# baofff/U-ViT

A PyTorch implementation of the paper "All are Worth Words: A ViT Backbone for Diffusion Models".

Repository: https://github.com/baofff/U-ViT
Canonical: https://ross.abutalabs.com/products/u-vit
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2023-03-25T02:43:10+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 98
- inputs: {"age_days": 1384, "days_push": 1257, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1110, forks 77 (observed 2026-08-28T04:03:37.502724+00:00)

## What it is
U-ViT is the official PyTorch implementation of a ViT-based backbone architecture for diffusion models from the CVPR 2023 paper 'All are Worth Words: A ViT Backbone for Diffusion Models'. It provides model code, pretrained checkpoints, and training/evaluation scripts for pixel-space, latent-space, and text-to-image diffusion models.

## Use cases
- train a diffusion model with a vision transformer backbone instead of a CNN U-Net
- generate images unconditionally or class-conditionally on CIFAR10, CelebA, or ImageNet
- build a text-to-image diffusion model
- run latent diffusion models with a transformer backbone
- research long skip connections in transformer architectures for generative modeling
- fine-tune pretrained U-ViT checkpoints on custom image generation tasks

## When to choose
- you need a transformer-based backbone for diffusion models with pretrained weights
- you want to reproduce or extend CVPR 2023 diffusion research
- you need training and evaluation scripts for pixel-space, latent, or text-to-image diffusion in PyTorch

## When to avoid
- you need a production-ready image generation service rather than research code
- you require a CNN-based U-Net backbone for diffusion models
- you lack GPU resources, since training diffusion models is compute-intensive

## Facets
- artifact type: library
- maturity: stable
- function: deep-learning, machine-learning, image-processing
- domain: deep-learning, machine-learning, image-processing, artificial-intelligence
- platform: python
- tags: vision-transformer, diffusion-models, image-generation, pytorch, research-code, text-to-image, cvpr-2023

## Member repositories
- baofff/U-ViT (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:37.502724+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:43:26.550519+00:00, confidence not recorded.
  - readme: https://github.com/baofff/U-ViT (fetched 2026-08-28T04:03:37.502724+00:00, sha 9d8b753a6376)
- Data as of 2026-08-30T08:39:29.467469+00:00.