Populate dataset card for REPA-E generated samples and artifacts

README.md

@@ -0,0 +1,110 @@
+---
+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](https://huggingface.co/papers/2504.10483). 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](https://end2end-diffusion.github.io/)
+- 📃 [Paper](https://huggingface.co/papers/2504.10483)
+- 💻 [Code Repository](https://github.com/End2End-Diffusion/REPA-E)
+
+## 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
+```python
+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:
+```python
+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](https://huggingface.co/REPA-E). To reproduce these results, download the respective checkpoints to the `pretrained` folder and run the evaluation script as detailed in the [GitHub repository](https://github.com/End2End-Diffusion/REPA-E/blob/main/README.md#5-generate-samples-and-run-evaluation).
+
+#### A. End-to-End Training (REPA-E)
+| Tokenizer | Generation Model | Epochs | gFID-50k ↓ | gFID-50k (CFG) ↓ |
+|:---------|:----------------|:-----:|:----:|:---:|
+| [**SD-VAE<sup>*</sup>**](https://huggingface.co/REPA-E/sdvae) | [**SiT-XL/2**](https://huggingface.co/REPA-E/sit-repae-sdvae) | 80 | 4.07 | 1.67<sup>a</sup> |
+| [**IN-VAE<sup>*</sup>**](https://huggingface.co/REPA-E/invae) | [**SiT-XL/1**](https://huggingface.co/REPA-E/sit-repae-invae) | 80 | 4.09 | 1.61<sup>b</sup> |
+| [**VA-VAE<sup>*</sup>**](https://huggingface.co/REPA-E/vavae) | [**SiT-XL/1**](https://huggingface.co/REPA-E/sit-repae-vavae) | 80 | 4.05 | 1.73<sup>c</sup> |
+
+\* 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)**](https://huggingface.co/REPA-E/e2e-vavae) | [**SiT-XL/1**](https://huggingface.co/REPA-E/sit-ldm-e2e-vavae) | REPA | 800 | **1.69** | **1.12**<sup>†</sup> |
+
+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:
+
+```bibtex
+@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},
+}
+```