Create README.md

README.md

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+---
+license: mit
+datasets:
+- Skylion007/openwebtext
+tags:
+- diffusion
+---
+
+# Generalized Interpolating Discrete Diffusion
+
+By Dimitri von Rütte, Janis Fluri, Yuhui Ding, Antonio Orvieto, Bernhard Schölkopf, Thomas Hofmann
+
+<div style="display: flex; gap: 8px;">
+  <a href="https://www.arxiv.org/abs/2503.04482"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-2503.04482-d22c2c.svg"></a>
+  <a href="https://colab.research.google.com/drive/1Xv4RyZhXHkIpIZeMYahl_4kMthLxKdg_?usp=sharing"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a>
+  <a href="https://github.com/dvruette/gidd"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-GIDD-blue"></a>
+</div>
+
+---
+
+![animation](https://github.com/dvruette/gidd/raw/main/animation.gif)
+
+We present Generalized Interpolating Discrete Diffusion (GIDD), a novel framework for training discrete diffusion models.
+GIDD can be seen as a generalization of the popular masked diffusion paradigm (MDM) to any diffusion process that can be written as a linear interpolation between a data distribution and some (time-variable) mixing distribution.
+We demonstrate the flexibility of GIDD by training models on a hybrid diffusion process that combines masking and uniform noise.
+The model therefore is trained to not only "fill in the blanks" (i.e. the masked tokens), but also to consider the correctness of already-filled-in tokens and, if necessary, replace incorrect tokens with more plausible ones.
+We show that GIDD models trained on hybrid noise have better sample quality (generative PPL) than mask-only models, and that they are able to identify and correct their own mistakes in generated samples through a self-correction step.
+This repository contains all training and evaluation code necessary for reproducing the results in the paper.
+
+
+
+### Pretrained Checkpoints
+Our trained checkpoints are available under the following links. All of them have been trained on 131B tokens from the [OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext) dataset with the [GPT-2 tokenizer](https://huggingface.co/openai-community/gpt2).
+
+| Model | Small (169.6M) | Base (424.5M) |
+|-------|-------|------|
+| GIDD+ (p_u = 0.0) | [dvruette/gidd-small-p_unif-0.0](https://huggingface.co/dvruette/gidd-small-p_unif-0.0) | [dvruette/gidd-base-p_unif-0.0](https://huggingface.co/dvruette/gidd-base-p_unif-0.0) |
+| GIDD+ (p_u = 0.1) | [dvruette/gidd-small-p_unif-0.1](https://huggingface.co/dvruette/gidd-small-p_unif-0.1) | dvruette/gidd-base-p_unif-0.1 |
+| GIDD+ (p_u = 0.2) | [dvruette/gidd-small-p_unif-0.2](https://huggingface.co/dvruette/gidd-small-p_unif-0.2) | [dvruette/gidd-base-p_unif-0.2](https://huggingface.co/dvruette/gidd-base-p_unif-0.2) |
+
+
+## Use the Model
+
+1. Install the GIDD repo:
+```bash
+pip install git+https://github.com/dvruette/gidd
+```
+
+2. For quickly downloading a trained model and playing around with it, the `GiddPipeline` class is most convenient:
+
+```python
+from gidd import GiddPipeline
+
+# Download a pretrained model from HuggingFace
+pipe = GiddPipeline.from_pretrained("dvruette/gidd-base-p_unif-0.1", trust_remote_code=True)
+
+# Generate samples
+texts = pipe.generate(num_samples=4, num_inference_steps=128)
+
+# Run self-correction step
+corrected_texts = pipe.self_correction(texts, num_inference_steps=128, early_stopping=True, temperature=0.1)
+
+print(corrected_texts)
+```
+