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description: Official implementations for paper: Anydoor: zero-shot object-level image customization
launage: Python
starts: 828
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recent stars: 89 stars today
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description: Official repo for VGen: a holistic video generation ecosystem for video generation building on diffusion models
launage: Python
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description: 无
launage: Python
starts: 1,780
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recent stars: 103 stars today
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description: A 6 Lesson course teaching everything you need to know about harnessing GitHub Copilot and an AI Paired Programing resource.
launage: Python
starts: 2,157
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recent stars: 1,273 stars today
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description: Rust implementation of Namada, a Proof-of-Stake L1 for interchain asset-agnostic privacy
launage: Rust
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description: Decentralized Autonomous Regulated Company (DARC), a company virtual machine that runs on any EVM-compatible blockchain, with on-chain law system, multi-level tokens and dividends mechanism.
launage: Solidity
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description: Easy to use open source fast database for search | Good alternative to Elasticsearch now | Drop-in replacement for E in the ELK soon
launage: C++
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recent stars: 27 stars today
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description: This is a repo with links to everything you'd ever want to learn about data engineering
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description: Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.
launage: Python
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description: Develop Desktop, Embedded, Mobile and WebAssembly apps with C# and XAML. The most popular .NET Foundation community project.
launage: C#
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description: Ask Questions in natural language and get Answers backed by private sources. Connects to tools like Slack, GitHub, Confluence, etc.
launage: Python
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description: World's most advanced database DevOps and CI/CD for Developer, DBA and Platform Engineering teams. The GitLab/GitHub for database DevOps.
launage: Go
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launage: TypeScript
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launage: Python
starts: 16,139
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description: To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.
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