GoKawiil############################################################################## # # # ____ _ __ __ _ .-----------. # # | _ \(_)/ _|/ _|_ _ ___(_) ___ _ __ | | # # | | | | | |_| |_| | | / __| |/ _ \| '_ \ | ░▒▓█▓▒░▒▓ | # # | |_| | | _| _| |_| \__ \ | (_) | | | | | ▒▓█████▓▒ | # # |____/|_|_| |_| \__,_|___/_|\___/|_| |_| | ▓███████▓ | # # | ↓ | # # ____ _ | █████████ | # # | __ ) ___ _ __ ___| |__ | ▓███████▓ | # # | _ \ / _ \ '_ \ / __| '_ \ | ▒▓█████▓▒ | # # | |_) | __/ | | | (__| | | | | | # # |____/ \___|_| |_|\___|_| |_| '-----------' # # # # Because ImageNet evaluation alone is no longer enough! # # # ##############################################################################
📣 Announcement post: Call for DiffusionBench: A Holistic Benchmark for Diffusion Transformers. Help us grow the benchmark with new evaluation axes, new metrics, and faithful reproductions of published methods.
This repo contains the unified codebase for DiffusionBench. It supports training and evaluation across different generation tasks (ImageNet, T2I, ...) through a single interface. Please see the sections below for the detailed structure. Come join us!
Text-to-image samples at 256×256 from models trained for 200K iterations using DiffusionBench.
Quickstart
Setup
# install uv project manager (if you don't already have it) curl -LsSf https://astral.sh/uv/install.sh | sh # install dependencies uv sync # prepare data uv run python scripts/prepare.py --data {all,imagenet,t2i,eval} # download pretrained models uv run hf download diffusion-bench/diffusion-bench --local-dir pretrained_models --exclude .gitattributes
Training
Reproduction flow: Stage 1 → Stage 2. Set these environment variables first (used for the output directory and W&B logging):
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