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Official implementation of TESS 2. TESS 2 is a state-of-the-art diffusion language model created by adapting existing pretrained autoregressive models to a diffusion paradigm. For more details, please check out our paper and model checkpoints on Hugging Face.
The main features of hamishivi/tess-2 are: Continuous Diffusion Models.
Open-source alternatives to hamishivi/tess-2 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…
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.
