" in the prompt. It also depends on if the fork you're using has that feature at all.
https://github.com/lstein/stable-diffusion has the feature, but you need to pass it outside the prompt. So if you use the `dream.py` prompt cli, you can pass it `"Hats on the ground" --seamless` and it should be perfectly tilable.
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Auto SD Workflow - Update 0.2.0 - "Collections", Password Protection, Brand new UI + more
From https://github.com/lstein/stable-diffusion
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Stable Diffusion GUIs for Apple Silicon
Stable Diffusion Dream Script: This is the original site/script for supporting macOS. I found this soon after Stable Diffusion was publicly released and it was the site which inspired me to try out using Stable Diffusion on a mac. They have a web-based UI (as well as command-line scripts) and a lot of documentation on how to get things working.
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Still can't believe this technology is real. My talentless 2 minute sketch on the left.
I’m pretty sure it works for M2 as well - basically the newer ARM-based Macs. The instructions to get it working are detailed! https://github.com/lstein/stable-diffusion
What are some alternatives?
nixpacks - App source + Nix packages + Docker = Image
waifu-diffusion - stable diffusion finetuned on weeb stuff
pytorch_wavelets - Pytorch implementation of 2D Discrete Wavelet (DWT) and Dual Tree Complex Wavelet Transforms (DTCWT) and a DTCWT based ScatterNet
taming-transformers - Taming Transformers for High-Resolution Image Synthesis
piku - The tiniest PaaS you've ever seen. Piku allows you to do git push deployments to your own servers.
stable-diffusion-webui - Stable Diffusion web UI
heroku-review-app-actions - GitHub action to automate managing review apps on your Heroku account
diffusers-uncensored - Uncensored fork of diffusers
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
txt2imghd - A port of GOBIG for Stable Diffusion
memray - Memray is a memory profiler for Python
dream-textures - Stable Diffusion built-in to Blender