| 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 | |
| This model implements the REPA-E approach for end-to-end tuning of latent diffusion transformers, as described in the paper [REPA-E: Unlocking VAE for End-to-End Tuning of Latent Diffusion Transformers](https://huggingface.co/papers/2504.10483). REPA-E enables stable and effective joint training of both the VAE and the diffusion model, leading to faster training and improved generation quality. | |
| For more information, please refer to the following resources: | |
| * **Project Page:** https://end2end-diffusion.github.io | |
| * **GitHub Repository:** https://github.com/REPA-E/REPA-E | |
| ## Usage | |
| You can use this model with the `diffusers` library. Here's a basic example: | |
| ```python | |
| from diffusers import DiffusionPipeline | |
| # Load the pipeline | |
| pipeline = DiffusionPipeline.from_pretrained("REPA-E/your-model-name") # Replace "REPA-E/your-model-name" | |
| # Generate an image | |
| image = pipeline().images[0] | |
| # Save the image | |
| image.save("generated_image.png") | |
| ``` | |
| Please refer to the GitHub repository for detailed instructions and more advanced usage examples. |