Improve model card

README.md

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----
-license: mit
----
+---
+license: mit
+library_name: diffusers
+pipeline_tag: image-to-image
+---
+
+---
+license: mit
+library_name: diffusers
+pipeline_tag: image-to-image
+---
+
+# REPA-E: Unlocking VAE for End-to-End Tuning of Latent Diffusion Transformers
+
+## About
+This model addresses the question of whether latent diffusion models and their VAE tokenizer can be trained end-to-end.  Using a representation-alignment (REPA) loss, REPA-E enables stable and effective joint training of both components, leading to significant training acceleration and improved VAE performance. The resulting E2E-VAE serves as a drop-in replacement for existing VAEs, improving convergence and generation quality across diverse LDM architectures.
+
+This model is based on the paper [REPA-E: Unlocking VAE for End-to-End Tuning of Latent Diffusion Transformers](https://huggingface.co/papers/2504.10483) and its official implementation is available on [Github](https://github.com/REPA-E/REPA-E). The project page can be found at [https://end2end-diffusion.github.io](https://end2end-diffusion.github.io).
+
+## Usage
+
+To use the REPA-E model, you can load it via the Hugging Face `DiffusionPipeline`. Below is a simplified example of how to use a pretrained REPA-E model for inference. For training examples and further details, please refer to the [Github repository](https://github.com/REPA-E/REPA-E).
+
+```python
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained("REPA-E/sit-repae-sdvae", trust_remote_code=True)
+image = pipeline().images[0]
+
+image.save("generated_image.png")
+```