Infopost | 2022.06.23
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Cattle |
Pickle molly |
import argparse import base64 import os from pathlib import Path import time from utils import parse_arg_boolean, parse_arg_dalle_version from consts import ModelSize from dalle_model import DalleModel dalle_model = None parser = argparse.ArgumentParser(description = "A DALL-E app to turn your textual prompts into visionary delights") parser.add_argument("--text", default = 'llama', help = 'Input strings') parser.add_argument("--model", type = parse_arg_dalle_version, default = ModelSize.MINI, help = "Mini, Mega, or Mega_full") parser.add_argument("--count", default = 6, help = 'Generate count') args = parser.parse_args() print(f"{time.strftime('%H:%M:%S')} Creating model") dalle_model = DalleModel(args.model) print(f"{time.strftime('%H:%M:%S')} Created model") def generate_images(): print(f"{time.strftime('%H:%M:%S')} Generating") generated_imgs = dalle_model.generate_images(args.text, args.count) print(f"{time.strftime('%H:%M:%S')} Generated") returned_generated_images = [] dir_name = os.path.join("./",f"{time.strftime('%Y%m%d_%H%M')}_{args. text}") Path(dir_name).mkdir(parents=True, exist_ok=True) for idx, img in enumerate(generated_imgs): img.save(os.path.join(dir_name, f'{idx}.png'), format="png") generate_images() print("Done")
pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax- releases/jax_cuda_releases.html
pip install https://storage.googleapis.com/jax-releases/cuda11/jaxlib-0.3. 10+cuda11.cudnn82-cp38-none-manylinux2014_x86_64.whl ERROR: jaxlib-0.3.10+cuda11.cudnn82-cp38-none-manylinux2014_x86_64.whl is not a supported wheel on this platform.
% nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2021 NVIDIA Corporation Cuda compilation tools, release 11.5, V11.5.119 Build cuda_11.5.r11.5
pip install cython
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2022.06.18
CarnageMore investment carnage, turf boot carnage, and sci-fi carnage. |
2022.06.25
DecidedReflecting on the SCOTUS decisions from this week. |
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2022.11.14
Stable Diffusion setupGetting Stable Diffusion up and running on Ubuntu 22.04 with an RTX3080 Ti. |
2022.11.19
PromptsExperimenting with prompts in Stable Diffusion. |
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ModsSince it was just the two-ish of us, Jes and I went to the Lodge for Thanksgiving lunch. |
cprimozic.net
Exploring Neural Networks Visually in the Browser - Casey Primozic's HomepageIntroduces a browser-based sandbox for building, training, visualizing, and experimenting with neural networks. Includes background information on the tool, usage information, technical implementation details, and a collection of observations and findings from using it myself. |
www.paepper.com
PyTorch multi-GPU training for faster machine learning results :: Päpper's Machine Learning Blog - This blog features state o...When you have a big data set and a complicated machine learning problem, chances are that training your model takes a couple of days even on a modern GPU. However, it is well-known that the cycle of having a new idea, implementing it and then verifying it should be as quick as possible. This is to ensure that you can efficiently test out new ideas. If you need to wait for a whole week for your training run, this becomes very inefficient. |
pyimagesearch.com
PNG Image to STL Converter in Python - PyImageSearchConvert your PNG images to STL effortlessly! Perfect for designers, engineers, and hobbyists trying to bring their designs to life. |