e2e-qwenimage-vae

🚀 Overall

⚡️ Quickstart

from diffusers import AutoencoderKLQwenImage

vae = AutoencoderKLQwenImage.from_pretrained("REPA-E/e2e-qwenimage-vae").to("cuda")

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

🧩 End-to-End Trained VAE Releases

📦 Requirements

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

pip install diffusers>=0.35.0
pip install torch>=2.5.0

🚀 Example Usage

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

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)

# QwenImage VAE expects an additional dimension for `num_frames`
image_ = image.unsqueeze(2)

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

# Squeeze the extra frame dimension
latents = latents.squeeze(2)
reconstructed = reconstructed.squeeze(2)