nix-stable-diffusion VS taming-transformers

I have been playing around with a memory efficient version of [stable-diffusion](https://github.com/basujindal/stable-diffusion), an AI model that outputs images based on input text. I think it is rather neat, and [this post](https://xeiaso.net/blog/stable-diffusion-nixos) by Xeiaso (good blog for nix resources and rants) inspired me to try and make the installation process more nix-friendly. So I have forked the original [stable-diffusion repo](https://github.com/CompVis/stable-diffusion) and added a [`default.nix`](https://github.com/Danielhp95/nix-stable-diffusion/blob/main/default.nix) which dumps you in a `nix-shell` with all dependencies: cuda (nvidia) stuff, python dependencies and C++ libraries for pytorch (deep learning framework). Although **note** that in order to run it you must first download the model weights from [this repo](https://huggingface.co/CompVis/stable-diffusion-v1-4) and place them in `models/ldm/stable-diffusion-v1/model.ckpt` directory.

taming-transformers

What’s your goal exactly? Are you trying to make a transformer based auto encoder of audio spectrograms? If so you should either start with either a proven ViT-based AE implementation (either a VAE or a VQ-GAN). But I don’t see why you necessarily need a ViT for this, if you’re working at a much smaller scale a convolutional architecture is plenty and much more amenable to beginners. See https://github.com/CompVis/taming-transformers for an example of a convolutional VQ GAN.

Command: "git" clone "https://github.com/CompVis/taming-transformers.git" "C:\AI\stable-diffusion-webui\repositories\taming-transformers"

See https://arxiv.org/abs/2012.09841 for prior work. SD authors swap out the Transformer and language modelling objective with a UNet diffusion objective. In general, the more inductive bias your model has, the more efficient it can be. ChatGPT runs purely on a Transformer architecture, which has far fewer priors than a CNN and requires far more parameters as a result. This may not be the case in the future.

There is definitely a cmd but I can't tell you. It's on GitHub https://github.com/CompVis/taming-transformers

I wasn’t able to find a “taming” folder within the site-packages folder so I decided to look up how to get VectorQuantizer2 and taming.modules.vqvae.quantize and found this link: https://github.com/CompVis/taming-transformers/blob/master/taming/modules/vqvae/quantize.py I copied the raw contents and pasted that to the terminal and I got this error: “zsh: parse error near `)’” I’m not sure how to fix this so I can install VectorQuantizer2 so I can use InvokeAI. How do I fix this?

I say this to everyone... Even if SD and the model is legit and legal. Do not go around commercialising it's outputs or claiming ownership over them... and if you do the properly cite the source of the model and system along with it. In https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers and https://huggingface.co/CompVis/stable-diffusion-v1-4 there are citiations provided for you to use for a reason. I recommend you to use them.

# Copy models as described in README cp ~/Downloads/model.ckpt . cp ~/Downloads/GFPGANv1.3.pth . # Clone other repos as mentioned in README mkdir repositories git clone https://github.com/CompVis/stable-diffusion.git repositories/stable-diffusion git clone https://github.com/CompVis/taming-transformers.git repositories/taming-transformers git clone https://github.com/sczhou/CodeFormer.git repositories/CodeFormer git clone https://github.com/salesforce/BLIP.git repositories/BLIP export NIXPKGS_ALLOW_UNFREE=1 nix-shell default.nix pip install torch --extra-index-url https://download.pytorch.org/whl/cu113 # Also from linux instructions. Can probably be added to default.nix python webui.py

Code for https://arxiv.org/abs/2012.09841 found: https://compvis.github.io/taming-transformers/