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RAE-collections
nyu-visionx/RAE-collections
RAE-collections is a unconditional image generation model from nyu-visionx. Use it for the unconditional image generation task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This repository contains the official PyTorch checkpoints for Representation Autoencoders.
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Public
Updated Mar 1, 2026
Repo size
36.6 GB
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49
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.pt36.5 GB · 100%
At a glance
- Task
- Unconditional Image Generation
- License
- mit
- Access
- Public
- Created
- Oct 13, 2025
- Updated
- Mar 1, 2026
- SHA
- 1be4f032
Model card
From the Hugging Face model README
RAE: Diffusion Transformers with Representation Autoencoders
This repository contains the official PyTorch checkpoints for Representation Autoencoders.
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.
Website: https://rae-dit.github.io/
- Task
- Unconditional Image Generation
- License
- mit
- Created
- Oct 13, 2025
- Updated
- Mar 1, 2026