| # Clone llama.cpp | |
| git clone https://github.com/ggerganov/llama.cpp.git | |
| cd llama.cpp | |
| # Build it | |
| make clean | |
| LLAMA_METAL=1 make | |
| # Download model | |
| export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin |
| """ | |
| 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 |
| # ██████╗ ███████╗███╗ ███╗ ██████╗ ██╗ ██╗███████╗ ██╗ ██╗██████╗ | |
| # ██╔══██╗██╔════╝████╗ ████║██╔═══██╗██║ ██║██╔════╝ ██║ ██║██╔══██╗ | |
| # ██████╔╝█████╗ ██╔████╔██║██║ ██║██║ ██║█████╗ ███████║██████╔╝ | |
| # ██╔══██╗██╔══╝ ██║╚██╔╝██║██║ ██║╚██╗ ██╔╝██╔══╝ ██╔══██║██╔═══╝ | |
| # ██║ ██║███████╗██║ ╚═╝ ██║╚██████╔╝ ╚████╔╝ ███████╗ ██║ ██║██║ | |
| # ╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝ ╚═════╝ ╚═══╝ ╚══════╝ ╚═╝ ╚═╝╚═╝ | |
| # | |
| # ██████╗ ██╗ ██████╗ █████╗ ████████╗██╗ ██╗ █████╗ ██████╗ ███████╗ | |
| # ██╔══██╗██║ ██╔═══██╗██╔══██╗╚══██╔══╝██║ ██║██╔══██╗██╔══██╗██╔════╝ | |
| # ██████╔╝██║ ██║ ██║███████║ ██║ ██║ █╗ ██║███████║██████╔╝█████╗ |
Note: This gist may be outdated, thanks to all contributors in comments.
adb is the Android CLI tool with which you can interact with your android device, from your PC
You must enable developer mode (tap 7 times on the build version in parameters) and install adb on your PC.
Don't hesitate to read comments, there is useful tips, thanks guys for this !
| """Script to illustrate usage of tf.estimator.Estimator in TF v1.3""" | |
| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data as mnist_data | |
| from tensorflow.contrib import slim | |
| from tensorflow.contrib.learn import ModeKeys | |
| from tensorflow.contrib.learn import learn_runner | |
| # Show debugging output |
| """ Some description. | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import sys | |
| import json | |
| import tqdm |
| #!/bin/bash | |
| # | |
| # Bash script to clean up stray branches that have no "upstream" branch | |
| # | |
| # USE AT OWN RISK | |
| # | |
| # gitclean [--no-interactive] | |
| # | |
| # - no-interactive: Automatically deletes the branch, without showing the diff | |
| # and asking for confirmation |
| import tensorflow as tf | |
| def transformer(U, theta, downsample_factor=1, name='SpatialTransformer', **kwargs): | |
| """Spatial Transformer Layer | |
| Implements a spatial transformer layer as described in [1]_. | |
| Based on [2]_ and edited by David Dao for Tensorflow. | |
| Parameters | |
| ---------- |
| from math import sqrt | |
| def put_kernels_on_grid (kernel, pad = 1): | |
| '''Visualize conv. filters as an image (mostly for the 1st layer). | |
| Arranges filters into a grid, with some paddings between adjacent filters. | |
| Args: | |
| kernel: tensor of shape [Y, X, NumChannels, NumKernels] | |
| pad: number of black pixels around each filter (between them) |
name: CaffeNet fine-tuned on the Oxford 102 category flower dataset
caffemodel: oxford102.caffemodel
caffemodel_url: https://s3.amazonaws.com/jgoode/oxford102.caffemodel
gist_id: 0179e52305ca768a601f
license: BSD-3
