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Lumina T2X

Lumina T2X is an open-source Python framework that uses flow-based diffusion transformers to generate media from text, including documented image, audio and music workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Lumina T2X — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Lumina T2X when you want a code-oriented framework for text-conditioned generation and are prepared to select a modality-specific implementation and follow its instructions.

Who it is for

  • Developers implementing text-conditioned media-generation workflows.
  • Researchers exploring diffusion-transformer models across media types.

Documented uses

  • Generate images from text prompts using Lumina-Next and Diffusers.
  • Train or run inference with the project's image-generation implementations, following their separate instructions.
  • Explore text-to-audio and text-to-music model workflows where the repository lists inference and checkpoints.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. The documented setup installs from GitHub; the image example uses CUDA.

  • The image-generation example depends on a Lumina-Next checkpoint and CUDA.
  • The README marks image training, inference and checkpoints as available; audio and music inference and checkpoints are also listed.
  • Text-to-video is listed with demos, but the open-source plan does not mark its training, inference or checkpoints as complete.
  • The README recommends Lumina-Next-T2I-Mini for training and inference and links separate instructions for each implementation.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Generate an image with Lumina-Next through Diffusers

Run the README's prompt-to-image inference example with a Lumina-Next checkpoint.

Before you start

  • The development version of Diffusers from its main branch, as specified by the README.
  • A Lumina-Next-SFT-Diffusers checkpoint accessible at the path used in the code.
  • CUDA, as used by the example's .to("cuda") call.

Steps

  1. Install the development version of Diffusers with the command below.

    Documented command
    pip install git+https://github.com/huggingface/diffusers

  2. Import LuminaText2ImgPipeline from diffusers and torch.

  3. Load the checkpoint with LuminaText2ImgPipeline.from_pretrained, using torch_dtype=torch.bfloat16, then move the pipeline to cuda.

  4. Call the pipeline with a text prompt and the desired height and width; retrieve the generated image from .images[0].

Expected outcome

The example returns an image generated from the supplied prompt at the requested dimensions.

Guide limitations

  • The README's checkpoint path is a local example path and must point to an available checkpoint.
  • The README recommends checking its task-specific inference instructions for further details.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
Alpha-VLLM/Lumina-T2X · Official homepage title · retained in its source language
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