bytetriper/RAE

Health v2 · maintenance only

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2001 stars · 87 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of 'Diffusion Transformers with Representation Autoencoders' (RAE), a two-stage image generation pipeline using frozen pretrained encoders (DINOv2, SigLIP2) with trained ViT decoders. Includes pretrained RAE decoders, LightningDiT/DiT-DH diffusion transformers, and training/sampling scripts for GPU and TPU.

Use cases

• generate high-fidelity images with diffusion transformers
• train a diffusion model on representation autoencoder latent space
• reproduce RAE paper results in PyTorch
• run image generation sampling on TPU with TorchXLA
• download pretrained RAE and DiT checkpoints for image synthesis
• train two-stage RAE plus DiT pipeline on ImageNet

When to choose

• you need a research-grade implementation of representation autoencoders for diffusion models
• you want pretrained DiT-based image generation weights
• you need GPU or TPU training and sampling scripts for latent diffusion transformers

When to avoid

• you need a production-ready image generation API or service
• you want a general-purpose diffusion library rather than a specific paper's codebase
• you cannot work with research code and changing APIs

Facets

library · maturity active

machine-learning deep-learning image-processing llm-training machine-learning deep-learning image-processing artificial-intelligence python diffusion-transformers autoencoders pytorch image-generation tpu research-code dinov2 siglip2 dit latent-space gpu linux docker

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