# tianweiy/DMD2

(NeurIPS 2024 Oral 🔥) Improved Distribution Matching Distillation for Fast Image Synthesis

Repository: https://github.com/tianweiy/DMD2
Canonical: https://ross.abutalabs.com/products/dmd2
Language: Python
License: NOASSERTION
License Family: other
Last push: 2025-03-05T06:04:36+00:00

## Health v2 (maintenance only)
Score: 28/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 9, release rhythm 35, longevity 59
- inputs: {"age_days": 832, "days_push": 546, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1438, forks 76 (observed 2026-08-28T04:04:43.907557+00:00)

## What it is
DMD2 is the official PyTorch implementation of Improved Distribution Matching Distillation, a NeurIPS 2024 method that distills diffusion models into fast one- or few-step image generators. It removes costly regression losses, adds a GAN loss, and supports distilling models like SDXL for high-quality few-step text-to-image synthesis.

## Use cases
- distill a diffusion model into a one-step generator
- generate images in a few steps instead of many
- speed up SDXL text-to-image inference
- reproduce DMD2 research results on ImageNet and COCO
- train a fast image generation model with a GAN loss
- reduce inference cost of diffusion models by 500x

## When to choose
- you need fast few-step text-to-image generation with near-teacher quality
- you want to distill SDXL or ImageNet diffusion models into efficient generators
- you are researching distribution matching distillation techniques

## When to avoid
- you just want to run a pretrained model without training or distillation
- you lack GPU resources for training large diffusion models
- you need a production-ready inference server rather than research code

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-training, image-processing
- domain: machine-learning, deep-learning, image-processing, artificial-intelligence
- platform: python
- tags: diffusion-models, model-distillation, text-to-image, few-step-generation, research-code, sdxl, gan-loss, gpu, linux

## Member repositories
- tianweiy/DMD2 (main) score 28

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:43.907557+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 2026-08-30T04:36:41.963027+00:00, confidence not recorded.
  - readme: https://github.com/tianweiy/DMD2 (fetched 2026-08-28T04:04:43.907557+00:00, sha ab7e18cf9ae7)
- Data as of 2026-08-30T08:39:29.467469+00:00.