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Guided Diffusion download for Linux

Free download Guided Diffusion Linux app to run online in Ubuntu online, Fedora online or Debian online

This is the Linux app named Guided Diffusion whose latest release can be downloaded as guided-diffusionsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.

Download and run online this app named Guided Diffusion with OnWorks for free.

请按照以下说明运行此应用程序:

- 1. 在您的 PC 中下载此应用程序。

- 2. 在我们的文件管理器 https://www.onworks.net/myfiles.php?username=XXXXX 中输入您想要的用户名。

- 3. 在这样的文件管理器中上传这个应用程序。

- 4. 从此网站启动OnWorks Linux online 或Windows online emulator 或MACOS online emulator。

- 5. 从您刚刚启动的 OnWorks Linux 操作系统,使用您想要的用户名转到我们的文件管理器 https://www.onworks.net/myfiles.php?username=XXXXX。

- 6. 下载应用程序,安装并运行。

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引导扩散


商品描述

The guided-diffusion repository is centered on diffusion models for image synthesis, with a focus on classifier guidance and improvements over earlier diffusion frameworks. It is derived from OpenAI’s improved-diffusion work, enhanced to include guided generation where a classifier (or other guidance mechanism) can steer sampling toward desired classes or attributes. The code provides model definitions (UNet, diffusion schedules), sampling and training scripts, and utilities for guidance and evaluation. A key insight is that combining diffusion sampling with classifier gradients allows fine control over the generated images, trading off diversity vs fidelity. The repository includes scripts such as image_train.py, image_sample.py, and classifier_train.py to train diffusion models, generate samples, and train guiding classifiers. It also ships with precomputed evaluation batches and baseline comparisons to support reproducible benchmarking of new models.



功能

  • Diffusion model architecture (UNet, noise schedules, training utilities)
  • Classifier-guided sampling: combining diffusion with classifier gradients
  • Scripts for training models (image_train.py), sampling (image_sample.py), and classifier training
  • Precomputed evaluation batches and baseline metrics for reproducibility
  • Modular code enabling new guidance modalities or architectural tweaks
  • Branching from improved-diffusion with enhancements in guided generation


程式语言

Python


分类

人工智能

This is an application that can also be fetched from https://sourceforge.net/projects/guided-diffusion.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.


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