9 Repos
Generative models using denoising diffusion processes for protein backbone and sequence design.
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This project is a comprehensive Chinese translation of a technical deep learning textbook, providing an educational resource on the theory and implementation of neural networks. It functions as a collaborative technical translation project designed to make complex academic AI literature accessible to non-English speakers. The project utilizes a community-driven translation model that integrates external suggestions and pull requests to refine linguistic accuracy and reduce bias. It employs standardized terminology mapping to ensure a uniform vocabulary throughout the translated content. To i
Covers the theory of denoising diffusion processes used to recover structure from noise.
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Implements the Denoising Diffusion Probabilistic Model for generating images and sequences using a U-Net backbone.
AlphaFold3 is a biomolecular structure prediction model and bioinformatics structural analysis tool. It uses a deep learning system to predict the three-dimensional shapes of proteins, DNA, RNA, and ligands. The system functions as a diffusion-based protein folding model that predicts the spatial coordinates of biomolecular atoms and interactions. It utilizes a GPU-accelerated inference pipeline to process genetic sequences and structural templates for molecular modeling. The project covers structural bioinformatics analysis and protein interaction modeling to determine the physical arrangem
Generative model using a diffusion architecture to predict spatial coordinates of biomolecular atoms.
IP-Adapter is a framework for conditioning pretrained text-to-image diffusion models to use image prompts as visual guides. It serves as a text-to-image model extension that transforms a text-based diffusion model to accept and process image inputs as primary generation sources. The system implements identity preservation to maintain consistent facial features across multiple outputs using a reference photo. It also enables style transfer workflows to produce image variations that preserve the artistic characteristics of a source image. Capabilities cover multi-modal prompting, including the
Utilizes a frozen pretrained diffusion model as the stable base engine for adaptation.
Tiny Universe is an educational monorepo that delivers multiple independent implementations of core AI subsystems as self-contained Jupyter notebooks. It provides from-scratch constructions of foundational architectures including a complete Transformer model built from the original paper specification, a denoising diffusion probabilistic model for image generation, and a ReAct-style autonomous agent framework that equips an LLM with tools for planning and multi-step task execution. The project distinguishes itself by covering the full lifecycle of modern AI systems through hands-on implementa
Implements a denoising diffusion probabilistic model for image generation from pure noise.
LLaDA is a masked diffusion language model and conditional text generator. It generates text by iteratively refining masked tokens through a diffusion process rather than predicting the next token in a sequence. The project functions as a vision-language diffusion model, converting visual inputs into text responses. It also serves as a preference optimization framework that uses log-likelihood estimation and evidence lower bounds to tune model responses. The system supports multi-round conversational AI and text sequence evaluation. It integrates vision-language embedding for cross-modal con
Implements a masked diffusion architecture that iteratively refines tokens into final text.
SCUBA-D: De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models
Backbone generation using diffusion with structured priors.
Reference: B. Ni, D.L. Kaplan, M.J. Buehler, Generative design of de novo proteins based on secondary structure constraints using an attention-based diffusion model, Chem, 2023
Attention-based diffusion for secondary-structure constrained design.
Fast Non-autoregressive Inverse Folding with Discrete Diffusion (NeurIPS MLSB 2023)
Non-autoregressive inverse folding using discrete diffusion.