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9 Repos

Awesome GitHub RepositoriesDiffusion Models

Generative models using denoising diffusion processes for protein backbone and sequence design.

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Awesome Diffusion Models GitHub Repositories

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  • exacity/deeplearningbook-chineseAvatar von exacity

    exacity/deeplearningbook-chinese

    37,285Auf GitHub ansehen↗

    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.

    TeX
    Auf GitHub ansehen↗37,285
  • lucidrains/denoising-diffusion-pytorchAvatar von lucidrains

    lucidrains/denoising-diffusion-pytorch

    10,614Auf GitHub ansehen↗

    Implementation of Denoising Diffusion Probabilistic Model in Pytorch

    Implements the Denoising Diffusion Probabilistic Model for generating images and sequences using a U-Net backbone.

    Pythonartificial-intelligencedeep-learninggenerative-model
    Auf GitHub ansehen↗10,614
  • google-deepmind/alphafold3Avatar von google-deepmind

    google-deepmind/alphafold3

    7,613Auf GitHub ansehen↗

    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.

    Python
    Auf GitHub ansehen↗7,613
  • tencent-ailab/ip-adapterAvatar von tencent-ailab

    tencent-ailab/IP-Adapter

    6,604Auf GitHub ansehen↗

    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.

    Jupyter Notebook
    Auf GitHub ansehen↗6,604
  • datawhalechina/tiny-universeAvatar von datawhalechina

    datawhalechina/tiny-universe

    4,505Auf GitHub ansehen↗

    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.

    Jupyter Notebookagentdiffusionevaluation-metrics
    Auf GitHub ansehen↗4,505
  • ml-gsai/lladaAvatar von ML-GSAI

    ML-GSAI/LLaDA

    3,580Auf GitHub ansehen↗

    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.

    Python
    Auf GitHub ansehen↗3,580
  • liuyf020419/scuba-dAvatar von liuyf020419

    liuyf020419/SCUBA-D

    63Auf GitHub ansehen↗

    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.

    Python
    Auf GitHub ansehen↗63
  • lamm-mit/proteindiffusiongeneratorAvatar von lamm-mit

    lamm-mit/ProteinDiffusionGenerator

    43Auf GitHub ansehen↗

    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.

    Jupyter Notebook
    Auf GitHub ansehen↗43
  • johnyang101/pmpnndiffJ

    johnyang101/pmpnndiff

    0Auf GitHub ansehen↗

    Fast Non-autoregressive Inverse Folding with Discrete Diffusion (NeurIPS MLSB 2023)

    Non-autoregressive inverse folding using discrete diffusion.

    Auf GitHub ansehen↗0
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  4. Diffusion Models

Unter-Tags erkunden

  • Masked Text DiffusionDiffusion processes specifically applied to discrete token sequences using masking. **Distinct from Diffusion Models:** Distinct from general Diffusion Models by focusing on masked token refinement for language instead of protein or image denoising.
  • SequenceDiffusion models that generate one-dimensional sequences such as time series or audio features. **Distinct from Diffusion Models:** Distinct from Diffusion Models: specifically targets one-dimensional sequence generation rather than image or video synthesis.