# XiangLi1999/Diffusion-LM

Diffusion-LM

Repository: https://github.com/XiangLi1999/Diffusion-LM
Canonical: https://ross.abutalabs.com/products/diffusion-lm
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-08-08T08:49:53+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1561, "days_push": 755, "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 1245, forks 164 (observed 2026-08-28T04:04:06.977509+00:00)

## What it is
Diffusion-LM is the official research code for the paper 'Diffusion-LM Improves Controllable Text Generation', implementing a diffusion-based language model over continuous embeddings. It provides training, decoding, and classifier-guided generation pipelines for controllable text generation tasks.

## Use cases
- generate text with diffusion models
- control text generation with syntactic constraints
- train a diffusion language model on custom text data
- reproduce results from the Diffusion-LM paper
- guide text generation with a classifier
- infill text conditioned on attributes like syntax trees

## When to choose
- you need controllable text generation via diffusion
- you want to experiment with or extend diffusion-based language models
- you are reproducing or building on the Diffusion-LM research

## When to avoid
- you need a production-ready text generation system
- you want simple autoregressive LLM inference without research setup
- you lack GPU resources for training diffusion models

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, nlp, llm-training
- domain: machine-learning, deep-learning
- platform: python
- tags: diffusion-models, controllable-text-generation, research-code, text-generation, academic-paper, natural-language-processing, research, linux, gpu

## Member repositories
- XiangLi1999/Diffusion-LM (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:06.977509+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 2026-08-30T05:08:07.628192+00:00, confidence not recorded.
  - readme: https://github.com/XiangLi1999/Diffusion-LM (fetched 2026-08-28T04:04:06.977509+00:00, sha f7182f530891)
- Data as of 2026-08-30T08:39:29.467469+00:00.