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license: mit |
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pipeline_tag: unconditional-image-generation |
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--- |
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# RAE: Diffusion Transformers with Representation Autoencoders |
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This repository contains the official PyTorch checkpoints for Representation Autoencoders. |
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Representation Autoencoders (RAE) are a class of autoencoders that utilize pretrained, frozen representation encoders such as DINOv2 and SigLIP2 as encoders with trained ViT decoders. RAE can be used in a two-stage training pipeline for high-fidelity image synthesis, where a Stage 2 diffusion model is trained on the latent space of a pretrained RAE to generate images. |
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Website: https://rae-dit.github.io/ |
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Code: https://github.com/bytetriper/RAE |
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Paper: https://huggingface.co/papers/2510.11690 |
