Improve model card

README.md

@@ -1 +1,13 @@
-Score Entropy Discrete Diffusion (SEDD) small model for use with inference code in https://github.com/louaaron/Score-Entropy-Discrete-Diffusion. Paper found at arxiv.org/abs/2310.16834
+---
+pipeline_tag: text-generation
+---
+
+# Think While You Generate: Discrete Diffusion with Planned Denoising
+
+This model was presented in the paper [Think While You Generate: Discrete Diffusion with Planned Denoising](https://huggingface.co/papers/2410.06264).
+
+## Abstract
+
+Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusion with Planned Denoising (DDPD), a novel framework that separates the generation process into two models: a planner and a denoiser. At inference time, the planner selects which positions to denoise next by identifying the most corrupted positions in need of denoising, including both initially corrupted and those requiring additional refinement. This plan-and-denoise approach enables more efficient reconstruction during generation by iteratively identifying and denoising corruptions in the optimal order. DDPD outperforms traditional denoiser-only mask diffusion methods, achieving superior results on language modeling benchmarks such as text8, OpenWebText, and token-based generation on ImageNet $256 \times 256$. Notably, in language modeling, DDPD significantly reduces the performance gap between diffusion-based and autoregressive methods in terms of generative perplexity.
+
+Code: https://github.com/liusulin/DDPD