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Dalle Pytorch

Dalle Pytorch is a Python and PyTorch library implementing and replicating DALL-E, a text-to-image transformer. Its README demonstrates training a DiscreteVAE and DALLE model, generating images from text tokens, and priming generation with an image crop; it also describes CLIP functionality for ranking generations.

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Dalle Pytorch — OpenViST project profile in Image & Video AI.
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Understand the project

Practical overview

When it fits

Use it when you want code-level access to a DALL-E-style training and image-generation workflow in Python and PyTorch, rather than a hosted prompt-and-generate service.

Who it is for

  • Python developers building a text-prompt-to-image application around a DALL-E-style model.
  • Researchers and learners experimenting with text-to-image transformers and model training.

Documented uses

  • Train a discrete image VAE and use it as the image component of a DALLE model.
  • Train a text-to-image model on text and image inputs.
  • Generate images from text tokens after training, optionally conditioning generation on an initial image crop.
  • Rank generated images using the CLIP functionality described by the project.
Before you choose

Requirements & limits

The public GitHub repository is MIT licensed. The README shows installation with pip and links to a Quick Start wiki and a Colab training notebook.

  • The documented generation example comes after training the VAE and DALLE model; it is not presented as an immediate, preconfigured prompt interface.
  • The README provides example model parameters and code but does not specify compute requirements.
  • The project is a Python library built around PyTorch, not a complete hosted application.

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

Train a VAE and DALL-E model, then generate images

Use the README’s PyTorch example to train a discrete VAE, train DALL-E with that VAE, and generate images from text-token sequences.

Before you start

  • Install the package with pip install dalle-pytorch.
  • Provide image data for training the VAE and DALL-E; the README’s code examples use random tensors as illustrative inputs.
  • Provide text-token sequences for DALL-E training and generation.

Steps

  1. Install dalle-pytorch with pip.

  2. Create a DiscreteVAE, pass training images to it with return_loss=True, and call loss.backward(); train with data to learn a codebook.

  3. Create a DALLE instance using a pretrained VAE, pass text tokens and images with return_loss=True, and call loss.backward() during training.

  4. After training, call dalle.generate_images(text) to obtain generated images.

Expected outcome

The example produces a batch of generated images; with its illustrated settings, the expected tensor shape is `(4, 3, 256, 256)`.

Guide limitations

  • The README says to train the VAE with a lot of data and train DALL-E for a long time with a lot of data; it does not specify a duration or dataset requirements.
  • The example uses tokenized text tensors and does not document a text-tokenization procedure.
  • The repository describes CLIP ranking as something it will also contain, but this usage example does not provide steps for ranking images.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
lucidrains/DALLE-pytorch · Official homepage title · retained in its source language
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