taming-transformers VS VQGAN-CLIP

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/

VQGAN-CLIP

I've screwed with many text-to-image models over the past couple of years, and I found that while I currently enjoy Stable Diffusion's coherency, I have a soft spot for the ImageNet model used by default for VQGAN+CLIP. It easily approaches the uncanny valley when generating people or animals, but makes for great abstract backgrounds and wallpapers. I already have nostalgia for generating images with it on my CPU overnight.

For someone only tangentially familiar with this space, how is this different than e.g. https://github.com/nerdyrodent/VQGAN-CLIP which you can also run at home? Is it the quality of the generated images?

Used https://github.com/nerdyrodent/VQGAN-CLIP

I don't have any of the DALL-Es but I do have a couple from github [1], [2] which gave these outputs[3]

[1] https://github.com/nerdyrodent/VQGAN-CLIP

If you want to try your hand at buggering your whole system - try playing with AI image generation as it uses all possible computer assets :D . There is a lot of forms and installations for those but I VQGANs from github the easiest. Problem is that some require familarity with shell, python and in some cases - you need to enable the Linux subsystem in Windows (is it called a subsystem? it is not exactly a VM). This one is the easiest to install out of all I tried. But I liked the results of Pixray most but I wrecked it. I use this one nowadays.

For those that are interested, I used VQGAN-CLIP, specifically this GitHub repository