TIGER-AI-Lab/ImagenHub
A one-stop library to standardize the inference and evaluation of all the conditional image generation models. [ICLR 2024]
Provides unified inference across 50+ conditional image generation models (text-to-image, image editing, inpainting, etc.) with curated evaluation datasets for 7 tasks and standardized metrics including LPIPS, CLIP-based scores, and human evaluation protocols (semantic consistency, perceptual quality). The library supports popular frameworks like Stable Diffusion, SDXL, and DALL-E through a modular architecture that abstracts model-specific implementations, enabling fair benchmarking and reproducible comparisons via YAML configuration files.
178 stars.
Stars
178
Forks
19
Language
Python
License
MIT
Category
Last pushed
Dec 02, 2025
Commits (30d)
0
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