taming-transformers
stable-diffusion
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taming-transformers | stable-diffusion | |
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35 | 186 | |
5,354 | 3,146 | |
3.9% | - | |
0.0 | 0.0 | |
about 1 month ago | 7 months ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | GNU General Public License v3.0 or later |
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taming-transformers
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Automatic1111 for Intel Arc (A380 Tested)
taming-transformers
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[R] My simple Transformer audio encoder gives the same output for each timestep after the encoder
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.
- Trying to make VqGAN+CLIP work again
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im so lost
Command: "git" clone "https://github.com/CompVis/taming-transformers.git" "C:\AI\stable-diffusion-webui\repositories\taming-transformers"
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Why is ChatGPT and other large language models not feasible to be used locally in consumer grade hardware while Stable Diffusion is?
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.
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1 or 2 Errors Installing Automatic1111 on Mac M1
There is definitely a cmd but I can't tell you. It's on GitHub https://github.com/CompVis/taming-transformers
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Trying to Install InvokeAI and VectorQuantizer2 and taming modules but get error “zsh: parse error near `)’” How to fix? (MAC M1)
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?
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AI Is Coming For Commercial Art Jobs. Can It Be Stopped? (Greg Rutkowski quoted)
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.
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Stable-diffusion in Nix
# 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
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[D] Where does VQ-GAN get its randomness from?
Code for https://arxiv.org/abs/2012.09841 found: https://compvis.github.io/taming-transformers/
stable-diffusion
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Possible to load Civitai models in basujindal optimizedSD fork?
I am using this repo: https://github.com/basujindal/stable-diffusion and it works fine with e.g. this model: https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
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40min to render 2x 256x256 pictures ..
That includes this optimized version : https://github.com/basujindal/stable-diffusion
- [Stable Diffusion] Coincé chez Unet: courir en mode EPS-Prédiction
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How to use safetensors locally (optimized-sd)?
Ah, I wasn't aware of that. I use this version, which was very easy to set up and use by CLI.
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[Stable Diffusion] stabile Diffusion 1.4 - CUDA-Speicherfehler
Used repo recommended in https://github.com/CompVis/stable-diffusion/issues/39 to use https://github.com/basujindal/stable-diffusion - same result.
- [Stable Diffusion] Aide avec Cuda hors de mémoire
- [Stable Diffusion] Comment créer notre propre modèle?
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Help installing optimisedSD please. Thank you so much!
As per the best solution I found, I have download this (https://github.com/basujindal/stable-diffusion) version and pasted the optimizedSD folder in the main (user>stable-diffusion-webui) folder as per site instruction.
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Stable Diffusion Web UI: Using Optimized SD Post-Installation
The git says you can simply grab the OptimizedSD folder and paste it into the installation path, which I did. However, I'm not sure how to call upon its functionality. Again, the reddit post says >Remember to call the optimized python script python optimizedSD/optimized_txt2img.py instead of standard scripts/txt2img. Though I'm not even sure where that script call is performed. Any ideas? Thanks in advance!
- [Stablediffusion] diffusion stable 1.4 - Erreur CUDA de mémoire insuffisante
What are some alternatives?
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
stable-diffusion
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]
diffusers-uncensored - Uncensored fork of diffusers
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
chaiNNer - A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling application, chaiNNer has grown into an extremely flexible and powerful programmatic image processing application.
stable-diffusion - A latent text-to-image diffusion model
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
stable-diffusion-webui - Stable Diffusion web UI