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REPA-E/
e2e-sd3.5-vae

Name: e2e-sd3.5-vae
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🚀 REPA-E for T2I

End-to-End Tuned VAEs for Supercharging Text-to-Image Diffusion Transformers

🌐 Project Page 🤗 Models 📃 Paper

🚀 Overall

We present REPA-E for T2I, a family of end-to-end tuned VAEs designed to supercharge text-to-image generation training. These models consistently outperform SD-3.5-VAE across all benchmarks (COCO-30K, DPG-Bench, GenAI-Bench, GenEval, and MJHQ-30K) without requiring any additional representation alignment losses.

For training, we adopt the official REPA-E training code to optimize the SD-3.5-VAE for 80 epochs with a batch size of 256 on the ImageNet-256 dataset. The REPA-E training effectively refines the VAE’s latent-space structure and enables faster convergence in downstream text-to-image latent diffusion model training.

This repository provides diffusers-compatible weights for the end-to-end trained SD-3.5-VAE. In addition, we release end-to-end trained variants of several other widely used VAEs to facilitate research and integration within text-to-image diffusion frameworks.

⚡️ Quickstart

python

from diffusers import AutoencoderKL

vae = AutoencoderKL.from_pretrained("REPA-E/e2e-sd3.5-vae").to("cuda")

Use vae.encode(...) / vae.decode(...) in your pipeline. (A full example is provided below.)

🧩 End-to-End Trained VAE Releases

Model	Hugging Face Link
E2E-FLUX-VAE	🤗 REPA-E/e2e-flux-vae
E2E-SD-3.5-VAE	🤗 REPA-E/e2e-sd3.5-vae
E2E-Qwen-Image-VAE	🤗 REPA-E/e2e-qwenimage-vae

📦 Requirements

The following packages are required to load and run the REPA-E VAEs with the diffusers library:

bash

pip install diffusers>=0.33.0
pip install torch>=2.3.1

🚀 Example Usage

Below is a minimal example showing how to load and use the REPA-E end-to-end trained SD-3.5-VAE with diffusers:

python

from io import BytesIO
import requests

from diffusers import AutoencoderKL
import numpy as np
import torch
from PIL import Image


response = requests.get("https://raw.githubusercontent.com/End2End-Diffusion/fuse-dit/main/assets/example.png")
device = "cuda"

image = torch.from_numpy(
    np.array(
        Image.open(BytesIO(response.content))
    )
).permute(2, 0, 1).unsqueeze(0).to(torch.float32) / 127.5 - 1
image = image.to(device)

vae = AutoencoderKL.from_pretrained("REPA-E/e2e-sd3.5-vae").to(device)

with torch.no_grad():
    latents = vae.encode(image).latent_dist.sample()
    reconstructed = vae.decode(latents).sample