README.md

metadata

task_categories:
  - unconditional-image-generation
tags:
  - vae
  - diffusion-models
  - imagenet
  - image-generation

REPA-E: Generated Samples and Artifacts

This repository hosts the generated image samples and artifacts associated with the paper REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers. These samples are instrumental for quantitative evaluation of the REPA-E method, which enables end-to-end tuning of latent diffusion models and VAEs, achieving state-of-the-art image generation performance.

• 🌐 Project Page
• 📃 Paper
• 💻 Code Repository

Overview

We address a fundamental question: Can latent diffusion models and their VAE tokenizer be trained end-to-end? While training both components jointly with standard diffusion loss is observed to be ineffective — often degrading final performance — we show that this limitation can be overcome using a simple representation-alignment (REPA) loss. Our proposed method, REPA-E, enables stable and effective joint training of both the VAE and the diffusion model.

REPA-E significantly accelerates training — achieving over 17× speedup compared to REPA and 45× over the vanilla training recipe. Interestingly, end-to-end tuning also improves the VAE itself: the resulting E2E-VAE provides better latent structure and serves as a drop-in replacement for existing VAEs (e.g., SD-VAE), improving convergence and generation quality across diverse LDM architectures. Our method achieves state-of-the-art FID scores on ImageNet 256×256: 1.12 with CFG and 1.69 without CFG. The generated .npz files for these evaluations can be found within this repository (e.g., under labelsampling-equal-run1).

Sample Usage

This section shows how to load and use the REPA-E fine-tuned VAE (E2E-VAE) in latent diffusion training, demonstrating how E2E-VAE acts as a drop-in replacement for the original VAE, enabling significantly accelerated generation performance.

⚡️ Quickstart

from diffusers import AutoencoderKL

# Load end-to-end tuned VAE (ImageNet VAE example)
vae = AutoencoderKL.from_pretrained("REPA-E/e2e-vavae-hf").to("cuda")

# Or load a text-to-image VAE
vae = AutoencoderKL.from_pretrained("REPA-E/e2e-flux-vae").to("cuda")

# Use in your pipeline with vae.encode(...) / vae.decode(...)

🧩 Complete Example

Full workflow for encoding and decoding images:

from io import BytesIO
import requests
from diffusers import AutoencoderKLQwenImage
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 = AutoencoderKLQwenImage.from_pretrained("REPA-E/e2e-qwenimage-vae").to(device)

# Add frame dimension (required for QwenImage VAE)
image_ = image.unsqueeze(2)

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

# Remove frame dimension
latents = latents.squeeze(2)
reconstructed = reconstructed.squeeze(2)

Quantitative Results

Tables below report generation performance using gFID on 50k samples, with and without classifier-free guidance (CFG). We compare models trained end-to-end with REPA-E and models using a frozen REPA-E fine-tuned VAE (E2E-VAE). Lower is better. All linked checkpoints below are hosted on our 🤗 Hugging Face Hub. To reproduce these results, download the respective checkpoints to the pretrained folder and run the evaluation script as detailed in the GitHub repository.

A. End-to-End Training (REPA-E)

Tokenizer	Generation Model	Epochs	gFID-50k ↓	gFID-50k (CFG) ↓
SD-VAE*	SiT-XL/2	80	4.07	1.67a
IN-VAE*	SiT-XL/1	80	4.09	1.61b
VA-VAE*	SiT-XL/1	80	4.05	1.73c

* The "Tokenizer" column refers to the initial VAE used for joint REPA-E training. The final (jointly optimized) VAE is bundled within the generation model checkpoint.

B. Traditional Latent Diffusion Model Training (Frozen VAE)

Tokenizer	Generation Model	Method	Epochs	gFID-50k ↓	gFID-50k (CFG) ↓
SD-VAE	SiT-XL/2	SiT	1400	8.30	2.06
SD-VAE	SiT-XL/2	REPA	800	5.84	1.28
VA-VAE	LightningDiT-XL/1	LightningDiT	800	2.05	1.25
E2E-VAVAE (Ours)	SiT-XL/1	REPA	800	1.69	1.12†

In this setup, the VAE is kept frozen and only the generator is trained. Models using our E2E-VAE (fine-tuned via REPA-E) consistently outperform baselines such as SD-VAE and VA-VAE, achieving state-of-the-art performance when incorporating the REPA alignment objective.

Note: The results for the last three rows (REPA, LightningDiT, and E2E-VAE) are obtained using the class-balanced sampling protocol (50 images per class).

Citation

If you find our work useful, please consider citing:

@article{leng2025repae,
  title={REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers},
  author={Xingjian Leng and Jaskirat Singh and Yunzhong Hou and Zhenchang Xing and Saining Xie and Liang Zheng},
  year={2025},
  journal={arXiv preprint arXiv:2504.10483},
}