# Alpha-VLLM/Lumina-DiMOO

Lumina-DiMOO - An Open-Sourced Multi-Modal Large Diffusion Language Model

Repository: https://github.com/Alpha-VLLM/Lumina-DiMOO
Canonical: https://ross.abutalabs.com/products/lumina-dimoo
Homepage: https://synbol.github.io/Lumina-DiMOO/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: diffusion-large-language-model, discrete-diffusion-models, unified-multimodal-understanding-and-generation
Last push: 2026-05-19T01:47:40+00:00

## Health v2 (maintenance only)
Score: 55/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 83, release rhythm 35, longevity 25
- inputs: {"age_days": 358, "days_push": 107, "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 1015, forks 63 (observed 2026-08-28T04:03:14.263932+00:00)

## What it is
Lumina-DiMOO is an open-source omni diffusion large language model that uses fully discrete diffusion to handle multimodal inputs and outputs. It supports text-to-image generation, image-to-image tasks like editing and inpainting, and image understanding, with training and inference code and checkpoints released.

## Use cases
- generate images from text prompts with a diffusion language model
- edit images or do subject-driven generation and inpainting
- understand and answer questions about images with a multimodal model
- research discrete diffusion models for multimodal learning
- speed up multimodal sampling with caching and test-time scaling
- fine-tune a unified multimodal model with GRPO-style training

## When to choose
- you need a unified model for both multimodal understanding and generation
- you want faster sampling than autoregressive or hybrid AR-diffusion models
- you are researching discrete diffusion language models
- you want an Apache-2.0 licensed open multimodal foundation model

## When to avoid
- you need a lightweight model for CPU-only or edge deployment
- you only need a production chatbot with mature ecosystem tooling
- you require long-form text-only generation with standard AR LLMs

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, llm-inference, llm-training
- domain: artificial-intelligence, large-language-models, image-processing, computer-vision, deep-learning
- platform: python
- tags: discrete-diffusion, multimodal, text-to-image, image-editing, diffusion-language-model, image-understanding, gpu, linux

## Member repositories
- Alpha-VLLM/Lumina-DiMOO (main) score 55

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:14.263932+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 2026-08-30T07:11:22.813618+00:00, confidence not recorded.
  - readme: https://github.com/Alpha-VLLM/Lumina-DiMOO (fetched 2026-08-28T04:03:14.263932+00:00, sha 7b3cc726733c)
  - homepage: https://synbol.github.io/Lumina-DiMOO/ (fetched 2026-08-29T13:10:35.732340+00:00, sha f740289fa2be)
- Data as of 2026-08-30T08:39:29.467469+00:00.