GitHub Weekly Hot Topics Issue 10 (2024/02/11-02/17) comb through the weekly hot GitHub projects, understand the hot technical trends, and take a look at the popular projects of the week.
#1 MoneyPrinter
- Project Name: MoneyPrinter - Automatically generate youtube clips
- GitHub link: https://github.com/FujiwaraChoki/MoneyPrinter
- 上周 Star 数：5300
The name of this project is so straightforward MoneyPrinter, this project uses a variety of AI tools to finally achieve the effect of entering the desired text and automatically outputting video clips.
The author's idea is very straightforward and down-to-earth, which is to integrate some existing technologies through python and finally achieve a usable effect. In fact, the content of the project is very simple and straightforward, and the core is only a few python files.
Let's talk about the author's implementation ideas, which are worth learning from.
- Use GPT to write video scripts
- Use pexels' API to search for relevant videos
- 用TikTok-Voice-TTS 来做语音生成
- Use MoviePy to cut and add subtitles
- Finally, you can also upload youtube via Google API
How, it seems very simple, many things are the same, and it is not difficult to do.
Below is its GitHub star, it seems that foreign friends also like the practical spirit of the project.
#2 PKL
- Project Name: PKL - Configuration-as-Code Language
- GitHub link: https://github.com/apple/pkl
- 上周 Star 数：4000+
It's an open-source project from Apple, and Pkl (pronounced Pickle) is a configuration-as-code language with a wealth of validation and tools. It can be used as a command-line tool, software library, or build plugin. PKLs can be scaled from small to large, from simple to complex, from ad-hoc to repetitive configuration tasks.
PKL was created because they believed that configuration was best expressed in a specialized configuration language; a mix of static configuration formats and general-purpose programming languages.
Current use cases mainly include generating static configurations and application runtime configurations*. *Currently hosting languages include Java, Kotlin, Swift and go
PKL supports the following output formats:
- JSON
- Jsonnet
- Pcf (a static subset of Pkl)
- (Java) Properties
- Property List
- XML
- YAML
And PKL provides better validation support for configuration content so that errors in content are found before deployment.
#3 StableCascade
- Project Name: StableCascade - Wensheng Diagram Model
- GitHub link: https://github.com/Stability-AI/StableCascade
- 上周 Star 数：3400+
Stable Cascade is a new generation of Wensheng graph model released by StabilityAI, Stable Cascade is a three-stage Wensheng graph diffusion model based on the Wuerstchen architecture, which is not only faster but also more effective than Stable Diffusion XL.
This project is the official codebase for Stable Cascade. We provide training and inference scripts, as well as a variety of different models that can be used.
Stable Cascade has achieved impressive results both visually and evaluatively. According to our evaluation, in almost all comparisons, Stable Cascade performed best in terms of fast alignment and aesthetic quality. The figure above shows the results of a human assessment using a combination of partial cues (links) and aesthetic cues. Specifically, Stable Cascade (30 inference steps) was compared to Playground v2 (50 inference steps), SDXL (50 inference steps), SDXL Turbo (1 inference step), and Würstchen v2 (30 inference steps).
Stable Cascade's focus on efficiency is evidenced by its architecture and the potential for higher compression. Although the largest model contains 1.4 billion more parameters than Stable Diffusion XL, it still has faster inference times, as shown in the figure below.
The Stable Cascade consists of three models: Stage A, Stage B, and Stage C, which represent the cascade of generated images, hence the name "Stable Cascade". Phases A and B are used to compress images, similar to the work of VAE in stable diffusion. However, as mentioned earlier, higher image compression can be achieved with this setting. In addition, Phase C is responsible for generating small 24 x 24 lurks given text prompts. The diagram below illustrates this visually. Note that Phase A is VAE, and both Phases B and C are diffusion models.
#4 ml-mgie
- Project Name: ML-MGIE - Image Editing Model
- GitHub link: https://github.com/apple/ml-mgie
- 上周 Star 数：1700+
This project is an open-source artificial intelligence model launched by Apple, which can interpret user commands based on multimodal large language models (MLLM) and handle pixel-level operations in various editing scenarios, such as global photo optimization, local editing, Photoshop-style modification, etc.
Key features of Apple ML-MGIE
- Visual perceptual response generation. ML-MGIE can generate responses to visual content through a language model, which means it can understand image content and generate relevant text descriptions or answer image-related questions. This feature is particularly useful in providing image descriptions, augmented reality applications, and visual data analysis.
- Cross-modal understanding. ML-MGIE has demonstrated a strong ability in cross-modal understanding, linking information from different modalities, such as text and images, for comprehensive understanding. For example, it can enhance scene understanding by analyzing image content and related text descriptions. This cross-modal understanding is essential for improving human-computer interaction, enhancing search engine results, and creating smarter educational tools.
- Image Editing Guidelines. An important application of ML-MGIE is to guide instruction-based image editing. It can edit the image according to the user's instructions, such as changing the color, shape, or size of the objects in the image. This is achieved by integrating multimodal large language models with diffusion models, where ML-MGIE shows superior performance compared to technologies such as InstructPix2Pix. This capability can be applied to automated image editing tools to improve the efficiency and accuracy of image editing.
#5 free-for-dog
- Project name: free-for-dev - a set of free development tools
- GitHub link: https://github.com/ripienaar/free-for-dev
- 上周 Star 数：1300 +
This project has compiled a collection of services that provide free packages for developers, including SaaS, PaaS, IaaS and other aspects to help developers quickly find the resources they need.
Because of the wide range of free development resources, the project remains focused, and the main target group is infrastructure developers (system administrators, DevOps practitioners, etc.), who can better find value in the project. The following is a partial table of contents, and there is still a lot of content.
Here I would also like to remind domestic developers that the content included in this project is mainly for overseas, and if you do overseas business, it should be very suitable, and if you are completely domestic development, you need to screen the content that suits you more carefully.