Practical overview
When it fits
Use it when you want to investigate or prototype diffusion-based sequence-to-sequence generation and can provide datasets, checkpoints and research computing resources.
Who it is for
- Researchers studying diffusion-based conditional language generation.
- Developers prototyping sequence-to-sequence text generation with Python training and decoding workflows.
Documented uses
- Train a conditional diffusion model on a prepared text dataset.
- Generate decoded text from a trained model.
- Explore dialogue, question generation, text simplification and paraphrase tasks.
Requirements & limits
The repository is public and lists an MIT license.
- The documented implementation uses Python, PyTorch and HuggingFace Transformers.
- Training requires prepared train, test and validation JSONL files.
- The README's reproduction examples use multiple NVIDIA A100 80G GPUs, so compute requirements are an important planning factor.
- DiffuSeq-v2 is presented as an accelerated sequence-to-sequence diffusion approach alongside the original DiffuSeq code.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare data and start a DiffuSeq training run
Set up the documented dependencies and launch training with prepared sequence-to-sequence datasets.
Before you start
- Python environment with the repository's PyTorch and HuggingFace Transformers dependencies.
- Dataset files placed under the datasets folder, including train.jsonl, test.jsonl and valid.jsonl.
- A training configuration and computing resources appropriate to the chosen dataset.
Steps
Install dependencies with the command below.
pip install -r requirements.txtPlace the prepared dataset files under the datasets folder.
From the scripts directory, run the command below.
bash train.sh
Expected outcome
A training run is launched using the repository's script and prepared dataset.
Guide limitations
- The README's example training configurations cite multiple NVIDIA A100 80G GPUs; compute needs vary with dataset and configuration.
- The guide covers launching training, not validating a completed run.
Decode text from a trained DiffuSeq model
Run the repository's documented decoding script using a model directory produced during training.
Before you start
- A model directory obtained during the training stage.
- The model_dir path in the decoding configuration.
Steps
Set the decoding configuration's model_dir to the trained model path.
From the scripts directory, run the command below.
bash run_decode.sh
Expected outcome
The decoding script runs against the configured trained model.
Guide limitations
- The README describes the decoding procedure but does not provide a hosted inference service.
- The guide does not cover evaluation of decoded output.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 6 November 2026
- Original website title
- Shark-NLP/DiffuSeq · Official homepage title · retained in its source language