| #!/bin/bash | |
| compile_flags=("" "--compile") | |
| fuse_qkv_flags=("" "--fuse_qkv") | |
| # quantizations=("fp16" "bf16" "fp8" "fp8_e4m3" "fp8_e5m2" "fp6" "int8wo" "int8dq" "int4dq" "int4wo" "autoquant" "sparsify") | |
| quantizations=("fp16" "bf16" "fp6" "int8wo" "int8dq" "int4dq" "int4wo" "autoquant" "sparsify") | |
| device="cuda" | |
| # Check if completed.txt exists and read it into an array | |
| if [ -f completed.txt ]; then |
| import torch | |
| import numpy as np | |
| import k_diffusion as K | |
| from PIL import Image | |
| from torch import autocast | |
| from einops import rearrange, repeat | |
| def pil_img_to_torch(pil_img, half=False): | |
| image = np.array(pil_img).astype(np.float32) / 255.0 |
| """ | |
| stable diffusion dreaming | |
| creates hypnotic moving videos by smoothly walking randomly through the sample space | |
| example way to run this script: | |
| $ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry | |
| to stitch together the images, e.g.: | |
| $ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4 |
| """Hack to add per-session state to Streamlit. | |
| Usage | |
| ----- | |
| >>> import SessionState | |
| >>> | |
| >>> session_state = SessionState.get(user_name='', favorite_color='black') | |
| >>> session_state.user_name | |
| '' |
Including iframe of an github (gists) embled script
So for include a github embled iframe into an iframe You may use hex code
see this exemple :
The github embled link : <script src="https://gist.github.com/lostsh/dfb8a51aeb3ad5d7e3cefbc66d72fa53.js"></script>
and ths iframe who contain this embled :
| import torch | |
| import torchvision | |
| class VGGPerceptualLoss(torch.nn.Module): | |
| def __init__(self, resize=True): | |
| super(VGGPerceptualLoss, self).__init__() | |
| blocks = [] | |
| blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval()) | |
| blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval()) | |
| blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval()) |
| #!/bin/bash | |
| # | |
| # script to extract ImageNet dataset | |
| # ILSVRC2012_img_train.tar (about 138 GB) | |
| # ILSVRC2012_img_val.tar (about 6.3 GB) | |
| # make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory | |
| # | |
| # https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md | |
| # | |
| # train/ |
UPDATE (Fall 2020): This gist is an updated version to the Windows 10 Fall Creators Update - Installing Node.js on Windows Subsystem for Linux (WSL) guide, I usually just keep here notes, configuration or short guides for personal use, it was nice to know it also helps other ppl, I hope this one too.
Windows updated windows subsystem for linux to version 2, as the F.A.Q stated you can still use WSL version 1 side by side with version 2. I'm not sure about existing WSL machines surviving the upgrade process, but as always backup and 🤞. NOTE: WSL version 1 is not replace/deprecated, and there ar
| #deb cdrom:[Ubuntu 16.04.2 LTS _Xenial Xerus_ - Release amd64 (20170215.2)]/ xenial main restricted | |
| # See http://help.ubuntu.com/community/UpgradeNotes for how to upgrade to | |
| # newer versions of the distribution. | |
| deb http://us.archive.ubuntu.com/ubuntu/ xenial main restricted | |
| # deb-src http://us.archive.ubuntu.com/ubuntu/ xenial main restricted | |
| ## Major bug fix updates produced after the final release of the | |
| ## distribution. | |
| deb http://us.archive.ubuntu.com/ubuntu/ xenial-updates main restricted |
I'm going to walk you through the steps for setting up a AWS Lambda to talk to the internet and a VPC. Let's dive in.
So it might be really unintuitive at first but lambda functions have three states.