End2End-Diffusion/diffusion-bench

Source: https://github.com/End2End-Diffusion/diffusion-bench

diffusion-bench

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#           Because ImageNet evaluation alone is no longer enough!           #
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📣 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):

export EXPERIMENT_NAME=<run-name>
export ENTITY=<wandb-entity>
export PROJECT=<wandb-project>
export WANDB_KEY=<key>

Stage 1. Train the RAE tokenizer:

uv run torchrun --standalone --nproc_per_node=8 \
    src/train_stage1.py \
    --config [STAGE1_CONFIG_PATH] \
    --results-dir results/stage1 --precision bf16 --compile --wandb

Stage 2. Train the diffusion model on VAE/RAE/Pixel space:

uv run torchrun --standalone --nproc_per_node=8 \
    src/train.py \
    --config [STAGE2_CONFIG_PATH] \
    --results-dir results/stage2 --precision bf16 --compile --wandb

Evaluation

Stage 2 training configs run online evaluation during training (the eval: block). For standalone evaluation of a released checkpoint, use the sampling/ configs — each embeds stage_2.ckpt (pointing into pretrained_models/) and the eval-time guidance, so the weights load automatically:

export EXPERIMENT_NAME=<run-name>

# stage 1 reconstruction (rFID/PSNR/SSIM/LPIPS)
uv run torchrun --nproc_per_node=8 src/offline_eval_stage1.py --config [STAGE1_CONFIG_PATH]

# stage 2 generation (FID/IS, GenEval/DPGBench/...)
uv run torchrun --nproc_per_node=8 src/offline_eval.py --config [STAGE2_CONFIG_PATH]

Available Configs

configs/
├── stage1/
└── stage2/
    ├── training/
    │   ├── imagenet/
    │   └── t2i/
    └── sampling/
        ├── imagenet/
        └── t2i/

Stage 2 spans VAE (11), RAE (6), REG (4), and Pixel (3) families, identical across ImageNet and T2I. Swap any config between tasks with a single path change. The sampling/ set mirrors training/ but adds the trained checkpoint and eval-time guidance, so it runs offline eval directly.

For ImageNet, pick the CFG-off baseline ([STAGE2_CONFIG_PATH].yaml) or the per-model best-CFG variant ([STAGE2_CONFIG_PATH]-cfg<scale>-t0.0-0.9.yaml).

Supported Methods

Compatibility

Contributing

We welcome contributions! Please refer to docs/contributors.md and docs/contributing.md for further details.

Acknowledgments

The codebase is built upon some amazing projects:

• RAE
• RAEv2
• REPA
• REPA-E
• JiT

We thank the authors for making their work publicly available.