DeepFaceLive
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DeepFaceLive | ploomber | |
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55 | 121 | |
13,912 | 3,374 | |
- | 1.0% | |
8.4 | 7.4 | |
11 months ago | 17 days ago | |
Python | Python | |
GNU General Public License v3.0 only | 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.
DeepFaceLive
- Virginia's Age Verification On Adult Websites Is Worse Than You Think
- How is the twitch trump biden debate stream made?
- DeepFaceLive: Real-time face swap for PC streaming or video calls
- Is it possible to sync a lip and facial expression animation with audio in real time?
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Val Kilmer Twitter Romance Scam
https://github.com/iperov/DeepFaceLive. This is the first result Google gives me. Rudimentary, but enough to possibly trick someone who isn't knowledgeable enough. Instead of a fully AI generated person, it is a digital face swap. Essentially, you are correct in that the technological leap towards making a fully interactive deep fake is likely far beyond current technological capabilities. Especially for a scammer. Digitally swapping a face sidesteps that technological limitation.
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Is there a way to do facial rigs on AI images?
A more lifelike deformer would be running a 'deepfake' layer over your face motion into your 2D character face, but so far I haven't tried it yet. Here is some example of a well known open source 'faceswapper' : https://github.com/iperov/DeepFaceLive
- DeepFaceLive issue #41: Stop Developing This Technology
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How to make a deep fake character for my youtube series
DeepFaceLive https://github.com/iperov/DeepFaceLive You have to create a celeb model first or download a publicly available model
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Correlation between advance of an AI and so-called safeguards?
"Advanced AI" is an expression that makes politicians and decision makers to go on a rampage. Don't get your hopes up, the way the world reacted to ChatGPT, Stable Diffusion and stuff like that already suggests all AIs will be pretty much government regulated eventually and people like Eugenia has no way to circumvent this. Add projects like https://github.com/iperov/DeepFaceLive into the mix and you can see the perfect storm brewing up already...
- Animate your stable diffusion portraits
ploomber
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Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor)
- One-click sharing powered by Ploomber Cloud: https://ploomber.io
Documentation: https://jupysql.ploomber.io
Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README).
We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!
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Runme – Interactive Runbooks Built with Markdown
For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel
And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber
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Rant: Jupyter notebooks are trash.
Develop notebook-based pipelines
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Who needs MLflow when you have SQLite?
Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.
We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.
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New to large SW projects in Python, best practices to organize code
I recommend taking a look at the ploomber open source. It helps you structure your code and parameterize it in a way that's easier to maintain and test. Our blog has lots of resources about it from testing your code to building a data science platform on AWS.
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A three-part series on deploying a Data Science Platform on AWS
Developing end-to-end data science infrastructure can get complex. For example, many of us might have struggled to try to integrate AWS services and deal with configuration, permissions, etc. At Ploomber, we’ve worked with many companies in a wide range of industries, such as energy, entertainment, computational chemistry, and genomics, so we are constantly looking for simple solutions to get them started with Data Science in the cloud.
- Ploomber Cloud - Parametrizing and running notebooks in the cloud in parallel
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Is Colab still the place to go?
If you like working locally with notebooks, you can run via the free tier of ploomber, that'll allow you to get the Ram/Compute you need for the bigger models as part of the free tier. Also, it has the historical executions so you don't need to remember what you executed an hour later!
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Alternatives to nextflow?
It really depends on your use cases, I've seen a lot of those tools that lock you into a certain syntax, framework or weird language (for instance Groovy). If you'd like to use core python or Jupyter notebooks I'd recommend Ploomber, the community support is really strong, there's an emphasis on observability and you can deploy it on any executor like Slurm, AWS Batch or Airflow. In addition, there's a free managed compute (cloud edition) where you can run certain bioinformatics flows like Alphafold or Cripresso2
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Saving log files
That's what we do for lineage with https://ploomber.io/
What are some alternatives?
DeepFaceLab - DeepFaceLab is the leading software for creating deepfakes.
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Wav2Lip - This repository contains the codes of "A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild", published at ACM Multimedia 2020. For HD commercial model, please try out Sync Labs
papermill - 📚 Parameterize, execute, and analyze notebooks
libreddit - Private front-end for Reddit
dagster - An orchestration platform for the development, production, and observation of data assets.
ColossalAI - Making large AI models cheaper, faster and more accessible
dvc - 🦉 ML Experiments and Data Management with Git
web2img - Bundle web files into a single image
argo - Workflow Engine for Kubernetes
Lemmy - 🐀 A link aggregator and forum for the fediverse
MLflow - Open source platform for the machine learning lifecycle