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Transformer Latent Diffusion

Transformer Latent Diffusion is an open-source Python project implementing text-to-image latent diffusion with a transformer denoiser in PyTorch. Its repository includes image generation, model training, and image-and-caption data preparation code, plus Colab notebooks for trying prompts and following the data and training workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Transformer Latent Diffusion — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to experiment with text-to-image generation or examine and adapt a PyTorch transformer-based diffusion training workflow, especially when you can work with prepared image and caption data.

Who it is for

  • Python developers exploring transformer-based image generation.
  • Researchers and practitioners who want to train or fine-tune a latent diffusion model using image-and-caption data.
  • Learners studying a relatively self-contained PyTorch diffusion implementation.

Documented uses

  • Generate an image from a text prompt using the project's inference code or linked Colab notebook.
  • Prepare latent and text embeddings from image URLs and captions for training.
  • Train a model from prepared files, with Accelerate support for multi-GPU notebook launches.
  • Inspect the transformer denoiser, diffusion process, data utilities, and training loop.
Before you choose

Requirements & limits

The public GitHub repository identifies the project as MIT licensed and links to Colab notebooks for inference, data preparation, and training.

  • This is a codebase and notebook workflow, not a hosted image-generation service.
  • The documented stack includes PyTorch, NumPy, einops, and Accelerate; data-processing and pretrained-model dependencies are also listed.
  • The README presents 256-pixel examples from a model it says had not yet converged, and reports that some 1024-pixel examples lack global coherence.
  • The README describes training on a single A100 and multi-GPU support through Accelerate; these are project-reported examples, not performance guarantees.

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

Prepare data and train a model in Colab

Turn an image-URL-and-caption dataset into model inputs, then use prepared latent and text-embedding files for training.

Before you start

  • A dataset of image URLs and captions.
  • Python dependencies listed by the project, including PyTorch, NumPy, einops, and Accelerate.
  • Latent and text-embedding files; the training example uses prepared files.

Steps

  1. Use train.download_and_process_data to create latent and text encodings as NumPy files; the repository links a Colab example for processing a dataset.

  2. Pass the resulting file paths through DataConfig and ModelConfig.

  3. Call train.main in an Accelerate notebook_launcher, following the linked Colab training example.

Expected outcome

A model-training run using the project's transformer latent diffusion code and prepared data files.

Guide limitations

  • The linked training example downloads preprocessed latents and embeddings; the README also describes preparing your own files in the preceding step.
  • The project does not establish this notebook workflow as a production deployment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
apapiu/transformer_latent_diffusion · Official homepage title · retained in its source language
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