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PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model
This repository provides the official implementation of the following paper:
This repository contains the code and models for our paper DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models.
This software project accompanies the research paper, DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation.
The main features of amayama/xdlm are: Applications.
Projects with overlapping indexed features include: apple/ml-planner — PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model. bansky-cl/diffuspoll — This repository provides the official implementation of the following paper:. bytedance/difflm — This repository contains the code and models for our paper DiffLM: Controllable Synthetic Data Generation via… bytedance/dplm — This repository contains the official implementation of training and inference as well as the pre-trained weights for… chenyuwang-monica/drakes — The repository contains the code for the DRAKES method presented in the paper: Fine-Tuning Discrete Diffusion Models… apple/ml-diffucoder — This software project accompanies the research paper, DiffuCoder: Understanding and Improving Masked Diffusion Models…