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Awesome GitHub RepositoriesGenerative Fidelity Benchmarks
Standardized tests for measuring the visual quality and distribution accuracy of generated images using metrics like FID.
Distinct from Diffusion Model Benchmarks: Focuses on visual fidelity and distribution quality rather than throughput and latency performance.
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Awesome Generative Fidelity Benchmarks GitHub Repositories
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- openai/consistency_models
openai/consistency_models
6,492在 GitHub 上查看This project is a framework for training and sampling generative models designed to produce high-quality images in few steps. It provides implementations for image generation models that transform random noise into structured visual data through an optimized sampling process. The system specializes in accelerating image generation through consistency distillation and consistency training. It includes tools to transform pre-trained diffusion models into faster versions by distilling knowledge from a teacher model into a student model, as well as methods to train consistency models from scratch
Benchmarks generative performance using FID, Precision, Recall, and Inception Score against sampled image batches.
Python
