2 dépôts
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
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- huggingface/diffusers
huggingface/diffusers
33,872Voir sur GitHubDiffusers 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 - sgl-project/sglang
sgl-project/sglang
29,079Voir sur GitHubSglang 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
