dalle-playground
warehouse
dalle-playground | warehouse | |
---|---|---|
35 | 274 | |
2,762 | 3,468 | |
- | 0.8% | |
3.2 | 9.7 | |
4 months ago | 6 days ago | |
JavaScript | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dalle-playground
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Discord bot with a locally-hosted SD backend.
Built on dalle-playground because it is simple and I like it.
- Neural photo engine with Intel compute stick?
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Free/open-source AI Text-To-Image Models that can be run on AWS?
[1] https://github.com/saharmor/dalle-playground
- ai_irl
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Running Dall-e mini on Windows? (Or: Are there any equivalent text-to-image AI's I can run on a windows PC with a 2080 TI?)
If you decide to abandon the idea of running locally and want to run in the cloud instead, https://github.com/saharmor/dalle-playground has a Google Colab notebook that's relatively easy to run (although Google Colab's free tier is relatively limited).
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Run Your Own DALL·E Mini (Craiyon) Server on EC2
Next, we want the code in the https://github.com/hrichardlee/dalle-playground repo, and we want to construct a pip environment from the backend/requirements.txt file in that repo. We were almost able to use the saharmor/dalle-playground repo as-is, but we had to make one change to add the jax[cuda] package to the requirements.txt file. In case you haven’t seen jax before, jax is a machine-learning library from Google, roughly equivalent to Tensorflow or PyTorch. It combines Autograd for automatic differentiation and XLA (accelerated linear algebra) for JIT-compiling numpy-like code for Google’s TPUs or Nvidia’s CUDA API for GPUs. The CUDA support requires explicitly selecting the [cuda] option when we install the package.
- Dream's over guys...
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How to run DALLE-2 locally
Is their any way to run DALLE-2 inside of a docker container similarly to this DALLE-PLAYGROUND repo on github?
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How difficult would it be to set up your own DALL-E (mini/mega) API for side-projects?
I know there are open source projects like dalle-playground. Would it literally be enough to host this app on an EC2 instance with the mini model?
- an AI image generator capable of taking a prompt and making it come to life.
warehouse
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Smooth Packaging: Flowing from Source to PyPi with GitLab Pipelines
python3 -m pip install \ --trusted-host test.pypi.org --trusted-host test-files.pythonhosted.org \ --index-url https://test.pypi.org/simple/ \ --extra-index-url https://pypi.org/simple/ \ piper_whistle==$(python3 -m src.piper_whistle.version)
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Pickling Python in the Cloud via WebAssembly
In my experience so far, I can use a vast amount of the Python Standard Library to build Wasm-powered serverless applications. The caveat I currently understand is that Python’s implementation of TCP and UDP sockets, as well as Python libraries that use threads, processes, and signal handling behind the scenes, will not compile to Wasm. It is worth noting that a similar caveat exists with libraries that I find on The Python Package Index (PyPI) site. While these caveats might limit what can be compiled to Wasm, there are still a ton of extremely powerful libraries to leverage.
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Introducing Flama for Robust Machine Learning APIs
We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.
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PyPI Packaging
From there, I needed to learn a bit about PyPi or Python Package Index, which is the home for all the wonderful packages that you know if you have ever run the handy pip install command. PyPi has a pretty quick and easy onboarding, which requires a secured account be created and, for the purposes of submitting packages from CLI, an API token be generated. This can be done in your PyPi profile. Once logg just navigate to https://pypi.org/manage/account/ and scroll down to the API tokens section. Click “Add Token” and follow the few steps to generate an API token which is your access point to uploading packages. With all this in place, I was able to use twine to handle the package upload. First I needed to install twine, again as simple as pip install twine. In order for twine to access my API token during the package upload process, it needed to read it from .pypirc file that contains the token info. For some that file may exist already, for me I was required to create it. Working in windows I simply used a text editor to create it in my home user directory ($HOME/.pypirc). The file contents had a TOML like format looked like this:
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Releasing my Python Project
I have published the package to Python Package Index, commonly called PyPi, and in this post, I'll be sharing the steps I had to follow in the process.
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Publishing my open source project to PyPI!
Register at PyPI.org
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Show HN: I mirrored all the code from PyPI to GitHub
According to the stats on the original link, there are over 25,000 identified secret ids/keys/tokens in the data. And it looks like that's just identifiable secrets, e.g. "Google API Keys" that I'm guessing are identifiable because they have a specific pattern, and may be missing other secrets that use less recognizable patterns.
I mean, sure, compared to the 478,876 Projects claimed on https://pypi.org/, that's a pretty small minority. On the other hand, I'd guess a many Python packages don't use these particular services, or even need to connect to a remote service at all, so the area for this class of mistake should be even smaller.
And mistakes do happen, but that's a pretty big thing to miss if you are knowingly publishing your code with the expectation other people will be reading it.
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Pezzo v0.5 - Dashboards, Caching, Python Client, and More!
PyPi package
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Modifying keywords in python package
Does pypi.org display the Union of all keywords, the keywords of the most recent release, the keywords of the first release or some other weird combination like the intersection?
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PyPI Requires 2FA for New User Registrations
https://peps.python.org/pep-0458/
Here's the in-progress roadmap: https://github.com/pypi/warehouse/issues/10672
If there's particular issues you believe you could pick off to help achieve the goal, much appreciated!
What are some alternatives?
dalle-mini - DALL·E Mini - Generate images from a text prompt
devpi
Dannjs - Easy to use Deep Neural Network Library for JavaScript.
bandersnatch
CogVideo - Text-to-video generation. The repo for ICLR2023 paper "CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers"
localshop - local pypi server (custom packages and auto-mirroring of pypi)
min-dalle - min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch
Poe the Poet - A task runner that works well with poetry.
nano-neuron - 🤖 NanoNeuron is 7 simple JavaScript functions that will give you a feeling of how machines can actually "learn"
scribd-downloader
pollinations - Generate Art
Python Packages Project Generator - 🚀 Your next Python package needs a bleeding-edge project structure.