taming-transformers VS stable-diffusion

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/

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 :)

I'm using lstein fork ("dream") and when I create an image from the terminal, it also writes back to the terminal like this:

> 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.

From https://github.com/lstein/stable-diffusion

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.

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