awesome-repositories.com
博客
MCP
awesome-repositories.com

通过 AI 驱动的搜索,发现最优秀的开源仓库。

探索精选搜索开源替代品自托管软件博客网站地图
项目MCP 服务器关于排名机制媒体报道
法律隐私政策服务条款
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 个仓库

Awesome GitHub RepositoriesDiffusion Model Benchmarks

Standardized tests for measuring the throughput and latency of generative image and video models.

Distinct from Performance Benchmarks: Focuses on diffusion-specific performance metrics, distinct from general LLM benchmarking.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Diffusion Model Benchmarks. Refine with filters or upvote what's useful.

Awesome Diffusion Model Benchmarks GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • huggingface/diffusershuggingface 的头像

    huggingface/diffusers

    33,872在 GitHub 上查看↗

    Diffusers is a PyTorch-based library and generative AI framework used to build, train, and deploy diffusion pipelines for producing multi-modal media. It provides a suite of tools for generating images, video, and audio from natural language descriptions, as well as specialized systems for text-to-image generation. The project differentiates itself through a modular architecture that separates noise schedulers, pretrained model blocks, and pipeline compositions. This structure allows for the construction of custom generation workflows and the ability to swap individual components of the diffu

    Includes utilities for measuring memory usage and latency of generative models to optimize production performance.

    Pythondeep-learningdiffusionflux
    在 GitHub 上查看↗33,872
  • sgl-project/sglangsgl-project 的头像

    sgl-project/sglang

    29,079在 GitHub 上查看↗

    Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr

    Measures serving throughput and latency for image and video generation models under concurrent request loads.

    Pythonattentionblackwellcuda
    在 GitHub 上查看↗29,079
  1. Home
  2. Artificial Intelligence & ML
  3. Machine Learning
  4. Infrastructure
  5. Evaluation & Validation
  6. Performance Benchmarks
  7. Diffusion Model Benchmarks