How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
This is the official implementation of the paper: A Cheaper and Better Diffusion Language Model with Soft-Masked Noise.
Continuous Latent Diffusion Language Model — a hierarchical latent-space text diffusion model with a block-causal DiT prior over a Text VAE.
We introduce Text-to-text Self-conditioned Simplex Diffusion (TESS), a text diffusion model that is fully non-autoregressive, employs a new form of self-conditioning, and applies the diffusion process on the logit simplex space rather than the typical learned embedding space.
Abstract: This paper presents the Text Encoding Diffusion Model (TEncDM), a novel approach to diffusion modeling that operates in the space of pre-trained language model encodings. In contrast to traditionally used embeddings, encodings integrate contextual information. In our approach, we also…
The main features of m0rjique/tencdm are: Continuous Diffusion Models.
Open-source alternatives to m0rjique/tencdm include: amazon-science/masked-diffusion-lm — This is the official implementation of the paper: A Cheaper and Better Diffusion Language Model with Soft-Masked Noise. ashaba1in/smoothie — Paper: https://arxiv.org/pdf/2505.18853. bytedance-seed/cola-dlm — Continuous Latent Diffusion Language Model — a hierarchical latent-space text diffusion model with a block-causal DiT… david3684/flm — Flow Map Language Models: One-step Language Modeling via Continuous Denoising. guangyliu/latentops — Source code of paper: Composable Text Controls in Latent Space with ODEs. allenai/tess-diffusion — We introduce Text-to-text Self-conditioned Simplex Diffusion (TESS), a text diffusion model that is fully…
